pax_global_header00006660000000000000000000000064145147752620014527gustar00rootroot0000000000000052 comment=642727d4fe0d6262019ee05a9dce9ff9fba045e4 LanguageMachines-timbl-642727d/000077500000000000000000000000001451477526200163105ustar00rootroot00000000000000LanguageMachines-timbl-642727d/.dockerignore000066400000000000000000000001241451477526200207610ustar00rootroot00000000000000.git .cache .* _* *.cache *.pyc build *.egg-info gource* *.tar.gz *.pdf TODO *.lock LanguageMachines-timbl-642727d/.github/000077500000000000000000000000001451477526200176505ustar00rootroot00000000000000LanguageMachines-timbl-642727d/.github/workflows/000077500000000000000000000000001451477526200217055ustar00rootroot00000000000000LanguageMachines-timbl-642727d/.github/workflows/badge.svg000066400000000000000000000061641451477526200234770ustar00rootroot00000000000000 LanguageMachines-timbl-642727d/.github/workflows/timbl.yml000066400000000000000000000074361451477526200235510ustar00rootroot00000000000000name: C/C++ CI on: push: branches: [master, valueclass] paths: - 'src/**' - 'include/**' - '.github/workflows/**' pull_request: branches: [master] jobs: notification: runs-on: ubuntu-latest name: Notify start to irc-gitlama outputs: branch: ${{ steps.extract_branch.outputs.branch }} steps: - name: Extract Branch name shell: bash run: echo "branch=$(echo ${GITHUB_REF#refs/heads/})" >> $GITHUB_OUTPUT id: extract_branch - name: IRC notification uses: Gottox/irc-message-action@v2 with: server: irc.uvt.nl channel: '#gitlama' nickname: GitHub message: > ${{ github.actor }} started a build of ${{ github.event.repository.name }} [${{ steps.extract_branch.outputs.branch }}] build: runs-on: ${{ matrix.os }} needs: notification strategy: matrix: os: [ubuntu-latest, macos-latest] compiler: [g++, clang++] steps: - name: Cancel Previous Runs uses: styfle/cancel-workflow-action@0.11.0 with: access_token: ${{ github.token }} - uses: actions/checkout@v3 - name: Install Build Environment run: | if [ "$RUNNER_OS" == "Linux" ]; then sudo apt-get install pkg-config autoconf-archive else brew upgrade; brew install pkg-config brew install autoconf-archive brew install autoconf brew install automake fi - name: Install Dependencies run: | if [ "$RUNNER_OS" == "Linux" ]; then sudo apt-get install libicu-dev libxml2-dev libbz2-dev sudo apt-get install zlib1g-dev cppcheck else brew install libxml2; brew install bzip2; brew install zlib; brew install cppcheck fi - name: install TiccUtils env: CXX: ${{ matrix.compiler }} run: | git clone https://github.com/LanguageMachines/ticcutils; cd ticcutils; bash bootstrap.sh; ./configure; make; sudo make install; cd ..; - name: bootstrap run: sh bootstrap.sh - name: configure env: CXX: ${{ matrix.compiler }} run: ./configure - name: compiler-id id: compiler run: | id=$(echo ${{matrix.compiler}} | cut -d\+ -f1) echo "id=$id" >> $GITHUB_ENV - name: Static Code-check run: cppcheck --enable=all --quiet --error-exitcode=0 . - name: make run: make - name: install run: sudo make install - name: make check env: CXX: ${{ matrix.compiler }} run: LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib make check continue-on-error: true - name: show log run: cat src/test-suite.log - name: Notify IRC of failure if: ${{ failure() }} uses: Gottox/irc-message-action@v2 with: server: irc.uvt.nl channel: '#gitlama' nickname: GH-${{ runner.os }}-${{ env.id }} message: "timbl [${{ needs.notification.outputs.branch }}] build with ${{ matrix.compiler }} by ${{ github.actor }} on ${{ matrix.os }}: \u00034FAIL\u0003" - name: Notify IRC of succes if: ${{ success() }} uses: Gottox/irc-message-action@v2 with: server: irc.uvt.nl channel: '#gitlama' nickname: GH-${{ runner.os }}-${{ env.id }} message: "timbl [${{ needs.notification.outputs.branch }}] build with ${{ matrix.compiler }} by ${{ github.actor }} on ${{ matrix.os }}: \u00033SUCCESS\u0003" LanguageMachines-timbl-642727d/.gitignore000066400000000000000000000003551451477526200203030ustar00rootroot00000000000000*~ *.gz Makefile Makefile.in compile config.guess config.h config.h.in config.log config.status config.sub configure INSTALL aclocal.m4 autom4te.cache/ depcomp install-sh libtool ltmain.sh missing stamp-h1 test-driver timbl.pc ChangeLog LanguageMachines-timbl-642727d/AUTHORS000066400000000000000000000016461451477526200173670ustar00rootroot00000000000000TiMBL authors Lead programmer: Ko van der Sloot Code, algorithm, and design contributions by: Peter Berck Antal van den Bosch Walter Daelemans Maarten van Gompel Ton Weijters Jakub Zavrel Contributors: People who contributed to Timbl by suggesting improvements, filing bug reports, asking the right questions etc.: Robert Andersson Vincent Van Asch Joris Bleys Johan Bos Joan Bresnan Stefan Breuer Sabine Buchholz Bertjan Busser Sander Canisius Giovanni Cassani Win Carus Felix Filoz Alan Frankel Sven Hartrumpf Iris Hendrickx Lyndon Hiew Steve Hunt Valentin Jijkoun Gunn Inger Lyse Svetoslav Marinov Erwin Marsi Liam McGrath Jens Nilsson Ties Kemper Tom DePlonty Adam Radziszewski Albert Russel Yvan Saeys Frank Scheelen Armin Schmidt Olaf Seibert Gabriel Skantze Carline Sporleder Herman Stehouwer Erik Tjong Kim Sang Joseph Turian Frederik Vaassen Corne Versloot Colin Wilson Linda Yung Bram Vandekerckhove Menno van Zaanen LanguageMachines-timbl-642727d/COPYING000077500000000000000000001045121451477526200173510ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read .LanguageMachines-timbl-642727d/Dockerfile000066400000000000000000000026621451477526200203100ustar00rootroot00000000000000FROM alpine:latest #VERSION can be: # - stable: builds latest stable versions from source (default) # - distro: uses packages as provided by Alpine Linux (may be slightly out of date) # - devel: latest development version (git master/main branch) ARG VERSION="stable" LABEL org.opencontainers.image.authors="Maarten van Gompel " LABEL description="timbl - tilburg memory-based learner" RUN mkdir -p /data RUN mkdir -p /usr/src/timbl COPY . /usr/src/timbl RUN if [ "$VERSION" = "distro" ]; then \ rm -Rf /usr/src/timbl &&\ echo -e "----------------------------------------------------------\nNOTE: Installing latest release as provided by Alpine package manager.\nThis version may diverge from the one in the git master tree or even from the latest release on github!\nFor development, build with --build-arg VERSION=development.\n----------------------------------------------------------\n" &&\ apk update && apk add timbl; \ else \ PACKAGES="libtar libbz2 icu-libs libxml2 libgomp libstdc++" &&\ BUILD_PACKAGES="build-base autoconf-archive autoconf automake libtool libtar-dev bzip2-dev icu-dev libxml2-dev git" &&\ apk add $PACKAGES $BUILD_PACKAGES &&\ cd /usr/src/ && ./timbl/build-deps.sh &&\ cd timbl && sh ./bootstrap.sh && ./configure && make && make install &&\ apk del $BUILD_PACKAGES && rm -Rf /usr/src; \ fi WORKDIR / ENTRYPOINT [ "timbl" ] LanguageMachines-timbl-642727d/MAINTAINERS000066400000000000000000000001551451477526200200060ustar00rootroot00000000000000Maarten van Gompel (KNAW Humanities Cluster) Ko van der Sloot LanguageMachines-timbl-642727d/Makefile.am000066400000000000000000000006201451477526200203420ustar00rootroot00000000000000 ACLOCAL_AMFLAGS =-I m4 --install SUBDIRS = src include demos docs m4 EXTRA_DIST = bootstrap.sh AUTHORS TODO NEWS README.md timbl.pc.in codemeta.json pkgconfigdir = $(libdir)/pkgconfig pkgconfig_DATA = timbl.pc ChangeLog: NEWS git pull; git2cl > ChangeLog docker: docker build -t timbl:latest . docker-dev: docker build -t timbl:dev --build-arg VERSION=development . deps: ./build-deps.sh LanguageMachines-timbl-642727d/NEWS000066400000000000000000000153131451477526200170120ustar00rootroot000000000000006.9 2023-10-21 [Ko van der Sloot] * better code: const correctness etc. 6.8.2 2023-02-22 [Ko van der Sloot] * plugged a memory leak * C++ code quality improved * removed dependency on deprecated sprintf function * removed dependency on libtar 6.8.1 2023-01-04 [Ko van der Sloot] * fix for some odd distro issues * for now re-added 2 backward compatability functions 6.8 2023-01-02 [Ko van der Sloot] * major code refactoring - BREAKS API and ABI - library bumped - getting rid of a lot of pointers and C-style arrays - removed C-style casts - Unicode is the default now for most functions. Some 'string' functions are still available in the API. - In general modernizing to C++11 - weeded out CppCheck warnings * improved GitHub action 6.7 2022-07-22 [Maarten van Gompel] * updated metadata (codemeta.json) following new (proposed) CLARIAH requirements (CLARIAH/clariah-plus#38) * added builds-deps.sh for automatically building and installing dependencies * added Dockerfile and instructions * no functional changes 6.6 2020-12-15 [Ko vd Sloot] * Internally we use NFC normalized UnicodeString's now. Timbl should be robust for UTF8 files, even exotic languages. * added some Unicode fuctions to the API * bumped library version * several code refactorings * added the possibility to use the options -f and -i, without -t 6.5 2020-04-15 [Ko vd Sloot] * adapted to the newest TiCC::CommandLine implementation * small code refactorings 6.4.14 2019-10-21 [Ko vd Sloot] * added JSON support. Still EXPERIMENTAL! the JSON syntax might change in the future. So handle with care. * confidence score calculation is now a real TimblExperiment member * removed Boost dependency. 6.4.13 2018-11-28 [Ko van der Sloot] - added a '--limit' option to use only the most significant features 6.4.12 2018-05-16 [Ko van der Sloot] Bugfix release: - updated usage(). Info on -G 2 option was wrong. - changed an error message to be more clear. - fixed building of the TeX documentation [Maarten van Gompel] - Added codemeta.json metadata 6.4.11 2018-01-09 [Ko van der Sloot] Bugfix release: - Fixed a major bug in similarity metric calculations. (Cosine and Dot product) 6.4.10 2017-11-09 [Ko van der Sloot] Bugfix release: - allow for spaces in TABBED input (they are significant) - corrected some typos in messages and man page - minor code refactorings 6.4.9 2017-05-04 [Ko van der Sloot] Maintenance release: - removed unused/non-functional functions from the API - code refactoring. Mostly based on CPPCHECK static analyzer. - small bugs: -e options didn't always do what you expected - added missing files in docs [Maarten van Gompel] - updated README.md 6.4.8 2016-07-11 [Ko van der Sloot] Maintance release: - code refactoring and improvement - relying more on ticcutils - fixed exit codes - accept long options: --version and --help - fix out-of-range problem in Sparse Format 6.4.7 2016-01-14 [Ko van der Sloot][Maarten van Gompel] * repository moved to GitHub * added travis support * code updates. (clearer code mainly) * depending a bit more on ticcutils (CommanLine, StringOps) * some small bug fixes (LOO with a 1 line file) 6.4.6 2014-09-23 [Ko van der Sloot] * release 6.4.5 2014-09-16 * small bug fixes 6.4.4 2013-04-03 * rely more on ticcutils stuff. A lot of functions are moved there * added a GetAccuracy option to the API * bug fix in Choppers.cxx 6.4.3 2012-10-11 * added an --occurrences option for training/testing files with an occurrence value. * made Tree.cxx and Trie.h 'omp thread-safe' and moved them to ticcutils * added a "Tabbed' inputformat (© Maarten van Gompel) * The Micro Avagare F-score calculation is now according to the Manual. There were small differences caused by a mixup of test and train data. 6.4.2 2011-12-20 * start to use Requires.private in timbl.pc * added a 'check' target to Make system * achieved a considerable speedup for multithreaded testing. * fixed a small problem in LogBuffer. Also simplified and cleaned up LogBuffer and LogStream code. All dependencies need recompiling! * implemented +vcf (confidence) output * The -T option for TreeOrdening is now named --Treeorder * fixed tiebreaking for -R (random) option, closes bug 43, again. * some small fixes for '-pedantic' compiler option * avoid zero result in Exponential Decay (bug 89). * removed unused relativeWeight() function. (was duplicated) 6.4.1 2011-08-25 [ Ko van der Sloot ] - added Version() and VersionName() functions. We want them for every member of the family - fixed a problem with including 'config'h' in the API - fixed a problem with normalization on empty distributions. - added a Confidence( class ) function to the instances API. returns the Weight of 'class'. Which is influenced by normalization! - added logProbability normalization - the +vS option was not always honoured. Now Timbl shuts it's big mouth better - Expand() is now also enabled for TRIBL and TRIBL2 6.4.0 - decapped Timbl and libTimbl to timbl and libtimbl this will shake the whole timbl family tree! - small fixes to survive -pedantic compiler option without warnings 6.3.4 - we now support incremental learning from a file on the command line. - implemented a --clones option to use multiple threads for testing - fixed bug 58. Emit Error when reading an InstanceBase without a test. - fixed bug 61. Give a Warning when a trainingset contains only 1 class. - cleaned up build system 6.3.3 - several small fixes. 6.3.2 - fixed bug 44. Segfault in weird cases - fixed bug 45. Needless processing of traindata when required option is missing - fixed bug 46. Tribl2 sometimes fails to correctly output +v+k+n - fixed bug 47. Unclear error message when InnerProduct fails - several small uncritical enhancements 6.3.1 - 2010-11-17 - Little API change in TimblOptions (more clear i hope) - Little bug fixes and improvement (logging mostly) - Moved LogStream stuff back in from TimblServer 6.2.3 forgot to edit this file 6.2.2 forgot to edit this file 6.2.1 - 2009-11-30 - Fixed compilation problem on Cygwin - Added functions to API - Improved server functionality (undocumented yet) 6.2.0 - 2009-11-03 - Stable release 6.1.99.0.20091021.1 - 2009-10-21 - Another snapshot from SVN. Now needs libxml2 for building. 6.1.99.0.20091014.1 - 2009-10-14 - Another snapshot from SVN. (By mistake, aka 6.2.0). 6.2.0.pre3 - 2009-10-05 - Another snapshot from SVN. 6.2.0-pre1 - 2009-09-03 - snapshot from SVN. 2009-08-31: Ko vd Sloot It's been a long time sine the prevous news lot of overhauling of the code took place 2008-03-04: Ko vd Sloot numerous small bug fixes. getting ready for 6.1.2 2007-12-03: Ko vd Sloot Packaging seems to be fine now. 2007-10-01: Ko vd Sloot first packaging attempt # $Id$ # $URL$ LanguageMachines-timbl-642727d/README000066400000000000000000000000521451477526200171650ustar00rootroot00000000000000Please see README.md for for information. LanguageMachines-timbl-642727d/README.md000066400000000000000000000105101451477526200175640ustar00rootroot00000000000000[![GitHub build](https://github.com/LanguageMachines/timbl/actions/workflows/timbl.yml/badge.svg?branch=master)](https://github.com/LanguageMachines/timbl/actions/) [![Language Machines Badge](http://applejack.science.ru.nl/lamabadge.php/timbl)](http://applejack.science.ru.nl/languagemachines/) [![DOI](https://zenodo.org/badge/20526237.svg)](https://zenodo.org/badge/latestdoi/20526237) =========================================== TiMBL: Tilburg Memory Based Learner =========================================== TiMBL 6.4 (c) CLS/ILK/CLiPS 1998 - 2023 Centre for Language Studies, Radboud University Nijmegen Induction of Linguistic Knowledge Research Group, Tilburg University and Centre for Dutch Language and Speech, University of Antwerp **Website:** https://languagemachines.github.io/timbl/ TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. For over fifteen years TiMBL has been mostly used in natural language processing as a machine learning classifier component, but its use extends to virtually any supervised machine learning domain. Due to its particular decision-tree-based implementation, TiMBL is in many cases far more efficient in classification than a standard k-nearest neighbor algorithm would be. ----------------------------------------------------------------------- This is a major extension to the sixth main release of TiMBL. Most significant change: **The main program is now called 'timbl' and not 'Timbl' anymore. Be warned!** This change is part of our effort to get our MBL software into software distributions like Debian, Ubuntu, RedHat . Comments and bug-reports are welcome at our issue tracker at https://github.com/LanguageMachines/timbl/issues or by mailing lamasoftware (at) science.ru.nl. Documentation and more info may be found on https://languagemachines.github.io/timbl . TiMBL is distributed under the GNU Public Licence v3 (see the file COPYING). ----------------------------------------------------------------------- This software has been tested on: - Intel platforms running several versions of Linux, including Ubuntu, Debian, Arch Linux, Fedora (both 32 and 64 bits) - MAC platform running OS X 10.10 Alternatively, with some effort, you may get it to work on a Windows platform using Cygwin. Compilers: - GCC (use 7.0 or later) - Clang Contents of this distribution: - Sources - Licensing information ( COPYING ) - Build system based on GNU Autotools - Container build file ( Dockerfile ) - Example data files ( in the demos directory ) - Documentation ( in the docs directory ) Dependencies: To be able to succesfully build TiMBL from the tarball, you need the following pakages: - ticcutils (https://github.com/LanguageMachines/ticcutils) - pkg-config - libxml2-dev To install TiMBL, first consult whether your distribution's package manager has an up-to-date package for TiMBL. To compile and install manually from source instead, provided you have all the dependencies installed: $ bash bootstrap.sh $ ./configure $ make $ make install If you want to automatically download and install the latest stable versions of the required dependencies, then run `./build-deps.sh` prior to the above. You can pass a target directory prefix as first argument and you may need to prepend `sudo` to ensure you can install there. The dependencies are: * [ticcutils](https://github.com/LanguageMachines/ticcutils) A `Dockerfile` for a container build is also available, specify `--build-arg VERSION=development` if you want the latest development version instead. You will still need to take care to install the following 3rd party dependencies through your distribution's package manager, as they are not provided by our script: * ``icu`` - A C++ library for Unicode and Globalization support. On Debian/Ubuntu systems, install the package libicu-dev. * A sane build environment with a C++ compiler (e.g. gcc 4.9 or above or clang), make, autotools, libtool, pkg-config LanguageMachines-timbl-642727d/TODO000066400000000000000000000001331451477526200167750ustar00rootroot00000000000000* reorganize clone() splitChild() and such in TimblExperiment. it is very confusing now. LanguageMachines-timbl-642727d/bootstrap.sh000077500000000000000000000042311451477526200206640ustar00rootroot00000000000000# bootstrap - script to bootstrap the distribution rolling engine # usage: # $ sh ./bootstrap && ./configure && make dist[check] # # this yields a tarball which one can install doing # # $ tar zxf PACKAGENAME-*.tar.gz # $ cd PACKAGENAME-* # $ ./configure # $ make # # make install # requirements: # GNU autoconf, from e.g. ftp.gnu.org:/pub/gnu/autoconf/ # GNU automake, from e.g. http://ftp.gnu.org/gnu/automake/ automake=automake aclocal=aclocal # if you want to autogenerate a ChangeLog form svn: # # svn2cl, a python script, as used in the GNU Enterprise project. # By jcater (Jason Cater), contributions by reinhard (Reinhard Müller). # Get it from # http://www.gnuenterprise.org/cgi-bin/viewcvs.cgi/*checkout*/gnue/trunk/gnue-common/utils/svn2cl . # svn2cl is used in Makefile.am too. # # (Another svn2cl implementation, in perl, is at # http://www.contactor.se/~dast/svn/archive-2002-04/0910.shtml) # # see also toplevel Makefile.am # test -f ChangeLog || { # svn log --verbose > ChangeLog #} if $automake --version|head -1 |grep ' 1\.[4-9]'; then echo "automake 1.4-1.9 is active. You should use automake 1.10 or later" if test -f /etc/debian_version; then echo " sudo apt-get install automake" echo " sudo update-alternatives --config automake" fi exit 1 fi # autoconf-archive Debian package, aclocal-archive RPM, obsolete/badly supported OS, installed in home dir acdirs="/usr/share/autoconf-archive/ /usr/share/aclocal/ /usr/local/share/aclocal/ $HOME/local/share/autoconf-archive/" found=false for d in $acdirs do if test -f ${d}libtool.m4 then found=true break fi done if ! $found then cat <&2 echo " Building latest stable release of main dependencies from source.">&2 echo "------------------------------------------------------------------------">&2 else echo "------------------------------------------------------------------------">&2 echo " Building development versions of main dependencie from source.">&2 echo " (This is experimental and may contain bugs! DO NOT PUBLISH!)">&2 echo "-----------------------------------------------------------------------">&2 fi PWD="$(pwd)" BUILDDIR="$(mktemp -dt "build-deps.XXXXXX")" cd "$BUILDDIR" BUILD_SOURCES="LanguageMachines/ticcutils" for SUFFIX in $BUILD_SOURCES; do \ NAME="$(basename "$SUFFIX")" git clone "https://github.com/$SUFFIX" cd "$NAME" REF=$(git tag -l | grep -E "^v?[0-9]+(\.[0-9])*" | sort -t. -k 1.2,1n -k 2,2n -k 3,3n -k 4,4n | tail -n 1) if [ "$VERSION" = "stable" ] && [ -n "$REF" ]; then git -c advice.detachedHead=false checkout "$REF" fi sh ./bootstrap.sh && ./configure --prefix "$PREFIX" && make && make install cd .. done cd "$PWD" [ -n "$BUILDDIR" ] && rm -Rf "$BUILDDIR" LanguageMachines-timbl-642727d/codemeta.json000066400000000000000000000110631451477526200207650ustar00rootroot00000000000000{ "@context": [ "https://doi.org/10.5063/schema/codemeta-2.0", "http://schema.org", "https://w3id.org/software-types" ], "@type": "SoftwareSourceCode", "identifier": "timbl", "name": "TiMBL", "version": "6.9", "description": "TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases.", "license": "https://spdx.org/licenses/GPL-3.0", "url": "https://languagemachines.github.io/timbl", "author": [ { "@type": "Person", "givenName": "Ko", "familyName": "van der Sloot", "email": "ko.vandersloot@let.ru.nl", "affiliation": { "@id": "https://www.ru.nl/clst", "@type": "Organization", "name": "Centre for Language and Speech Technology", "url": "https://www.ru.nl/clst", "parentOrganization": { "@id": "https://www.ru.nl/cls", "@type": "Organization", "name": "Centre for Language Studies", "url": "https://www.ru.nl/cls", "parentOrganization": { "@id": "https://www.ru.nl", "name": "Radboud University", "@type": "Organization", "url": "https://www.ru.nl", "location": { "@type": "Place", "name": "Nijmegen" } } } }, "position": 1 }, { "@id": "https://orcid.org/0000-0003-2493-656X", "@type": "Person", "givenName": "Antal", "familyName": "van den Bosch", "email": "antal.vandenbosch@let.ru.nl", "affiliation": { "@id": "https://cls.ru.nl" }, "position": 2 }, { "@type": "Person", "givenName": "Walter", "familyName": "Daelemans", "position": 3 }, { "@id": "https://orcid.org/0000-0002-1046-0006", "@type": "Person", "givenName": "Maarten", "familyName": "van Gompel", "email": "proycon@anaproy.nl", "affiliation": { "@id": "https://knaw.huc.nl" }, "position": 4 }, { "@type": "Person", "givenName": "Ton", "familyName": "Weijters", "position": 5 }, { "@type": "Person", "givenName": "Jakub", "familyName": "Zavrel", "position": 6 } ], "sourceOrganization": { "@id": "https://www.ru.nl/clst" }, "programmingLanguage": { "@type": "ComputerLanguage", "identifier": "c++", "name": "C++" }, "operatingSystem": [ "Linux", "BSD", "macOS" ], "codeRepository": "https://github.com/LanguageMachines/timbl", "softwareRequirements": [ { "@type": "SoftwareApplication", "identifier": "libxml2", "name": "libxml2" }, { "@type": "SoftwareApplication", "identifier": "ticcutils", "name": "ticcutils" } ], "readme": "https://github.com/LanguageMachines/timbl/blob/master/README.md", "issueTracker": "https://github.com/LanguageMachines/timbl/issues", "contIntegration": "https://travis-ci.org/LanguageMachines/timbl", "releaseNotes": "https://github.com/LanguageMachines/timbl/releases", "developmentStatus": "https://www.repostatus.org/#active", "keywords": [ "nlp", "natural language processing", "memory based learning", "machine learning", "knn", "k-nearest neighbours", "decision tree" ], "referencePublication": [ { "@type": "TechArticle", "name": "TiMBL: Tilburg Memory Based Learner, Reference Guide", "author": [ "Walter Daelemans", "Jakub Zavrel", "Ko van der Sloot", "Antal van den Bosch" ], "url": "https://github.com/LanguageMachines/timbl/raw/master/docs/Timbl_6.4_Manual.pdf" }, { "@type": "Book", "name": "Memory-Based Language Processing", "author": [ "Walter Daelemans", "Antal van den Bosch" ], "url": "http://ilk.uvt.nl/mblp", "publisher": "Cambridge University Press" } ], "dateCreated": "1998", "targetProduct": [ { "@type": "SoftwareLibrary", "executableName": "libtimbl", "name": "libtimbl", "runtimePlatform": [ "Linux", "BSD", "macOS" ], "description": "Memory-based Learning Library with API for C++" }, { "@type": "CommandLineApplication", "executableName": "timbl", "name": "timbl", "runtimePlatform": [ "Linux", "BSD", "macOS" ], "description": "Memory-based learner, command-line tool" } ] } LanguageMachines-timbl-642727d/configure.ac000066400000000000000000000040021451477526200205720ustar00rootroot00000000000000# -*- Autoconf -*- # Process this file with autoconf to produce a configure script. AC_PREREQ([2.69]) AC_INIT([timbl],[6.9],[lamasoftware@science.ru.nl]) #also adapt in codemeta.json! AM_INIT_AUTOMAKE AC_CONFIG_SRCDIR([.]) AC_CONFIG_MACRO_DIR([m4]) AC_CONFIG_HEADERS([config.h]) if test x"${CXXFLAGS+set}" = xset; then # the user set CXXFLAGS; don't override it. cxx_flags_were_set=true else cxx_flags_were_set=false fi if $cxx_flags_were_set; then CXXFLAGS=$CXXFLAGS fi # Checks for programs. AC_PROG_CXX( [g++ c++] ) # libtool stuff LT_INIT # when running tests, use CXX AC_LANG([C++]) AC_OPENMP if test "x$ac_cv_prog_cxx_openmp" != "x"; then if test "x$ac_cv_prog_cxx_openmp" != "xunsupported"; then CXXFLAGS="$CXXFLAGS $OPENMP_CXXFLAGS" AC_DEFINE([HAVE_OPENMP], [1] , [Define to 1 if you have OpenMP] ) else AC_MSG_NOTICE([We don't have OpenMP for Clang. Multithreaded operation is di sabled]) fi fi #checks for libraries. # Checks for header files. AC_CHECK_HEADERS([sys/time.h]) # Checks for typedefs, structures, and compiler characteristics. AC_HEADER_STDBOOL AC_C_INLINE AC_TYPE_SIZE_T # Checks for library functions. AC_CHECK_FUNCS([floor gettimeofday pow rint sqrt ]) PKG_PROG_PKG_CONFIG if test "x$prefix" = "xNONE"; then prefix="/usr/local" fi if test "x$PKG_CONFIG_PATH" = x; then export PKG_CONFIG_PATH="$prefix/lib/pkgconfig" else export PKG_CONFIG_PATH="$prefix/lib/pkgconfig:$PKG_CONFIG_PATH" fi AC_OSX_PKG( [icu4c] ) PKG_PROG_PKG_CONFIG PKG_CHECK_MODULES([XML2], [libxml-2.0 >= 2.6.16] ) CXXFLAGS="$CXXFLAGS $XML2_CFLAGS" LIBS="$LIBS $XML2_LIBS" PKG_CHECK_MODULES([ticcutils], [ticcutils >= 0.30] ) CXXFLAGS="$CXXFLAGS $ticcutils_CFLAGS" LIBS="$LIBS $ticcutils_LIBS" PKG_CHECK_MODULES([ICU], [icu-uc >= 50 icu-io] ) CXXFLAGS="$CXXFLAGS $ICU_CFLAGS" LIBS="$ICU_LIBS $LIBS" AC_CONFIG_FILES([ Makefile timbl.pc m4/Makefile src/Makefile docs/Makefile include/Makefile include/timbl/Makefile demos/Makefile ]) AC_OUTPUT LanguageMachines-timbl-642727d/demos/000077500000000000000000000000001451477526200174175ustar00rootroot00000000000000LanguageMachines-timbl-642727d/demos/.gitignore000066400000000000000000000001701451477526200214050ustar00rootroot00000000000000*~ *.o *.lo Makefile Makefile.in .deps/ .libs/ tse api_test1 api_test2 api_test3 api_test4 api_test5 api_test6 classify LanguageMachines-timbl-642727d/demos/Makefile.am000066400000000000000000000012401451477526200214500ustar00rootroot00000000000000# $Id$ # $URL: $ AM_CPPFLAGS = -I@top_srcdir@/include AM_CXXFLAGS = -std=c++0x noinst_PROGRAMS = api_test1 api_test2 api_test3 api_test4 api_test5 api_test6\ tse classify LDADD = ../src/libtimbl.la tse_SOURCES = tse.cxx classify_SOURCES = classify.cxx api_test1_SOURCES = api_test1.cxx api_test2_SOURCES = api_test2.cxx api_test3_SOURCES = api_test3.cxx api_test4_SOURCES = api_test4.cxx api_test5_SOURCES = api_test5.cxx api_test6_SOURCES = api_test6.cxx exdir = $(datadir)/doc/@PACKAGE@/examples ex_DATA = dimin.script dimin.train dimin.test cross_val.test \ small_1.train small_2.train small_3.train small_4.train small_5.train EXTRA_DIST = $(ex_DATA) LanguageMachines-timbl-642727d/demos/api_test1.cxx000077500000000000000000000023401451477526200220360ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include "timbl/TimblAPI.h" int main(){ Timbl::TimblAPI My_Experiment( "-a IGTREE +vDI+DB+F", "test1" ); My_Experiment.SetOptions( "-w3 -vDB" ); My_Experiment.ShowSettings( std::cout ); My_Experiment.Learn( "dimin.train" ); My_Experiment.Test( "dimin.test", "my_first_test.out" ); My_Experiment.SetOptions( "-mM" ); My_Experiment.Test( "dimin.test", "my_first_test.out" ); } LanguageMachines-timbl-642727d/demos/api_test2.cxx000077500000000000000000000023261451477526200220430ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include "timbl/TimblAPI.h" int main(){ Timbl::TimblAPI *My_Experiment = new Timbl::TimblAPI( "-a IB2 +vF+DI+DB" , "test2" ); My_Experiment->SetOptions( "-b100" ); My_Experiment->ShowSettings( std::cout ); My_Experiment->Learn( "dimin.train" ); My_Experiment->Test( "dimin.test", "my_second_test.out" ); delete My_Experiment; exit(1); } LanguageMachines-timbl-642727d/demos/api_test3.cxx000077500000000000000000000021001451477526200220320ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include "timbl/TimblAPI.h" using Timbl::TimblAPI; int main(){ TimblAPI *My_Experiment = new TimblAPI( "-t cross_validate" ); My_Experiment->Test( "cross_val.test" ); delete My_Experiment; exit(0); } LanguageMachines-timbl-642727d/demos/api_test4.cxx000077500000000000000000000041371451477526200220470ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include #include "timbl/TimblAPI.h" using namespace Timbl; int main(){ TimblAPI *My_Experiment = new TimblAPI( "-a IB1 +vDI+DB +mM" , "test4" ); My_Experiment->ShowSettings( std::cout ); My_Experiment->Learn( "dimin.train" ); My_Experiment->Test( "dimin.test", "inc1.out" ); My_Experiment->SaveWeights( "wg.1.wgt" ); My_Experiment->WriteArrays( "arr.1.arr" ); My_Experiment->Increment( "=,=,=,=,+,k,e,=,-,r,@,l,T" ); My_Experiment->Test( "dimin.test", "inc2.out" ); My_Experiment->SaveWeights( "wg.2.wgt" ); My_Experiment->WriteArrays( "arr.2.arr" ); My_Experiment->Increment( "+,zw,A,rt,-,k,O,p,-,n,O,n,E" ); My_Experiment->Test( "dimin.test", "inc3.out" ); My_Experiment->SaveWeights( "wg.3.wgt" ); My_Experiment->WriteArrays( "arr.3.arr" ); My_Experiment->Decrement( "+,zw,A,rt,-,k,O,p,-,n,O,n,E" ); My_Experiment->Test( "dimin.test", "inc4.out" ); My_Experiment->SaveWeights( "wg.4.wgt" ); My_Experiment->WriteArrays( "arr.4.arr" ); My_Experiment->Decrement( "=,=,=,=,+,k,e,=,-,r,@,l,T" ); My_Experiment->Test( "dimin.test", "inc5.out" ); My_Experiment->SaveWeights( "wg.5.wgt" ); My_Experiment->WriteArrays( "arr.5.arr" ); delete My_Experiment; exit(1); } LanguageMachines-timbl-642727d/demos/api_test5.cxx000077500000000000000000000060051451477526200220440ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include #include "timbl/TimblAPI.h" using std::endl; using std::cout; using std::string; using namespace Timbl; int main(){ TimblAPI *My_Experiment = new TimblAPI( "-a IB1 +vDI+DB+n +mM +k4 " , "test5" ); My_Experiment->Learn( "dimin.train" ); { icu::UnicodeString line = "=,=,=,=,+,k,e,=,-,r,@,l,T"; const neighborSet *neighbours1 = My_Experiment->classifyNS( line ); if ( neighbours1 ){ cout << "Classify OK on " << line << endl; cout << neighbours1; } else { cout << "Classify failed on " << line << endl; neighbours1 = new neighborSet(); } neighborSet neighbours2; line = "+,zw,A,rt,-,k,O,p,-,n,O,n,E"; if ( My_Experiment->classifyNS( line, neighbours2 ) ){ cout << "Classify OK on " << line << endl; cout << neighbours2; } else { cout << "Classify failed on " << line << endl; } line = "+,z,O,n,-,d,A,xs,-,=,A,rm,P"; const neighborSet *neighbours3 = My_Experiment->classifyNS( line ); if ( neighbours3 ){ cout << "Classify OK on " << line << endl; cout << neighbours3; } else { cout << "Classify failed on " << line << endl; neighbours3 = new neighborSet(); } neighborSet uit2; { neighborSet uit; uit.setShowDistance(true); uit.setShowDistribution(true); cout << " before first merge " << endl; cout << uit; uit.merge( *neighbours1 ); cout << " after first merge " << endl; cout << uit; uit.merge( *neighbours3 ); cout << " after second merge " << endl; cout << uit; uit.merge( neighbours2 ); cout << " after third merge " << endl; cout << uit; uit.truncate( 3 ); cout << " after truncate " << endl; cout << uit; cout << " test assignment" << endl; uit2 = *neighbours1; } cout << "assignment result: " << endl; cout << uit2; { cout << " test copy construction" << endl; neighborSet uit(uit2); cout << "result: " << endl; cout << uit; } cout << "almost done!" << endl; } delete My_Experiment; cout << "done!" << endl; } LanguageMachines-timbl-642727d/demos/api_test6.cxx000077500000000000000000000036301451477526200220460ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include "timbl/TimblAPI.h" using std::cout; using std::endl; using namespace Timbl; int main(){ TimblAPI My_Experiment( "-a IB1 +vDI+DB -G 0 -k3", "test6" ); My_Experiment.Learn( "dimin.train" ); const ClassDistribution *vd; const TargetValue *tv = My_Experiment.Classify( std::string("-,=,O,m,+,h,K,=,-,n,I,N,K"), vd ); cout << "resulting target: " << tv << endl; cout << "resulting Distribution: " << vd << endl; ClassDistribution::dist_iterator it=vd->begin(); while ( it != vd->end() ){ cout << it->second << " OR "; cout << it->second->Value() << " " << it->second->Weight() << endl; ++it; } cout << "the same with neighborSets" << endl; const neighborSet *nb = My_Experiment.classifyNS( "-,=,O,m,+,h,K,=,-,n,I,N,K" ); WClassDistribution *vd2 = nb->bestDistribution(); vd2->Normalize(); cout << "default answer " << vd2 << endl; decayStruct *dc = new expDecay(0.3); delete vd2; vd2 = nb->bestDistribution( dc ); delete dc; cout << "with exponenial decay, alpha = 0.3 " << vd2 << endl; delete vd2; } LanguageMachines-timbl-642727d/demos/classify.cxx000077500000000000000000000063331451477526200217700ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include #include #include "timbl/TimblAPI.h" using namespace std; using namespace Timbl; char inf[] = "./dimin.train"; char test_f[] = "./dimin.test"; int main(){ string Bresult; double Distance; TimblAPI *Exp = new TimblAPI( "-a TRIBL" ); Exp->SetOptions( "+vS +x -N30 -q2" ); Exp->ShowOptions( cout ); Exp->Learn( inf ); ifstream testfile; string Buffer; testfile.open( test_f, ios::in ); cout << "\nStart testing, using TRIBL" << endl; while ( getline( testfile, Buffer ) ){ const TargetValue *tv = Exp->Classify( Buffer, Distance ); if ( tv ) cout << Buffer << "\t --> " << tv << " " << Distance << endl; else cout << Buffer << "\t --> (nill)" << endl; } testfile.close(); delete Exp; Exp = new TimblAPI( "-a IB1" ); Exp->SetOptions( "+vS" ); Exp->ShowOptions( cout ); Exp->Learn( inf ); testfile.clear(); testfile.open( test_f, ios::in ); cout << "\nStart testing, using IB" << endl; while ( getline( testfile, Buffer ) ){ if ( Exp->Classify( Buffer, Bresult, Distance ) ){ cout << Buffer << "\t --> " << Bresult << " " << Distance << endl; } else cout << Buffer << "\t --> (nill)" << endl; } testfile.close(); delete Exp; Exp = new TimblAPI( "-a IGTREE" ); Exp->SetOptions( "+vS -N40" ); Exp->ShowOptions( cout ); Exp->Learn( inf ); Exp->WriteInstanceBase( "dimin.tree" ); Exp->SaveWeights( "dimin.wgt" ); cout << "\nStart testing, using IGTree, first run" << endl; testfile.clear(); testfile.open( test_f, ios::in ); while ( getline( testfile, Buffer ) ){ if ( Exp->Classify( Buffer, Bresult, Distance ) ){ cout << Buffer << "\t --> " << Bresult << " " << Distance << endl; } else cout << Buffer << "\t --> (nill)" << endl; } testfile.close(); delete Exp; Exp = new TimblAPI( "-a IGTREE" ); Exp->SetOptions( "+vS" ); Exp->ShowOptions( cout ); Exp->GetInstanceBase( "dimin.tree" ); Exp->GetWeights( "dimin.wgt" ); cout << "\nStart testing, using IGTree, second run, (retrieved Tree)" << endl; testfile.clear(); testfile.open( test_f, ios::in ); while ( getline( testfile, Buffer ) ){ if ( Exp->Classify( Buffer, Bresult, Distance ) ){ cout << Buffer << "\t --> " << Bresult << " " << Distance << endl; } else cout << Buffer << "\t --> (nill)" << endl; } testfile.close(); exit(1); } LanguageMachines-timbl-642727d/demos/cross_val.test000077500000000000000000000001061451477526200223130ustar00rootroot00000000000000small_1.train small_2.train small_3.train small_4.train small_5.train LanguageMachines-timbl-642727d/demos/dimin.script000077500000000000000000000032741451477526200217560ustar00rootroot00000000000000# # Copyright (c) 1998 - 2011 # ILK - Tilburg University # CLiPS - University of Antwerp # # This file is part of timbl # # timbl is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # timbl is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see . # # For questions and suggestions, see: # http://ilk.uvt.nl/software.html # or send mail to: # timbl@uvt.nl # example script file for tse demo program. # # create an experiment with name exp1 # new exp1 # # make sure that we see something happen # exp1.set +v+f+di+O # # set the desired weighting to IG # exp1.set +w IG # # now train ( prepare is implicit) # exp1.train ./dimin.train # # save the Instancebase for later use. # exp1.save tree.tmp exp1.show options # # # first we start with OVERLAP metric # exp1.set -mO # # and test exp1.test ./dimin.test a1.tmp # # now we try the Value Difference Metric exp1.set -mM # exp1.test ./dimin.test a2.tmp # # start a new experiment: new exp2 # # fill it with de tree generated with exp1 # exp2.get tree.tmp # # let's make a lot of noice! exp2.set +v +o+f+di+n+db # # now delete exp1, to demonstrate that it works. free exp1 # # end perform a test with exp2 exp2.test ./dimin.test exp2.out.tmp # # ready LanguageMachines-timbl-642727d/demos/dimin.test000077500000000000000000000612721451477526200214330ustar00rootroot00000000000000=,=,=,=,=,=,=,=,+,p,e,=,T =,=,=,=,+,k,u,=,-,bl,u,m,E +,m,I,=,-,d,A,G,-,d,},t,J -,t,@,=,-,l,|,=,-,G,@,n,T -,=,I,n,-,str,y,=,+,m,E,nt,J =,=,=,=,=,=,=,=,+,br,L,t,J =,=,=,=,+,zw,A,=,-,m,@,r,T =,=,=,=,-,f,u,=,+,dr,a,l,T =,=,=,=,=,=,=,=,+,l,e,w,T =,=,=,=,+,tr,K,N,-,k,a,rt,J =,=,=,=,+,=,o,=,-,p,u,=,T =,=,=,=,=,=,=,=,+,l,A,m,E =,=,=,=,=,=,=,=,+,l,A,p,J =,=,=,=,=,=,=,=,+,sx,E,lm,P +,l,a,=,-,d,@,=,-,k,A,st,J -,s,i,=,-,f,E,r,-,st,O,k,J =,=,=,=,=,=,=,=,+,sp,a,n,T =,=,=,=,=,=,=,=,+,st,o,t,J =,=,=,=,+,sp,a,r,-,b,u,k,J +,h,I,N,-,k,@,l,-,bl,O,k,J -,m,e,=,-,d,A,l,+,j,O,n,E -,sn,u,=,-,p,@,=,+,r,K,=,T =,=,=,=,=,=,=,=,+,sp,A,N,E +,k,a,=,-,k,@,=,-,n,E,st,J =,=,=,=,+,v,u,=,-,r,I,N,K =,=,=,=,=,=,=,=,+,v,A,t,J -,r,@,=,+,G,I,s,-,t,@,r,T =,=,=,=,+,=,O,p,-,tr,E,k,J 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=,=,=,=,+,sn,L,=,-,t,@,r,T +,h,a,=,-,v,@,m,-,pl,a,ts,J =,=,=,=,=,=,=,=,+,z,e,=,T =,=,=,=,-,k,A,l,+,k,u,n,T LanguageMachines-timbl-642727d/demos/small_1.train000077500000000000000000000006701451477526200220140ustar00rootroot00000000000000Rockwell,PUNT,PUNT,PUNT,NNP,PUNT,PUNT,PUNT,I International,Rockwell,PUNT,PUNT,NNP,NNP,PUNT,PUNT,I CorpPUNT,International,Rockwell,PUNT,NNP,NNP,NNP,PUNT,I 's,CorpPUNT,International,Rockwell,POS,NNP,NNP,NNP,I Tulsa,'s,CorpPUNT,International,NNP,POS,NNP,NNP,B unit,Tulsa,'s,CorpPUNT,NN,NNP,POS,NNP,I said,unit,Tulsa,'s,VBD,NN,NNP,POS,I it,said,unit,Tulsa,PRP,VBD,NN,NNP,O signed,it,said,unit,VBD,PRP,VBD,NN,I a,signed,it,said,DT,VBD,PRP,VBD,O LanguageMachines-timbl-642727d/demos/small_2.train000077500000000000000000000006701451477526200220150ustar00rootroot00000000000000tentative,a,signed,it,JJ,DT,VBD,PRP,I agreement,tentative,a,signed,NN,JJ,DT,VBD,I extending,agreement,tentative,a,VBG,NN,JJ,DT,I its,extending,agreement,tentative,PRP$,VBG,NN,JJ,O contract,its,extending,agreement,NN,PRP$,VBG,NN,I with,contract,its,extending,IN,NN,PRP$,VBG,I Boeing,with,contract,its,NNP,IN,NN,PRP$,O CoPUNT,Boeing,with,contract,NNP,NNP,IN,NN,I to,CoPUNT,Boeing,with,TO,NNP,NNP,IN,I provide,to,CoPUNT,Boeing,VB,TO,NNP,NNP,O LanguageMachines-timbl-642727d/demos/small_3.train000077500000000000000000000006441451477526200220170ustar00rootroot00000000000000structural,provide,to,CoPUNT,JJ,VB,TO,NNP,O parts,structural,provide,to,NNS,JJ,VB,TO,I for,parts,structural,provide,IN,NNS,JJ,VB,I Boeing,for,parts,structural,NNP,IN,NNS,JJ,O 's,Boeing,for,parts,POS,NNP,IN,NNS,I 747,'s,Boeing,for,CD,POS,NNP,IN,B jetliners,747,'s,Boeing,NNS,CD,POS,NNP,I PUNT,jetliners,747,'s,PUNT,NNS,CD,POS,I Rockwell,PUNT,PUNT,jetliners,NNP,PUNT,PUNT,NNS,O said,Rockwell,PUNT,PUNT,VBD,NNP,PUNT,PUNT,I LanguageMachines-timbl-642727d/demos/small_4.train000077500000000000000000000006041451477526200220140ustar00rootroot00000000000000the,said,Rockwell,PUNT,DT,VBD,NNP,PUNT,O agreement,the,said,Rockwell,NN,DT,VBD,NNP,I calls,agreement,the,said,VBZ,NN,DT,VBD,I for,calls,agreement,the,IN,VBZ,NN,DT,O it,for,calls,agreement,PRP,IN,VBZ,NN,O to,it,for,calls,TO,PRP,IN,VBZ,I supply,to,it,for,VB,TO,PRP,IN,O 200,supply,to,it,CD,VB,TO,PRP,O additional,200,supply,to,JJ,CD,VB,TO,I shipsets,so-called,additional,200,NNS,JJ,JJ,CD,I LanguageMachines-timbl-642727d/demos/small_5.train000077500000000000000000000005251451477526200220170ustar00rootroot00000000000000for,shipsets,so-called,additional,IN,NNS,JJ,JJ,I the,for,shipsets,so-called,DT,IN,NNS,JJ,O planes,the,for,shipsets,NNS,DT,IN,NNS,I PUNT,planes,the,for,PUNT,NNS,DT,IN,I These,PUNT,PUNT,planes,DT,PUNT,PUNT,NNS,O include,These,PUNT,PUNT,VBP,DT,PUNT,PUNT,I among,KOMMA,include,These,IN,KOMMA,VBP,DT,O other,among,KOMMA,include,JJ,IN,KOMMA,VBP,O LanguageMachines-timbl-642727d/demos/tse.cxx000077500000000000000000000303151451477526200207430ustar00rootroot00000000000000/* Copyright (c) 1998 - 2015 ILK - Tilburg University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: http://ilk.uvt.nl/software.html or send mail to: timbl@uvt.nl */ #include #include #include #include #include "timbl/TimblAPI.h" using namespace Timbl; using std::ifstream; using std::ofstream; using std::ios; using std::cerr; using std::cin; using std::cout; using std::endl; using std::istream; using std::string; using std::equal; using std::getline; #define MAX_EXP 10 #define MAX_PARAMS 256 bool nocase_cmp( char c1, char c2 ){ return toupper(c1) == toupper(c2); } bool compare_nocase_n( const string& s1, const string& s2, size_t n ){ if ( equal( s1.begin(), s1.begin()+n, s2.begin(), nocase_cmp ) ){ return true; } else { return false; } } // // here are the various scripting actions: // enum ActionType { UnknownAct, New, Free, Set, Show, Classify, Train, Test, Increment, Decrement, Expand, Remove, SaveTree, GetTree, SaveW, GetW, Quit }; /* The following scripting commands are implemented: QUIT stop all further actions. NEW name create an experiment with name 'name' of type 'algo' algo can be IB1, IB2, TRIBL or IGTREE. Default: IBL FREE name delete the experiment with name 'name' .SET option value set option of experiment name to value .SHOW OPTIONS show all posible options with their default and current values for experiment 'name' .SHOW SETTINGS show all options with current settings of 'name' .TRAIN file build an instancebase from file. .TEST file1 [file2] classify all lines from file1, write results to file2 or to file1.out if parameter file2 is not present .EXPAND file1 increment the database with contents of file1 .REMOVE file1 decrement the database with contents of file1 .CLASSIFY line classify this line .ADD line increment the database with line .REM line decrement the database with line .SAVE file save the instancebase of experiment name to file. .GET file get the instancebase for experiment name from file. .SAVEW file save the current weights of experiment name to file. .GETW file get new weights for experiment name from file. */ TimblAPI *experiments[MAX_EXP]; int exp_cnt = 0; // the following functions implement a simple parser to parse the // script file, recognize experiment names en actions to perform // on those experiments int fill_params( string *params, const string& line ){ // chop line into a bunch of parameters. int i; for ( i=0; i < MAX_PARAMS; i++ ) params[i] = ""; i = 0; size_t len = line.length(); if ( line[0] == '"' && line[len-1] == '"' ) { params[0] = string( line, 1, len-2 ); return 1; } for ( size_t u_i = 0; u_i < len; u_i++) { if ( line[u_i] == ',' || line[u_i] == ' ' ){ if ( params[i] != "" ) // Don't accept zero length strings ! ++i; if ( i >= MAX_PARAMS ) break; } else params[i] += line[u_i]; } // u_i if ( i >= MAX_PARAMS ){ cerr << "too many parameters!" << endl; return MAX_PARAMS+1; } if ( params[i] != "" ){ // last param ended at line end i++; } return i; } int lookup( const string& name ){ // search for an experiment with the name 'name' in the list of // known experiments. int i; for ( i=0; i < exp_cnt; i++ ){ if ( name == experiments[i]->ExpName() ) return i; } return -1; } ActionType parse( const string& Buffer, int &pos, string *pars, int &len ){ // here we parse lines of the script-file: // first we take the first part and see if it is a NEW or FREE // command which need special attention. // otherwise we asume it to be the name of an experiment. string Buf = TiCC::trim( Buffer ); len = 0; if ( compare_nocase_n( Buf, "NEW ", 4 ) ){ len = fill_params( pars, Buf.substr(4) ); if ( ( pos = lookup( pars[0] ) ) != -1 ){ cerr << "you can't renew an experiment: " << Buf << endl; return UnknownAct; } return New; } else if ( compare_nocase_n( Buf, "FREE ", 5 ) ){ len = fill_params( pars, Buf.substr(5) ); if ( (pos = lookup( pars[0] ) ) == -1 ){ cerr << "you can't free this unknown experiment: " << Buf << endl; return UnknownAct; } return Free; } else if ( compare_nocase_n( Buf, "QUIT", 4 ) ){ return Quit; } else { string::size_type p = Buf.find( '.' ); if ( p == string::npos ){ cerr << "missing experiment reference!" << endl; return UnknownAct; } else { string expname = Buf.substr(0, p ); pos = lookup( expname ); // do we know it. if ( pos == -1 ){ return UnknownAct; // error } Buf = Buf.substr( p+1 ); // A well known experiment, so now we can see what we // must do. if ( compare_nocase_n( Buf, "SET ", 4 ) ){ len = fill_params( pars, Buf.substr(4) ); return Set; } else if ( compare_nocase_n( Buf, "SHOW ", 5 ) ){ len = fill_params( pars, Buf.substr(5) ); return Show; } else if ( compare_nocase_n( Buf, "GET ", 4 ) ){ len = fill_params( pars, Buf.substr(4) ); return GetTree; } else if ( compare_nocase_n( Buf, "GETW ", 5 ) ){ len = fill_params( pars, Buf.substr(5) ); return GetW; } else if ( compare_nocase_n( Buf, "SAVE ", 5 ) ){ len = fill_params( pars, Buf.substr(5) ); return SaveTree; } else if ( compare_nocase_n( Buf, "SAVEW ", 6 ) ){ len = fill_params( pars, Buf.substr(6) ); return SaveW; } else if ( compare_nocase_n( Buf, "TRAIN ", 6 ) ){ len = fill_params( pars, Buf.substr(6) ); return Train; } else if ( compare_nocase_n( Buf, "EXPAND ", 7 ) ){ len = fill_params( pars, Buf.substr(7) ); return Expand; } else if ( compare_nocase_n( Buf, "REMOVE ", 7 ) ){ len = fill_params( pars, Buf.substr(7) ); return Remove; } else if ( compare_nocase_n( Buf, "TEST ", 5 ) ){ len = fill_params( pars, Buf.substr(5) ); return Test; } else if ( compare_nocase_n( Buf, "CLASSIFY ", 9 ) ){ len = fill_params( pars, Buf.substr(9) ); return Classify; } else if ( compare_nocase_n( Buf, "ADD ", 4 ) ){ len = fill_params( pars, Buf.substr(4) ); return Increment; } else if ( compare_nocase_n( Buf, "REM ", 4 ) ){ len = fill_params( pars, Buf.substr(4) ); return Decrement; } else return UnknownAct; } } } void one_command( istream &in_file, int &line_count ) { // the actual "engine" // get a line from in_file, parse it and take appropiate action // Most of the time by directly calling a MBL Class function. // of course some sanity checking is done here and there static string *params = NULL; int pos = -1, len; if ( params == 0 ){ params = new string[MAX_PARAMS+1]; } string Buffer; getline( in_file, Buffer ); line_count++; if ( Buffer == "" || Buffer[0] == '#' ){ return; } cerr << "TSE script, executing line: " << line_count<< endl << "=== " << Buffer << endl; ActionType action = parse( Buffer, pos, params, len ); if ( len >= MAX_PARAMS ){ cerr << "Too many parameters, skipping....." << endl; return; } switch ( action ){ case Quit: exit(1); break; case New: { if ( exp_cnt == MAX_EXP ){ cerr << "To many different experiments in one run" << endl; exit(1); } if ( len == 0 ){ cerr << " Wrong number of parameters for New" << endl; exit(1); } string cmnd; if ( len == 1 ){ cerr << "1 parameters " << params[0] << endl; cmnd = "-a IB1"; } else { for ( int i=1; i < len; ++i ) cmnd += params[i] + " "; } experiments[exp_cnt++] = new TimblAPI( cmnd, params[0] ); cerr << "Created a new experiment: " << experiments[exp_cnt-1]->ExpName() << endl; break; } case Free: delete experiments[pos]; exp_cnt--; for ( ; pos < exp_cnt; pos++ ){ experiments[pos] = experiments[pos+1]; } experiments[exp_cnt] = 0; break; case GetTree: if ( len == 0 ) cerr << "missing filename to retrieve InstanceBase" << endl; else experiments[pos]->GetInstanceBase( params[0] ); break; case SaveTree: if ( len == 0 ){ params[0] = experiments[pos]->ExpName() + ".tree"; } else experiments[pos]->WriteInstanceBase(params[0]); break; case GetW: if ( len == 0 ) { params[0] = experiments[pos]->ExpName() + ".weights"; } else experiments[pos]->GetWeights(params[0]); break; case SaveW: if ( len == 0 ){ params[0] = experiments[pos]->ExpName() + ".weights"; } else experiments[pos]->SaveWeights(params[0]); break; case Show: if ( len != 1 ) cerr << "missing information about WHAT to show" << endl; else { if ( compare_nocase( params[0], "OPTIONS" ) ) experiments[pos]->ShowOptions( cerr ); else if ( compare_nocase( params[0], "SETTING" ) ) experiments[pos]->ShowSettings( cerr ); else cerr << "don't know how to show '" << params[0] << "'" << endl; } break; case Train: if ( len == 1 ) experiments[pos]->Learn(params[0]); else cerr << "missing filename for Train" << endl; break; case Expand: if ( len == 1 ) experiments[pos]->Expand(params[0]); else cerr << "missing filename for Expand" << endl; break; case Remove: if ( len == 1 ) experiments[pos]->Remove(params[0]); else cerr << "missing filename for Remove" << endl; break; case Test: { switch ( len ){ case 0: cerr << "missing filename for Test" << endl; return; break; case 1: params[1] = params[0] + ".out"; break; case 2: break; default: cerr << "too many parameters for Test, (ignored)" << endl; } experiments[pos]->Test( params[0], params[1] ); break; } case Classify: if ( len == 1 ){ const TargetValue *tv = experiments[pos]->Classify(params[0]); cout << "classify: " << params[0] << " ==> " << tv << endl; } else cerr << "missing instancestring for Add" << endl; break; case Increment: if ( len == 1 ) experiments[pos]->Increment(params[0]); else cerr << "missing instancestring for Add" << endl; break; case Decrement: if ( len == 1 ) experiments[pos]->Decrement(params[0]); else cerr << "missing instancestring for Remove" << endl; break; case Set: if ( len != 1 ){ for ( int j=1; j < len; j++ ) params[0] += params[j]; } if ( !experiments[pos]->SetOptions( params[0] ) ) cerr << "problem with Set " << params[0] << endl; break; case UnknownAct: if ( pos < 0 ) cerr << "[" << line_count << "]" << Buffer << " ==> Unknown experiment, skipped\n" << endl; else cerr << "[" << line_count << "] " << Buffer << " ==> Unknown action, skipped\n" << endl; break; } } int main(int argc, char *argv[] ){ // the following trick makes it possible to parse lines from cin // as well from a user supplied file. istream *script_file; ifstream test_file; if ( argc > 1 ){ if ( (test_file.open( argv[1], ios::in ), !test_file.good() ) ){ cerr << argv[0] << " - couldn't open scriptfile " << argv[1] << endl; exit(1); } cout << "reading script from: " << argv[1] << endl; script_file = &test_file; } else script_file = &cin; int line = 0; while ( !(*script_file).eof() ) one_command( *script_file, line ); exit(0); } LanguageMachines-timbl-642727d/docs/000077500000000000000000000000001451477526200172405ustar00rootroot00000000000000LanguageMachines-timbl-642727d/docs/Makefile.am000066400000000000000000000000721451477526200212730ustar00rootroot00000000000000# $Id$ # $URL$ man1_MANS = timbl.1 EXTRA_DIST = timbl.1 LanguageMachines-timbl-642727d/docs/Timbl_6.4_Manual.pdf000066400000000000000000021036251451477526200226770ustar00rootroot00000000000000%PDF-1.5 %쏢 5 0 obj <> stream xYْ}m F]e`y{lq{RRUzX"Lr̓'O_5Ѡa~u_ψ_û&.4Ik\Hq=n87)lxȍnK*<&ȥQ[f-vqt8D/ 6<$rCaJ95i۝Uov)iG4wK{w,Rz~Y '^e[҆oڙ7Qw1*Л<' WI?\|(!hp_s&aO+7š!B{٨)D㳝 _Ny !h+t3951b͗w<X>c]A($咍7^<1f'S2&V(#וػ%[kL6GXÓ'HM8 _\Gt-c#n.4 Z+sR*2RxGne+|5s0JQbٍb '],ٯr7òN|Xy*芫\iRf|e(67".Usʡ#KCf^ÑqǢF3j UH,aUM(/TW=2ER&۪zWxEbjEJ 3kP2[CY[XiLҪ}rbԫ0i.PERѪS'1gM+=|W'Z5aQǎ2B}rkIr%8J󹵒[\n۸yxU›\rыIHklPwo_<|ًmQuW2Dz:}Б@IܽdS?7bJáNkw \q!Y,Acx*,-9tUjr Kˌˎz!-xrbp܉\̽ z dkۚ8%M,Rbĭ 4 6X)Uj$3Mhν!btsPB8w"fm3'UyǬ I oJ6A>w&]$NB!&%m~<3=15J>NbK~{ڛ] n/%3*yi -ocl~13sCMb Kq"͐+g'H;jDYO/ޭ(gC4K!!D; 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startxref 549640 %%EOF LanguageMachines-timbl-642727d/docs/texfiles/000077500000000000000000000000001451477526200210635ustar00rootroot00000000000000LanguageMachines-timbl-642727d/docs/texfiles/Timbl_6.3_API.tex000066400000000000000000001733001451477526200237370ustar00rootroot00000000000000% TiMBL 6.3 API \documentclass{report} \usepackage{epsf} \usepackage{a4wide} \usepackage{palatino} \usepackage{fullname} \usepackage{url} \newcommand{\chisq}{{$ \chi^2 $}} \author{Ko van der Sloot\\ \ \\ Induction of Linguistic Knowledge\\ Computational Linguistics\\ Tilburg University \\ \ \\ P.O. Box 90153, NL-5000 LE, Tilburg, The Netherlands \\ URL: http://ilk.uvt.nl} \title{{\huge TiMBL: Tilburg Memory-Based Learner} \\ \vspace*{0.5cm} {\bf version 6.3} \\ \vspace*{0.5cm}{\huge API Reference Guide}\\ \vspace*{1cm} {\it ILK Technical Report -- ILK 10-03}} %better paragraph indentation \parindent 0pt \parskip 9pt \begin{document} \maketitle \tableofcontents \chapter*{Preface} This is a brief description of the TimblAPI class, the application programming interface to the Timbl\footnote{\url{http://ilk.uvt.nl/timbl}} software package, and its main functions. For an introduction into Timbl, consult the Timbl Reference Guide \cite{Daelemans+10}. Although most of the API can be traced in the {\tt TimblAPI.h} file, the reverse is not true; some functions {\tt TimblAPI.h} are still ``work in progress'' and some others are artefacts to simplify the implementation of the TiMBL main program\footnote{Timbl.cxx is therefore {\em not} a good example of how to use the API.}. To learn more about using the API, you should study programs such as {\tt classify.cxx}, {\tt tse.cxx}, and the examples given in this manual, which can all be found in the {\tt demos} directory of this distribution. As you can readily gather from these examples, the basic thing you need to do to get access to the TimblAPI functions is to include {\tt TimblAPI.h} in the program, and to include {\tt libTimbl.a} in your linking path. {\bf Important note}: The described functions return a result (mostly a bool) to indicate succes or failure. To simplify the examples, we ignore these return values. This is, of course, bad practice, to be avoided in real life programming.\footnote{as stated by commandment 6 of ``The Ten Commandments for C Programmers''' by Henry Spencer: If a function be advertised to return an error code in the event of difficulties, thou shalt check for that code, yea, even though the checks triple the size of thy code and produce aches in thy typing fingers, for if thou thinkest ``it cannot happen to me'', the gods shall surely punish thee for thy arrogance.} {\bf Warning}: Although the TiMBL internals perform some sanity checking, it is quite possible to combine API functions such that some undetermined state is reached, or even a conflict arises. The effect of the {\tt SetOptions()} function, for instance, might be quite surprising. If you have created your own program with the API it might be wise to test against well-know data to see if the results make sense. \chapter{Changes} \label{changes} \section{From version 6.2 to 6.3} No changes to the API are made for this release. This Manual is made up to date (preserving the beta-state). \section{From version 6.1 to 6.2} In version 6.2, some additional functions were added to the API: {\tt matchDepth()}, {\tt matchedAtLeaf()}, {\tt WriteMatrices()}, {\tt GetMatrices()} and {\tt ShowStatistics()}. These reflect the additional functionality of Timbl 6.2. The API is still experimental, and contains more functions than described in this manual. Using these `undocumented' features is, as usual, unwise. \section{From version 5.1 to 6.1} The major change in 6.0 is the introduction of the {\tt neighborSet} class, with some special Classify functions. We added Classify functions that deliver pointers into Timbl's internal data. This is fast, but dangerous. Also, a {\tt WriteInstanceBaseXml()} function is added, which comes in handy when you want to know more about the instance base. Two more examples demonstrating neighborSets and such are added in Appendix B. From version 6.0 to 6.1, the API has not changed. \section{From version 5.0 to 5.1} The API is quite stable at the moment. Most TiMBL changes did not affect the API. The only real API change is in the {\tt GetWeights()} function. (see the section on Storing and retrieving intermediate results). A few options were added to Timbl, influencing the table in Appendix A. We have also changed and enhanced the examples in Appendix B. \chapter{Quick-start} \section{Setting up an experiment} There is just one way to start a TiMBL experiment, which is to call the TimblAPI constructor: \begin{footnotesize} \begin{verbatim} TimblAPI( const std::string& args, const std::string& name ="" ); \end{verbatim} \end{footnotesize} args is used as a "command line" and is parsed for all kind of options which are used to create the right kind of experiment with the desired settings for metric, weighting etc. If something is wrong with the settings, {\em no}\/ object is created. The most important option is {\tt -a} to set the kind of algorithm, e.g. {\tt -a IB1} to invoke an IB1 experiment or {\tt -a IGTREE} to invoke an IGTREE experiment. A list of possible options is give in Appendix A. The optional name can be useful if you have multiple experiments. In case of warnings or errors, this name is appended to the message. For example: \begin{footnotesize} \begin{verbatim} TimblAPI *My_Experiment = new TimblAPI( "-a IGTREE +vDI+DB", "test1" ); \end{verbatim} \end{footnotesize} {\tt My\_Experiment} is created as an IGTREE experiment with the name "test1", and the verbosity is set to DI+DB, meaning that the output will contain DIstance and DistriBution information. The counterpart to creation is the {\tt \~{ }TimblAPI()} destructor, which is called when you delete an experiment: \begin{footnotesize} \begin{verbatim} delete My_Experiment; \end{verbatim} \end{footnotesize} \section{Running an experiment} Assuming that we have appropriate datafiles (such as the example files {\tt dimin.train} and {\tt dimin.test} in the TiMBL package), we can get started right away with the functions {\tt Learn()} and {\tt Test()}. \subsection{Training} \begin{footnotesize} \begin{verbatim} bool Learn( const std::string& f ); \end{verbatim} \end{footnotesize} This function takes a file with name 'f', and gathers information such as: number of features, number and frequency of feature values and the same for class names. After that, these data are used to calculate a lot of statistical information, which will be used for testing. Finally, an InstanceBase is created, tuned to the current algorithm. \subsection{Testing} \begin{footnotesize} \begin{verbatim} bool Test( const std::string& in, const std::string& out, const std::string& perc = "" ); \end{verbatim} \end{footnotesize} Test a file given by 'in' and write results to 'out'. If 'perc' is not empty, then a percentage score is written to file 'perc'. For example: \begin{footnotesize} \begin{verbatim} My_Experiment->Learn( "dimin.train" ); My_Experiment->Test( "dimin.test", "my_first_test" ); \end{verbatim} \end{footnotesize} An InstanceBase will be created from dimin.train, then dimin.test is tested against that InstanceBase and output is written to my\_first\_test. \subsection{Special cases of {\tt Learn()} and {\tt Test()}} There are special cases where {\tt Learn()} behaves differently: \begin{itemize} \item When the algorithm is IB2, {\tt Learn()} will automatically take the first $n$ lines of f (set with the {\tt -b n} option) to bootstrap itself, and then the rest of f for IB2-learning. After Learning IB2, you can use {\tt Test()} as usual. \item When the algorithm is CV, {\tt Learn()} is not defined, and all work is done in a special version of {\tt Test()}. 'f' is assumed to give the name of a file, which, on separate lines, gives the names of the files to be cross-validated. Also, if {\em featureWeights}\/ or {\em probabilities}\/ are read from user-defined datafiles, a special {\tt CVprepare()} function must be called, to make the weigthing, weightFilename and probabilityFileName known to the {\tt Test()} function. See Appendix B for a complete CV example (program {\tt api\_test3}). %TODO: een voorbeeld met CVPrepare erbij! \end{itemize} \section{More about settings} After an experiment is set up with the TimblAPI constructor, many options can be changed "on the fly" with: \begin{footnotesize} \begin{verbatim} bool SetOptions( const std::string& opts ); \end{verbatim} \end{footnotesize} Here, `opts' is interpreted as a list of option settings, just like in the TimblAPI constructor. When an error in the opts string is found, {\tt SetOptions()} returns false. Whether any options are really set or changed in that case is undefined. Note that a few options can only be set {\em once}\/ when creating the experiment, most notably the algorithm. Any attempt to change these options will result in a failure. See Appendix A for all valid options and information about the possibility to change them within a running experiment. Note: {\tt SetOptions()} is lazy; changes are cached until the moment they are really needed, so you can do several {\tt SetOptions()} calls with even different values for the same option. Only the last one seen will be used for running the experiment. To see which options are in effect, you can use the calls {\tt ShowOptions()} and {\tt ShowSettings()}. \begin{footnotesize} \begin{verbatim} bool ShowOptions( std::ostream& ); \end{verbatim} \end{footnotesize} Shows all options with their possible and current values. \begin{footnotesize} \begin{verbatim} bool ShowSettings( std::ostream& ); \end{verbatim} \end{footnotesize} Shows all options and their currect values. For example: \begin{footnotesize} \begin{verbatim} My_Experiment->SetOptions( "-w2 -m:M" ); My_Experiment->SetOptions( "-w3 -v:DB" ); My_Experiment->ShowSettings( cout ) \end{verbatim} \end{footnotesize} See Appendix B (program {\tt api\_test1}) for the output. \section{Storing and retrieving intermediate results} To speed up testing, or to manipulate what is happening internally, we can store and retrieve several important parts of our experiment: The InstanceBase, the FeatureWeights, the ProbabilityArrays and the ValueDistance Matrices. Saving is done with: \begin{footnotesize} \begin{verbatim} bool WriteInstanceBase( const std::string& f ); bool SaveWeights( const std::string& f ); bool WriteArrays( const std::string& f ); bool WriteMatrices( const std::string& f ); \end{verbatim} \end{footnotesize} Retrieve with their counterparts: \begin{footnotesize} \begin{verbatim} bool GetInstanceBase( const std::string& f ); bool GetWeights( const std::string& f, Weighting w ); bool GetArrays( const std::string& f ); bool GetMatrices( const std::string& f ); \end{verbatim} \end{footnotesize} All use `f' as a filename for storing/retrieving. {\tt GetWeights} needs information to decide {\em which}\/ weighting to retrieve. Weighting is defined as the enumerated type: \begin{footnotesize} \begin{verbatim} enum Weighting { UNKNOWN_W, UD, NW, GR, IG, X2, SV }; \end{verbatim} \end{footnotesize} Some notes: \begin{enumerate} \item The InstanceBase is stored in a internal format, with or without hashing, depending on the {\tt -H} option. The format is described in the TiMBL manual. Remember that it is a bad idea to edit this file in any way. \item {\tt GetWeights()} can be used to override the weights that {\tt Learn()} calculated. {\tt UNKNOWN\_W} should not be used. \item The Probability arrays are described in the TiMBL manual. They can be manipulated to tune the MVDM similarity metric. \end{enumerate} If you like you may dump the Instancebase in an XML format. No Retrieve function is available for this format. \begin{footnotesize} \begin{verbatim} bool WriteInstanceBaseXml( const std::string& f ); \end{verbatim} \end{footnotesize} \chapter{Classify functions} \section{Classify functions: Elementary} After an experiment is trained with {\tt Learn()}, we do not have to use {\tt Test()} to do bulk-testing on a file. We can create our own tests with the {\tt Classify} functions: \begin{footnotesize} \begin{verbatim} bool Classify( const std::string& Line, std::string& result ); bool Classify( const std::string& Line, std::string& result, double& distance ); bool Classify( const std::string& Line, std::string& result, std::string& Distrib, double& distance ); \end{verbatim} \end{footnotesize} Results are stored in 'result' (the assigned class). 'distance' will get the calculated distance, and 'Distrib' the distribution at 'distance' which is used to calculate 'result'. Distrib will be a string like ``\{ NP 2, PP 6 \}''. It is up to you to parse and interpret this. (In this case: There were 8 classes assigned at 'distance', 2 NP's and 6 PP's, giving a 'result' of ``PP''.) If you want to perform analyses on these distributions, it might be a good idea to read the next section about the other range of Classify() functions. A main disadvantage compared to using {\tt Test()} is that {\tt Test()} is optimized. {\tt Classify()} has to test for sanity of its input and also whether a {\tt SetOptions()} has been performed. This slows down the process. A good example of the use of {\tt Classify()} is the {\tt classify.cxx} program in the TiMBL Distribution. Depending on the Algorithm and Verbosity setting, it may be possible to get some extra information on the details of each classification using: \begin{footnotesize} \begin{verbatim} const bool ShowBestNeighbors( std::ostream& os, bool distr ) const; \end{verbatim} \end{footnotesize} Provided that the option {\tt +v n} or {\tt +v k} is set and we use IB1 or IB2, output is produced similar to what we see in the TiMBL program. When 'distr' is true, their distributions are also displayed. Bear in mind: The {\tt +vn} option is expensive in time and memory and does not work for IGTREE, TRIBL, and TRIBL2. Two other functions provide the results as given by the {\tt +vmd} verbosity option: \begin{footnotesize} \begin{verbatim} size_t matchDepth() const; bool matchedAtLeaf() const; \end{verbatim} \end{footnotesize} The first returns the matching Depth in the InstanceBase; the second flags whether it was a Leaf or a Non-Terminal Node. \section{Classify functions: Advanced} A faster, but more dangerous version of Classify is also available. It is faster because it returns pointers into Timbl's internal datastructures. It is dangerous because it returns pointers into Timbl's internal datastructures (using 'const' pointers, so it is fortunately difficult te really damage Timbl) \begin{footnotesize} \begin{verbatim} const TargetValue *Classify( const std::string& ); const TargetValue *Classify( const std::string&, const ValueDistribution *& ); const TargetValue *Classify( const std::string&, double& ); const TargetValue *Classify( const std::string&, const ValueDistribution *&, double& ); \end{verbatim} \end{footnotesize} A ValueDistribution is a list-like object (but it is not a real list!) that contains TargetValues objects and weights. It is the result of combining all nearest neighbors and applying the desired weightings. Timbl chooses a best TargetValue from this ValueDistribution and the Classify functions return that as their main result. {\bf Important}: Because these functions return pointers into Timbl's internal representation, the results are only valid until the next Classify function is called (or the experiment is deleted). Both the TargetValue and ValueDistribution objects have output operators defined, so you can print them. TargetValue also has a {\tt Name()} function, which returns a std::string so you can collect results. ValueDistribution has an iterator-like interface which makes it possible to walk through the Distribution. An iterator on a {\tt ValueDistribution *vd} is created like this: \begin{footnotesize} \begin{verbatim} ValueDistribution::dist_iterator it=vd->begin(); \end{verbatim} \end{footnotesize} Unfortunately, the iterator cannot be printed or used directly. It walks through a map-like structure with pairs of values, of which only the {\tt second} part is of interest to you. You may print it, or extract its {\tt Value()} (which happens to be a TargetValue pointer) or extract its {\tt Weight()}, which is a {\tt double}. Like this: \begin{footnotesize} \begin{verbatim} while ( it != vd->end() ){ cout << it->second << " has a value: "; cout << it->second->Value() << " an a weight of " << it->second->Weight() << endl; ++it; } \end{verbatim} \end{footnotesize} Printing {\tt it->second} is the same as printing the TargetValue plus its Weight. In the {\em demos}\/ directory you will find a complete example in api\_test6. {\bf Warning}: it is possible to search the Timbl code for the internal representation of the TargetValue and ValueDistribution objects, but please DON'T DO THAT. The representation might change between Timbl versions. \section{Classify functions: neighborSets} A more flexible way of classifying is to use one of these functions: \begin{footnotesize} \begin{verbatim} const neighborSet *classifyNS( const std::string& ); bool classifyNS( const std::string&, neighborSet& ); \end{verbatim} \end{footnotesize} The first function will classify an instance and return a pointer to a {\tt neighborSet} object. This object may be seen as an container which holds both distances and distributions up to a certain depth, (which is {\em at least}\/ the number of neighbors (-k option) that was used for the classifying task.) It is a const object, so you cannot directly manipulate its internals, but there are some functions defined to get useful information out of the neighborSet. Important: The neighborSet {\em will be overwritten}\/ on the next call to any of the classify functions. Be sure to get all the results out before that happens. To make life easy, a second variant can be used, which fills a neighborSet object that you provide (the same could be achieved by a copy of the result of the first function). {\bf Note}: NeighborSets can be large, and copying therefore expensive, so you should only do this if you really have to. \subsection{How to get results from a neighborSet} No metric functions (such as exponential decay and the like) are performed on the neighborSet. You are free to insert your own metrics, or use Timbls built-in metrics. \begin{footnotesize} \begin{verbatim} double getDistance( size_t n ) const; double bestDistance() const; const ValueDistribution *getDistribution( size_t n ) const; ValueDistribution *bestDistribution( const decayStruct * ds=0, size_t n=0 ) const ; \end{verbatim} \end{footnotesize} {\tt getDistance( n )} will return the distance of the neighbor(s) at n. {\tt bestDistance()} is simply {\tt getDistance(0)}. {\tt getDistribution( n )} will return the distribution of neighbor(s) at n. {\tt bestDistribution()} will return the Weighted distribution calculated using the first n elements in the container and a metric specified by the {\tt decayStruct}. The default n=0, means: use the whole container. An empty decay struct means zeroDecay. The returned ValueDistribution object is handed to you, and you are responsible for deleting it after using it (see the previous section for more details about ValueDistributions). A decayStruct is one of: \begin{footnotesize} \begin{verbatim} class zeroDecay(); class invLinDecay(); class invDistDecay(); class expDecay( double alpha ); class expDecay( double alpha, double beta ); \end{verbatim} \end{footnotesize} For example, to get a ValueDistribution form a neighborSet {\tt nb}, using 3 neighbors and exponential decay with alpha=0.3, you can do: \begin{footnotesize} \begin{verbatim} decayStruct *dc = new expDecay(0.3); ValueDistribution *vd = nb->bestDistribution( dc, 3 ); \end{verbatim} \end{footnotesize} \subsection{Useful operations on neighborSet objects} You can print neighborSet objects: \begin{footnotesize} \begin{verbatim} std::ostream& operator<<( std::ostream&, const neighborSet& ); std::ostream& operator<<( std::ostream&, const neighborSet * ); \end{verbatim} \end{footnotesize} You may create a neighborSet yourself, and assign and delete them: \begin{footnotesize} \begin{verbatim} neighborSet(); neighborSet( const neighborSet& ); neighborSet& operator=( const neighborSet& ); ~neighborSet(); \end{verbatim} \end{footnotesize} If you create an neighborSet, you might want to reserve space for it, to avoid needless reallocations. Also it can be cleared, and you can ask the size (just like with normal containers): \begin{footnotesize} \begin{verbatim} void reserve( size_t ); void clear(); size_t size() const; \end{verbatim} \end{footnotesize} Two neighborSets can be merged: \begin{footnotesize} \begin{verbatim} void merge( const neighborSet& ); \end{verbatim} \end{footnotesize} A neighborSet can be truncated at a certain level. This is useful after merging neighborSets. Merging sets with depth k and n will result in a set with a depth somewhere within the range $[max(k,n), k+n]$. \begin{footnotesize} \begin{verbatim} void truncate( size_t ); \end{verbatim} \end{footnotesize} \chapter{Advanced Functions} \section{Modifying the InstanceBase} The instanceBase can be modified with the functions: \begin{footnotesize} \begin{verbatim} bool Increment( const std::string& Line ); bool Decrement( const std::string& Line ); \end{verbatim} \end{footnotesize} These functions add an Instance (as described by Line) to the InstanceBase, or remove it. This can only be done for IB1-like experiments (IB1, IB2, CV and LOO), and enforces a lot of statistical recalculations. More sophisticated are: \begin{footnotesize} \begin{verbatim} bool Expand( const std::string& File ); bool Remove( const std::string& File ); \end{verbatim} \end{footnotesize} which use the contents of File to do a bulk of Increments or Decrements, and recalculate afterwards. \section{Getting more information out of Timbl} There are a few convenience functions to get extra information on TiMBL and its behaviour: \begin{footnotesize} \begin{verbatim} bool WriteNamesFile( const std::string& f ); \end{verbatim} \end{footnotesize} Create a file which resembles a C4.5 namesfile. \begin{footnotesize} \begin{verbatim} Algorithm Algo() \end{verbatim} \end{footnotesize} Give the current algorithm as a type enum Algorithm. First, the declaration of the Algorithm type: \begin{footnotesize} \begin{verbatim} enum Algorithm { UNKNOWN_ALG, IB1, IB2, IGTREE, TRIBL, TRIBL2, LOO, CV }; \end{verbatim} \end{footnotesize} This can be printed with the helper function: \begin{footnotesize} \begin{verbatim} const std::string to_string( const Algorithm ) \end{verbatim} \end{footnotesize} \begin{footnotesize} \begin{verbatim} Weighting CurrentWeighting() \end{verbatim} \end{footnotesize} Gives the current weighting as a type enum Weighting. Declaration of Weighting: \begin{footnotesize} \begin{verbatim} enum Weighting { UNKNOWN_W, UD, NW, GR, IG, X2, SV }; \end{verbatim} \end{footnotesize} This can be printed with the helper function: \begin{footnotesize} \begin{verbatim} const std::string to_string( const Weighting ) \end{verbatim} \end{footnotesize} \begin{footnotesize} \begin{verbatim} Weighting CurrentWeightings( std::vector& v ) \end{verbatim} \end{footnotesize} Returns the current weighting as a type enum Weighting and also a vector v with all the current values of this weighting. \begin{footnotesize} \begin{verbatim} std::string& ExpName() \end{verbatim} \end{footnotesize} Returns the value of 'name' given at the construction of the experiment \begin{footnotesize} \begin{verbatim} static std::string VersionInfo( bool full = false ) \end{verbatim} \end{footnotesize} Returns a string containing the Version number, the Revision and the Revision string of the current API implementation. If full is true, also information about the date and time of compilation is included. \chapter{Server mode} \label{Using TiMBL as a Server} \begin{footnotesize} \begin{verbatim} bool StartServer( const int port, const int max_c ); \end{verbatim} \end{footnotesize} Starts a TimblServer on 'port' with maximally 'max\_c' concurrent connections to it. Starting a server makes sense only after the experiment is trained. \clearpage \chapter{Annotated example programs} \subsection{example 1, {\tt api\_test1.cxx}} \begin{footnotesize} \begin{verbatim} #include "TimblAPI.h" int main(){ TimblAPI My_Experiment( "-a IGTREE +vDI+DB+F", "test1" ); My_Experiment.SetOptions( "-w3 -vDB" ); My_Experiment.ShowSettings( std::cout ); My_Experiment.Learn( "dimin.train" ); My_Experiment.Test( "dimin.test", "my_first_test.out" ); My_Experiment.SetOptions( "-mM" ); My_Experiment.Test( "dimin.test", "my_first_test.out" ); } \end{verbatim} \end{footnotesize} Output: \begin{footnotesize} \begin{verbatim} Current Experiment Settings : FLENGTH : 0 MAXBESTS : 500 TRIBL_OFFSET : 0 INPUTFORMAT : Unknown TREE_ORDER : Unknown ALL_WEIGHTS : false WEIGHTING : x2 [Note 1] BIN_SIZE : 20 IB2_OFFSET : 0 KEEP_DISTRIBUTIONS : false DO_SLOPPY_LOO : false TARGET_POS : 18446744073709551615 DO_SILLY : false DO_DIVERSIFY : false DECAY : Z SEED : -1 BEAM_SIZE : 0 DECAYPARAM_A : 1.00000 DECAYPARAM_B : 1.00000 NORMALISATION : None NORMFACTOR : 1.00000 EXEMPLAR_WEIGHTS : false IGNORE_EXEMPLAR_WEIGHTS : true NO_EXEMPLAR_WEIGHTS_TEST : true VERBOSITY : F+DI [Note 2] EXACT_MATCH : false HASHED_TREE : true GLOBAL_METRIC : O METRICS : MVD_LIMIT : 1 NEIGHBORS : 1 PROGRESS : 100000 CLIP_FACTOR : 10 Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 -test1-Phase 1: Reading Datafile: dimin.train -test1-Start: 0 @ Mon May 31 11:03:34 2010 -test1-Finished: 2999 @ Mon May 31 11:03:34 2010 -test1-Calculating Entropy Mon May 31 11:03:34 2010 Lines of data : 2999 DB Entropy : 1.6178929 Number of Classes : 5 Feats Vals X-square Variance InfoGain GainRatio 1 3 128.41828 0.021410184 0.030971064 0.024891536 2 50 364.75812 0.030406645 0.060860038 0.027552191 3 19 212.29804 0.017697402 0.039562857 0.018676787 4 37 449.83823 0.037499019 0.052541227 0.052620750 5 3 288.87218 0.048161417 0.074523225 0.047699231 6 61 415.64113 0.034648310 0.10604433 0.024471911 7 20 501.33465 0.041791818 0.12348668 0.034953203 8 69 367.66021 0.030648567 0.097198760 0.043983864 9 2 169.36962 0.056475363 0.045752381 0.046816705 10 64 914.61906 0.076243669 0.21388759 0.042844587 11 18 2807.0418 0.23399815 0.66970458 0.18507018 12 43 7160.3682 0.59689631 1.2780762 0.32537181 Feature Permutation based on Chi-Squared : < 12, 11, 10, 7, 4, 6, 8, 2, 5, 3, 9, 1 > -test1-Phase 2: Building index on Datafile: dimin.train -test1-Start: 0 @ Mon May 31 11:03:34 2010 -test1-Finished: 2999 @ Mon May 31 11:03:34 2010 -test1- Phase 3: Learning from Datafile: dimin.train -test1-Start: 0 @ Mon May 31 11:03:34 2010 -test1-Finished: 2999 @ Mon May 31 11:03:34 2010 Size of InstanceBase = 148 Nodes, (5920 bytes), 99.61 % compression Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: my_first_test.out Algorithm : IGTree Weighting : Chi-square Feature 1 : 128.418283576224439 Feature 2 : 364.758115277811896 Feature 3 : 212.298037236345095 Feature 4 : 449.838231470681876 Feature 5 : 288.872176256387263 Feature 6 : 415.641126446691771 Feature 7 : 501.334653478280984 Feature 8 : 367.660212489714240 Feature 9 : 169.369615106487458 Feature 10 : 914.619058199288816 Feature 11 : 2807.041753278295346 Feature 12 : 7160.368151902808677 -test1-Tested: 1 @ Mon May 31 11:03:34 2010 -test1-Tested: 2 @ Mon May 31 11:03:34 2010 -test1-Tested: 3 @ Mon May 31 11:03:34 2010 -test1-Tested: 4 @ Mon May 31 11:03:34 2010 -test1-Tested: 5 @ Mon May 31 11:03:34 2010 -test1-Tested: 6 @ Mon May 31 11:03:34 2010 -test1-Tested: 7 @ Mon May 31 11:03:34 2010 -test1-Tested: 8 @ Mon May 31 11:03:34 2010 -test1-Tested: 9 @ Mon May 31 11:03:34 2010 -test1-Tested: 10 @ Mon May 31 11:03:34 2010 -test1-Tested: 100 @ Mon May 31 11:03:34 2010 -test1-Ready: 950 @ Mon May 31 11:03:34 2010 Seconds taken: 0.1331 (7135.13 p/s) overall accuracy: 0.962105 (914/950) Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Warning:-test1-Metric must be Overlap for IGTree test. [Note 3] \end{verbatim} \end{footnotesize} Notes: \begin{enumerate} \item The {\tt -w2} of the first {\tt SetOptions()} is overruled with {\tt -w3} from the second {\tt SetOptions()}, resulting in a weighting of 3 or Chi-Square. \item The first {\tt SetOptions()} sets the verbosity with {\tt +F+DI+DB}. The second {\tt SetOptions()}, however, sets the verbosity with {\tt -vDB}, and the resulting verbosity is therefore {\tt F+DI}. \item Due to the second {\tt SetOptions()}, the default metric is set to MVDM --- this is however not applicable to IGTREE. This raises a warning when we start to test. \end{enumerate} Result in my\_first\_test.out (first 20 lines): \begin{footnotesize} \begin{verbatim} =,=,=,=,=,=,=,=,+,p,e,=,T,T 6619.8512628162 =,=,=,=,+,k,u,=,-,bl,u,m,E,P 2396.8557978603 +,m,I,=,-,d,A,G,-,d,},t,J,J 6619.8512628162 -,t,@,=,-,l,|,=,-,G,@,n,T,T 6619.8512628162 -,=,I,n,-,str,y,=,+,m,E,nt,J,J 6619.8512628162 =,=,=,=,=,=,=,=,+,br,L,t,J,J 6619.8512628162 =,=,=,=,+,zw,A,=,-,m,@,r,T,T 6619.8512628162 =,=,=,=,-,f,u,=,+,dr,a,l,T,T 6619.8512628162 =,=,=,=,=,=,=,=,+,l,e,w,T,T 13780.219414719 =,=,=,=,+,tr,K,N,-,k,a,rt,J,J 6619.8512628162 =,=,=,=,+,=,o,=,-,p,u,=,T,T 3812.8095095379 =,=,=,=,=,=,=,=,+,l,A,m,E,E 3812.8095095379 =,=,=,=,=,=,=,=,+,l,A,p,J,J 6619.8512628162 =,=,=,=,=,=,=,=,+,sx,E,lm,P,P 6619.8512628162 +,l,a,=,-,d,@,=,-,k,A,st,J,J 6619.8512628162 -,s,i,=,-,f,E,r,-,st,O,k,J,J 6619.8512628162 =,=,=,=,=,=,=,=,+,sp,a,n,T,T 6619.8512628162 =,=,=,=,=,=,=,=,+,st,o,t,J,J 6619.8512628162 =,=,=,=,+,sp,a,r,-,b,u,k,J,J 6619.8512628162 +,h,I,N,-,k,@,l,-,bl,O,k,J,J 6619.8512628162 \end{verbatim} \end{footnotesize} \clearpage \subsection{example 2, {\tt api\_test2.cxx}} This demonstrates IB2 learning. Our example program: \begin{footnotesize} \begin{verbatim} #include "TimblAPI.h" int main(){ TimblAPI *My_Experiment = new TimblAPI( "-a IB2 +vF+DI+DB" , "test2" ); My_Experiment->SetOptions( "-b100" ); My_Experiment->ShowSettings( std::cout ); My_Experiment->Learn( "dimin.train" ); My_Experiment->Test( "dimin.test", "my_second_test.out" ); delete My_Experiment; exit(1); } \end{verbatim} \end{footnotesize} We create an experiment for the IB2 algorithm, with the {\tt -b} option set to 100, so the first 100 lines of {\tt dimin.train} will be used to bootstrap the learning, as we can see from the output: \begin{footnotesize} \begin{verbatim} Current Experiment Settings : FLENGTH : 0 MAXBESTS : 500 TRIBL_OFFSET : 0 INPUTFORMAT : Unknown TREE_ORDER : G/V ALL_WEIGHTS : false WEIGHTING : gr BIN_SIZE : 20 IB2_OFFSET : 100 KEEP_DISTRIBUTIONS : false DO_SLOPPY_LOO : false TARGET_POS : 4294967295 DO_SILLY : false DO_DIVERSIFY : false DECAY : Z SEED : -1 BEAM_SIZE : 0 DECAYPARAM_A : 1.00000 DECAYPARAM_B : 1.00000 NORMALISATION : None NORM_FACTOR : 1.00000 EXEMPLAR_WEIGHTS : false IGNORE_EXEMPLAR_WEIGHTS : true NO_EXEMPLAR_WEIGHTS_TEST : true VERBOSITY : F+DI+DB EXACT_MATCH : false HASHED_TREE : true GLOBAL_METRIC : O METRICS : MVD_LIMIT : 1 NEIGHBORS : 1 PROGRESS : 100000 CLIP_FACTOR : 10 Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 -test2-Phase 1: Reading Datafile: dimin.train -test2-Start: 0 @ Mon May 31 11:03:34 2010 -test2-Finished: 2999 @ Mon May 31 11:03:34 2010 -test2-Calculating Entropy Mon May 31 11:03:34 2010 Lines of data : 2999 [Note 1] DB Entropy : 1.6178929 Number of Classes : 5 Feats Vals InfoGain GainRatio 1 3 0.030971064 0.024891536 2 50 0.060860038 0.027552191 3 19 0.039562857 0.018676787 4 37 0.052541227 0.052620750 5 3 0.074523225 0.047699231 6 61 0.10604433 0.024471911 7 20 0.12348668 0.034953203 8 69 0.097198760 0.043983864 9 2 0.045752381 0.046816705 10 64 0.21388759 0.042844587 11 18 0.66970458 0.18507018 12 43 1.2780762 0.32537181 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > -test2-Phase 2: Learning from Datafile: dimin.train -test2-Start: 0 @ Mon May 31 11:03:34 2010 -test2-Finished: 100 @ Mon May 31 11:03:34 2010 Size of InstanceBase = 954 Nodes, (38160 bytes), 26.62 % compression -test2-Phase 2: Appending from Datafile: dimin.train (starting at line 101) -test2-Start: 101 @ Mon May 31 11:03:34 2010 -test2-Learning: 101 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 102 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 103 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 104 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 105 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 106 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 107 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 108 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 109 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 110 @ Mon May 31 11:03:34 2010 added:0 -test2-Learning: 200 @ Mon May 31 11:03:34 2010 added:9 -test2-Learning: 1100 @ Mon May 31 11:03:34 2010 added:66 -test2-Finished: 2999 @ Mon May 31 11:03:35 2010 in total added 173 new entries [Note 2] Size of InstanceBase = 2232 Nodes, (89280 bytes), 32.40 % compression DB Entropy : 1.61789286 Number of Classes : 5 Feats Vals InfoGain GainRatio 1 3 0.03097106 0.02489154 2 50 0.06086004 0.02755219 3 19 0.03956286 0.01867679 4 37 0.05254123 0.05262075 5 3 0.07452322 0.04769923 6 61 0.10604433 0.02447191 7 20 0.12348668 0.03495320 8 69 0.09719876 0.04398386 9 2 0.04575238 0.04681670 10 64 0.21388759 0.04284459 11 18 0.66970458 0.18507018 12 43 1.27807625 0.32537181 Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: my_second_test.out Algorithm : IB2 Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Feature 1 : 0.026241147173103 Feature 2 : 0.030918769841214 Feature 3 : 0.021445836516602 Feature 4 : 0.056561885447060 Feature 5 : 0.048311436541460 Feature 6 : 0.027043360641622 Feature 7 : 0.037453180788027 Feature 8 : 0.044999091421718 Feature 9 : 0.048992032381874 Feature 10 : 0.044544230779268 Feature 11 : 0.185449683494634 Feature 12 : 0.324719540921155 -test2-Tested: 1 @ Mon May 31 11:03:35 2010 -test2-Tested: 2 @ Mon May 31 11:03:35 2010 -test2-Tested: 3 @ Mon May 31 11:03:35 2010 -test2-Tested: 4 @ Mon May 31 11:03:35 2010 -test2-Tested: 5 @ Mon May 31 11:03:35 2010 -test2-Tested: 6 @ Mon May 31 11:03:35 2010 -test2-Tested: 7 @ Mon May 31 11:03:35 2010 -test2-Tested: 8 @ Mon May 31 11:03:35 2010 -test2-Tested: 9 @ Mon May 31 11:03:35 2010 -test2-Tested: 10 @ Mon May 31 11:03:35 2010 -test2-Tested: 100 @ Mon May 31 11:03:35 2010 -test2-Ready: 950 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0456 (20826.48 p/s) overall accuracy: 0.941053 (894/950), of which 15 exact matches [Note 3] There were 43 ties of which 32 (74.42%) were correctly resolved \end{verbatim} \end{footnotesize} Notes: \begin{enumerate} \item IB2 is bootstrapped with 100 lines, but for the statistics all 2999 lines are used. \item As we see here, 173 entries from the input file had a mismatch, and were therefore entered in the Instancebase. \item We see that IB2 scores 94.11 \%, compared to 96.21 \% for IGTREE in our first example. For this data, IB2 is not a good algorithm. However, it saves a lot of space, and is faster than IB1. Yet, IGTREE is both faster and better. Had we used IB1, the score would have been 96.84 \%. \end{enumerate} \clearpage \subsection{example 3, {\tt api\_test3.cxx}} This demonstrates Cross Validation. Let's try the following program: \begin{footnotesize} \begin{verbatim} #include "TimblAPI.h" using Timbl::TimblAPI; int main(){ TimblAPI *My_Experiment = new TimblAPI( "-t cross_validate" ); My_Experiment->Test( "cross_val.test" ); delete My_Experiment; exit(0); } \end{verbatim} \end{footnotesize} This program creates an experiment, which defaults to IB1 and because of the special option ``-t cross\_validate'' will start a CrossValidation experiment.\\ Learn() is not possible now. We must use a special form of Test(). ``cross\_val.test'' is a file with the following content: \begin{footnotesize} \begin{verbatim} small_1.train small_2.train small_3.train small_4.train small_5.train \end{verbatim} \end{footnotesize} All these files contain an equal part of a bigger dataset, and My\_Experiment will run a CrossValidation test between these files. Note that output filenames are generated and that you cannot influence that. The output of this program is: \begin{footnotesize} \begin{verbatim} Starting Cross validation test on files: small_1.train small_2.train small_3.train small_4.train small_5.train Examine datafile 'small_1.train' gave the following results: Number of Features: 8 InputFormat : C4.5 Starting to test, Testfile: small_1.train Writing output in: small_1.train.cv Algorithm : CV Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Tested: 1 @ Mon May 31 11:03:35 2010 Tested: 2 @ Mon May 31 11:03:35 2010 Tested: 3 @ Mon May 31 11:03:35 2010 Tested: 4 @ Mon May 31 11:03:35 2010 Tested: 5 @ Mon May 31 11:03:35 2010 Tested: 6 @ Mon May 31 11:03:35 2010 Tested: 7 @ Mon May 31 11:03:35 2010 Tested: 8 @ Mon May 31 11:03:35 2010 Tested: 9 @ Mon May 31 11:03:35 2010 Tested: 10 @ Mon May 31 11:03:35 2010 Ready: 10 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0006 (16207.46 p/s) overall accuracy: 0.800000 (8/10) Examine datafile 'small_2.train' gave the following results: Number of Features: 8 InputFormat : C4.5 Starting to test, Testfile: small_2.train Writing output in: small_2.train.cv Algorithm : CV Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Tested: 1 @ Mon May 31 11:03:35 2010 Tested: 2 @ Mon May 31 11:03:35 2010 Tested: 3 @ Mon May 31 11:03:35 2010 Tested: 4 @ Mon May 31 11:03:35 2010 Tested: 5 @ Mon May 31 11:03:35 2010 Tested: 6 @ Mon May 31 11:03:35 2010 Tested: 7 @ Mon May 31 11:03:35 2010 Tested: 8 @ Mon May 31 11:03:35 2010 Tested: 9 @ Mon May 31 11:03:35 2010 Tested: 10 @ Mon May 31 11:03:35 2010 Ready: 10 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0005 (19646.37 p/s) overall accuracy: 0.800000 (8/10) Examine datafile 'small_3.train' gave the following results: Number of Features: 8 InputFormat : C4.5 Starting to test, Testfile: small_3.train Writing output in: small_3.train.cv Algorithm : CV Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Tested: 1 @ Mon May 31 11:03:35 2010 Tested: 2 @ Mon May 31 11:03:35 2010 Tested: 3 @ Mon May 31 11:03:35 2010 Tested: 4 @ Mon May 31 11:03:35 2010 Tested: 5 @ Mon May 31 11:03:35 2010 Tested: 6 @ Mon May 31 11:03:35 2010 Tested: 7 @ Mon May 31 11:03:35 2010 Tested: 8 @ Mon May 31 11:03:35 2010 Tested: 9 @ Mon May 31 11:03:35 2010 Tested: 10 @ Mon May 31 11:03:35 2010 Ready: 10 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0005 (20202.02 p/s) overall accuracy: 0.900000 (9/10) Examine datafile 'small_4.train' gave the following results: Number of Features: 8 InputFormat : C4.5 Starting to test, Testfile: small_4.train Writing output in: small_4.train.cv Algorithm : CV Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Tested: 1 @ Mon May 31 11:03:35 2010 Tested: 2 @ Mon May 31 11:03:35 2010 Tested: 3 @ Mon May 31 11:03:35 2010 Tested: 4 @ Mon May 31 11:03:35 2010 Tested: 5 @ Mon May 31 11:03:35 2010 Tested: 6 @ Mon May 31 11:03:35 2010 Tested: 7 @ Mon May 31 11:03:35 2010 Tested: 8 @ Mon May 31 11:03:35 2010 Tested: 9 @ Mon May 31 11:03:35 2010 Tested: 10 @ Mon May 31 11:03:35 2010 Ready: 10 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0005 (19880.72 p/s) overall accuracy: 0.800000 (8/10) Examine datafile 'small_5.train' gave the following results: Number of Features: 8 InputFormat : C4.5 Starting to test, Testfile: small_5.train Writing output in: small_5.train.cv Algorithm : CV Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Tested: 1 @ Mon May 31 11:03:35 2010 Tested: 2 @ Mon May 31 11:03:35 2010 Tested: 3 @ Mon May 31 11:03:35 2010 Tested: 4 @ Mon May 31 11:03:35 2010 Tested: 5 @ Mon May 31 11:03:35 2010 Tested: 6 @ Mon May 31 11:03:35 2010 Tested: 7 @ Mon May 31 11:03:35 2010 Tested: 8 @ Mon May 31 11:03:35 2010 Ready: 8 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0004 (19093.08 p/s) overall accuracy: 1.000000 (8/8) \end{verbatim} \end{footnotesize} What has happened here? \begin{enumerate} \item TiMBL trained itself with inputfiles small\_2.train through small\_5.train. (in fact using the {\tt Expand()} API call. \item Then TiMBL tested small\_1.train against the InstanceBase. \item Next, small\_2.train is removed from the database (API call {\tt Remove()} ) and small\_1.train is added. \item Then small\_2.train is tested against the InstanceBase. \item And so forth with small\_3.train $\ldots$ \end{enumerate} \clearpage \subsection{example 4, {\tt api\_test4.cxx}} This program demonstrates adding and deleting of the InstanceBase. It also proves that weights are (re)calculated correctly each time (which also explains why this is a time-consuming thing to do). After running this program, wg.1.wgt should be equal to wg.5.wgt and wg.2.wgt equal to wg.4.wgt . Important to note is also, that while we do not use a weighting of X2 or SV here, only the ``simple'' weights are calculated and stored. Further, arr.1.arr should be equal to arr.5.arr and arr.2.arr should be equal to arr.4.arr First the program: \begin{footnotesize} \begin{verbatim} #include #include "TimblAPI.h" int main(){ TimblAPI *My_Experiment = new TimblAPI( "-a IB1 +vDI+DB +mM" , "test4" ); My_Experiment->ShowSettings( std::cout ); My_Experiment->Learn( "dimin.train" ); My_Experiment->Test( "dimin.test", "inc1.out" ); My_Experiment->SaveWeights( "wg.1.wgt" ); My_Experiment->WriteArrays( "arr.1.arr" ); My_Experiment->Increment( "=,=,=,=,+,k,e,=,-,r,@,l,T" ); My_Experiment->Test( "dimin.test", "inc2.out" ); My_Experiment->SaveWeights( "wg.2.wgt" ); My_Experiment->WriteArrays( "arr.2.arr" ); My_Experiment->Increment( "+,zw,A,rt,-,k,O,p,-,n,O,n,E" ); My_Experiment->Test( "dimin.test", "inc3.out" ); My_Experiment->SaveWeights( "wg.3.wgt" ); My_Experiment->WriteArrays( "arr.3.arr" ); My_Experiment->Decrement( "+,zw,A,rt,-,k,O,p,-,n,O,n,E" ); My_Experiment->Test( "dimin.test", "inc4.out" ); My_Experiment->SaveWeights( "wg.4.wgt" ); My_Experiment->WriteArrays( "arr.4.arr" ); My_Experiment->Decrement( "=,=,=,=,+,k,e,=,-,r,@,l,T" ); My_Experiment->Test( "dimin.test", "inc5.out" ); My_Experiment->SaveWeights( "wg.5.wgt" ); My_Experiment->WriteArrays( "arr.5.arr" ); delete My_Experiment; exit(1); } \end{verbatim} \end{footnotesize} This produces the following output: \begin{footnotesize} \begin{verbatim} Current Experiment Settings : FLENGTH : 0 MAXBESTS : 500 TRIBL_OFFSET : 0 IG_THRESHOLD : 1000 INPUTFORMAT : Unknown TREE_ORDER : G/V ALL_WEIGHTS : false WEIGHTING : gr BIN_SIZE : 20 IB2_OFFSET : 0 KEEP_DISTRIBUTIONS : false DO_SLOPPY_LOO : false TARGET_POS : 18446744073709551615 DO_SILLY : false DO_DIVERSIFY : false DECAY : Z SEED : -1 BEAM_SIZE : 0 DECAYPARAM_A : 1.00000 DECAYPARAM_B : 1.00000 NORMALISATION : None NORM_FACTOR : 1.00000 EXEMPLAR_WEIGHTS : false IGNORE_EXEMPLAR_WEIGHTS : true NO_EXEMPLAR_WEIGHTS_TEST : true VERBOSITY : DI+DB EXACT_MATCH : false HASHED_TREE : true GLOBAL_METRIC : M METRICS : MVD_LIMIT : 1 NEIGHBORS : 1 PROGRESS : 100000 CLIP_FACTOR : 10 Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 -test4-Phase 1: Reading Datafile: dimin.train -test4-Start: 0 @ Mon May 31 11:03:35 2010 -test4-Finished: 2999 @ Mon May 31 11:03:35 2010 -test4-Calculating Entropy Mon May 31 11:03:35 2010 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > -test4-Phase 2: Learning from Datafile: dimin.train -test4-Start: 0 @ Mon May 31 11:03:35 2010 -test4-Finished: 2999 @ Mon May 31 11:03:35 2010 Size of InstanceBase = 19231 Nodes, (769240 bytes), 49.77 % compression Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: inc1.out Algorithm : IB1 Global metric : Value Difference, Prestored matrix Deviant Feature Metrics:(none) Size of value-matrix[1] = 168 Bytes Size of value-matrix[2] = 968 Bytes Size of value-matrix[3] = 968 Bytes Size of value-matrix[4] = 168 Bytes Size of value-matrix[5] = 168 Bytes Size of value-matrix[6] = 1904 Bytes Size of value-matrix[7] = 1904 Bytes Size of value-matrix[8] = 504 Bytes Size of value-matrix[9] = 104 Bytes Size of value-matrix[10] = 2904 Bytes Size of value-matrix[11] = 1728 Bytes Size of value-matrix[12] = 1248 Bytes Total Size of value-matrices 12736 Bytes Weighting : GainRatio -test4-Tested: 1 @ Mon May 31 11:03:35 2010 -test4-Tested: 2 @ Mon May 31 11:03:35 2010 -test4-Tested: 3 @ Mon May 31 11:03:35 2010 -test4-Tested: 4 @ Mon May 31 11:03:35 2010 -test4-Tested: 5 @ Mon May 31 11:03:35 2010 -test4-Tested: 6 @ Mon May 31 11:03:35 2010 -test4-Tested: 7 @ Mon May 31 11:03:35 2010 -test4-Tested: 8 @ Mon May 31 11:03:35 2010 -test4-Tested: 9 @ Mon May 31 11:03:35 2010 -test4-Tested: 10 @ Mon May 31 11:03:35 2010 -test4-Tested: 100 @ Mon May 31 11:03:35 2010 -test4-Ready: 950 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0791 (12003.74 p/s) overall accuracy: 0.964211 (916/950), of which 62 exact matches There were 6 ties of which 6 (100.00%) were correctly resolved -test4-Saving Weights in wg.1.wgt -test4-Saving Probability Arrays in arr.1.arr Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: inc2.out Algorithm : IB1 Global metric : Value Difference, Prestored matrix Deviant Feature Metrics:(none) Size of value-matrix[1] = 168 Bytes Size of value-matrix[2] = 968 Bytes Size of value-matrix[3] = 968 Bytes Size of value-matrix[4] = 168 Bytes Size of value-matrix[5] = 168 Bytes Size of value-matrix[6] = 1904 Bytes Size of value-matrix[7] = 1904 Bytes Size of value-matrix[8] = 504 Bytes Size of value-matrix[9] = 104 Bytes Size of value-matrix[10] = 2904 Bytes Size of value-matrix[11] = 1728 Bytes Size of value-matrix[12] = 1248 Bytes Total Size of value-matrices 12736 Bytes Weighting : GainRatio -test4-Tested: 1 @ Mon May 31 11:03:35 2010 -test4-Tested: 2 @ Mon May 31 11:03:35 2010 -test4-Tested: 3 @ Mon May 31 11:03:35 2010 -test4-Tested: 4 @ Mon May 31 11:03:35 2010 -test4-Tested: 5 @ Mon May 31 11:03:35 2010 -test4-Tested: 6 @ Mon May 31 11:03:35 2010 -test4-Tested: 7 @ Mon May 31 11:03:35 2010 -test4-Tested: 8 @ Mon May 31 11:03:35 2010 -test4-Tested: 9 @ Mon May 31 11:03:35 2010 -test4-Tested: 10 @ Mon May 31 11:03:35 2010 -test4-Tested: 100 @ Mon May 31 11:03:35 2010 -test4-Ready: 950 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0866 (10965.92 p/s) overall accuracy: 0.964211 (916/950), of which 62 exact matches There were 6 ties of which 6 (100.00%) were correctly resolved -test4-Saving Weights in wg.2.wgt -test4-Saving Probability Arrays in arr.2.arr Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: inc3.out Algorithm : IB1 Global metric : Value Difference, Prestored matrix Deviant Feature Metrics:(none) Size of value-matrix[1] = 168 Bytes Size of value-matrix[2] = 968 Bytes Size of value-matrix[3] = 968 Bytes Size of value-matrix[4] = 168 Bytes Size of value-matrix[5] = 168 Bytes Size of value-matrix[6] = 1904 Bytes Size of value-matrix[7] = 1904 Bytes Size of value-matrix[8] = 504 Bytes Size of value-matrix[9] = 104 Bytes Size of value-matrix[10] = 2904 Bytes Size of value-matrix[11] = 1728 Bytes Size of value-matrix[12] = 1248 Bytes Total Size of value-matrices 12736 Bytes Weighting : GainRatio -test4-Tested: 1 @ Mon May 31 11:03:35 2010 -test4-Tested: 2 @ Mon May 31 11:03:35 2010 -test4-Tested: 3 @ Mon May 31 11:03:35 2010 -test4-Tested: 4 @ Mon May 31 11:03:35 2010 -test4-Tested: 5 @ Mon May 31 11:03:35 2010 -test4-Tested: 6 @ Mon May 31 11:03:35 2010 -test4-Tested: 7 @ Mon May 31 11:03:35 2010 -test4-Tested: 8 @ Mon May 31 11:03:35 2010 -test4-Tested: 9 @ Mon May 31 11:03:35 2010 -test4-Tested: 10 @ Mon May 31 11:03:35 2010 -test4-Tested: 100 @ Mon May 31 11:03:35 2010 -test4-Ready: 950 @ Mon May 31 11:03:35 2010 Seconds taken: 0.0740 (12844.09 p/s) overall accuracy: 0.964211 (916/950), of which 62 exact matches There were 6 ties of which 6 (100.00%) were correctly resolved -test4-Saving Weights in wg.3.wgt -test4-Saving Probability Arrays in arr.3.arr Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: inc4.out Algorithm : IB1 Global metric : Value Difference, Prestored matrix Deviant Feature Metrics:(none) Size of value-matrix[1] = 168 Bytes Size of value-matrix[2] = 968 Bytes Size of value-matrix[3] = 968 Bytes Size of value-matrix[4] = 168 Bytes Size of value-matrix[5] = 168 Bytes Size of value-matrix[6] = 1904 Bytes Size of value-matrix[7] = 1904 Bytes Size of value-matrix[8] = 504 Bytes Size of value-matrix[9] = 104 Bytes Size of value-matrix[10] = 2904 Bytes Size of value-matrix[11] = 1728 Bytes Size of value-matrix[12] = 1248 Bytes Total Size of value-matrices 12736 Bytes Weighting : GainRatio -test4-Tested: 1 @ Mon May 31 11:03:36 2010 -test4-Tested: 2 @ Mon May 31 11:03:36 2010 -test4-Tested: 3 @ Mon May 31 11:03:36 2010 -test4-Tested: 4 @ Mon May 31 11:03:36 2010 -test4-Tested: 5 @ Mon May 31 11:03:36 2010 -test4-Tested: 6 @ Mon May 31 11:03:36 2010 -test4-Tested: 7 @ Mon May 31 11:03:36 2010 -test4-Tested: 8 @ Mon May 31 11:03:36 2010 -test4-Tested: 9 @ Mon May 31 11:03:36 2010 -test4-Tested: 10 @ Mon May 31 11:03:36 2010 -test4-Tested: 100 @ Mon May 31 11:03:36 2010 -test4-Ready: 950 @ Mon May 31 11:03:36 2010 Seconds taken: 0.0727 (13075.49 p/s) overall accuracy: 0.964211 (916/950), of which 62 exact matches There were 6 ties of which 6 (100.00%) were correctly resolved -test4-Saving Weights in wg.4.wgt -test4-Saving Probability Arrays in arr.4.arr Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 Starting to test, Testfile: dimin.test Writing output in: inc5.out Algorithm : IB1 Global metric : Value Difference, Prestored matrix Deviant Feature Metrics:(none) Size of value-matrix[1] = 168 Bytes Size of value-matrix[2] = 968 Bytes Size of value-matrix[3] = 968 Bytes Size of value-matrix[4] = 168 Bytes Size of value-matrix[5] = 168 Bytes Size of value-matrix[6] = 1904 Bytes Size of value-matrix[7] = 1904 Bytes Size of value-matrix[8] = 504 Bytes Size of value-matrix[9] = 104 Bytes Size of value-matrix[10] = 2904 Bytes Size of value-matrix[11] = 1728 Bytes Size of value-matrix[12] = 1248 Bytes Total Size of value-matrices 12736 Bytes Weighting : GainRatio -test4-Tested: 1 @ Mon May 31 11:03:36 2010 -test4-Tested: 2 @ Mon May 31 11:03:36 2010 -test4-Tested: 3 @ Mon May 31 11:03:36 2010 -test4-Tested: 4 @ Mon May 31 11:03:36 2010 -test4-Tested: 5 @ Mon May 31 11:03:36 2010 -test4-Tested: 6 @ Mon May 31 11:03:36 2010 -test4-Tested: 7 @ Mon May 31 11:03:36 2010 -test4-Tested: 8 @ Mon May 31 11:03:36 2010 -test4-Tested: 9 @ Mon May 31 11:03:36 2010 -test4-Tested: 10 @ Mon May 31 11:03:36 2010 -test4-Tested: 100 @ Mon May 31 11:03:36 2010 -test4-Ready: 950 @ Mon May 31 11:03:36 2010 Seconds taken: 0.0732 (12975.31 p/s) overall accuracy: 0.964211 (916/950), of which 62 exact matches There were 6 ties of which 6 (100.00%) were correctly resolved -test4-Saving Weights in wg.5.wgt -test4-Saving Probability Arrays in arr.5.arr \end{verbatim} \end{footnotesize} \clearpage \subsection{example 5, {\tt api\_test5.cxx}} This program demonstrates the use of neighborSets to classify and store results. It also demonstrates some neighborSet basics. \begin{footnotesize} \begin{verbatim} #include #include #include "TimblAPI.h" using std::endl; using std::cout; using std::string; using namespace Timbl; int main(){ TimblAPI *My_Experiment = new TimblAPI( "-a IB1 +vDI+DB+n +mM +k4 " , "test5" ); My_Experiment->Learn( "dimin.train" ); { string line = "=,=,=,=,+,k,e,=,-,r,@,l,T"; const neighborSet *neighbours1 = My_Experiment->classifyNS( line ); if ( neighbours1 ){ cout << "Classify OK on " << line << endl; cout << neighbours1; } else cout << "Classify failed on " << line << endl; neighborSet neighbours2; line = "+,zw,A,rt,-,k,O,p,-,n,O,n,E"; if ( My_Experiment->classifyNS( line, neighbours2 ) ){ cout << "Classify OK on " << line << endl; cout << neighbours2; } else cout << "Classify failed on " << line << endl; line = "+,z,O,n,-,d,A,xs,-,=,A,rm,P"; const neighborSet *neighbours3 = My_Experiment->classifyNS( line ); if ( neighbours3 ){ cout << "Classify OK on " << line << endl; cout << neighbours3; } else cout << "Classify failed on " << line << endl; neighborSet uit2; { neighborSet uit; uit.setShowDistance(true); uit.setShowDistribution(true); cout << " before first merge " << endl; cout << uit; uit.merge( *neighbours1 ); cout << " after first merge " << endl; cout << uit; uit.merge( *neighbours3 ); cout << " after second merge " << endl; cout << uit; uit.merge( neighbours2 ); cout << " after third merge " << endl; cout << uit; uit.truncate( 3 ); cout << " after truncate " << endl; cout << uit; cout << " test assignment" << endl; uit2 = *neighbours1; } cout << "assignment result: " << endl; cout << uit2; { cout << " test copy construction" << endl; neighborSet uit(uit2); cout << "result: " << endl; cout << uit; } cout << "almost done!" << endl; } delete My_Experiment; cout << "done!" << endl; } \end{verbatim} \end{footnotesize} Its expected output is (without further comment): \begin{footnotesize} \begin{verbatim} Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 -test5-Phase 1: Reading Datafile: dimin.train -test5-Start: 0 @ Mon May 31 11:03:36 2010 -test5-Finished: 2999 @ Mon May 31 11:03:36 2010 -test5-Calculating Entropy Mon May 31 11:03:36 2010 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > -test5-Phase 2: Learning from Datafile: dimin.train -test5-Start: 0 @ Mon May 31 11:03:36 2010 -test5-Finished: 2999 @ Mon May 31 11:03:36 2010 Size of InstanceBase = 19231 Nodes, (769240 bytes), 49.77 % compression Classify OK on =,=,=,=,+,k,e,=,-,r,@,l,T # k=1 { T 1.00000 } 0.0000000000000 # k=2 { T 1.00000 } 0.0031862902473388 # k=3 { T 1.00000 } 0.0034182315118303 # k=4 { T 1.00000 } 0.0037433772844615 Classify OK on +,zw,A,rt,-,k,O,p,-,n,O,n,E # k=1 { E 1.00000 } 0.0000000000000 # k=2 { E 1.00000 } 0.056667880327190 # k=3 { E 1.00000 } 0.062552636617742 # k=4 { E 1.00000 } 0.064423860361889 Classify OK on +,z,O,n,-,d,A,xs,-,=,A,rm,P # k=1 { P 1.00000 } 0.059729836255170 # k=2 { P 1.00000 } 0.087740769132651 # k=3 { P 1.00000 } 0.088442788919723 # k=4 { P 1.00000 } 0.097058649951429 before first merge after first merge # k=1 { P 1.00000 } 0.059729836255170 # k=2 { P 1.00000 } 0.087740769132651 # k=3 { P 1.00000 } 0.088442788919723 # k=4 { P 1.00000 } 0.097058649951429 after second merge # k=1 { P 2.00000 } 0.059729836255170 # k=2 { P 2.00000 } 0.087740769132651 # k=3 { P 2.00000 } 0.088442788919723 # k=4 { P 2.00000 } 0.097058649951429 after third merge # k=1 { E 1.00000 } 0.0000000000000 # k=2 { E 1.00000 } 0.056667880327190 # k=3 { P 2.00000 } 0.059729836255170 # k=4 { E 1.00000 } 0.062552636617742 # k=5 { E 1.00000 } 0.064423860361889 # k=6 { P 2.00000 } 0.087740769132651 # k=7 { P 2.00000 } 0.088442788919723 # k=8 { P 2.00000 } 0.097058649951429 after truncate # k=1 { E 1.00000 } 0.0000000000000 # k=2 { E 1.00000 } 0.056667880327190 # k=3 { P 2.00000 } 0.059729836255170 test assignment assignment result: # k=1 { P 1.00000 } 0.059729836255170 # k=2 { P 1.00000 } 0.087740769132651 # k=3 { P 1.00000 } 0.088442788919723 # k=4 { P 1.00000 } 0.097058649951429 test copy construction result: # k=1 { P 1.00000 } 0.059729836255170 # k=2 { P 1.00000 } 0.087740769132651 # k=3 { P 1.00000 } 0.088442788919723 # k=4 { P 1.00000 } 0.097058649951429 almost done! done! \end{verbatim} \end{footnotesize} \clearpage \subsection{example 6, {\tt api\_test6.cxx}} This program demonstrates the use of ValueDistributions, TargetValues an neighborSets for classification. \begin{footnotesize} \begin{verbatim} #include #include "TimblAPI.h" using std::cout; using std::endl; using namespace Timbl; int main(){ TimblAPI My_Experiment( "-a IB1 +vDI+DB -k3", "test6" ); My_Experiment.Learn( "dimin.train" ); const ValueDistribution *vd; const TargetValue *tv = My_Experiment.Classify( "-,=,O,m,+,h,K,=,-,n,I,N,K", vd ); cout << "resulting target: " << tv << endl; cout << "resulting Distribution: " << vd << endl; ValueDistribution::dist_iterator it=vd->begin(); while ( it != vd->end() ){ cout << it->second << " OR "; cout << it->second->Value() << " " << it->second->Weight() << endl; ++it; } cout << "the same with neighborSets" << endl; const neighborSet *nb = My_Experiment.classifyNS( "-,=,O,m,+,h,K,=,-,n,I,N,K" ); ValueDistribution *vd2 = nb->bestDistribution(); cout << "default answer " << vd2 << endl; decayStruct *dc = new expDecay(0.3); delete vd2; vd2 = nb->bestDistribution( dc ); delete dc; cout << "with exponenial decay, alpha = 0.3 " << vd2 << endl; delete vd2; } \end{verbatim} \end{footnotesize} This is the output produced: \begin{footnotesize} \begin{verbatim} Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 -test6-Phase 1: Reading Datafile: dimin.train -test6-Start: 0 @ Mon May 31 11:03:36 2010 -test6-Finished: 2999 @ Mon May 31 11:03:36 2010 -test6-Calculating Entropy Mon May 31 11:03:36 2010 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > -test6-Phase 2: Learning from Datafile: dimin.train -test6-Start: 0 @ Mon May 31 11:03:36 2010 -test6-Finished: 2999 @ Mon May 31 11:03:36 2010 Size of InstanceBase = 19231 Nodes, (769240 bytes), 49.77 % compression resulting target: K resulting Distribution: { E 1.00000, K 7.00000 } E 1 OR E 1 K 7 OR K 7 the same with neighborSets default answer { E 1.00000, K 7.00000 } with exponenial decay, alpha = 0.3 { E 0.971556, K 6.69810 } \end{verbatim} \end{footnotesize} \end{document} LanguageMachines-timbl-642727d/docs/texfiles/Timbl_6.3_Manual.tex000066400000000000000000004776231451477526200245620ustar00rootroot00000000000000% TiMBL 6.3 manual \documentclass{report} \usepackage{epsf} \usepackage{epsfig} \usepackage{a4wide} \usepackage{palatino} \usepackage{fullname} \usepackage{url} \newcommand{\chisq}{{$ \chi^2 $}} \author{Walter Daelemans* \and Jakub Zavrel*$\dagger$ \and Ko van der Sloot \and Antal van den Bosch\\ \ \\ Induction of Linguistic Knowledge Research Group\\ Tilburg centre for Cognition and Communication \\ Tilburg University \\ \\ (*) CLiPS - Computational Linguistics Group\\ Department of Linguistics \\ University of Antwerp\\ \\ ($\dagger$) Textkernel B.V.\\ \\ P.O. Box 90153, NL-5000 LE, Tilburg, The Netherlands \\ URL: http://ilk.uvt.nl\thanks{This document is available from http://ilk.uvt.nl/downloads/pub/papers/ilk.1001.pdf. All rights reserved Induction of Linguistic Knowledge, Tilburg University and CLiPS, University of Antwerp.}} \title{{\huge TiMBL: Tilburg Memory-Based Learner} \\ \vspace*{0.5cm} {\bf version 6.3} \\ \vspace*{0.5cm}{\huge Reference Guide}\\ \vspace*{1cm} {\it ILK Technical Report -- ILK 10-01}} %better paragraph indentation \parindent 0pt \parskip 9pt \begin{document} \pagenumbering{roman} \maketitle \tableofcontents \chapter*{Preface} Memory-Based Learning ({\sc mbl}) is an elegantly simple and robust machine-learning method applicable to a wide range of tasks in Natural Language Processing (NLP). In our research group at Tilburg University, we have been working since the end of the 1980s on the development of Memory-Based Learning techniques and algorithms. The foundations are bundled in \namecite{Daelemans+05}. Section~\ref{furtherreading} provides a historical overview of work on the application of {\sc mbl} in NLP. With the establishment of the ILK (Induction of Linguistic Knowledge) research group in 1997, and with the increasing use of {\sc mbl} at the CNTS (now CLiPS) research group of the University of Antwerp, the need for a well-coded and uniform tool for our main algorithms became more urgent. TiMBL was the result of combining ideas from a number of different {\sc mbl} implementations, cleaning up the interface, and using a whole bag of tricks to make it more efficient. We think it has become a useful tool for NLP research, and, for that matter, for many other domains where classification tasks are learned from examples, so we started to release the software in 1999. With the release of the sixth version of TiMBL we moved to releasing our software under the GPL license, for anyone to use under the conditions stated in the license. Memory-Based Learning is a direct descendant of the classical $k$-Nearest Neighbor ($k$-NN) approach to classification, which has become known as a powerful pattern classification algorithm for numeric data. In typical NLP learning tasks, however, the focus is on discrete data, very large numbers of examples, and many attributes of differing relevance. Moreover, classification speed is a critical issue in any realistic application of Memory-Based Learning. These constraints demand non-trivial data-structures and speedup optimizations for the core $k$-NN classifier. Our approach has resulted in an architecture which compresses the typical flat file organization found in straightforward $k$-NN implementations, into a decision-tree structure. While the decision tree can be used to retrieve the exact $k$-nearest neighbors (as happens in the {\sc ib1} algorithm within TiMBL), it can also be deterministically traversed as in a decision-tree classifier (the method adopted by the {\sc igtree} algorithm). We believe that our optimizations make TiMBL one of the fastest discrete $k$-NN implementations around. The main effort in the development and maintenance of this software was and continues to be invested by Ko van der Sloot. The code started as a rewrite of {\tt nibl}, a piece of software developed by Peter Berck from a Common Lisp implementation by Walter Daelemans of {\sc ib1-ig}. Some of the index optimizations in TiMBL are due to Jakub Zavrel. The code has benefited substantially from trial, error and scrutiny by all past and present members of the ILK and CLiPS (formerly CNTS) groups in Tilburg and Antwerp. We are furthermore indebted to Ton Weijters of Eindhoven Technical University for his inspirational early work on $k$-NN and for his involvements in {\sc igtree}. Our sincere thanks go to the many users of TiMBL who have contributed to it immensely by giving us feedback and reporting bugs, and to the two organisations that have supported and enabled its development: NWO, the Netherlands Organization for Scientific Research, and the School of Humanities of Tilburg University. NWO funding has spanned three subsequent periods. From 1997 until 2001 development was part of the ``Induction of Linguistic Knowledge'' research programme, partially funded by the Netherlands Organization for Scientific Research (NWO) and Tilburg University. Between 2001 and 2006 it was funded as part of the ``Memory Models of Language'' research project under the NWO {\em Vernieuwingsimpuls}\/ programme, and since 2006 it is funded as part of the ``Implicit Linguistics'' research project under the NWO Vici programme. The current release (version 6.3) succeeds major release 6.2. The most significant change is that all {\em server}-related functionality is moved to a separate TimblServer package. An elaborate description of the changes from version 1.0 up to 6.3 can be found in Chapter~\ref{changes}. Although all new features have been tested for some time in our research groups, the software may still contain bugs and inconsistencies in some places. We would appreciate it if you would send bug reports, ideas about enhancements of the software and the manual, and any other comments you might have, to {\tt Timbl@uvt.nl}. This reference guide is structured as follows. In Chapter~\ref{license} you can find the terms of the license according to which you are allowed to use TiMBL. The subsequent chapter gives some instructions on how to install the TiMBL package on your computer. Chapter~\ref{changes} lists the changes that have taken place up to the current version. Next, Chapter~\ref{tutorial} offers a quick-start tutorial for readers who want to get to work with TiMBL right away. The tutorial describes, step-by-step, a case study with a sample data set (included with the software) representing the linguistic domain of predicting the diminutive inflection of Dutch nouns. Readers who are interested in the theoretical and technical details of Memory-Based Learning and of this implementation can refer to Chapter~\ref{algorithms}. Chapter~\ref{reference} provides full reference to the command line options of TiMBL and supported file formats. \chapter{GNU General Public License} \label{license} \pagenumbering{arabic} TiMBL is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. TiMBL is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with TiMBL. If not, see $<$http://www.gnu.org/licenses/$>$. In publication of research that makes use of TiMBL 6.3, a citation should be given of: {\em ``Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch (2009). TiMBL: Tilburg Memory Based Learner, version 6.3, Reference Guide. ILK Technical Report 10-01 Available from \\ {\tt http://ilk.uvt.nl/downloads/pub/papers/ilk1001.pdf}''} For information about commercial licenses for TiMBL 6.3, contact {\tt Timbl@uvt.nl}, or send your request in writing to: Prof. dr.~Walter Daelemans\\ CLiPS - Language Technology Group\\ Dept. of Linguistics \\ University of Antwerp\\ Prinsstraat 13, L-203, B-2000 Antwerp \\ Belgium \pagestyle{headings} \chapter{Installation} \vspace{-1cm} You can get the TiMBL package as a gzipped tar archive from: {\tt http://ilk.uvt.nl/timbl} Following the links from that page, you can download the file {\tt timbl-6.3.tar.gz}. This file contains the complete source code (C++) for the TiMBL program, a few sample data sets, the license, and documentation. The installation should be relatively straightforward on most UNIX systems. To install the package on your computer, unzip the downloaded file ({\tt >} is the command line prompt): {\tt > tar xfz timbl-6.3.0.tar.gz} This will make a directory {\tt timbl-6.3.0} under your current directory. Alternatively you can do: {\tt > gunzip timbl-6.3.0.tar.gz} and unpack the tar archive: {\tt > tar xf timbl-6.3.0.tar} Go to the timbl-6.3.0 directory, and configure the package by typing {\tt > cd timbl-6.3.0} \\ {\tt > ./configure --prefix=} If you do not use the {\tt --prefix} option, TiMBL will try to install itself in the directory {\tt /usr/local/}. If you do not have {\tt root} access you can specify a different installation location such as {\tt \$HOME/install} It is not obligatory to install TiMBL, but if you plan to install TiMBL-based extensions such as TimblServer\footnote{\url{http://ilk.uvt.nl/timbl}}, Mbt\footnote{\url{http://ilk.uvt.nl/mbt}}, Dimbl\footnote{\url{http://ilk.uvt.nl/dimbl}}, or Tadpole\footnote{\url{http://ilk.uvt.nl/tadpole}}, or you want to build your own extensions using the TiMBL API, installing is the best choice. After {\tt configure} you can build TiMBL: {\tt > make} and (as recommended) install: {\tt > make install } If the process was completed successfully, you should now have an executable file named {\tt Timbl} in the directory {\tt /bin}, and a static library {\tt libTimbl.a} in the directory {\tt /lib}. Additionally, several demo programs named {\tt api\_test*}, {\tt classify} and {\tt tse} are created in the {\tt ./demos} subdirectory. Within the {\tt } directory a subdirectory is also created: {\tt share/doc/timbl} where the TiMBL 6.3 documentation can be found, and which in turn contains a subdirectory {\tt examples} with example data files. Some of these data sets are used in the Quick Start Section~\ref{tutorial} of this document; other data and source files are referred to in the API documentation. The latter, along with a pdf version of this document, can also be found in the {\tt doc} directory. Note that the API documentation is a beta-state document. TiMBL should now be ready for use. If you want to run the examples and demos from this manual, you should act as follows: \begin{itemize} \item Be sure to add {\tt /bin} to your PATH. In many shells something like {\tt > export PATH=\$PATH:/bin } will do. \item copy all the files from {\tt /share/doc/timbl/examples} to some working location. (By default, TiMBL writes its results to the directory where it finds the data.) \item and test: {\tt cd} to the working location, and then {\tt Timbl -f dimin.train -t dimin.test} \end{itemize} If you did not install TiMBL, the executable can be found in the {\tt src} directory of the build. The demo files can be found in the {\tt demo} directory. The e-mail address for problems with the installation, bug reports, comments and questions is {\tt Timbl@uvt.nl}. \chapter{Changes} \label{changes} This chapter gives a brief overview of the changes from all previously released versions (1.0 up to 6.3) for users already familiar with the program. \section{From version 6.2 to 6.3} \begin{itemize} \item All server-related functionality is removed from Timbl. A new TimblServer package is available wich provides the same interface as Timbl up to version 6.3, but also adds some extra features, such as running multiple but separate experiments on one TCP port. See the TimblServer package for more details. \item Starting with Timbl 6.3 we support installable packages for Debian and Ubuntu (.deb), RedHat (.rpm) and MacOSX (Fink)\footnote{\url{http://ilk.uvt.nl/timbl-packages}}. \item Some bugs and inconsistencies have been fixed. \end{itemize} \section{From version 6.1 to 6.2} Version 6.2 differs from 6.1 in a great number of internal changes aimed at making the code better maintainable and extendible, in some minor bug fixes, and in the following more prominent changes: \begin{itemize} \item A new distance metric, the Dice coefficient, has been added; the metric can be set with {\tt -mDC}. Analogous to the Levenshtein ({\tt -mL}) metric, the Dice coefficient operates at the feature value level; it computes the overlap in character bigrams of two value strings. \item Value difference matrices, as used by the {\sc mvdm} and Jeffrey divergence distance metrics, can now be written to file, and read into TiMBL, allowing for user-defined value difference metrics to be used. The new command line options are {\tt --matrixout=} and {\tt --matrixin=}. \item The {\sc IGTree} algorithm has been optimized beyond the improvements introduced in version 6.0. With very large training sets, {\sc IGTree} was reported to be exponentially slower in the later stages of training. Trees are now built in near-linear time. \end{itemize} Finally, besides minor bug fixes, a great number of internal changes were made to make the code better maintainable and extendible. \section{From version 6.0 to 6.1} Version 6.1 differs from 6.0 mainly in the changed configuration. It is now based on autotools and is delivered as an installable package. Some bugs have been fixed as well. \section{From version 5.1 to 6.0} Version 6.0 differs from 5.1 firstly in terms of internal changes aimed at increasing classification speed and lowering memory usage, of which the most prominent are \begin{itemize} \item The {\sc IGTree} algorithm has been optimized. Learning has been made more memory-lean, while classification has been optimized so that it is now orders of magnitude faster than before on most data sets. \item {\sc Mvdm} matrices are partly prestored; only the {\sc mvdm} values of pairs of frequent values are precomputed. The threshold frequency $n$ can now be determined with {\tt -c n}. This way memory can be traded for speed, up to a point. The default value 10 remains the recommended one. \end{itemize} Also, two metrics and several verbosity options and other command-line switches are added: \begin{itemize} \item Two distance metrics are added: {\tt -mC} sets the Cosine metric, and {\tt -mL} sets the Levenshtein metric. The latter metric operates at the feature-value level, and thus offers an alternative to the all-or-nothing Overlap metric for string-valued features. \item Class distribution output generated with {\tt +v db} can be normalized so that they add to $1.0$, with the additional {\tt -G} option (or {\tt -G0}). As a simple smoothing option, with {\tt -G1:double} all class votes are incremented by {\tt double} before normalization. For example, {\tt -G1:1} (or {\tt -G1} for short) is ``add one''-smoothing; {\tt -G1:0.5} adds $0.5$ to all class votes. \item With {\tt -Beam=n} (from version 6.2 onwards: {\tt --Beam=n}), where $n$ is an integer, the {\tt +v db} output is constrained to the $n$ classes receiving the highest votes. This special limit is useful in cases in which the {\tt +v db} output, typically used for further processing, generates far too much output in its default unconstrained setting. \item Class distributions are not stored on non-terminal nodes with {\sc IGTree} and {\sc tribl} by default. To revert this default, e.g. to be able to use {\tt +v db} with {\sc IGTree}, the setting {\tt +D} can be used. \item With {\tt -T n}, the user can specify that the $n$th column in the training set of labeled examples contains the label to be predicted, while all other columns represent the input features. By default, the last column is assumed to contain the class labels. \item After classification, TiMBL reports its classification speed at microsecond precision instead of in seconds. \item The verbosity option {\tt +v md} displays the level at which a classification was made by {\sc IGTree} ({\tt -a1}), and whether the class label was obtained from a leaf node or an end node. \item With {\tt -X [file]}, TiMBL dumps its internal TiMBL tree into a file containing an XML tree. This option is analogous to {\tt -I [file]}, which prints a TiMBL tree in TiMBL's proprietary format, the difference being that the latter format can be read into TiMBL again. \item Several minor bugs have been resolved. \end{itemize} \section{From version 5.0 to 5.1} Version 5.1 adds speed and memory improvements that are notable with datasets that have very large amounts of examples, features, feature values, or classes (and, especially, combinations of those). Previous versions exhibited exponential slowdown in some worst cases; this has been largely countered. On the outside, TiMBL has been updated in the following aspects: \begin{itemize} \item TiMBL offers extended performance reporting: next to accuracy it reports on micro and macro-averages of F-score and AUC (area under the ROC-curve) with {\tt +v as}. Optionally, it also shows each individual class' precision, recall (or true positive rate), and false positive rate with {\tt +v cs}. \item TiMBL always uses gain ratio feature weighting as the default case, if not specified by the user, also with the {\sc mvdm} and Jeffrey Divergence similarity metrics. \item Two additional feature orderings for the internal TiMBL trees are added, {\tt -TGxE} and {\tt -TIxE} (gain ratio $\times$ entropy and information gain $\times$ entropy, respectively) to potentially tackle the problem of unbalanced trees. \item Bugs in leave-one-out testing with numeric features and with exemplar weighting were fixed. \end{itemize} \section{From version 4.3 to 5.0} Version 5.0 is the conclusion of a number of recodings (mostly involving more generic treatment of variables to improve robustness, but also the removal of inverted indexing on the internal tree representation) that have changed the internals of TiMBL considerably. On the outside, TiMBL displays the following new characteristics: \begin{itemize} \item Next to the Overlap, {\sc mvdm}, and Numeric distance functions, TiMBL now features the Jeffrey divergence distance function and the Dot-product distance function. \item The exponential-decay distance weighting function can be set using a second parameter, which can change the shape of the function from normal exponential to bell-shaped. \item In addition to the ``binary'' format, TiMBL can now read a more generic sparse data format. This format allows instances to be coded by tuples of $<$ feature number, feature value $>$ where the value can be symbolic or numeric rather than only binary. \item Tree files generated by TiMBL versions 1.*, 2.* and 3.* are no longer supported. \item The command line interface has had the following additions, including the ones reflecting the above changes: \begin{itemize} \item {\tt -m J} activates the Jeffrey divergence distance metric. \item {\tt -m D} activates the Dot-product distance metric. \item {\tt -d ED::} (without whitespace) sets the $\alpha$ and new $\beta$ parameters. If unspecified, as in {\tt -d ED:} or the older (deprecated) {\tt -d ED }, $\beta$ is set to $1.0$. \item {\tt -F Sparse} declares that training and test files are in the sparse $<$ feature number, feature value $>$ tuple-format described in more detail in section~\ref{commandline}. \item {\tt +v k} is a new verbosity option that prints all class distributions per $k$-nearest distance per classified instance in the output file. It works analogous to the {\tt +v n} option, but does not print the neighbors themselves. \end{itemize} \end{itemize} \section{From version 3.0 to 4.3} As the last upgrade of the version 4 strain, version 4.3 added some command line functionality and internal code changes to version 4.2. Minor progressive changes from 4.0 to 4.3 are found at the bottom of this list and are marked as such. \begin{itemize} \item Distance weighting of the $k$ nearest neighbors. This classical exemplar weighting scheme \cite{Dudani76} allows closer nearest neighbors in the $k$ to have a more prominent vote in classification. TiMBL incorporates linear, inversed, and exponential distance weighting. \item Incremental edited memory-based learning with {\sc ib2} \cite{Aha+91}. This incremental version of {\sc ib1} adds instances to memory only when those instances are misclassified by the then-current set of instances in memory. \item Frequency-filtered {\sc mvdm} distance metric. The option, which is not selected by default, is an add-on of the {\sc mvdm} metric, that backs off from the {\sc mvdm} metric to the Overlap distance function whenever one or both in a pair of matched values occurs fewer times in the training material than a user-determined threshold. \item {\sc tribl2}. The {\sc tribl2} algorithm has been implemented as an additional trade-off between {\sc igtree} and {\sc ib1}. In contrast to {\sc tribl}, {\sc tribl2} uses no threshold parameter. \item Exemplar weighting. TiMBL can read additional numeric exemplar weights (generated externally) when reading a data file, and use these weights during neighbor distance computation in $k$-NN classification. \item Cross-validation testing. Analogous to the leave-one-out testing option, with cross-validation testing it is possible to let TiMBL run systematic tests on different values of parameters, without completely re-initializing the classifier in every fold of the validation experiment. \item The number of concurrent connections to a TiMBL server has been restricted, but can be set to different values. \item The command line interface has had several additions reflecting the above changes, plus one extra verbosity option: \begin{itemize} \item the {\tt -d metriccode} option sets the distance weighting metric. Three metrics are available: inverse distance (code ID), inverse linear (IL), and exponential decay (ED, which takes an extra argument $a$, without whitespace, determining the factor of the exponential function). By default, no distance weighting is used (code Z). See Chapter~\ref{algorithms} for descriptions. \item the {\tt -L n} option sets the frequency threshold in the optional switch (backoff) from {\sc mvdm} or Jeffrey divergence to Overlap; whenever in an {\sc mvdm} or Jeffrey divergence distance computation one or both of a pair of values occur fewer than {\tt n} times, Overlap is used rather than the {\sc mvdm} metric. The default value for {\tt n} is 1 (no switching). \item the {\tt -a 3} or {\tt -a IB2} switch invokes the {\sc ib2} algorithm. This algorithm expects to have the {\tt -b} switch set. \item the {\tt -b n} option sets the number ($n$) of lines counting from the top of the training set file, which form the bootstrap set of memorized instances to which {\sc ib2} will start adding instances incrementally. \item the {\tt -a 4} or {\tt -a TRIBL2} switch invokes the {\sc tribl2} algorithm. \item the {\tt -C n} switch (default: {\tt n} set to 10) restricts the number of concurrent connections to a TiMBL server (cf. the {\tt -S} switch). \item the {\tt +v/-v} option has {\tt cm} as a new optional argument; it returns the confusion matrix, obtained after testing, between predicted and actual classes in the test data. \end{itemize} \item The ``programmer's reference'' or API section has been separated from this manual. This new API, describing the underlying structure of TiMBL, is available as a separate document in the TiMBL software distribution. \item Two bugs relating to a type of sparse data problem have been resolved. The first involved leave-one-out experiments on data sets with features that have values that occur only once in the training data. The second bug occurred with the use of the {\tt -F Binary} option with the same type of data. \item {\bf [4.1]} Exemplar weights are stored in the TiMBL-tree. \item {\bf [4.1]} The core representation of TiMBL-trees has been modified, causing no changes at the surface except that the {\sc tribl} variant uses less memory. \item {\bf [4.2]} Feature value and class information in the internal TiMBL tree is hashed, by default, except with binary features. Hashing can be explicitly set on or off through the flag {\tt +H} or {\tt -H}. \item {\bf [4.2]} The discretization of numeric features, used for computing feature weights, has changed from linear binning between minimum and maximum values, to equal-content binning. \item {\bf [4.2]} Tie resolution between equal class distributions in the nearest neighbors set is resolved by first expanding the $k$ by one value. If the tie persists after the enlargement of the nearest neighbor set, the original tie resolution method is applied. \item {\bf [4.3]} Internal changes in the code (with no effect on learning and classification functionality) have been implemented with respect to namespaces. \item {\bf [4.3]} A progress marker (one dot per 10 seconds) in computationally intensive operations on the internal representation of the instance base (e.g. pruning {\sc igtree}s) is added in TiMBL's screen output. \item A number of bugs have been fixed, notably to handle erroneous input more robustly. \end{itemize} \section{From version 2.0 to 3.0} \begin{itemize} \item Server functionality. Apart from the standard processing of test items from a file, alternatively you can now specify a portnumber with {\tt -S portnumber} to open a socket and send commands for classification of test patterns or change of parameters to it. A sample client program is included in the distribution. This allows fast response times when small amounts of test material are presented at various intervals. It also opens the possibility of having large numbers of ``classification agents'' cooperate in real time, or of classication of the same data with different parameters. \item Leave-one-out testing. To get an estimate of the classification error, without setting aside part of one's data as a test set, one can now test by ``leave-one-out'' ({\tt -t leave\_one\_out}), in effect testing on every case once, while training on the rest of the cases, without completely re-initializing the classifier for every test case. \item Support for sparse binary features. For tasks with large numbers of sparse binary features, TiMBL now allows for an input format which lists only the ``active'' features, avoiding the listing of the many (zero-valued) features for each case. This format is described in Section~\ref{binaryformat}. \item Additional feature weighting metrics. We have added chi-squared and shared variance measures as weighting schemes. These weighting metrics are sometimes more robust to large numbers of feature values and other forms of data sparseness. \item Different metrics (Overlap, {\sc mvdm} or Numeric) can be applied to different features. \item The command line interface has slightly been cleaned up, and re-organized: \begin{itemize} \item The {\tt -m metricnumber} switch to choose metrics has been replaced by the use of a specification string following {\tt -m}. E.g.~you can specify to use {\sc mvdm} as the default metric, but use Overlap on features 5-7,9, Numeric on feature 1, and ignore feature 10 ({\tt -m M:O5-7,9:N1:I10}). \item All of the output needed for analysing the matching of nearest neighbors has been moved to the verbosity setting. \item Verbosity levels and some other options can be switched on {\tt +v} and off {\tt -v}, even between different classification actions. \item Because of the large amount of verbosity levels, the {\tt +v} option takes mnemonic abbreviations as arguments instead of numeric verbosity levels. Although the old (numeric) format is still supported, it's use is not encouraged as it will disappear in future versions. \end{itemize} \item Because of significant optimizations in the nearest neighbor search, the default is no longer to use inverted indexes. These can however still be turned on by using the {\tt +-} switch on the command line. \item You can now choose the output filename or have it generated by TiMBL on the basis of the test filename and the parameters. \item You can use TiMBL in a pipeline of commands by specifying '-' as either input, output or both. \item Several problems with the display of nearest neighbors in the output have been fixed. \item The API has been adapted a bit to allow more practical use of it. \end{itemize} \section{From version 1.0 to 2.0} \begin{itemize} \item We have added a new algorithm: {\sc tribl}, a hybrid between the fast {\sc igtree} algorithm and real nearest neighbor search (for more details, see~\ref{tribl}, or~\namecite{Daelemans+97d}). This algorithm is invoked with the {\tt -a 2} switch and requires the specification of a so-called {\sc tribl}-offset, the feature where {\sc igtree} stops and case bases are stored under the leaves of the constructed tree. \item Support for numeric features. Although the package has retained its focus on discrete features, it can now also process numeric features, scale them, and compute feature weights on them. You specify which features are numeric with the {\tt -N} option on the command line. \item The organization of the code is much more object-oriented than in version 1.0. %The main benefit of this is that: \item A Memory-Based Learning API is made available. You can define Memory-Based classification objects in your own C++ programs and access all of the functionality of TiMBL by linking to the TiMBL library. \item It has become easier to examine the way decisions are made from nearest neighbors, because several verbosity-levels allow you to dump similarity values ({\tt -D}), distributions ({\tt -v 16}), and nearest neighbor sets ({\tt -v 32}) to the output file. The {\tt -d} option for writing the distributions no longer exists. \item Better support for the manipulation of {\sc mvdm} matrices. Using the {\tt -U} and {\tt -u} options it is now possible to respectively save and read back value difference matrices (see Section~\ref{mvdmformat}). \item Both ``pre-stored'' and ``regular'' {\sc mvdm} experiments now generate filenames with ``{\tt mvd}'' in the suffix. This used to be ``{\tt pvd}'' and ``{\tt mvd}'' respectively. \item a number of minor bugs have been fixed. \end{itemize} \chapter{Quick-start Tutorial} \label{tutorial} This quick-start tutorial is meant to get you started with TiMBL right away. We discuss how to format the data of a task to serve as training examples, which choices can be made during the construction of the classifier, how various choices can be evaluated in terms of their generalization accuracy, and various other practical issues. The reader who is interested in more background information on TiMBL implementation issues and a formal description of Memory-Based Learning, is advised to read Chapter~\ref{algorithms}. Memory-Based Learning ({\sc mbl}) is based on the idea that intelligent behavior can be obtained by analogical reasoning, rather than by the application of abstract {\em mental rules} as in rule induction and rule-based processing. In particular, {\sc mbl} is founded in the hypothesis that the extrapolation of behavior from stored representations of earlier experience to new situations, based on the similarity of the old and the new situation, is of key importance. {\sc mbl} algorithms take a set of examples (fixed-length patterns of feature-values and their associated class) as input, and produce a {\em classifier} which can classify new, previously unseen, input patterns. Although TiMBL was designed with linguistic classification tasks in mind, it can in principle be applied to any kind of classification task with symbolic or numeric features and discrete (non-continuous) classes for which training data is available. As an example task for this tutorial we go through the application of TiMBL to the prediction of Dutch diminutive suffixes. The necessary data sets are included in the TiMBL distribution, so you can replicate the examples given below on your own system. \section{Data} The operation of TiMBL will be illustrated below by means of a real natural language processing task: prediction of the diminutive suffix form in Dutch~\cite{Daelemans+97b}. In Dutch, a noun can receive a diminutive suffix to indicate {\em small size} literally or metaphorically attributed to the referent of the noun; e.g. {\em mannetje} means {\em little man}. Diminutives are formed by a productive morphological rule which attaches a form of the Germanic suffix {\em -tje} to the singular base form of a noun. The suffix shows variation in its form (Table \ref{variation}). The task we consider here is to predict which suffix form is chosen for previously unseen nouns on the basis of their form. \begin{table}[ht] \begin{center} \begin{tabular}{l|l|l} Noun & Form & Suffix \\ \noalign{\smallskip} \hline \noalign{\smallskip} huis (house) & huisje & {\em -je} \\ man (man) & mannetje & {\em -etje\/} \\ raam (window) & raampje & {\em -pje\/} \\ woning (house) & woninkje & {\em -kje\/} \\ baan (job) & baantje & {\em -tje\/} \\ \end{tabular} \caption{Allomorphic variation in Dutch diminutives.}\label{variation} \end{center} \end{table} For these experiments, we collect a representation of nouns in terms of their syllable structure as training material\footnote{These words were collected form the {\sc celex} lexical database~\cite{Baayen+93}.}. For each of the last three syllables of the noun, four different features are collected: whether the syllable is stressed or not (values - or +), the string of consonants before the vocalic part of the syllable (i.e. its onset), its vocalic part (nucleus), and its post-vocalic part (coda). Whenever a feature value is not present (e.g. a syllable does not have an onset, or the noun has less than three syllables), the value `=' is used. The class to be predicted is either E ({\em -etje}), T ({\em -tje}), J ({\em -je}), K ({\em -kje}), or P ({\em -pje}). Some examples are given below (the word in the rightmost column is only provided for convenience and is not used). The values of the syllabic content features are given in phonetic notation. \begin{table}[ht] \begin{center} \begin{tabular}{cccccccccccc|l|l|l} + & b & i & = & - & z & @ & = & - & m & A & nt & J & {\em biezenmand} \\ = & = & = & = & = & = & = & = & + & b & I & x & E & {\em big}\\ = & = & = & = & + & b & K & = & - & b & a & n & T & {\em bijbaan}\\ = & = & = & = & + & b & K & = & - & b & @ & l & T & {\em bijbel}\\ \end{tabular} \end{center} \end{table} Our goal is to use TiMBL in order to train a classifier that can predict the class of new, previously unseen words as correctly as possible, given a set of training examples that are described by the features given above. Because the basis of classification in TiMBL is the storage of all training examples in memory, a test of the classifier's accuracy must be done on a separate test set. We will call these datasets {\tt dimin.train} and {\tt dimin.test}, respectively. The training set {\tt dimin.train} contains 2999 words and the test set contains 950 words, none of which are present in the training set. Although a single train/test partition suffices here for the purposes of explanation, it does not factor out the bias of choosing this particular split. Unless the test set is sufficiently large, a more reliable generalization accuracy measurement is used in real experiments, e.g.~10-fold cross-validation~\cite{Weiss+91}. This means that 10 separate experiments are performed, and in each ``fold'' 90\% of the data is used for training and 10\% for testing, in such a way that each instance is used as a test item exactly once. Another reliable way of testing the real error of a classifier is leave-one-out~\cite{Weiss+91}. In this approach, every data item in turn is selected once as a test item, and the classifier is trained on all remaining items. Accuracy of the classifier is then the number of data items correctly predicted. With the option {\tt -t leave\_one\_out}, this testing methodology is used by TiMBL. We will use this option in the tutorial on the file {\tt dimin.data}, the union of {\tt dimin.train} and {\tt dimin.test}. \section{Using TiMBL} Different formats are allowed for training and test data files. TiMBL is able to guess the type of format in most cases. We will use comma-separated values here, with the class as the last value. This format is called C4.5 format in TiMBL because it is the same as that used in Quinlan's well-known C4.5 program for induction of decision trees~\cite{Quinlan93}. See Section~\ref{fileformats} for more information about this and other file formats. An experiment is started by executing TiMBL with the two files ({\tt dimin.train} and {\tt dimin.test}) as arguments (``$>$'' is the command line prompt): {\footnotesize \begin{verbatim} > Timbl -f dimin.train -t dimin.test \end{verbatim} } Upon completion, a new file has been created with name {\small\tt dimin.test.IB1.O.gr.k1.out}, which is identical to the input test file except that an extra comma-separated column is added with the class predicted by TiMBL. The name of the file provides information about the {\sc mbl} algorithms and metrics used in the experiment (the default values in this case). We will describe these shortly. Apart from the result file, information about the operation of the algorithm is also sent to the standard output. It is therefore advisable to redirect the output to a file in order to make a log of the results. {\footnotesize \begin{verbatim} > Timbl -f dimin.train -t dimin.test > dimin-exp1 \end{verbatim} } The defaults used in this case work reasonably well for most problems. We will now provide a point by point explanation of what goes on in the output. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} TiMBL 6.3.0 (c) ILK 1998 - 2010. Tilburg Memory Based Learner Induction of Linguistic Knowledge Research Group, Tilburg University CLiPS Computational Linguistics Group, University of Antwerp Mon Oct 19 21:30:00 2009 Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} TiMBL has detected 12 features and the C4.5 input format (comma-separated features, class at the end). %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Phase 1: Reading Datafile: dimin.train Start: 0 @ Mon Oct 19 21:30:00 2009 Finished: 2999 @ Mon Oct 19 21:30:00 2009 Calculating Entropy Mon Oct 19 21:30:00 2009 Lines of data : 2999 DB Entropy : 1.6178929 Number of Classes : 5 Feats Vals InfoGain GainRatio 1 3 0.030971064 0.024891536 2 50 0.060860038 0.027552191 3 19 0.039562857 0.018676787 4 37 0.052541227 0.052620750 5 3 0.074523225 0.047699231 6 61 0.10604433 0.024471911 7 20 0.12348668 0.034953203 8 69 0.097198760 0.043983864 9 2 0.045752381 0.046816705 10 64 0.21388759 0.042844587 11 18 0.66970458 0.18507018 12 43 1.2780762 0.32537181 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} Phase 1 is the training data analysis phase. Time stamps for start and end of analysis are provided. Some preliminary analysis of the training data is done: number of training items, number of classes, entropy of the training data. For each feature, the number of values, and four variants of an information-theoretic measure of feature relevance are given. These are used both for memory organization during training and for feature relevance weighting during testing (see Chapter~\ref{algorithms}). Finally, an ordering (permutation) of the features is given. This ordering is used for building the tree-index to the case-base. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Phase 2: Learning from Datafile: dimin.train Start: 0 @ Mon Oct 19 21:30:00 2009 Finished: 2999 @ Mon Oct 19 21:30:00 2009 Size of InstanceBase = 19231 Nodes, (769240 bytes), 49.77 % compression Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} Phase 2 is the learning phase: all training items are stored in an efficient way in memory for use during testing. Again timing information (real time) is provided, as well as information about the size of the data structure representing the stored examples and the amount of compression achieved. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Starting to test, Testfile: dimin.test Writing output in: dimin.test.IB1.O.gr.k1.out Algorithm : IB1 Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Feature 1 : 0.024891535617620 Feature 2 : 0.027552191321752 Feature 3 : 0.018676787182524 Feature 4 : 0.052620750282779 Feature 5 : 0.047699230752236 Feature 6 : 0.024471910753751 Feature 7 : 0.034953203413051 Feature 8 : 0.043983864437713 Feature 9 : 0.046816704745507 Feature 10 : 0.042844587034556 Feature 11 : 0.185070180760327 Feature 12 : 0.325371814230901 Tested: 1 @ Mon Oct 19 21:30:00 2009 Tested: 2 @ Mon Oct 19 21:30:00 2009 Tested: 3 @ Mon Oct 19 21:30:00 2009 Tested: 4 @ Mon Oct 19 21:30:00 2009 Tested: 5 @ Mon Oct 19 21:30:00 2009 Tested: 6 @ Mon Oct 19 21:30:00 2009 Tested: 7 @ Mon Oct 19 21:30:00 2009 Tested: 8 @ Mon Oct 19 21:30:00 2009 Tested: 9 @ Mon Oct 19 21:30:00 2009 Tested: 10 @ Mon Oct 19 21:30:00 2009 Tested: 100 @ Mon Oct 19 21:30:00 2009 Ready: 950 @ Mon Oct 19 21:30:00 2009 Seconds taken: 0.0650 (14609.99 p/s) overall accuracy: 0.968421 (920/950), of which 39 exact matches There were 5 ties of which 5 (100.00%) were correctly resolved \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} In Phase 3, the trained classifier is applied to the test set. Because we have not specified which algorithm to use, the default settings are used ({\sc ib1} with information-theoretic feature weighting). This algorithm computes the similarity between a test item and each training item in terms of {\em weighted overlap}: the total difference between two patterns is the sum of the relevance weights of those features which are not equal. The class for the test item is decided on the basis of the least distant item(s) in memory. To compute relevance, Gain Ratio is used (an information-theoretic measure, see Section~\ref{infogain}). Time stamps indicate the progress of the testing phase. Finally, accuracy on the test set is logged, and the number of exact matches\footnote{An exact match in this experiment can occur when two different nouns have the same feature-value representation.} and ties (two or more classes are equally frequent in the nearest neighbor set). In this experiment, the diminutive suffix form of 96.8\% of the new words was correctly predicted. Train and test set overlap in 39 items, and the algorithm had to break five ties, all of which were broken correctly. The meaning of the output file names can be explained now:\\ {\tt dimin.test.IB1.O.gr.k1.out} means output file ({\tt .out}) for {\tt dimin.test} with algorithm {\sc mbl} (={\sc ib1}), similarity computed as {\em weighted overlap} ({\tt .O}), relevance weights computed with {\em gain ratio} ({\tt .gr}), and number of most similar memory patterns on which the output class was based equal to 1 ({\tt .k1}). \section{Algorithms and metrics} A precise discussion of the different algorithms and metrics implemented in TiMBL is given in Chapter~\ref{algorithms}. We will discuss the effect of the most important ones on our data set. A first choice in algorithms is between using {\sc ib1} and {\sc igtree}. In the trade-off between generalization accuracy and efficiency, {\sc ib1} usually, but not always, leads to more accuracy at the cost of more memory and slower computation, whereas {\sc igtree} is a fast heuristic approximation of {\sc ib1}, but sometimes less accurate. The {\sc igtree} algorithm is used when {\tt -a 1} is given on the command line, whereas the {\sc ib1} algorithm used above (the default) would have been specified explicitly by {\tt -a 0}. {\footnotesize \begin{verbatim} > Timbl -a1 -f dimin.train -t dimin.test \end{verbatim}} We see that {\sc igtree} performs only slightly worse (96.6\%) than {\sc ib1} (96.8\%) for this train-test partitioning of the data --- it uses less memory and is faster, however. When using the {\sc ib1} algorithm, there is a choice of metrics for influencing the definition of similarity. With {\em weighted overlap}, each feature is assigned a weight, determining its relevance in solving the task. With the {\em modified value difference metric} ({\sc mvdm}), each pair of values of a particular feature is assigned a value difference. The intuition here is that in our diminutive problem, for example, the codas $n$ and $m$ should be regarded as being more similar than $n$ and $p$. These pair-wise differences are computed for each pair of values in each feature (see Section~\ref{mvdm}). Selection between weighted overlap and {\sc mvdm} is done by means of the {\tt -mM} parameter. The following selects {\sc mvdm}, whereas {\tt -mO} ({\em weighted overlap}) is the default. {\footnotesize \begin{verbatim} > Timbl -mM -f dimin.train -t dimin.test \end{verbatim} } Especially when using {\sc mvdm}, but also in other cases, it may be useful to extrapolate not just from the most similar example in memory, which is the default, but from several. This can be achieved by using the $-k$ parameter followed by the wanted number of nearest neighbors. E.g., the following applies {\sc ib1} with the {\sc mvdm} metric, with extrapolation from the 5 nearest neighbors. {\footnotesize \begin{verbatim} > Timbl -mM -k5 -f dimin.train -t dimin.test \end{verbatim} } Whenever more than one nearest neighbor is taken into account for extrapolation, it may be useful to weigh the influence of the neighbors on the final decision as a function of their distance from the test item. Several possible implementations of this distance function are provided. E.g., the following provides inverse distance: {\footnotesize \begin{verbatim} > Timbl -mM -k5 -dID -f dimin.train -t dimin.test \end{verbatim} } Within the {\sc ib1} {\em weighted overlap}\/ option, the default feature weighting method is gain ratio. Other feature relevance weighting methods are available as well. By setting the parameter {\tt -w} to 0, an {\em unweighted overlap}\/ definition of similarity is created where each feature is considered equally relevant. In that case, similarity reduces to the number of equal values in the same position in the two patterns being compared. As an alternative weighting, users can provide their own weights by using the {\tt -w} parameter with a filename in which the feature weights are stored (see Section~\ref{weightformat} for a description of the format of the weights file). \begin{table} \begin{center} \begin{tabular}{l|rrrr} & no weight & gain & information & chi \\ & (overlap) & ratio & gain & squared \\ \noalign{\smallskip} \hline \noalign{\smallskip} Overlap, $-k1$ & 86.4 & 96.8 & 96.7 & 96.7 \\ Overlap, $-k3$ & 73.1 & 96.4 & 96.8 & 96.9 \\ Overlap, $-k5$ & 62.6 & 95.4 & 96.1 & 96.1 \\ \hline \noalign{\smallskip} {\sc mvdm}, $-k1$ & 95.8 & 96.4 & 96.2 & 96.3 \\ {\sc mvdm}, $-k3$ & 97.3 & 97.6 & 97.6 & 97.6 \\ {\sc mvdm}, $-k5$ & {\bf 97.8} & 97.7 & 97.7 & 97.7 \\ \hline \noalign{\smallskip} \end{tabular} \caption{Some results for diminutive prediction.} \label{diminresults} \end{center} \end{table} Table \ref{diminresults} shows a small matrix indicating the effect of distance metric (Overlap versus {\sc mvdm}) and weighting method choice on generalization accuracy, using the same training and test set as before, and increasing $k$ from 1 to 3 and 5. While increasing $k$ leads to a deterioration of generalization accuracy with the Overlap function, it leads to improvements with {\sc mvdm}. Another clear contrast is that the absence of feature weighting leads to the lowest scores with the Overlap function, and the highest score with {\sc mvdm} and $k=5$. Given that TiMBL offers several more hyperparameters than only $k$, the distance metric, and the feature weighting metric, it should be obvious that even with a single training and test set experiment, a large experimental matrix can be explored. Unfortunately, the location of the cell with the highest number in this matrix cannot be predicted upfront. It is therefore useful to try out a large set of reasonable combinations of options by cross-validation on the training data to achieve best results with {\sc mbl} \cite{VandenBosch04b}. The option {\tt -t @f} where {\tt f} is the name of a file, allows you to predefine various combinations of options to be tested and test them without having the training stages repeated each time. See Chapter \ref{commandline}. \section{More options} Several input and output options exist to make life easier while experimenting. See Chapter~\ref{commandline} for a detailed description of these options. One especially useful option for testing linguistic hypotheses is the ignore option, which allows you to skip certain features when computing similarity. E.g. if we want to test the hypothesis that only the rime (nucleus and coda) and the stress of the last syllable are actually relevant in determining the form of the diminutive suffix, we can execute the following with the previously best parameter settings to disregard all but the fourth-last and the last two features. As a result we get an accuracy of 97.1\%. {\footnotesize \begin{verbatim} > Timbl -mM:I1-8,10 -k5 -w0 -f dimin.train -t dimin.test \end{verbatim} } The {\tt +/-v} (verbosity) option allows you to control the amount of information that is generated in the output, ranging from nearly nothing ({\tt +v s}) to a lot ({\tt +v as+cs+di+db+n+k}). Specific verbosity settings exist for dumping option settings ({\tt +v o}), feature relevance weights (default), value-class conditional probabilities ({\tt +v p}), exact matches ({\tt +v e}), distributions ({\tt +v db}), a confusion matrix ({\tt +v cm}), advanced statistics besides accuracy: micro-average and macro-average F-score and AUC ({\tt +v as}), per-class advanced statistics ({\tt +v cs}), the nearest neighbors on which decision are based ({\tt +v n}), just the class distributions per $k$-nearest distance per classified instance ({\tt +v k}), or the distances to the nearest neighbor ({\tt +v di}). E.g. the following command results in an output file with distributions. {\footnotesize \begin{verbatim} > Timbl +v db -f dimin.train -t dimin.test \end{verbatim} } The resulting output file {\tt dimin.test.IB1.O.gr.k1.out} contains lines like the following. {\footnotesize \begin{verbatim} +,t,L,=,-,m,@,=,-,l,I,N,E,E { E 1.00000 } =,=,=,=,=,=,=,=,+,pr,O,p,J,J { E 3.00000, J 12.0000 } =,=,=,=,=,=,=,=,+,w,e,t,J,J { J 2.00000 } =,=,=,=,+,t,L,n,-,h,L,s,J,J { J 1.00000 } =,=,=,=,=,=,=,=,+,t,L,n,T,T { T 1.00000 } =,=,=,=,=,=,=,=,+,z,o,m,P,P { P 3.00000 } +,d,a,=,-,m,@,s,-,kr,A,ns,J,J { J 1.00000 } =,=,=,=,+,=,a,rd,-,m,A,n,E,E { E 2.00000 } =,=,=,=,=,=,=,=,+,f,M,n,T,T { T 43.0000, E 20.0000 } -,d,u,=,-,k,@,=,-,m,A,nt,J,J { J 1.00000 } \end{verbatim} } This information can e.g. be used to assign a certainty to a decision of the classifier, or to make available a second-best back-off option. Another verbosity option, {\tt +v di}, displays the distance to the nearest neighbor: {\footnotesize \begin{verbatim} > Timbl +v di -f dimin.train -t dimin.test +,l,a,=,-,d,@,=,-,k,A,st,J,J 0.070701 -,s,i,=,-,f,E,r,-,st,O,k,J,J 0.000000 =,=,=,=,=,=,=,=,+,sp,a,n,T,T 0.042845 =,=,=,=,=,=,=,=,+,st,o,t,J,J 0.042845 =,=,=,=,+,sp,a,r,-,b,u,k,J,J 0.024472 +,h,I,N,-,k,@,l,-,bl,O,k,J,J 0.147489 -,m,e,=,-,d,A,l,+,j,O,n,E,E 0.182421 -,sn,u,=,-,p,@,=,+,r,K,=,T,T 0.046229 =,=,=,=,=,=,=,=,+,sp,A,N,E,E 0.042845 +,k,a,=,-,k,@,=,-,n,E,st,J,J 0.114685 \end{verbatim} } This can be used to study how very similar instances (low distance) and less similar patterns (higher distance) are used in the process of generalization. The listing of nearest neighbors is useful for the analysis of the behavior of a classifier. It can be used to interpret why particular decisions or errors occur. {\footnotesize \begin{verbatim} > Timbl +v n+k -mM -k3 -w0 -f dimin.train -t dimin.test +,m,I,=,-,d,A,G,-,d,},t,J,J { J 3.00000 } # k=1, 1 Neighbor(s) at distance: 0.99179269134432 # +,p,a,=,-,t,@,rs,-,f,A,t,{ J 1.00000 } # k=2, 1 Neighbor(s) at distance: 0.99458957262696 # +,h,o,=,-,n,@,G,-,b,A,k,{ J 1.00000 } # k=3, 1 Neighbor(s) at distance: 1.0088291749842 # +,h,E,r,-,d,@,rs,-,t,A,s,{ J 1.00000 } -,t,@,=,-,l,|,=,-,G,@,n,T,T { T 3.00000 } # k=1, 1 Neighbor(s) at distance: 0.33024081383366 # -,x,@,=,+,h,|,=,-,G,@,n,{ T 1.00000 } # k=2, 1 Neighbor(s) at distance: 0.49144604610567 # -,d,@,r,-,w,a,=,-,G,@,n,{ T 1.00000 } # k=3, 1 Neighbor(s) at distance: 0.56944572926932 # -,st,@,=,-,l,I,=,-,N,@,=,{ T 1.00000 } \end{verbatim} } A confusion matrix, printed when the {\tt +v cm} option is selected, can bring to light specific errors of the classifier that would not be apparent from the overall accuracy. Applied to the diminutive data, the following confusion matrix is computed and printed: {\footnotesize \begin{verbatim} > Timbl +v cm -f dimin.train -t dimin.test Confusion Matrix: T E J P K ----------------------------------- T | 453 0 2 0 0 E | 0 87 4 1 8 J | 1 4 347 0 0 P | 0 3 0 24 0 K | 0 7 0 0 9 -*- | 0 0 0 0 0 \end{verbatim} } The confusion matrix associates the class predicted by TiMBL (vertically) with the real class of the test items given (horizontally). All cells outside the diagonal contain errors of one class being mistaken for another. For example, the K class ({\em -kje}) is mispredicted seven times as class E ({\em -etje}). (The bottom line, labeled with {\tt -*-}, would contain aggregate counts of classes occuring in the test data that did not occur in the training data. In the diminutive data this does not occur.) In general, a confusion matrix allows a more fine-grained analysis of experimental results and better experimental designs (some parameter settings may work for some classes but not for others, or some may improve recall, and others precision, e.g.). From such a matrix, not only accuracy can be derived, but also a number of additional metrics that have become popular in machine learning, information retrieval, and subsequently also in computational linguistics: {\em recall}, {\em precision}, and their harmonic mean {\em F-score}, as well as {\em true positive rate}, {\em false positive rate}, and their joint measure {\em AUC} in ROC space. The details of these advanced statistics are given in Section~\ref{advancedstats}. They can be reported by TiMBL using the {\tt +v as} and {\tt +v cs} verbosity options: {\footnotesize \begin{verbatim} > Timbl +v as+cs -f dimin.train -t dimin.test Scores per Value Class: class | TP FP TN FN precision recall(TPR) FPR F-score AUC T | 453 1 494 2 0.99780 0.99560 0.00202 0.99670 0.99679 E | 87 14 836 13 0.86139 0.87000 0.01647 0.86567 0.92676 J | 347 6 592 5 0.98300 0.98580 0.01003 0.98440 0.98788 P | 24 1 922 3 0.96000 0.88889 0.00108 0.92308 0.94390 K | 9 8 926 7 0.52941 0.56250 0.00857 0.54545 0.77697 F-Score beta=1, microav: 0.968123 F-Score beta=1, macroav: 0.863060 AUC, microav: 0.980729 AUC, macroav: 0.926462 overall accuracy: 0.968421 (920/950), of which 39 exact matches There were 5 ties of which 5 (100.00%) were correctly resolved \end{verbatim} } We hope that this tutorial has made it clear that, once you have coded your data in fixed-length feature-value patterns, it should be relatively straightforward to get the first results using TiMBL. You can then experiment with different metrics and algorithms to try and further improve your results. \chapter{Memory-based learning algorithms} \label{algorithms} TiMBL is a program implementing several memory-based learning algorithms. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. The main differences among the algorithms incorporated in TiMBL lie in: \begin{itemize} \item The definition of {\em similarity}, \item The way the instances are stored in memory, and \item The way the search through memory is conducted. \end{itemize} In this chapter, various choices for these issues are described. We start in Section~\ref{mbl} with a formal description of the basic memory-based learning algorithm, i.e.~a nearest neighbor search. We then introduce different distance metrics, such as Information Gain weighting, which allows us to deal with features of differing importance, and the Modified Value Difference metric, which allows us to make a graded guess of the match between two different symbolic values, and describe the standard versus three distance-weighted versions of the class voting mechanism of the nearest neighbor classifier. In Section~\ref{indexing}, we give a description of various algorithmic optimizations for nearest neighbor search. Sections~\ref{igtree} to~\ref{ib2} describe three variants of the standard nearest neighbor classifier implemented within TiMBL, that optimize some intrinsic property of the standard algorithm. First, in Section~\ref{igtree}, we describe {\sc igtree}, which replaces the exact nearest neighbor search with a very fast heuristic that exploits the difference in importance between features. Second, in Section~\ref{tribl}, we describe the {\sc tribl} algorithm, which is a hybrid between {\sc igtree} and nearest neighbor search. Third, Section~\ref{ib2} describes the {\sc ib2} algorithm, which incrementally and selectively adds instances to memory during learning. The chapter is concluded by Section~\ref{furtherreading}, which provides an overview of further reading into theory and applications of memory-based learning to natural language processing tasks. \section{Memory-based learning} \label{mbl} Memory-based learning is founded on the hypothesis that performance in cognitive tasks is based on reasoning on the basis of similarity of new situations to {\em stored representations of earlier experiences}, rather than on the application of {\em mental rules}\/ abstracted from earlier experiences (as in rule induction and rule-based processing). The approach has surfaced in different contexts using a variety of alternative names such as similarity-based, example-based, exemplar-based, analogical, case-based, in\-stance-based, and lazy learning~\cite{Stanfill+86,Aha+91,Cost+93,Kolodner93,Aha97a}. Historically, memory-based learning algorithms are descendants of the $k$-nearest neighbor (henceforth $k$-{\sc nn}) algorithm \cite{Cover+67,Devijver+82,Aha+91}. An {\sc mbl} system, visualized schematically in Figure~\ref{mbl-method}, contains two components: a {\em learning component}\/ which is memory-based (from which {\sc mbl} borrows its name), and a {\em performance component}\/ which is similarity-based. The learning component of {\sc mbl} is memory-based as it involves adding training instances to memory (the {\em instance base} or case base); it is sometimes referred to as `lazy' as memory storage is done without abstraction or restructuring. An instance consists of a fixed-length vector of $n$ feature-value pairs, and an information field containing the classification of that particular feature-value vector. In the performance component of an {\sc mbl} system, the product of the learning component is used as a basis for mapping input to output; this usually takes the form of performing classification. During classification, a previously unseen test example is presented to the system. The similarity between the new instance $X$ and all examples $Y$ in memory is computed using some {\em distance metric} $\Delta(X,Y)$. The extrapolation is done by assigning the most frequent category within the found set of most similar example(s) (the $k$-nearest neighbors) as the category of the new test example. In case of a tie among categories, a tie breaking resolution method is used. This method is described in subsection~\ref{tiebreaking}. \begin{figure}[htb] \begin{center} \leavevmode \epsfxsize=8cm \epsffile{mble-method.eps} \caption{General architecture of an {\sc mbl} system. } \label{mbl-method} \end{center} \end{figure} \subsection{The Overlap metric} \label{overlap} The most basic metric that works for patterns with symbolic features is the {\bf Overlap metric}\footnote{This metric is also referred to as Hamming distance, Manhattan metric, city-block distance, or L1 metric.} given in Equations~\ref{distance} and~\ref{overlapeq}; where $\Delta(X,Y)$ is the distance between instances $X$ and $Y$, represented by $n$ features, and $\delta$ is the distance per feature. The distance between two patterns is simply the sum of the differences between the features. The $k$-{\sc nn} algorithm with this metric is called {\sc ib1} \cite{Aha+91}. \begin{equation} \Delta(X,Y) = \sum_{i=1}^{n} \delta(x_{i},y_{i}) \label{distance} \end{equation} where: \begin{equation} \delta(x_{i}, y_{i}) = \left\{ \begin{array}{ll} abs(\frac{x_{i}-y_{i}}{max_{i}-min_{i}}) & \mbox{if numeric, else}\\ 0 & \mbox{if $x_{i} = y_{i}$}\\ 1 & \mbox{if $x_{i} \neq y_{i}$}\\ \end{array} \right. \label{overlapeq} \end{equation} The major difference with the {\sc ib1} algorithm originally proposed by \cite{Aha+91}, is that in our version the value of $k$ refers to $k$-nearest {\em distances}\/ rather than $k$-nearest examples. With $k=1$, for instance, TiMBL's nearest neighbor set can contain several instances that are equally distant to the test instance. Arguably, our $k$-NN kernel could therefore be called $k$-nearest distances classification. Another difference with the original {\sc ib1} as well as with other implementations such as $k$-NN in the {\sc weka} machine learning toolkit \cite{Witten+99} is the way in which ties are resolved in choosing the majority category among the set of nearest neighbors. Since this method is independent of the distance function we discuss this issue separately in subsection~\ref{tiebreaking}. \paragraph{Variations on Overlap: Levenshtein and Dice coefficient metrics} The Overlap metric is all-or-nothing. For measuring the similarity between numeric or atomically symbolic values this may suffice, but there are cases (such as in natural language processing) in which string-valued feature values occur that can mismatch with other string values in a meaningfully graded way. For example, the value pair ``bathe'' and ``bathes'' only differs in one letter; counting them as more similar than ``bathe'' and ``rumour'', for example, may be useful for the classification task at hand. We implemented two additional metrics, Levenshtein distance and the Dice coefficient, that each provide a graded similarity score between pairs of strings. {\bf Levenshtein} distance is a classic {\em edit distance}\/ metric \cite{Levenshtein66} that counts the number of insertions, deletions, and substitutions to transform the one string into the other. In our (dynamic programming) implementation the three operations count equally heavily. The {\bf Dice} coefficient computes the overlap between the occurrences of character bigrams in two strings as in Equation~\ref{dice}, where $n_{x_{i} \cap y{i}}$ is the number of character bigrams (uniquely) occuring both in string value $x_{i}$ and in string value $y_{i}$ (and where $i$ is the index of the feature as introduced in Equation~\ref{distance})\footnote{Strings of length one are not handled by Dice; we back off to Overlap in these cases.}. The equation subtracts the similarity from 1, because we assume $\delta$ to produce a distance, not a similarity. \begin{equation} \delta(x_{i}, y_{i}) = 1 - \frac{2 n_{x_{i} \cap y_{i}}}{n_{x_{i}} + n_{y_{i}}} \label{dice} \end{equation} \subsection{Information-gain and gain ratio feature weighting} \label{infogain} The distance metric in Equation~\ref{overlapeq} straightforwardly counts the number of (mis)matching feature-values in both patterns. In the absence of information about feature relevance, this is a reasonable choice. Otherwise, we can add domain knowledge bias to weight or select different features (see e.g.~\namecite{Cardie96} for an application of linguistic bias in a language processing task), or look at the behavior of features in the set of examples used for training. We can compute statistics about the relevance of features by looking at which features are good predictors of the class labels. Information Theory gives us a useful tool for measuring feature relevance in this way~\cite{Quinlan86,Quinlan93}. {\bf Information Gain} (IG) weighting looks at each feature in isolation, and measures how much information it contributes to our knowledge of the correct class label. The Information Gain of feature $i$ is measured by computing the difference in uncertainty (i.e.\ entropy) between the situations without and with knowledge of the value of that feature (Equation~\ref{IGgain}). \begin{equation} w_{i} = H(C) - \sum_{v \in V_{i}} P(v) \times H(C|v) \label{IGgain} \end{equation} Where $C$ is the set of class labels, $H(C) = - \sum_{c \in C} P(c) \log_{2} P(c)$ is the entropy of the class labels, and $V_{i}$ is the set of values for feature $i$. The probabilities are estimated from relative frequencies in the training set. For numeric features, an intermediate step needs to be taken to apply the symbol-based computation of IG. All real values of a numeric features are temporarily discretized into a number (the default is 20) of intervals. Instances are ranked on their real value, and then spread evenly over the intervals; each interval contains the same number of instances (i.e., by default, $1/20$th of the total amount of instances). Instances in each of these intervals are then used in the IG computation as all having the same unordered, symbolic value per group. Note again that this discretization is only temporary; it is not used in the computation of the distance metric. It is important to realize that the IG weight is really a probability-weighted average of the informativity of the different values of the feature. On the one hand, this pre-empts the consideration of values with low frequency but high informativity. Such values ``disappear'' in the average. On the other hand, this also makes the IG weight very robust to estimation problems. Each parameter (weight) is estimated on the whole data set. Information Gain, however, tends to overestimate the relevance of features with large numbers of values. Imagine a data set of hospital patients, where one of the available features is a unique ``patient ID number''. This feature will have very high Information Gain, but it does not give any generalization to new instances. To normalize Information Gain for features with different numbers of values, Quinlan~\cite{Quinlan93} has introduced a normalized version, called {\bf Gain Ratio}, which is Information Gain divided by $si(i)$ (split info), the entropy of the feature-values (Equation~\ref{splitinfo}). \begin{equation} w_{i} = \frac{H(C) - \sum_{v \in V_{i}} P(v) \times H(C|v)}{si(i)} \label{IGgainratio} \end{equation} \begin{equation} si(i) = - \sum_{v \in V_{i}} P(v) \log_{2} P(v) \label{splitinfo} \end{equation} The resulting Gain Ratio values can then be used as weights $w_{f}$ in the weighted distance metric (Equation~\ref{distancew})\footnote{In a generic use IG refers both to Information Gain and to Gain Ratio throughout this manual. In specifying parameters for the software, the distinction between both needs to be made, because they often result in different behavior.}. The $k$-{\sc nn} algorithm with this metric is called {\sc ib1-ig} \cite{Daelemans+92b}. \begin{equation} \Delta(X,Y) = \sum_{i=1}^{n}\ w_{i} \ \delta(x_{i},y_{i}) \label{distancew} \end{equation} The possibility of automatically determining the relevance of features implies that many different and possibly irrelevant features can be added to the feature set. This is a very convenient methodology if domain knowledge does not constrain the choice enough beforehand, or if we wish to measure the importance of various information sources experimentally. However, because IG values are computed for each feature independently, this is not necessarily the best strategy. Sometimes better results can be obtained by leaving features out than by letting them in with a low weight. Very redundant features can also be challenging for {\sc ib1-ig}, because IG will overestimate their joint relevance. Imagine an informative feature which is duplicated. This results in an overestimation of IG weight by a factor two, and can lead to accuracy loss, because the doubled feature will dominate the distance metric. \subsection{Chi-squared and shared variance feature weighting} \label{chisquared} Unfortunately, as~\namecite{White+94} have shown, the Gain Ratio measure still has an unwanted bias towards features with more values. The reason for this is that the Gain Ratio statistic is not corrected for the number of degrees of freedom of the contingency table of classes and values. \namecite{White+94} proposed a feature selection measure based on the chi-squared statistic, as values of this statistic can be compared across conditions with different numbers of degrees of freedom. The \chisq statistic is computed from the same contingency table as the Information Gain measure by the following formula (Equation~\ref{chisq-eq}). \begin{equation} \chi^{2} = \sum_{i} \sum_{j} \frac{(E_{ij} - O_{ij})^{2}} {E_{ij}} \label{chisq-eq} \end{equation} where $O_{ij}$ is the observed number of cases with value $v_{i}$ in class $c_{j}$, i.e.~$O_{ij} = n_{ij}$, and $E_{ij}$ is the expected number of cases which should be in cell ($v_{i}$, $c_{j}$) in the contingency table, if the null hypothesis (of no predictive association between feature and class) is true (Equation~\ref{chisq-expect-eq}). Let $n_{.j}$ denote the marginal for class $j$ (i.e.~the sum over column $j$ of the table), $n_{i.}$ the marginal for value $i$, and $n_{..}$ the total number of cases (i.e.~the sum of all the cells of the contingency table). \begin{equation} E_{ij} = \frac{n_{.j} n_{i.}}{n_{..}} \label{chisq-expect-eq} \end{equation} The \chisq statistic is well approximated by the chi-square distribution with $\nu = (m-1)(n-1)$ degrees of freedom, where $m$ is the number of values and $n$ is the number of classes. We can then either use the \chisq values as feature weights in Equation~\ref{distancew}, or we can explicitly correct for the degrees of freedom by using the {\bf Shared Variance} measure (Equation~\ref{shared-variance-eq}). \begin{equation} SV_{i} = \frac{ \chi^2_{i}}{N \times ( min(|C|,|V_{i}|)-1 ) } \label{shared-variance-eq} \end{equation} Where $|C|$ and $|V_{i}|$ are the number of classes and the number of values of feature $i$, respectively, and $N$ is the number of instances\footnote{Note that with two classes, the shared variance weights of all features are simply divided by $N$, and will not be different from \chisq weights.}. We will refer to these variations of {\sc mbl} as {\sc ib1-\chisq} and {\sc ib1-sv}. One should keep in mind, that the correspondence to the chi-square distribution generally becomes poor if the expected frequencies in the contingency table cells become small. A common recommendation is that the \chisq test cannot be trusted when more than $20\%$ of the expected frequencies are less than $5$, or any are less than $1$. Chi-squared and shared variance weights of {\em numeric}\/ features are computed via a discretization preprocessing step (also used with computing IG and GR weights). Values are first discretized into a number (20 by default) of equally-spaced intervals between the minimum and maximum values of the feature. These groups are then used as discrete values in computing chi-squared and shared variance weights. \subsection{Modified value difference and Jeffrey divergence metrics} \label{mvdm} It should be stressed that the choice of representation for instances in {\sc mbl} remains the key factor determining the strength of the approach. The features and categories in NLP tasks are usually represented by symbolic labels. The metrics that have been described so far, i.e.~Overlap and IG Overlap, are limited to exact match between feature-values. This means that all values of a feature are seen as equally dissimilar. However, if we think of an imaginary task in e.g.~the phonetic domain, we might want to use the information that 'b' and 'p' are more similar than 'b' and 'a'. For this purpose a metric was defined by \namecite{Stanfill+86} and further refined by \namecite{Cost+93}. It is called the (Modified) Value Difference Metric ({\sc mvdm}; Equation~\ref{MVDMeq}), and it is a method to determine the similarity of the values of a feature by looking at co-occurrence of values with target classes. For the distance between two values $v_{1},\ v_{2}$ of a feature, we compute the difference of the conditional distribution of the classes $C_{i}$ for these values. \begin{equation} \delta(v_{1}, v_{2}) = \sum_{i=1}^{n} \left| P(C_{i}|v_{1}) - P(C_{i}|v_{2}) \right| \label{MVDMeq} \end{equation} For computational efficiency, all pairwise $\delta(v_{1}, v_{2})$ values can be pre-comput\-ed before the actual nearest neighbor search starts. Although the {\sc mvdm} metric does not explicitly compute feature relevance, an implicit feature weighting effect is present. If features are very informative, their conditional class probabilities will on average be very skewed towards a particular class. This implies that on average the $\delta(v_{1}, v_{2})$ will be large. For uninformative features, on the other hand, the conditional class probabilities will be pretty uniform, so that on average the $\delta(v_{1}, v_{2})$ will be very small. {\sc mvdm} differs considerably from Overlap based metrics in its composition of the nearest neighbor sets. Overlap causes an abundance of ties in nearest neighbor position. For example, if the nearest neighbor is at a distance of one mismatch from the test instance, then the nearest neighbor set will contain the entire partition of the training set that matches all the other features but contains {\em any} value for the mismatching feature (see~\namecite{Zavrel+97} for a more detailed discussion). With the {\sc mvdm} metric, however, the nearest neighbor set will either contain patterns which have the value with the lowest $\delta(v_{1}, v_{2})$ in the mismatching position, or {\sc mvdm} will select a totally different nearest neighbor which has less exactly matching features, but a smaller distance in the mismatching features. In sum, this means that the nearest neighbor set is usually much smaller for {\sc mvdm} at the same value of $k$. In NLP tasks we have found it very useful to experiment with values of $k$ larger than one for {\sc mvdm}, because this re-introduces some of the beneficial smoothing effects associated with large nearest neighbor sets. One cautionary note about this metric is connected with data sparsity. In many practical applications, we are confronted with a very limited set of examples, with values occuring only a few times or once in the whole data set. If two such values occur with the same class, {\sc mvdm} will regard them as identical, and if they occur with two different classes their distance will be maximal. In cases of such extreme behaviour on the basis of low-frequency evidence, it may be safer to back off to the Overlap metric, where only an exact value match yields zero distance. TiMBL offers this back-off from {\sc mvdm} to Overlap through a frequency threshold, that switches from the {\sc mvdm} to the Overlap metric when one or both of a pair of matched values occurs fewer times in the learning material than this threshold. Jeffrey divergence, offered as a close neighbor alternative to {\sc mvdm}, is a statistical dissimilarity metric that can be used to compute the distance between class distributions of two values of the same feature. Functionally it is quite similar to {\sc mvdm}. It is best known for its application as a distance function in unsupervised vector space models, e.g. in image retrieval, where it is applied to histogram vectors. While {\sc mvdm} computes a straightforward geometrical distance between two class distribution vectors, Jeffrey divergence introduces a logarithm term, as seen in Equation~\ref{jd}. Jeffrey divergence is a symmetric variant of Kullback-Leibner distance; the $m$ term given in Equation~\ref{jdm} is used for this purpose. \begin{equation} \delta(v_{1}, v_{2}) = \sum_{i=1}^{n} ( P(C_{i}|v_{1}) log \frac{P(C_{i}|v_{1})}{m} + P(C_{i}|v_{2}) log \frac{P(C_{i}|v_{2})}{m} ) \label{jd} \end{equation} \begin{equation} m = \frac{P(C_{i}|v_{1}) + P(C{i}|v_{2})}{2} \label{jdm} \end{equation} Compared to {\sc mvdm}, Jeffrey divergence assigns relatively larger distances to value pairs of which the class distributions are more orthogonal. In other words, it assigns more prominence to zero probabilities, which in the case of sparse data (e.g, with Zipfian distributions of values) are generally better estimations than non-zero probabilities. This makes Jeffrey divergence in principle more robust than {\sc mvdm} with respect to sparse data. As with {\sc mvdm}, TiMBL offers an optional frequency-thresholded back-off from Jeffrey divergence to the Overlap metric to further remedy some negative effects due to data sparseness. \subsection{Dot-product and cosine metrics} \label{dotproduct} When features have numeric or binary values, TiMBL can also compute the distance between two instances via the dot product (or inner product) of their feature-value vectors. The dot product (which is higher with better matches) is subsequently inversed to a distance by subtracting it from the maximum dot product attainable, i.e. that on an exact match. In Equation~\ref{doteq} this maximal dot product is referred to as $dot_{max}$. \begin{equation} \label{doteq} \Delta(X,Y) = dot_{max} - \sum_{i=1}^{n} w_{i} x_{i} y_{i} \end{equation} As with the other distance metrics incorporated in TiMBL, we include the feature weight $w_{i}$ in the metric. When no weighting is set ({\tt -w 0}), all weights are set to $1.0$, and equation~\ref{doteq} reduces to the normal unweighted dot product. The dot-product metric is typically used with binary vectors or sparse vectors in general. When either $x_{i}$ or $y_{i}$ has a zero value, that value pair is not counted in the dot product. Consequently, the significant deviation from the Overlap metric is that matching values that both have a zero value do not count here, whereas they count as much as any other value match in the Overlap metric. A commonly used variant of the dot product metric, e.g. in information retrieval, is the cosine metric, which corrects for large differences in the length of the instance vectors. The cosine metric divides the dot product metric by the product of the length of the two vectors. As with the dot product, TiMBL converts the cosine metric similarity to a distance by subtracting it from a $cos_{max}$ term that is larger than the maximal cosine similarity, as given in Equation~\ref{coseq}. Again, feature weighting is included in the formula: \begin{equation} \label{coseq} \Delta(X,Y) = cos_{max} - \frac{\sum_{i=1}^{n} w_{i} x_{i} y_{i}}{\sqrt{\sum_{i=1}^{n} w_{i} x_{i}^2 \sum_{i=1}^{n} w_{i} y_{i}^2}} \end{equation} Due to its internal tree structure, TiMBL is not particularly suited to handle feature vectors with thousands or more features. Many features cause very deep and usually very unbalanced trees, from which retrieval can be rather inefficient (especially when there is little variance in the feature weights). Other internal data structures such as inverted indices are typically more suited to these types of vector spaces. For now, inverted indices are not implemented in TiMBL. \subsection{Distance-weighted class voting} \label{distweightvote} The most straightforward method for letting the $k$ nearest neighbors vote on the class of a new case is the {\em majority voting} method, in which the vote of each neighbor receives equal weight, and the class with the highest number of votes is chosen (or in case of a tie, some tie resolution is performed, cf. Subsection~\ref{tiebreaking}). We can see the voting process of the $k$-NN classifier as an attempt to make an optimal class decision, given an estimate of the conditional class probabilities in a local region of the data space. The radius of this region is determined by the distance of the $k$-furthest neighbor. Sometimes, if $k$ is small, and the data is very sparse, or the class labels are noisy, the ``local'' estimate is very unreliable. As it turns out in experimental work, using a larger value of $k$ can often lead to higher accuracy. The reason for this is that in densely populated regions, with larger $k$ the local estimates become more reliable, because they are "smoother". However, when the majority voting method is used, smoothing can easily become oversmoothing in sparser regions of the same data set. The reason for this is that the radius of the $k$-NN region can become extended far beyond the local neighborhood of the query point, but the far neighbors will receive equal influence as the close neighbors. This can result in classification errors that could easily have been avoided if the measure of influence would somehow be correlated with the measure of similarity. To remedy this, we have implemented three types of distance weighted voting functions in TiMBL. A voting rule in which the votes of different members of the nearest neighbor set are weighted by a function of their distance to the query, was first proposed by Dudani~\shortcite{Dudani76}. In this scheme, henceforth referred to as IL (for inverse-linear), a neighbor with smaller distance is weighted more heavily than one with a greater distance: the nearest neighbor gets a weight of 1, the furthest neighbor a weight of 0 and the other weights are scaled linearly to the interval in between (\namecite{Dudani76}, Equation 1.). \begin{equation} \label{dudani_eq} w_{j}= \left \{ \begin{array}{ll} \frac{d_{k} - d_{j}}{d_{k} - d_{1}} & \mbox{if $d_{k} \not= d_{1}$ } \\ 1 & \mbox{if $d_{k} = d_{1}$}\\ \end{array} \right. \end{equation} Where $d_{j}$ is the distance to the query of the $j$'th nearest neighbor, $d_{1}$ the distance of the nearest neighbor, and $d_{k}$ of the furthest ($k$'th) neighbor. Dudani (\namecite{Dudani76}, eq.2,3) further proposed the {\em inverse distance weight} (henceforth ID). In Equation~\ref{inverseweight} a small constant is usually added to the denominator to avoid division by zero~\cite{Wettschereck94}. \begin{equation} \label{inverseweight} w_{j}= \left \{ \begin{array}{ll} \frac{1}{d_{j} + \epsilon} \\ \end{array} \right. \end{equation} Another weighting function considered here is based on the work of \namecite{Shepard87}, who argues for a universal perceptual law which states that the relevance of a previous stimulus for the generalization to a new stimulus is an exponentially decreasing function of its distance in a psychological space (henceforth ED). This gives the weighed voting function of Equation~\ref{expdecayweight}, where $\alpha$ and $\beta$ are constants determining the slope and the power of the exponential decay function. \begin{equation} \label{expdecayweight} w_{j}= e^{-\alpha d_{j}^\beta} \end{equation} Note that in Equations~\ref{inverseweight} and ~\ref{expdecayweight} the weight of the nearest and furthest neighbors and the slope between them depend on their absolute distance to the query. This assumes that the relationship between absolute distance and the relevance gradient is fixed over different datasets. This assumption is generally false; even within the same dataset, different feature weighting metrics can cause very different absolute distances. Figure~\ref{dist-weight-fig} visualises a part of the curves of ID and ED, the latter with a few varied settings of $\alpha$ and $\beta$. Generally, both distance weighting functions assign highly differing weights for close neighbors, and less differing weights for more distant neighbors. ID assigns very high votes (distance weights) to nearest neighbors at distances approaching 0.0 - in effect it assigns absolute preference to exact matches. In contrast, all ED variants have a vote of 1.0 for exact matches, and have a shallower curve than the ID curve for higher distances. Higher values of $\alpha$ in the ED function assign relatively higher weights to exact matches. When $\beta$ is set to larger values than $1.0$, the ED curve becomes bell-shaped, effectively assigning relatively less different weights between exact-matching neighbors and near-exact matching instances. \begin{figure}[htb] \begin{center} \leavevmode \epsfxsize=0.8\columnwidth \epsffile{distanceweight-ided.eps} \caption{Visualisation of the Inverse Distance weighting function (IL) and three variants of the Exponential Decay distance weighting function (ED) varying settings of $\alpha$ (1) and $\beta$ (b). } \label{dist-weight-fig} \end{center} \end{figure} Following Dudani's proposal, the benefits of weighted voting for $k$-NN have been discussed widely, e.g. \cite{Bailey+78,Morin+81,MacLeod+87}, but mostly from an analytical perspective. With the popularity of Instance-Based Learning applications, these issues have gained a more practical importance. In his thesis on $k$-NN classifiers, \namecite{Wettschereck94} cites Dudani, but proceeds to work with Equation~\ref{inverseweight}. He tested this function on a large amount of datasets and found weak evidence for performance increase over majority voting. An empirical comparison of the discussed weighted voting methods in~\cite{Zavrel97} has shown that weighted voting indeed often outperforms unweighted voting, and that Dudani's original method (Equation~\ref{dudani_eq}) mostly outperforms the other two methods. From that set of experiments, it also seems that Dudani's method shows its optimal performance at much larger values of $k$ than the other voting methods. \subsection{Tie breaking} \label{tiebreaking} Thus far we have described the last step of $k$-NN classification as taking the majority category among the set of nearest neighbors, where their vote is either unweighted or weighted by their distance (subsection~\ref{distweightvote}). Especially in case of unweighted voting, ties can occur; e.g.\ of a set of ten nearest neighbors, five vote for class $A$, and the other five for $B$. The procedure for breaking this tie in the $k$-NN classifier in TiMBL is as follows. First, the value of the $k$ parameter is incremented by $1$, and the additional nearest neighbors at this new $k$th distance are added to the current nearest neighbor set ($k$ is subsequently reset to its user-specified value). If the tie in the class distribution persists, then the class label is selected with the highest overall occurrence in the training set. If that is also equal, then the first class is taken that was encountered when reading the training instance file. Optionally, TiMBL can be set to avoid ties by making a {\em random}\/ choice of a classification from a class distribution in a nearest-neighbor set, weighted by the distribution of the classes in the set. \subsection{Exemplar weighting} \label{exemplar} Exemplar weighting in memory-based learning captures the intuition that some instances are better (more reliable, more typical, more regular) nearest neighbors than others. Classification accuracy could benefit from giving these instances some priority in the $k$-NN classification process. This idea has been explored in the context of on the one hand classification \cite{Salzberg90,Zhang92}, and on the other hand editing bad instances from memory \cite{Aha+91}. \namecite{Salzberg90}, as a classic example, uses {\em class-prediction strength}: the ratio of the number of times the instance type is a nearest neighbor of another instance with the same class and the number of times that the instance type is the nearest neighbor of another instance type regardless of the class. Another example is {\em typicality}\/ as used by \namecite{Zhang92}. Exemplar weights could in principle be used either as weights in the class voting (as distance weights, cf. Subsection~\ref{distweightvote}), or as weights in the distance metric (eq.~\ref{distancew}). TiMBL supports only the latter type, and in this respect exemplar weighting is not an intrinsic part of TiMBL. TiMBL does not compute exemplar weighting metrics itself, but only allows users to specify preprocessed exemplar weights with the {\tt -s} input option. Subsequently, when the distance between a test instance and a memory instance is computed, TiMBL uses the memory instance's weight as follows, where $\Delta^{E}(X,Y)$ is the exemplar-weighted distance between instances $X$ and $Y$, and $ew_{X}$ is the exemplar weight of memory instance $X$: \begin{equation} \Delta^{E}(X,Y) = \frac{\Delta(X,Y)}{ew_{X} + \epsilon} \label{exweight} \end{equation} $\epsilon$ is the smallest non-zero number, and is used to avoid division by zero. Exemplar weights approaching zero yield very large distances; relatively higher values yield relatively smaller distances. Note that when a training instance occurs more than once in a training set, TiMBL expects it to have the same example weight with all occurrences; TiMBL cannot handle different example weights for the same instance type. TiMBL produces a warning ({\em Warning: deviating exemplar weight in line \#$\ldots$}), and uses the first weight found for the instance. \section{Indexing optimizations} \label{indexing} The discussion of the algorithm and the metrics in the section above is based on a naive implementation of nearest neighbor search: a flat array of instances which is searched from beginning to end while computing the similarity of the test instance with each training instance. Such an implementation, unfortunately, reveals the flip side of the lazy learning coin. Although learning is very cheap: just storing the instances in memory, the computational price of classification can become very high for large data sets. The computational cost is proportional to $N$, the number of instances in the training set, times $f$, the number of features. In our current implementation of {\sc ib1} we use tree-based indexing to alleviate these costs. \subsection{Tree-based indexing} The tree-based memory indexing operation replaces the flat array by a tree structure. Instances are stored in the tree as paths from a root node to a leaf, the arcs of the path are the consecutive feature-values, and the leaf node contains a {\em distribution}\/ of classes, i.e.~a count of how many times which class occurs with this pattern of feature-values. % (see Figure~\ref{example2}). Due to this storage structure, instances with identical feature-values are collapsed into a single path, and only their separate class information needs to be stored in the distribution at the leaf node. Many different {\bf tokens} of a particular {\bf instance type} share one path from the root to a leaf node. Moreover, instances which share a prefix of feature-values, also share a partial path. This reduces storage space (although at the cost of some book-keeping overhead) and has two implications for nearest neighbor search efficiency. %\begin{figure}[htb] % \begin{center} % \leavevmode % \epsfxsize=0.8\columnwidth % \epsffile{example2.eps}\ % \caption{A tree-structured storage of the instance % base from figure~\ref{example1}. An exact match for % the test is in this case directly found by a top down % traversal of the tree (grey path). If there is no % exact match, all paths are interpreted as instances % and the distances are computed. The order of the % features in this tree is based on Gain Ratio. % } % \label{example2} % \end{center} %\end{figure} First, the tree can be searched top-down very quickly for {\em exact matches}. When $k=1$, an exact match ($\Delta(X,Y)=0$) can never be beaten, so then it is possible to omit any further distance computations. The shortcut is built into TiMBL, but by default it is not used with $k>1$. TiMBL does, however, offer the possibility to use the shortcut at any value of $k$, with the command line switch ({\tt +x}. Using it can speed up classification radically for some types of data, but with $k>1$, the shortcut is not guaranteed to give the same performance (for better or for worse) as classification without it. Second, the distance computation for the nearest neighbor search can re-use partial results for paths which share prefixes. This re-use of partial results is in the direction from the root to the leaves of the tree. When we have proceeded to a certain level of the tree, we know how much similarity (Equation~\ref{overlapeq}) can still contribute to the overall distance (Equation~\ref{distance}), and discard whole branches of the tree which will never be able to rise above the partial similarity of the current least similar nearest neighbor. By doing the search depth first\footnote{Suggested by Gert Durieux.}, the similarity threshold quickly gets initialized to a good value, so that large parts of the search space can be pruned\footnote{With the special command line setting {\tt --silly=true} this tree search shortcut is switched off; as the name of the setting suggests, this is not recommended, except for explicit speed comparisons.}. Disregarding this last constraint on search, the number of feature-value comparisons is equal to the number of arcs in the tree. Thus if we can find an ordering of the features which produces more overlap between partial paths, and hence a smaller tree, we can gain both space and time improvements. An ordering which was found to produce small trees for many of our NLP data sets is Gain Ratio divided by the number of feature-values (this is the default setting). Through the {\tt -T} command line switch, however, the user is allowed to experiment with different orderings. Note that different orderings may only affect classification speed, not the actual classifications. \section{IGTree} \label{igtree} Using Information Gain rather than unweighted Overlap distance to define similarity in {\sc ib1} improves its performance on several {\sc nlp} tasks \cite{Daelemans+92b,VandenBosch+93,VandenBosch97}. The positive effect of Information Gain on performance prompted us to develop an alternative approach in which the instance memory is restructured in such a way that it contains the same information as before, but in a compressed decision tree structure. We call this algorithm {\sc igtree}~\cite{Daelemans+97} %(see Figure~\ref{example3} %for an illustration). In this structure, similar to the tree-structured instance base described above, instances are stored as paths of connected nodes which contain classification information. Nodes are connected via arcs denoting feature values. Information Gain is used to determine the order in which instance feature-values are added as arcs to the tree. The reasoning behind this compression is that when the computation of information gain points to one feature clearly being the most important in classification, search can be restricted to matching a test instance to those memory instances that have the same feature-value as the test instance at that feature. Instead of indexing all memory instances only once on this feature, the instance memory can then be optimized further by examining the second most important feature, followed by the third most important feature, etc. Again, compression is obtained as similar instances share partial paths. %\begin{figure}[htb] % \begin{center} % \leavevmode % \epsfxsize=0.7\columnwidth % \epsffile{example3.eps} % \caption{A pruned {\sc igtree} for the instance base % of Figure~\ref{example1}. The classification for % the test instance is found by top down search of the % tree, and returning the class label (default) of the % node after the last matching feature-value (arc). Note % that this tree is essentially a compressed version of % the tree in Figure~\ref{example2}. % } % \label{example3} % \end{center} %\end{figure} Because {\sc igtree} makes a heuristic approximation of nearest neighbor search by a top down traversal of the tree in the order of feature relevance, we no longer need to store all the paths. The idea is that it is not necessary to fully store those feature-values of the instance that have lower Information Gain than those features which already fully disambiguate the instance classification. Apart from compressing all training instances in the tree structure, the {\sc igtree} algorithm also stores with each non-terminal node information concerning the {\em most probable} or {\em default} classification given the path thus far, according to the bookkeeping information maintained by the tree construction algorithm. This extra information is essential when processing unknown test instances. Processing an unknown input involves traversing the tree (i.e., matching all feature-values of the test instance with arcs in the order of the overall feature Information Gain), and either retrieving a classification when a leaf is reached (i.e., an exact match was found), or using the default classification on the last matching non-terminal node if an exact match fails. In sum, it can be said that in the trade-off between computation during learning and computation during classification, the {\sc igtree} approach chooses to invest more time in organizing the instance base using Information Gain and compression, to obtain simplified and faster processing during classification, as compared to {\sc ib1} and {\sc ib1-ig}. The generalization accuracy of {\sc igtree} is usually comparable to that of {\sc ib1-ig}; often slightly worse, but sometimes even better. The two causes for {\sc IGTree}'s surprisingly good accuracies attained with dramatically faster classification are that (i) most 'unseen' instances contain large parts that fully match stored parts of training instances, and (ii) the probabilistic information stored at non-terminal nodes (i.e., the default classifications) still produces strong `best guesses' when exact matching fails. The difference between the top-down traversal of the tree and precise nearest neighbor search becomes more pronounced when the differences in informativity between features are small. In such a case a slightly different weighting would have produced a switch in the ordering and a completely different tree. The result can be a considerable change in classification outcomes, and hence also in accuracy. However, we have found in our work on NLP datasets that when the goal is to obtain a very fast classifier for processing large amounts of text, the tradeoff between a somewhat lower accuracy against stellar speed increases can be very attractive. It should be noted that by design, {\sc igtree} is not suited for numeric features, as it does not use some type of discretization. If present in data, numbers will simply be treated as literal strings by {\sc igtree}. Moreover, one should realize that the success of {\sc igtree} is determined by a good judgement of feature relevance ordering. Hence {\sc igtree} is not to be used with e.g. ``no weights'' ({\tt -w 0}). Also, setting the {\tt -k} parameter obviously has no effect on {\sc igtree} performance. \section{The TRIBL and TRIBL2 hybrids} \label{tribl} The application of {\sc igtree} on a number of common machine-learning datasets suggested that it is not applicable to problems where the relevance of the predictive features cannot be ordered in a straightforward way, e.g.~if the differences in Information Gain are only very small. In those cases, {\sc ib1-ig} or even {\sc ib1} tend to perform significantly better than {\sc igtree}. For this reason we have designed {\sc tribl} \cite{Daelemans+97d} and {\sc tribl2} as hybrid combinations of {\sc igtree} and {\sc ib1}. Both algorithms aim to exploit the trade-off between (i) optimization of search speed (as in {\sc igtree}), and (ii) maximal generalization accuracy. They do that by splitting the classification of new instances into a quick decision-tree ({\sc igtree}) traversal based on the first (most important and most class-disambiguating) features, followed by a slow but relatively accurate $k$-NN ({\sc ib1}) classification based on the remaining less important features. The difference between {\sc tribl} and {\sc tribl2} is that the former algorithm fixes the point in the feature ordering where {\sc igtree} is succeeded by {\sc ib1}, while {\sc tribl2} determines this switching point automatically per classification. We briefly describe both variants. For {\sc tribl}, a parameter ({\tt -q}) determines the switching point in the feature ordering from {\sc igtree} to {\sc ib1}. A heuristic that we have used with some success is based on {\em average feature information gain}; when the Information Gain of a feature exceeds the average Information Gain of all features $+$ one standard deviation of the average, then the feature is used for constructing an {\sc igtree}, including the computation of defaults on nodes. When the Information Gain of a feature is below this threshold, and the node is still ambiguous, tree construction halts and the leaf nodes at that point represent case bases containing subsets of the original training set. During search, the normal {\sc igtree} search algorithm is used, until the case-base nodes are reached, in which case regular {\sc ib1} nearest neighbor search is used on this sub-case-base. {\sc tribl2} does not employ a fixed switching point. Rather, during the classification of an instance it continues to use {\sc igtree} as long as it finds matching feature values in the weighting-governed feature ordering. Only when it finds a mismatch it reverts to {\sc ib1} classification on all remaining features. The reasoning behind this mismatch-based switching is that it offers a fairly optimal minimalisation of the use of {\sc ib1}; it is only invoked when mismatching occurs, which is the typical point in which {\sc ib1} can improve on decision-tree-style classification, which does not consider the other potentially matching features in the ordering \cite{Daelemans+99}. \section{IB2: Incremental editing} \label{ib2} In memory-based learning it seems sensible to keep any instance in memory that plays a (potentially) positive role in the correct classification of other instances. Alternatively, when it plays no role at all, or when it is disruptive for classification, it may be a good idea to discard, or {\em edit} it from memory. On top of not harming or even improving generalization performance, the editing of instances from memory could also alleviate the practical processing burden of the $k$-NN classifier kernel, since it would have less instances to compare new instances to. This potential double pay-off spawned a distinct line of work on editing in the $k$-NN classifier quite early \namecite{Hart68} and \namecite{Wilson72}. TiMBL offers an implementation of one particular editing algorithm called {\sc ib2} \cite{Aha+91}, an extension to the basic {\sc ib1} algorithm introduced in the same article. {\sc ib2} implements an incremental editing strategy. Starting from a seed memory filled with a certain (usually small) number of labeled training instances, {\sc ib2} adds instances incrementally to memory only when they are {\em misclassified}\/ by the $k$-NN classifier on the basis of the instances in memory at that point. These instances are added, since they are assumed to be representatives of a part of the complete instance space in which they themselves and potentially more nearest-neighbor instances have a particular class different from the class of neigboring instances already in memory. The economical idea behind {\sc ib2} is that this way typically only instances on the boundaries of such areas are stored, and not the insides of the areas; the classification of instances that would be positioned well inside such areas is assumed to be safeguarded by the memorized boundary instances surrounding it. Although the {\sc ib2} may optimize storage considerably, its strategy to store all misclassified instances incrementally makes {\sc ib2} sensitive to noise \cite{Aha+91}. It is also yet unclear what the effect is of the size of the seed. \section{Advanced evaluation metrics} \label{advancedstats} Aside from accuracy (the percentage of correctly classified test instances), TiMBL offers some more evaluation metrics that have become common in information retrieval and machine learning in general, namely precision, recall, and F-score, and ROC-space (with dimensions true positive rate and false positive rate), and AUC. We describe these metrics in more detail here. \begin{figure} \begin{center} \epsfig{file=pos-neg.eps, width=0.5\textwidth} \vspace*{-0.5cm} \end{center} \caption{Class-specific confusion matrix containing the basic counts used in the advanced performance metrics. \label{confmat}} \end{figure} Figure~\ref{confmat} displays the general confusion matrix\index{confusion matrix} for one class $C$, splitting all classifications on a test set into four cells. The TP or true positives cell contains a count of examples that have class $C$ and are predicted to have this class correctly by the classifier. The FP or false positives cell contains a count of examples of a different class that the classifier incorrectly classified as $C$. The FN or false negatives cell contains examples of class $C$ for which the classifier predicted a different class label than $C$. On the basis of these four numbers and the total number of positive examples $P=TP+FN$ and negative examples $N=FP+TN$, we can compute the following performance measures: \begin{description} \item[Precision]\index{precision} $= \frac{TP}{TP+FP}$, or the proportional number of times the classifier has correctly made the decision that some instance has class $C$. \item[Recall or True Positive Rate (TPR)]\index{TPR}\index{recall}\index{true positive rate} $= \frac{TP}{P}$, or the proportional number of times an example with class $C$ in the test data has indeed been classified as class $C$ by the classifier. \item[False Positive Rate (FPR)]\index{FPR}\index{false positive rate} $= \frac{FP}{N}$, or the proportional number of times an example with a different class than $C$ in the test data has been classified as class $C$ by the classifier. \item[F-score]\index{F-score} $= \frac{2 \times precision \times recall}{precision + recall}$, or the harmonic mean\index{harmonic mean} of precision and recall \cite{VanRijsbergen79}, is a commonly used metric to summarize precision and recall in one measure. The left part of Figure~\ref{spaces} shows F-score isolines in the two-dimensional space of recall (x-axis) and precision (y-axis). The curvature of the isolines is caused by the harmonic aspect of the formula (in contrast, the normal mean has straight isolines orthogonal to the $x=y$ diagonal), which penalizes large differences between precision and recall. The isolines could be likened to height isolines in a map, where the peak of the hill is at the upper right corner of the space. \item [AUC]\index{AUC}\index{area under the curve} or {\em area under the curve}\/ in the so-called ROC\index{ROC space} or {\em receiver operator characteristics}\/\index{receiver operator characteristics} space \cite{Egan75,Swets+00}, is the surface of the grey area in the right graph of Figure~\ref{spaces}. The ROC space is defined by the two dimensions FPR (false positive rate, x-axis) and TPR (true positive rate, or recall, y-axis). The difference with F-score is that it does not make use of the statistically unreliable precision metric; rather, it takes into account all cells of the matrix in Figure~\ref{confmat} including the TN (true negative) cell (for a more detailed description and arguments for using ROC analysis, cf. \cite{Fawcett04}). Its ``peak'' is in the upper left corner, at a FPR of zero and a TPR of 1. Rather than using the harmonic mean, it is common to report on the AUC, area under the classifier's TPR-FPR curve, where in the case of a discrete-output classifier such as {\sc TiMBL} this can be taken to mean the two lines connecting the experiment's TPR and FPR to the $(0,0)$ coordinate and the $(1,1)$ coordinate, respectively; the AUC is then the grey area between these points and coordinate $(1,0)$. \end{description} While these advanced statistics can be computed per class, they can also be averaged to produce a single outcome for a full test set. Common methods for averaging F-scores and AUC scores are micro-averaging and macro-averaging. In micro-averaging, each class' F-score or AUC is weighted proportionally to the frequency of the class in the test set. A macro-average sums the F-scores or AUCs and divides the total by the number of classes in the training set. In computing these averages, TiMBL bases itself on the classes in the training set. When a class does not re-occur in test material, it can have no recall, but it can have precision, hence it is always incorporated in averages. A class that occurs in test material but not in training material can never be predicted correctly, and is never included in averages. \begin{figure} \begin{center} \begin{minipage}[t]{0.53\textwidth} \epsfig{file=fspace.eps, width=\textwidth} \end{minipage}\hfill \begin{minipage}[t]{0.47\textwidth} \epsfig{file=roc-auc.eps, width=\textwidth} \end{minipage} \end{center} \caption{Precision--recall space with F-score isolines (left), and ROC space with an experimental outcome marked by the dot, and the outcome's AUC, the shaded surface between the dot and coordinates $(0,0)$, $(1,0)$, and $(1,1)$ (right). \label{spaces}} \end{figure} \section{NLP applications of TiMBL} \label{furtherreading} This section provides a brief historical overview of work, performed in the Tilburg and Antwerp groups and by others, with the application of {\sc mbl}-type algorithms to NLP tasks. For more historical background, see \cite{Daelemans+05}. The Tilburg and Antwerp groups have published a number of articles containing descriptions of the algorithms and specialised metrics collected in TiMBL, usually demonstrating their functioning using NLP tasks. The {\sc ib1-ig} algorithm was first introduced in \cite{Daelemans+92b} in the context of a comparison of memory-based approaches with error-back\-propagation learning for a hyphenation task. Predecessor versions of {\sc igtree} can be found in \cite{Daelemans+93c,VandenBosch+93} where they are applied to grapheme-to-phoneme conversion. See \cite{Daelemans+97} for a description and review of {\sc igtree} and {\sc ib1-ig}. {\sc tribl} is described in \cite{Daelemans+97d}. Experiments with distance-weighted class voting are described in \cite{Zavrel97}. Aspects of using binary-valued (unpacked multi-valued) features are discussed in \cite{VandenBosch+00}. Comparisons between memory-based learning and editing variants are reported in \cite{VandenBosch99,Daelemans+99}. A hybrid of TiMBL and the {\sc ripper} rule-induction algorithm \cite{Cohen95} is described in \cite{VandenBosch00,VandenBosch04}. Using TiMBL as a classifier combination method is discussed in \cite{Halteren+01}. \namecite{Raaijmakers00} describes an extension of TiMBL with error-correcting output codes. \namecite{Hendrickx+04} report on an experiment to import maximum-entropy matrices to replace {\sc mvdm} matrices \cite{Hendrickx+04}, improving over the maximum-entropy classifier. \namecite{VandenBosch04} presents a search algorithm to find optimal combinations of parameter settings automatically, given a labeled training set of examples, showing large gains of the default settings (also of other machine learning algorithms). Parallelization of TiMBL, through splitting either the training set or the test set in $n$ pieces in shared-memory multi-processor architectures, is explored in \cite{VandenBosch+07b}. The memory-based algorithms implemented in the TiMBL package have been targeted to a large range of Natural Language Processing tasks. Examples of applications in the morpho-phonological and speech areas are hyphenation and syllabification \cite{Daelemans+92b}; classifiying phonemes in speech \cite{Kocsor+00}; assignment of word stress \cite{Daelemans+94}; grapheme-to-phoneme conversion, \cite{VandenBosch+93,Daelemans+96,Canisius+06}; diminutive formation \cite{Daelemans+98a}; and morphological analysis \cite{VandenBosch+96,VandenBosch+99,Canisius+06}. Although these examples are applied mostly to Germanic languages (English, Dutch, and German), applications to other languages with more complicated writing systems or morphologies, or with limited resources, have also been presented: for example, letter-phoneme conversion in Scottish Gaelic \cite{Wolters+97}, morphological analysis of Arabic \cite{Marsi+05}, or diacritic restoration in languages with a diacritic-rich writing system \cite{Mihalcea02,DePauw+07}. At the syntactic sentence level TiMBL has been applied to part of speech tagging \cite{Daelemans+96b,Zavrel+99,Halteren+01}; PP-attachment \cite{Zavrel+97b}; subcategorization \cite{Buchholz98}; phrase chunking \cite{Veenstra98,Sang+99}; shallow parsing \cite{Daelemans+99a,Buchholz+99,Yeh00}; clause identification \cite{Orasan00,Sang01}; detecting the scope of negation markers \cite{Morante+08c}; sentence-boundary detection \cite{Stevenson+00}; predicting the order of prenominal adjectives for generation \cite{Malouf00} and article generation \cite{Minnen+00}; and, beyond the sentence level, to anaphora resolution \cite{Preiss02,Mitkov+02,Hoste05}. More recently, memory-based learning has been integrated as a classifier engine in more complicated dependency parsing systems \cite{Nivre+04,Sagae+05,Canisius+06b}, or dependency parsing in combination with semantic role labeling \cite{Morante+09b}. Memory-based learning has been applied succesfully to lexical semantics, in particular to word sense disambiguation \cite{Veenstra+00,Stevenson+99,Kokkinakis00,Mihalcea02,Hoste+02,DeCadt+04}, but also in other lexical semantic tasks such as determining noun countability \cite{Baldwin+03}, animacy \cite{Orasan+01}, and semantic relations within noun compounds \cite{Kim+06b,Nastase+06}. On the textual level, TiMBL has been used for information extraction \cite{Zavrel+00b,Zavrel+03,Ahn06}, text classification \cite{Spitters00}, question classification \cite{Garcia+06,Dridan+07}, spam filtering \cite{Androutsopoulos+00}, named-entity recognition \cite{Buchholz+00,Hendrickx+03,DeMeulder+03,Sporleder+06b,Leveling+06}, and error detection in textual databases \cite{Sporleder+06}. In the field of discourse and dialogue modeling, TiMBL has been used for shallow semantic analysis of speech-recognised utterances \cite{Gustafson+99,Krahmer+01,VandenBosch+01,Lendvai+02a,Lendvai+03}, in disfluency detection in transcribed spontaneous speech \cite{Lendvai+03c}, and in classifying ellipsis in dialogue \cite{Fernandez+04}. Relations to statistical language processing, in particular the interesting equivalence relations with back-off smoothing in probabilistic classifiers, are discussed in \cite{Zavrel+97}. Relations between classification-based word prediction and statistical language modeling are identified in \cite{VandenBosch05,VandenBosch06}. In machine translation, $k$-nearest neighbor classification offers a conceptual bridge between example-based machine translation (EBMT) and statistical MT. Pure memory-based approaches are described in \cite{VandenBosch+07,Canisius+09,VandenBosch+09,VanGompel+09}; hybrids with statistical machine translation are presented in \cite{Stroppa+07,Haque+09}. The first dissertation-length study devoted to the approach is \cite{VandenBosch97}, in which the approach is compared to alternative learning methods for NLP tasks related to English word pronunciation (stress assignment, syllabification, morphological analysis, alignment, grapheme-to-phoneme conversion). TiMBL is also central in the Ph.D. theses of \namecite{Buchholz02}, \namecite{Lendvai04}, \namecite{Hendrickx05} \namecite{Hoste05}, \namecite{Keuleers08}, and \namecite{Canisius09}. In 1999 a special issue of the {\em Journal for Experimental and Theoretical Artificial Intelligence} (Vol.~11(3), edited by Walter Daelemans) was devoted to Memory-Based Language Processing. The introduction to this special issue discusses the inspiration sources and alternative developments related to the memory-based approach taken in TiMBL \cite{Daelemans99b}. Whereas most work using TiMBL has been oriented towards natural language engineering applications, the linguistic and psycholinguistic relevance of memory-based learning is another focus of research in Antwerp, Tilburg and elsewhere. Work in this area has been done on stress assignment in Dutch \cite{Daelemans+94,Gillis+00}, reading aloud \cite{VandenBosch+00b}, phonological bootstrapping \cite{Durieux+00}, the prediction of linking morphemes in Dutch \cite{Krott+01}, morphology \cite{Eddington00,Eddington03}, and the Dutch plural inflection \cite{Keuleers+07}. A comparison to other analogical methods for linguistics is provided in \cite{Daelemans+97f,Daelemans02}. \ \\ {\it All Tilburg/Antwerp papers referred to in this section, as well as more recent papers, are available in electronic form from the {\sc ILK} home page: {\tt http://ilk.uvt.nl} and the {\sc CLiPS} home page: \\ {\tt http://www.clips.ua.ac.be/}.} \chapter{Software usage and options} \label{reference} \section{Command line options} \label{commandline} The user interacts with TiMBL through the use of command line arguments. When you have installed TiMBL successfully, and you type {\tt Timbl} at the command line without any further arguments, it will print an overview of the most basic command line options. {\footnotesize \begin{verbatim} TiMBL 6.3.0 (c) ILK 1998 - 2010. Tilburg Memory Based Learner Induction of Linguistic Knowledge Research Group, Tilburg University CLiPS Computational Linguistics Group, University of Antwerp Mon Oct 19 22:33:13 2009 usage: Timbl -f data-file {-t test-file} or see: Timbl -h for all possible options \end{verbatim} } If you are satisfied with all of the default settings, you can proceed with just these basics: \begin{description} \item {\tt -f } : supplies the name of the file with the training items. \item {\tt -t } : supplies the name of the file with the test items. \item {\tt -h} : prints a glossary of all available command line options. \end{description} The presence of a training file will make TiMBL pass through the first two phases of its cycle. In the first phase it examines the contents of the training file, and computes a number of statistics on it (feature weights etc.). In the second phase the instances from the training file are stored in memory. If no test file is specified, the program exits, possibly writing some of the results of learning to files (see below). If there is a test file, the selected classifier, trained on the present training data, is applied to it, and the results are written to a file the name of which is a combination of the name of the test file and a code representing the chosen algorithm settings. TiMBL then reports the percentage of correctly classified test items. The default settings for the classification phase are: a Memory-Based Learner, with Gain Ratio feature weighting, with $k=1$, and with optimizations for speedy search. If you need to change the settings, because you want to use a different type of classifier, or because you need to make a trade-off between speed and memory-use, then you can use the options that are shown using {\tt -h}. The sections below provide a reference to the use of these command line arguments, and they are roughly ordered by the type of action that the option has effect on. Note that some options (listed with ``{\tt +/-}'') can be turned on ({\tt +}) or off ({\tt -}). \subsection{Algorithm and metric selection} \begin{description} \item {\tt -a or } : determines the classification algorithm. Possible values are: \begin{description} \item {\tt 0} or {\tt IB1} -- the {\sc ib1} ($k$-NN) algorithm (default). See Sections~\ref{mbl} and~\ref{indexing}. \item {\tt 1} or {\tt IGTREE} -- {\sc igtree}, decision-tree-based optimization. See Section~\ref{igtree}. \item {\tt 2} or {\tt TRIBL} -- {\sc tribl}, a hybrid of {\sc ib1} and {\sc igtree}. See Section~\ref{tribl}. \item {\tt 3} or {\tt IB2} -- {\sc ib2}, incremental edited memory-based learning. See Section~\ref{ib2}. \item {\tt 4} or {\tt TRIBL2} -- {\sc tribl2}, a non-parameteric version of {\sc tribl}. See Section~\ref{tribl}. \end{description} \item {\tt -m } : determines which distance metrics are used for each feature. The format of this string is as follows:\\ {\tt GlobalMetric:MetricRange:MetricRange}\\ Where {\tt GlobalMetric} is used for alle features except for the ones that are assigned other metrics by following the restrictions given by {\tt :MetricRange}. A range can be written using comma's for lists, and hyphens for intervals. The metric code can be one of the following nine: \begin{itemize} \item {\tt O} -- Overlap (default; see Subsection~\ref{overlap}) \item {\tt M} -- Modified value difference ({\sc mvdm}; see Subsection~\ref{mvdm}) \item {\tt J} -- Jeffrey divergence (see Subsection~\ref{mvdm}) \item {\tt D} -- Dot product (see Subsection~\ref{dotproduct}) \item {\tt C} -- Cosine (see Subsection~\ref{dotproduct}) \item {\tt N} -- Numeric (all features are numeric. see Subsection~\ref{overlap}) \item {\tt L} -- Levenshtein (see Subsection~\ref{overlap}) \item {\tt DC} -- Dice coefficient (see Subsection~\ref{overlap}) \item {\tt I} -- Ignore (ignore specified features) \end{itemize} For example, {\tt -mO:N3:I2,5-7} sets the global metric to overlap, declares the third feature to be numeric, and ignores features 2 and 5, 6, and 7. Ignore {\em can}\/ be the global metric; it must be followed by a {\tt MetricRange} string with metric {\tt O}, {\tt M}, {\tt J}, {\tt D}, or {\tt N} specifying in the range which features are {\em not}\/ ignored. \item {\tt -w } : chooses between feature-weighting possibilities. The weights are used in the metric of {\sc ib1} and in the ordering of the {\sc igtree}. Possible values are: \begin{description} \item n=0 -- No weighting, i.e. all features have the same importance (weight = 1). \item n=1 -- Gain Ratio weighting (default). See section~\ref{infogain}. \item n=2 -- Information Gain weighting. See section~\ref{infogain}. \item n=3 -- Chi-squared ($\chi^2$) weighting. See section~\ref{chisquared}. \item n=4 -- Shared variance weighting. See section~\ref{chisquared}. \item n=$<$filename$>$:$<$number$>$ or n=$<$filename$>$ -- Instead of the five weight settings above we can supply a filename to the {\tt -w} option. This causes TiMBL to read this file and use its contents as weights. If only $<$filename$>$ is given as an argument, the file is supposed to contain one list of feature weights for all features. The $<$filename$>$:$<$number$>$ option assumes that a weights file generated by TiMBL with the {\tt -W} option (and possibly edited by the user) is read back in; the number refers to one of the five numbers above. See section~\ref{weightformat} for a description of the format of weights files. \end{description} \item {\tt -k } : number of nearest neighbors used for extrapolation. Only applicable in conjunction with {\sc ib1} ({\tt -a 0}), {\sc tribl} ({\tt -a 2}), {\sc tribl2} ({\tt -a 4}) and {\sc ib2} ({\tt -a 3}). The default is 1. Especially with the {\sc mvdm} metric it is often useful to determine a good value larger than 1 for this parameter (usually an odd number, to avoid ties). Note that due to ties (instances with exactly the same similarity to the test instance) the number of instances used to extrapolate might in fact be much larger than this parameter. \item {\tt -d } : The type of class voting weights that are used for extrapolation from the nearest neighbor set. {\tt val} can be one of: \begin{itemize} \item {\tt Z} : normal majority voting; all neighbors have equal weight (default). \item {\tt ID}: Inverse Distance weighting. See Section~\ref{distweightvote}, Equation~\ref{dudani_eq}. \item {\tt IL}: Inverse Linear weighting. See Section~\ref{distweightvote}, Equation~\ref{inverseweight}. \item {\tt ED::}: Exponential Decay weighting with decay parameters {\tt a} ($\alpha$) and {\tt b} ($\beta$). No spaces are allowed in the string. Parameter {\tt b} can be left unspecified: {\tt ED:} assumes $\beta=1$. The syntax used in previous TiMBL versions ({\tt ED}) is still supported but deprecated. See Section~\ref{distweightvote}, Equation~\ref{expdecayweight}. \end{itemize} \item {\tt -L } : frequency threshold for switching from the {\sc mvdm} or Jeffrey Divergence to the Overlap distance metric. The default is 1 (never switch). When in a pair of matched values one or both values occur less frequently than {\tt n} times in the learning material, TiMBL switches from {\sc mvdm} or Jeffrey Divergence to Overlap. Higher values of {\tt n} force TiMBL to use the Overlap metric more. Only applicable in conjunction with the {\sc mvdm} ({\tt -mM}) and Jeffrey divergence ({\tt -mJ}) distance metrics. \item {\tt -b } : determines n ($\geq 1$), the number of instances, to be taken from the top of the training file, to act as the bootstrap set of memorized instances before {\sc ib2} starts adding new instances. Only applicable in conjunction with {\sc ib2} ({\tt -a 3}). \item {\tt -q } : {\tt n} is the {\sc tribl} offset, the index number of the feature (counting from 1) after which {\sc tribl} should switch from {\sc igtree} to {\sc ib1}. Only applicable in conjunction with {\sc tribl} ({\tt -a 2}). \item {\tt -R } : Resolve ties in the classifier randomly, using a random generator with seed n. {\tt -R } causes the classification to be based on a random pick (with seed n) of a category according to the probability distribution in the nearest neighbor set. By default, {\tt -R} is not used, but rather the deterministic tie resolution scheme described in Subsection~\ref{overlap}. \item {\tt -t $<$@file$>$} : If the filename given after {\tt -t} starts with '{\tt @}', TiMBL will read commands for testing from {\tt file}. This file should contain one set of instructions per line. On each line new values can be set for the following command line options: {\tt -B -d -e -k -L -M -o -p -Q -R -t -u +/-v -w +/-x +/-\%}. It is compulsory that each line in {\tt file} contains a {\tt -t } argument to specify the name of the test file. \item {\tt -t } : the string {\tt } is the literal name of the file with the test items. \item {\tt -t leave\_one\_out} : No test file is read, but testing is done on each pattern of the training file, by treating each pattern of the training file in turn as a test case (and the whole remainder of the file as training cases). Only applicable in conjunction with {\sc ib1} ({\tt -a0}). \item {\tt -t cross\_validate} : An $n$-fold cross-validation experiment is performed on the basis of $n$ files (e.g. $1/n$ partitionings of an original data file). The names of these $n$ files need to be in a text file (one name per line) which is given as argument of {\tt -f}. In each fold $f=1 \ldots n$, file number $f$ is taken as test set, and the remaining $n-1$ files are concatenated to form the training set. Only applicable in conjunction with {\sc ib1} ({\tt -a0}). \end{description} \subsection{Input options} \begin{description} \item {\tt -f } : the string {\tt } is the literal name of the file with the training items, or (in conjunction with {\tt -t cross\_validate}, the file containing the names of the cross-validation files. \item {\tt -F } : Force TiMBL to interpret the training and test file as a specific data format. Possible values for this parameter are: {\tt Compact, C4.5, ARFF, Columns, Sparse, Binary} (case-insensitive). The default is that TiMBL guesses the format from the contents of the first line of the data file. ARFF is not automatically detected. See section~\ref{dataformats} for description of the data formats and the guessing rules. The {\tt Compact} format cannot be used with numeric features. \item {\tt -l } : Feature length. Only applicable with the Compact data format; {\tt } is the number of characters used for each feature-value and category symbol. \item {\tt -i } : Skip the first two training phases: instead of processing a training file, read a previously saved (see {\tt -I} option) instance-base or {\sc igtree} from the file {\tt treefile}. See section~\ref{treeformat} for the format of this file. \item {\tt --matrixin=} : Read value distance metrics (such as {\sc mvdm} or Jeffrey divergence matrices written to file with {\tt --matrixout=} : Replace the automatically computed value-class probability matrix with the matrices provided in this file. \item {\tt -P } : Specify a path to read the data files from. This path is ignored if the name of the data file already contains path information. \item {\tt -s}: Use the whitespace-delimited exemplar weights, given after each training instance in the training file {\tt }, during classification. {\tt } may contain exemplar weights, but they are not used in classification. If the test file does not have an exemplar weights column, you must specify {\tt -s1}. Exemplar weights can also be ignored (in both training and test files) by specifying {\tt -s0}. \end{description} \subsection{Output options} \begin{description} \item {\tt -I } : After phase two of learning, save the resulting tree-based representation of the instance-base or {\sc igtree} in a file. This file can later be read back in using the {\tt -i} option (see above). For {\sc igtree} this also automatically saves the current weights into {\tt treefile.wgt} unless this is overridden by {\tt -W}. See section~\ref{treeformat} for a description of the resulting file's format. \item {\tt --matrixout=} : Store calculated {\sc mvdm} or Jeffrey divergence distance metrics in file {\tt filename}. \item {\tt -X } : instead of the proprietary file format written with the {\tt -I} switch, {\tt -X} writes the TiMBL tree into an XML tree in {\tt }. This XML file cannot be read back into TiMBL. \item {\tt -W } : Save the currently used feature-weights in a file. \item {\tt -U } : Write the automatically computed value-class probability matrix to this file. \item {\tt -n } : Save the feature-value and target category symbols in a C4.5 style ``names file'' with the name {\tt }. Take caution of the fact that TiMBL does not mind creating a file with ',' '.' '$|$' and ':' values in features; C4.5 will produce errors on this. \item {\tt -p } : Indicate progress during training and testing after every n processed patterns. The default setting is 100,000. \item {\tt -e } : During testing, compute and print an estimate on how long it will take to classify n test patterns. Off by default. \item {\tt +/-v } : Verbosity Level; determines how much information is written to the output during a run. Unless indicated otherwise, this information is written to standard error. The use of {\tt +} turns a given verbosity level {\bf on}, whereas {\tt -} turns it {\bf off} (only useable in non-commandline contexts, such as client/server communication or {\tt -t @testcommandfile}). This parameter can take on the following values (case-insensitive): \begin{description} \item {\tt s}: work silently (turns off all set verbosity levels). \item {\tt o}: show all options set. \item {\tt f}: show calculated feature weights. (default) \item {\tt p}: show {\sc mvdm} matrices. \item {\tt e}: show exact matches. \item {\tt as}: show overall advanced statistics (micro and macro averages of F-score and AUC). \item {\tt cm}: show confusion matrix between actual and predicted classes. \item {\tt cs}: show per-class statistics (precision, recall, true positive rate, false positive rate, F-score, AUC). \item {\tt di}: add the distance of the nearest neighbor to the output file. \item {\tt db}: add class distribution in the nearest neighbor set to the output file. \item {\tt md}: add matching depth and node type (N for non-ending node, L for leaf) to output file. \item {\tt k}: add a summary of class distribution information of all nearest neighbors to the output file (sets {\tt -x}) \item {\tt n}: add nearest neigbors to the output file (sets {\tt -x}) \item {\tt b}: provide branching statistics of the internal tree, overall and per level. \end{description} You may combine levels using '{\tt +}' e.g. {\tt +v p+db} or {\tt -v o+di}. \item {\tt -G }: Normalize class distributions generated by {\tt +v db}. \begin{description} \item {\tt 0 (zero)}: Normalize distributions so that they add up to 1.0 \item {\tt 1:}: Smooth by adding floating-point $f$ to all class votes (e.g. {\tt -G1:1} performs add-one smoothing). \end{description} \item {\tt --Beam=}: Limit the number of returned classes and class votes returned by {\tt +v db} to $n$. Default is infinity (no limit). \item {\tt +/- \%} : Write the percentage of correctly classified test instances, the number of correctly classified instances, and the total number of classified instances (one number per line, three lines in total) to a file with the same name as the output file, but with the suffix ``{\tt .\%}''. \item {\tt -o $<$filename$>$} : Write the test output to filename. Useful for different runs with the same settings on the same testfile, where the default output file name would normally be the same. \item {\tt -O $<$path$>$} : Write all output to the path given here. The default is to write all output to the directory where the test file is located. \item {\tt -V} : Show the TiMBL version number. \end{description} \subsection{Internal representation options} \begin{description} \item {\tt -N } : (maximum) number of features. Obligatory for Sparse and Binary formats. When larger than a pre-defined constant (default 2500), N needs to be set explicitly for all algorithms. \item {\tt +/- x} : turns the shortcut search for exact matches on or off in {\sc ib1} (and {\sc ib2}, {\sc tribl}, and {\sc tribl2}). The default is to be off ({\tt -x}). Turning it on makes {\sc ib1} generally faster, but with $k>1$ the shortcut produces different results from a genuine $k$ nearest neighbors search, since absolute preference is given to the exact match. \item {\tt -M } : Set the maximum number of nearest neighbors printed using the {\tt +vn} verbosity option. By default this is set to 500, but when you are interested in the contents of really large nearest neighbor sets (which is possible with large $k$ or large data sets with few features), {\tt n} can be increased up to 100,000. \item {\tt +/- H} : Turn on/off hashing of feature values and class labels in TiMBL trees. Hashing is done by default, but with short (e.g. one-character) feature values and/or classes less memory is used when hashing is set off. \item {\tt -B } : Number of bins used for discretizing numeric data (only used for computing feature weights). \item {\tt -c } : Clipping (threshold) frequency for prestoring {\sc mvdm} matrices. Cells in the matrix are only stored if both feature values occur more than {\tt } times. \item {\tt -T } : Set the ordering of the TiMBL tree (with {\sc ib1} and {\sc ib2}), i.e., rank the features according to the metric identified by {\tt }. The default ordering is {\tt G/V} (according to gain ratio divided by the number of values), but some orderings may produce faster classification. Note that different orderings do {\em not}\/ change the classification behavior of {\sc ib1} and {\sc ib2}. {\tt } can take the following values: \begin{description} \item {\tt DO}: no ordering (the ordering of the features in the data file is taken) \item {\tt GRO}: gain ratio (eq.~\ref{IGgainratio}) \item {\tt IGO}: information gain (eq.~\ref{IGgain}) \item {\tt 1/V}: $1/V$, where $V$ is the number of values \item {\tt G/V}: gain ratio divided by the number of values \item {\tt I/V}: information gain divided by the number of values \item {\tt X2O}: \chisq \ (eq.~\ref{chisq-eq}) \item {\tt X/V}: \chisq \ divided by the number of values \item {\tt SVO}: shared variance (eq.~\ref{shared-variance-eq}) \item {\tt S/V}: shared variance divided by the number of values \item {\tt GxE}: gain ratio $\times si$, where $si$ is the split info of the feature (eq.~\ref{splitinfo}) \item {\tt IxE}: information gain $\times si$ \item {\tt 1/S}: $1/si$ \end{description} \end{description} \subsection{Hidden options} The commandline interface to TiMBL contains several hidden options that have been built in over time for particular reasons. Some have survived over time, and although their use is not for the faint-hearted, some may offer interesting functionalities. A small list of disclosed hidden options follows. \begin{description} \item {\tt --sloppy=\{true|false\}}: in combination with leave-one-out (LOO) testing, this option turns off all weight recomputation. By default, leaving out one training example out causes all feature weights, value-class matrices, and derived metrics such as {\sc mvdm} to be recomputed, because strictly the example-specific statistics should be absent when it is held out and classified. {\tt --sloppy} skips this, causing a significant speedup, and usually slightly better LOO scores. Use only if your experimental method allows it. Default value is {\tt false}. \item {\tt --silly=\{true|false\}}: set to {\tt true}, switches off the optimized nearest-neighbor search in {\sc ib1} and {\sc tribl}. This causes TiMBL to compare all feature values of a test instance to full paths in the TiMBL tree. This causes TiMBL to slow down dramatically on most datasets. Setting is available to enable testing the effect of optimized search. Default value is {\tt false}. \item{\tt --Diversify}: modifies all features weights by subtracting the smallest weight (plus $\epsilon$) from all weights. The smallest weight thus becomes $\epsilon$. This modification ``diversifies'' the feature weights, and was introduced to enhance the effect of {\sc Dimbl}, the multi-CPU variant of TiMBL\footnote{For {\sc Dimbl}, see \url{http://ilk.uvt.nl/dimbl}}. \end{description} \section{File formats} \label{fileformats} This section describes the format of the input and output files used by TiMBL. Where possible, the format is illustrated using the classical ``objects'' data set, which consists of 12 instances of 5 different everyday objects (nut, screw, key, pen, scissors), described by 3 discrete features (size, shape, and number of holes). \subsection{Data files} \label{dataformats} The training and test sets for the learner consist of descriptions of instances in terms of a fixed number of feature-values. TiMBL supports a number of different formats for the instances, but they all have in common that the files should contain one instance per line. The number of instances is determined automatically, and the format of each instance is inferred from the format of the first line in the training set. The last feature of the instance is assumed to be the target category. Should the guess of the format by TiMBL turn out to be wrong, you can force it to interpret the data as a particular format by using the {\tt -F} option. Note that TiMBL, by default, will interpret features as having {\em symbolic, discrete values}. Unless you specify explicitly that certain features are numeric, using the {\tt -m} option, TiMBL will interpret numbers as just another string of characters. If a feature is numeric, its values will be scaled to the interval [0,1] for purposes of distance computation (see Equation~\ref{overlapeq}). The computation of feature weights will be based on a discretization of the feature. Once TiMBL has determined the input format, it will skip and complain about all lines in the input which do not respect this format (e.g.~have a different number of feature-values with respect to that format). During testing, TiMBL writes the classifications of the test set to an output file. The format of this output file is by default the same as the input format, with the addition of the predicted category being appended after the correct category. If we turn on higher levels of verbosity, the output files will also contain distributions, distances and nearest neighbor sets. \subsubsection{Column format} \label{comlumnformat} The {\bf column format} uses white space as the separator between features. White space is defined as a sequence of one or more spaces or tab characters. Every instance of white space is interpreted as a feature separator, so it is not possible to have feature-values containing white space. The column format is auto-detected when an instance of white space is detected on the first line {\em before a comma has been encountered}. The example data set looks like this in the column format: \begin{footnotesize} \begin{verbatim} small compact 1 nut small long none screw small long 1 key small compact 1 nut large long 1 key small compact none screw small compact 1 nut large long none pen large long 2 scissors large long 1 pen large other 2 scissors small other 2 key \end{verbatim} \end{footnotesize} \subsubsection{C4.5 format} \label{c45format} This format is a derivative of the format that is used by the well-known C4.5 decision tree learning program~\cite{Quinlan93}. The separator between the features is a comma, and the category (viz. the last feature on the line) is followed by a period (although this is not mandatory: TiMBL is robust to missing periods)\footnote{The periods after the category are not reproduced in the output}. White space within the line is taken literally, so the pattern {\tt a,\ b\ c,d} will be interpreted as {\tt `a',`\ b\ c',`d'}. An exception is the class label, which should not contain any whitespace. When using this format, especially with linguistic data sets or with data sets containing floating point numbers, one should take special care that commas do not occur as feature values and that periods do not occur within the category. Note that TiMBL's C4.5 format does not require a so called {\em namesfile}. However, TiMBL can produce such a file for C4.5 with the {\tt -n} option. The C4.5 format is auto-detected when a comma is detected on the first line {\em before any white space has been encountered}. The example data set looks like this in the C4.5 format: \begin{footnotesize} \begin{verbatim} small,compact,1,nut. small,long,none,screw. small,long,1,key. small,compact,1,nut. large,long,1,key. small,compact,none,screw. small,compact,1,nut. large,long,none,pen. large,long,2,scissors. large,long,1,pen. large,other,2,scissors. small,other,2,key. \end{verbatim} \end{footnotesize} \subsubsection{ARFF format} \label{arffformat} ARFF is a format that is used by the WEKA machine learning workbench~\cite{Garner95,Witten+99}\footnote{WEKA is available from the Waikato University Department of Computer Science, \url{http://www.cs.waikato.ac.nz/~ml/weka}}. Although TiMBL at present does {\em not}\/ entirely follow the ARFF specification, it still tries to do as well as it can in reading this format. The ARFF format is {\em not}\/ autodetected, and needs to be specified on the commanline with {\tt -F ARFF}. In ARFF data, the actual data are preceded by a information on feature types, feature names, and names of values in case of symbolic features. TiMBL ignores all of these lines, and starts reading data from after the {\tt @data} statement until the end of the file. Feature-values are supposed to be separated by commas; white space is deleted entirely, so the pattern {\tt a, b c,d} will be interpreted as {\tt `a',`bc',`d'}. There should be no whitespace in class labels. We hope to include better support for the ARFF format in future releases. \begin{footnotesize} \begin{verbatim} % There are 4 attributes. % There are 12 instances. % Attribute information: Ints Reals Enum Miss % 'size' 0 0 12 0 % 'shape' 0 0 12 0 % 'n_holes' 9 0 3 0 % 'class.' 0 0 12 0 @relation 'example.data' @attribute 'size' { small, large} @attribute 'shape' { compact, long, other} @attribute 'n_holes' { 1, none, 2} @attribute 'class.' { nut., screw., key., pen., scissors.} @data small,compact,1,nut. small,long,none,screw. small,long,1,key. small,compact,1,nut. large,long,1,key. small,compact,none,screw. small,compact,1,nut. large,long,none,pen. large,long,2,scissors. large,long,1,pen. large,other,2,scissors. small,other,2,key. \end{verbatim} \end{footnotesize} \subsubsection{Compact format} \label{compactformat} The compact format is especially useful when dealing with very large data files. Because this format does not use any feature separators, file-size is reduced considerably in some cases. The price of this is that all features and class labels must be of equal length (in characters) and TiMBL needs to know beforehand what this length is. You must tell TiMBL by using the {\tt -l} option. The compact format is auto-detected when neither of the other formats applies. The same example data set might look like this in the column format (with two characters per feature): \begin{footnotesize} \begin{verbatim} smco1_nu smlonosc smlo1_ke smco1_nu lalo1_ke smconosc smco1_nu lalonope lalo2_sc lalo1_pe laot2_sc smot2_ke \end{verbatim} \end{footnotesize} \subsubsection{Sparse format} The sparse format is relevant for data with features of which a significant portion of the values is $0.0$ (numeric), $0$ (binary), or some ``null'' symbolic value. Storing only the non-null values typically takes less space on disk. Consider, for example, a data set in text classification with 10,000 features each representing the tf*idf weight of a term. It would be uneconomical to store instances as long lines of \begin{footnotesize} \begin{verbatim} 0.02, 0.0, 0.0, 0.0, 0.54, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... , 0.01,sports \end{verbatim} \end{footnotesize} The Sparse format allows to store such an instance as \begin{footnotesize} \begin{verbatim} (1,0.02)(5,0.54)...(10000,0.01)sports \end{verbatim} \end{footnotesize} That is, a sequence of ($,$) expressions between parentheses each indicating that the feature number $index$ has value $value$, with the class label at the end, directly following the last parenthesis. The feature index is assumed to start at 1. In case of symbolic values, whitespace included in the parentheses are considered significant (i.e., part of the values). A case with only null values can be represented as either `{\tt class} or {\tt ,class}. This option must be specified by the user ({\tt -F Sparse}); it is not guessed from the data. It must also be accompanied by a user declaration of the number of features ({\tt -N }). \subsubsection{Sparse Binary format} \label{binaryformat} The sparse binary format, a simplified version of the Sparse format, is especially useful when dealing with large numbers of two-valued (binary) features, of which each case only has a very few active ones, such as e.g. in text categorization. Thus instead of representing a case as: \begin{footnotesize} \begin{verbatim} 1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,small. \end{verbatim} \end{footnotesize} We can represent it as: \begin{footnotesize} \begin{verbatim} 1,8,11,12,18,small. \end{verbatim} \end{footnotesize} This format allows one to specify only the index numbers of the active features (indexes start at one), while implicitly assuming that the value for all the remaining features is zero. Because each case has a different number of active features, we must specify in some other way what the actual number of features is. This must be done using the {\tt -N} option. As the format is very similar to numeric features, it must always be user-declared using {\tt -F Binary}. The last feature of a line is always interpreted as being the category string. A case with only zeroes can be represented as either `{\tt class} or {\tt ,class}. \subsection{Weight files} \label{weightformat} The feature weights used for computing similarities and for the internal organization of the memory base can be saved to a file {\tt -W}. These files can be read back into TiMBL with {\tt -w $<$filename$>$:$<$weight number$>$}, where the weight number refers to the five options in TiMBL. It is also possible to change these files manually before reading them in -- and additionally it is also possible to write a file from scratch and read this into TiMBL. This allows the experimenter to handcraft feature weights. The generic format for the weights file is as follows. The weights file may contain comments on lines that start with a \# character. The other lines contain the number of the feature followed by its numeric weight. An example of such a file is provided below. The numbering of the weights starts with 1. In this example, the data set has three features. \begin{footnotesize} \begin{verbatim} # DB Entropy: 2.29248 # Classes: 5 # Lines of data: 12 # Fea. Weight 1 0.765709 2 0.614222 3 0.73584 \end{verbatim} \end{footnotesize} Weight files written by TiMBL are of the same format, but write all weights in a concatenation, separated by \# lines that carry the abbreviated name of the weight (nw, gr, ig, x2, sv). The following example illustrates this format (which can be edited manually, as long as the same number of lines is kept): \begin{footnotesize} \begin{verbatim} # DB Entropy: 1.61789 # Classes: 5 # Lines of data: 2999 # nw # Fea. Weight 1 1 2 1 3 1 # # gr # Fea. Weight 1 0.0428445870345557 2 0.185070180760327 3 0.325371814230901 # # ig # Fea. Weight 1 0.213887591411729 2 0.669704582861074 3 1.27807624584789 # # sv # Fea. Weight 1 0.0762436694064095 2 0.233998145488354 3 0.596896311429044 # # x2 # Fea. Weight 1 914.619058199289 2 2807.0417532783 3 7160.36815190281 # \end{verbatim} \end{footnotesize} \subsection{Value difference files} \label{mvdmformat} Using the {\sc mvdm} metric, it can sometimes be interesting to inspect the matrix of conditional class probabilities from Equation~\ref{MVDMeq}. By using the {\tt -U} option, we can write the computed matrix to a file. This way we can see which values are considered to be similar by the metric. For each feature a row vector is given for each value, of the conditional probabilities of all the classes (columns) given that value. \begin{footnotesize} \begin{verbatim} targets A, B, C, D, E. feature # 1 Matrix: small 0.429 0.286 0.286 0.000 0.000 large 0.000 0.000 0.200 0.400 0.400 feature # 2 Matrix: compact 0.750 0.250 0.000 0.000 0.000 long 0.000 0.167 0.333 0.333 0.167 other 0.000 0.000 0.500 0.000 0.500 feature # 3 Matrix: 1 0.500 0.000 0.333 0.167 0.000 none 0.000 0.667 0.000 0.333 0.000 2 0.000 0.000 0.333 0.000 0.667 \end{verbatim} \end{footnotesize} As long as this format is observed, the file can be modified (manually or by substituting some other vector-based representations for the values), and the new matrix can be read in and used with the {\sc mvdm} metric. \subsection{Tree files} \label{treeformat} Although the learning phase in TiMBL is relatively fast, it can be useful to store the internal representation of the data set both for later usage and for faster subsequent learning. In TiMBL, the data set is stored internally in a tree structure (see Section~\ref{indexing}). When using {\sc ib1}, this tree representation contains all the training cases as full paths in the tree. When using {\sc igtree}, unambiguous paths in the tree are pruned before it is used for classification or written to file; on the same data, {\sc igtree} trees are usually considerably smaller than {\sc ib1} trees. In either tree type, the arcs represent feature values and nodes contain class distribution information. The features are in the same order throughout the tree. This order is either determined by memory-size considerations in {\sc ib1}, or by feature relevance in {\sc igtree}. It can explicitly be manipulated using the {\tt -T} option. We strongly advise to refrain from manually editing the tree file. However, the syntax of the tree file is as follows. First a header consisting of information about the status of the tree, the feature-ordering (the permutation from the order in the data file to the order in the tree), and the presence of numeric features is provided\footnote{Although in this header each line starts with '\#', these lines cannot be seen as comment lines.}. Subsequently, unless hashing has been set off explicitly ({\tt -H}), a legenda is given of numeric hash codes for the class names (one unique integer per class) and feature value names (one unique integer per value). Subsequently, the tree's nodes and arcs are given in a proprietary non-indented bracket notation. Starting from the root node, each node is denoted by an opening parenthesis ``('', followed by an integer coding the default class. After this, there is the class distribution list, within curly braces ``\{ \}'', containing a non-empty list of category codes followed by integer counts. After this comes an optional comma-separated list of arcs to child nodes, within ``[ ]'' brackets. An arc is labeled with a coded feature value. The node that the arc leads to again has a class distribution, and any number of child nodes pointed to by arcs. The {\sc ib1} tree constructed from our example data set looks as follows: \begin{footnotesize} \begin{verbatim} # Status: complete # Permutation: < 1, 3, 2 > # Numeric: . # Version 4 (Hashed) # Classes 1 nut 2 screw 3 key 4 pen 5 scissors Features 1 small 2 compact 3 1 4 long 5 none 6 large 7 2 8 other (1{ 1 3, 2 2, 3 3, 4 2, 5 2 }[1(1[3(1[2(1{ 1 3 }) ,4(3{ 3 1 }) ] ) ,5(2[2(2{ 2 1 }) ,4(2{ 2 1 }) ] ) ,7(3[8(3{ 3 1 }) ] ) ] ) ,6(4[3(3[4(3{ 3 1, 4 1 }) ] ) ,5(4[4(4{ 4 1 }) ] ) ,7(5[4(5{ 5 1 }) ,8(5{ 5 1 }) ] ) ] ) ] ) \end{verbatim} \end{footnotesize} The corresponding compressed {\sc igtree} version is considerably smaller. \begin{footnotesize} \begin{verbatim} # Status: pruned # Permutation: < 1, 3, 2 > # Numeric: . # Version 4 (Hashed) # Classes 1 nut 2 screw 3 key 4 pen 5 scissors Features 1 small 2 compact 3 1 4 long 5 none 6 large 7 2 8 other (1{ 1 3, 2 2, 3 3, 4 2, 5 2 }[1(1{ 1 3, 2 2, 3 2 }[3(1{ 1 3, 3 1 }[4(3{ 3 1 }) ] ) ,5(2{ 2 2 }) ,7(3{ 3 1 }) ] ) ,6(4{ 3 1, 4 2, 5 2 }[3(3{ 3 1, 4 1 }) ,7(5{ 5 2 }) ] ) ] ) \end{verbatim} \end{footnotesize} TiMBL tree files generated by versions 1.0 to 3.0 of TiMBL, which do not contain hashed class and value names, are no longer recognized in current TiMBL versions. Backward compatibility to trees generated by versions 1.0 to 3.0 is preserved in TiMBL version 4 up to release 4.3.1. \clearpage \bibliographystyle{fullname} \bibliography{../../ilkbib/ilk} \end{document} LanguageMachines-timbl-642727d/docs/texfiles/Timbl_6.4_Manual.tex000066400000000000000000005153771451477526200245620ustar00rootroot00000000000000% TiMBL 6.4 manual \documentclass{book} \usepackage{epsf} \usepackage{epsfig} \usepackage{a4wide} \usepackage{palatino} \usepackage{fullname} \usepackage{url} \newcommand{\chisq}{{$ \chi^2 $}} \author{Walter Daelemans$*$ \and Jakub Zavrel$\dagger$ \and Ko van der Sloot$\dagger\dagger$\\ \and Antal van den Bosch$\dagger\dagger$\\ \ \\ (*) CLiPS - Computational Linguistics Group\\ Department of Linguistics \\ University of Antwerp\\ \\ ($\dagger$) Textkernel B.V.\\ \\ % (**) Induction of Linguistic Knowledge Research Group\\ % Tilburg center for Cognition and Communication \\ % Tilburg University \\ \\ ($\dagger\dagger$) Centre for Language Studies \\ Radboud University Nijmegen \\ \\ URL: http://ilk.uvt.nl\thanks{This document is available from http://ilk.uvt.nl/downloads/pub/papers/ilk.XXXX.pdf. All rights reserved Centre for Language Studies, Nijmegen \ Induction of Linguistic Knowledge, Tilburg University and CLiPS, University of Antwerp.}} \title{{\huge TiMBL: Tilburg Memory-Based Learner} \\ \vspace*{0.5cm} {\bf version 6.4} \\ \vspace*{0.5cm}{\huge Reference Guide}\\ \vspace*{1cm} {\it ILK Technical Report -- ILK 11-01}} %better paragraph indentation \parindent 0pt \parskip 9pt \begin{document} \pagenumbering{roman} \maketitle \tableofcontents \chapter*{Preface} Memory-Based Learning ({\sc mbl}) is an elegantly simple and robust machine-learning method applicable to a wide range of tasks in Natural Language Processing (NLP). Starting from our research group at Tilburg University, we have been working since the end of the 1980s on the development of Memory-Based Learning techniques and algorithms. The foundations are bundled in \namecite{Daelemans+05}. Section~\ref{furtherreading} provides a historical overview of work on the application of {\sc mbl} in NLP. With the establishment of the ILK (Induction of Linguistic Knowledge) research group in 1997, and with the increasing use of {\sc mbl} at the CNTS (now CLiPS) research group of the University of Antwerp, the need for a well-coded and uniform tool for our main algorithms became more urgent. TiMBL was the result of combining ideas from a number of different {\sc mbl} implementations, cleaning up the interface, and using a whole bag of tricks to make it more efficient. We think it has become a useful tool for NLP research, and, for that matter, for many other domains where classification tasks are learned from examples, so we started to release the software in 1999. With the release of the sixth version of TiMBL we moved to releasing our software under the GPL license, for anyone to use, adapt and improve under the conditions stated in the license. Memory-Based Learning is a direct descendant of the classical $k$-Nearest Neighbor ($k$-NN) approach to classification, which has become known as a powerful pattern classification algorithm for numeric data. In typical NLP learning tasks, however, the focus is on discrete data, very large numbers of examples, and many attributes of differing relevance. Moreover, classification speed is a critical issue in any realistic application of Memory-Based Learning. These constraints demand non-trivial data structures and speedup optimizations for the core $k$-NN classifier. Our approach has resulted in an architecture which compresses the typical flat file organization found in straightforward $k$-NN implementations, into a decision-tree structure. While the decision tree can be used to retrieve the exact $k$-nearest neighbors (as happens in the {\sc ib1} algorithm within TiMBL), it can also be deterministically traversed as in a decision-tree classifier (the method adopted by the {\sc IGTree} algorithm). We believe that our optimizations make TiMBL one of the fastest discrete $k$-NN implementations around. The main effort in the development and maintenance of this software was and continues to be invested by Ko van der Sloot. The code started as a rewrite of {\tt nibl}, a piece of software developed by Peter Berck from a Common Lisp implementation by Walter Daelemans of {\sc ib1-ig}. Some of the index optimizations in TiMBL are due to Jakub Zavrel. The code has benefited substantially from trial, error and scrutiny by all past and present members of the ILK and CLiPS (formerly CNTS) groups in Tilburg and Antwerp. We are furthermore indebted to Ton Weijters of Eindhoven Technical University for his inspirational early work on $k$-NN and for his ideas on {\sc IGTree}. Our sincere thanks go to the many users of TiMBL who have contributed to it immensely by giving us feedback and reporting bugs, and to the two organisations that have supported and enabled its development: NWO, the Netherlands Organization for Scientific Research, and the School of Humanities of Tilburg University. NWO funding has spanned three subsequent periods totalling fourteen years. From 1997 until 2001 development was part of the ``Induction of Linguistic Knowledge'' research project funded by NWO's {\em Pionier}\/ programme. Between 2001 and 2006 it was funded as part of the ``Memory Models of Language'' research project under the NWO {\em Vernieuwingsimpuls}\/ programme, and between 2006 and 2011 as part of the ``Implicit Linguistics'' research project under the NWO Vici programme. The current release (version 6.4) coincides with a release of a Debian Science package for TiMBL. Its most significant change is a multiprocessor option that ``clones'' TiMBL internally so that it can classify a test set on $n$ available processors. An elaborate description of the changes from version 1.0 up to 6.4 can be found in Chapter~\ref{changes}. Although all new features have been tested for some time in our research groups, the software may still contain bugs and inconsistencies in some places. We would appreciate it if you would send bug reports, ideas about enhancements of the software and the manual, and any other comments you might have, to {\tt Timbl@uvt.nl}. This reference guide is structured as follows. In Chapter~\ref{license} you can find the terms of the open source license according to which you are allowed to use TiMBL. The subsequent chapter gives some instructions on how to install the TiMBL package on your computer. Chapter~\ref{changes} lists the changes that have taken place up to the current version. Next, Chapter~\ref{tutorial} offers a quick-start tutorial for readers who want to get to work with TiMBL right away. The tutorial describes, step-by-step, a case study with a sample data set (included with the software) representing the linguistic domain of predicting the diminutive inflection of Dutch nouns. Readers who are interested in the theoretical and technical details of Memory-Based Learning and of this implementation can refer to Chapter~\ref{algorithms}. Chapter~\ref{reference} provides full reference to the command line options of TiMBL and supported file formats. \chapter{GNU General Public License} \label{license} \pagenumbering{arabic} TiMBL is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. TiMBL is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with TiMBL. If not, see $<$http://www.gnu.org/licenses/$>$. In publication of research that makes use of TiMBL 6.4, a citation should be given of: {\em ``Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch (2012). TiMBL: Tilburg Memory Based Learner, version 6.4, Reference Guide. ILK Technical Report XX-XX Available from \\ {\tt http://ilk.uvt.nl/downloads/pub/papers/ilkXXXX.pdf}''} For information about commercial licenses for TiMBL 6.4, contact {\tt Timbl@uvt.nl}, or send your request in writing to: Prof. dr.~Walter Daelemans\\ CLiPS - Language Technology Group\\ Dept. of Linguistics \\ University of Antwerp\\ Prinsstraat 13, L-203, B-2000 Antwerp \\ Belgium \pagestyle{headings} \chapter{Installation} \vspace{-1cm} You can get the TiMBL package as a gzipped tar archive from: {\tt http://ilk.uvt.nl/timbl} Following the links from that page, you can download the file {\tt timbl-latest.tar.gz}. This file contains the complete source code (C++) for the TiMBL program, a few sample data sets, the license, and documentation. The installation should be relatively straightforward on most UNIX systems. To install the package on your computer, unzip the downloaded file ({\tt >} is the command line prompt): {\tt > tar xfz timbl-latest.tar.gz} This will make a directory {\tt timbl-6.4} under your current directory. (The numbering may be higher; the current guide refers to version 6.4). Alternatively you can do: {\tt > gunzip timbl-6.4.tar.gz} and unpack the tar archive: {\tt > tar xf timbl-6.4.tar} Go to the timbl-6.4 directory, and configure the package by typing {\tt > cd timbl-6.4} \\ {\tt > ./configure --prefix=} If you do not use the {\tt --prefix} option, TiMBL will try to install itself in the directory {\tt /usr/local/}. If you do not have {\tt root} access you can specify a different installation location such as {\tt \$HOME/install}. It is not obligatory to install TiMBL, but if you plan to install TiMBL-based extensions such as TimblServer\footnote{\url{http://ilk.uvt.nl/timbl}}, Mbt\footnote{\url{http://ilk.uvt.nl/mbt}}, or Frog\footnote{\url{http://ilk.uvt.nl/frog}}, or you want to build your own extensions using the TiMBL API, installing is the best choice. After {\tt configure} you can build TiMBL: {\tt > make} and (as recommended) install: {\tt > make install } If the process was completed successfully, you should now have an executable file named {\tt timbl} (low caps) in the directory {\tt /bin}, and a static library {\tt libTimbl.a} in the directory {\tt /lib}. Additionally, several demo programs named {\tt api\_test*}, {\tt classify} and {\tt tse} are created in the {\tt ./demos} subdirectory. Within the {\tt } directory a subdirectory is also created: {\tt share/doc/timbl} where the TiMBL 6.4 documentation can be found, and which in turn contains a subdirectory {\tt examples} with example data files. Some of these data sets are used in the Quick Start Section~\ref{tutorial} of this document; other data and source files are referred to in the API documentation. The latter, along with a pdf version of this document, can also be found in the {\tt doc} directory. Note that the API documentation is a beta-state document. TiMBL should now be ready for use. If you want to run the examples and demos from this manual, you should act as follows: \begin{itemize} \item Be sure to add {\tt /bin} to your PATH. In many shells something like {\tt > export PATH=\$PATH:/bin } will do. \item copy all the files from {\tt /share/doc/timbl/examples} to some working location. (By default, TiMBL writes its results to the directory where it finds the data.) \item and test: {\tt cd} to the working location, and then {\tt timbl -f dimin.train -t dimin.test} \end{itemize} If you did not install TiMBL, the executable can be found in the {\tt src} directory of the build. The demo files can be found in the {\tt demo} directory. The email address for problems with the installation, bug reports, comments and questions is {\tt Timbl@uvt.nl}. \chapter{Changes} \label{changes} This chapter gives a brief overview of the changes from all previously released versions (1.0 up to 6.4) for users already familiar with the program. \section{From version 6.3 to 6.4} \begin{itemize} \item With {\tt --clones=}, TiMBL can be told to classify a test set using $n$ processors in parallel. This option will speed up classification near-linearly with respect to $n$, although with larger $n$ the overhead causes the speedup to be increasingly sub-linear. \item We added Jensen-Shannon divergence as a pairwise similarity metric for feature values. With {\sc mvdm} and Jeffrey divergence, TiMBL now offers three class distribution distance functions for value differences. \item Euclidean distance was long overdue as an optional distance function ({\tt -mE}) for numeric features. \item {\tt -w 5} or {\tt -w SD} sets the feature weighting of numeric features to the standard deviation of its values. \item With {\tt --occurrences=\{train|test|both\}}, instances in instance base files can be accompanied by a (long) integer specifying a number of token occurrences of that instance, so that it does not count as a single instance in the TiMBL tree, but as {\tt } instances. These counts can be exclusively present in the training set, in the test set, or in both if a training and test action is invoked at once. \item The verbosity option {\tt +v b} adds overall statistics of IGTree nodes (leafs and non-ending nodes) and branching at each tree level. \item The executable file is now called {\tt timbl} instead of {\tt Timbl} to be more GNU/Linux compliant. Previous users of TiMBL may want to check scripts for this name change. \end{itemize} \section{From version 6.2 to 6.3} \begin{itemize} \item All server-related functionality is removed from TiMBL. A new TimblServer package is available wich provides the same interface as Timbl up to version 6.3, but also adds some extra features, such as running multiple but separate experiments on one TCP port. See the TimblServer package for more details. \item Starting with Timbl 6.3 we support installable packages for Debian and Ubuntu (.deb), RedHat (.rpm) and MacOSX (Fink)\footnote{\url{http://ilk.uvt.nl/timbl-packages}}. \item Some bugs and inconsistencies have been fixed. \end{itemize} \section{From version 6.1 to 6.2} Version 6.2 differs from 6.1 in a great number of internal changes aimed at making the code better maintainable and extendible, in some minor bug fixes, and in the following more prominent changes: \begin{itemize} \item A new distance metric, the Dice coefficient, has been added; the metric can be set with {\tt -mDC}. Analogous to the Levenshtein ({\tt -mL}) metric, the Dice coefficient operates at the feature value level; it computes the overlap in character bigrams of two value strings. \item Value difference matrices, as used by the {\sc mvdm} and Jeffrey divergence distance metrics, can now be written to file, and read into TiMBL, allowing for user-defined value difference metrics to be used. The new command line options are {\tt --matrixout=} and {\tt --matrixin=}. \item The {\sc IGTree} algorithm has been optimized beyond the improvements introduced in version 6.0. With very large training sets, {\sc IGTree} was reported to be exponentially slower in the later stages of training. Trees are now built in near-linear time. \end{itemize} Finally, besides minor bug fixes, a great number of internal changes were made to make the code better maintainable and extendible. \section{From version 6.0 to 6.1} Version 6.1 differs from 6.0 mainly in the changed configuration. It is now based on autotools and is delivered as an installable package. Some bugs have been fixed as well. \section{From version 5.1 to 6.0} Version 6.0 differs from 5.1 firstly in terms of internal changes aimed at increasing classification speed and lowering memory usage, of which the most prominent are \begin{itemize} \item The {\sc IGTree} algorithm has been optimized. Learning has been made more memory-lean, while classification has been optimized so that it is now orders of magnitude faster than before on most data sets. \item {\sc Mvdm} matrices are partly prestored; only the {\sc mvdm} values of pairs of frequent values are precomputed. The threshold frequency $n$ can now be determined with {\tt -c n}. This way memory can be traded for speed, up to a point. The default value 10 remains the recommended one. \end{itemize} Also, two metrics and several verbosity options and other command-line switches are added: \begin{itemize} \item Two distance metrics are added: {\tt -mC} sets the Cosine metric, and {\tt -mL} sets the Levenshtein metric. The latter metric operates at the feature-value level, and thus offers an alternative to the all-or-nothing Overlap metric for string-valued features. \item Class distribution output generated with {\tt +v db} can be normalized so that they add to $1.0$, with the additional {\tt -G} option (or {\tt -G0}). As a simple smoothing option, with {\tt -G1:double} all class votes are incremented by {\tt double} before normalization. For example, {\tt -G1:1} (or {\tt -G1} for short) is ``add one''-smoothing; {\tt -G1:0.5} adds $0.5$ to all class votes. \item With {\tt -Beam=n} (from version 6.2 onwards: {\tt --Beam=n}), where $n$ is an integer, the {\tt +v db} output is constrained to the $n$ classes receiving the highest votes. This special limit is useful in cases in which the {\tt +v db} output, typically used for further processing, generates far too much output in its default unconstrained setting. \item Class distributions are not stored on non-terminal nodes with {\sc IGTree} and {\sc tribl} by default. To revert this default, e.g. to be able to use {\tt +v db} with {\sc IGTree}, the setting {\tt +D} can be used. \item With {\tt -T n}, the user can specify that the $n$th column in the training set of labeled examples contains the label to be predicted, while all other columns represent the input features. By default, the final column is assumed to contain the class labels. \item After classification, TiMBL reports its classification speed at microsecond precision instead of in seconds. \item The verbosity option {\tt +v md} displays the level at which a classification was made by {\sc IGTree} ({\tt -a1}), and whether the class label was obtained from a leaf node or an end node. \item With {\tt -X [file]}, TiMBL dumps its internal TiMBL tree into a file containing an XML tree. This option is analogous to {\tt -I [file]}, which prints a TiMBL tree in TiMBL's proprietary format, the difference being that the latter format can be read into TiMBL again. \item Several minor bugs have been resolved. \end{itemize} \section{From version 5.0 to 5.1} Version 5.1 adds speed and memory improvements that are notable with datasets that have very large amounts of examples, features, feature values, or classes (and, especially, combinations of those). Previous versions exhibited exponential slowdown in some worst cases; this has been largely countered. On the outside, TiMBL has been updated in the following aspects: \begin{itemize} \item TiMBL offers extended performance reporting: next to accuracy it reports on micro and macro-averages of F-score and AUC (area under the ROC-curve) with {\tt +v as}. Optionally, it also shows each individual class' precision, recall (or true positive rate), and false positive rate with {\tt +v cs}. \item TiMBL always uses gain ratio feature weighting as the default case, if not specified by the user, also with the {\sc mvdm} and Jeffrey Divergence similarity metrics. \item Two additional feature orderings for the internal TiMBL trees are added, {\tt -TGxE} and {\tt -TIxE} (gain ratio $\times$ entropy and information gain $\times$ entropy, respectively) to potentially tackle the problem of unbalanced trees. \item Bugs in leave-one-out testing with numeric features and with exemplar weighting were fixed. \end{itemize} \section{From version 4.3 to 5.0} Version 5.0 is the conclusion of a number of recodings (mostly involving more generic treatment of variables to improve robustness, but also the removal of inverted indexing on the internal tree representation) that have changed the internals of TiMBL considerably. On the outside, TiMBL displays the following new characteristics: \begin{itemize} \item Next to the Overlap, {\sc mvdm}, and Numeric distance functions, TiMBL now features the Jeffrey divergence distance function and the Dot-product distance function. \item The exponential-decay distance weighting function can be set using a second parameter, which can change the shape of the function from normal exponential to bell-shaped. \item In addition to the ``binary'' format, TiMBL can now read a more generic sparse data format. This format allows instances to be coded by tuples of $<$ feature number, feature value $>$ where the value can be symbolic or numeric rather than only binary. \item Tree files generated by TiMBL versions 1.*, 2.* and 3.* are no longer supported. \item The command line interface has had the following additions, including the ones reflecting the above changes: \begin{itemize} \item {\tt -mJ} activates the Jeffrey divergence distance metric. \item {\tt -mD} activates the Dot-product distance metric. \item {\tt -dED::} (without whitespace) sets the $\alpha$ and new $\beta$ parameters. If unspecified, as in {\tt -d ED:} or the older (deprecated) {\tt -d ED }, $\beta$ is set to $1.0$. \item {\tt -F Sparse} declares that training and test files are in the sparse $<$ feature number, feature value $>$ tuple-format described in more detail in section~\ref{commandline}. \item {\tt +v k} is a new verbosity option that prints all class distributions per $k$-nearest distance per classified instance in the output file. It works analogous to the {\tt +v n} option, but does not print the neighbors themselves. \end{itemize} \end{itemize} \section{From version 3.0 to 4.3} As the last upgrade of the version 4 strain, version 4.3 added some command line functionality and internal code changes to version 4.2. Minor progressive changes from 4.0 to 4.3 are found at the bottom of this list and are marked as such. \begin{itemize} \item Distance weighting of the $k$ nearest neighbors. This classical exemplar weighting scheme \cite{Dudani76} allows closer nearest neighbors in the $k$ to have a more prominent vote in classification. TiMBL incorporates linear, inversed, and exponential distance weighting. \item Incremental edited memory-based learning with {\sc ib2} \cite{Aha+91}. This incremental version of {\sc ib1} adds instances to memory only when those instances are misclassified by the then-current set of instances in memory. \item Frequency-filtered {\sc mvdm} distance metric. The option, which is not selected by default, is an add-on of the {\sc mvdm} metric, that backs off from the {\sc mvdm} metric to the Overlap distance function whenever one or both in a pair of matched values occurs fewer times in the training material than a user-determined threshold. \item {\sc tribl2}. The {\sc tribl2} algorithm has been implemented as an additional trade-off between {\sc IGTree} and {\sc ib1}. In contrast to {\sc tribl}, {\sc tribl2} uses no threshold parameter. \item Exemplar weighting. TiMBL can read additional numeric exemplar weights (generated externally) when reading a data file, and use these weights during neighbor distance computation in $k$-NN classification. \item Cross-validation testing. Analogous to the leave-one-out testing option, with cross-validation testing it is possible to let TiMBL run systematic tests on different values of parameters, without completely re-initializing the classifier in every fold of the validation experiment. \item The number of concurrent connections to a TiMBL server has been restricted, but can be set to different values. \item The command line interface has had several additions reflecting the above changes, plus one extra verbosity option: \begin{itemize} \item the {\tt -d metriccode} option sets the distance weighting metric. Three metrics are available: inverse distance (code ID), inverse linear (IL), and exponential decay (ED, which takes an extra argument $a$, without whitespace, determining the factor of the exponential function). By default, no distance weighting is used (code Z). See Chapter~\ref{algorithms} for descriptions. \item the {\tt -L n} option sets the frequency threshold in the optional switch (backoff) from {\sc mvdm} or Jeffrey divergence to Overlap; whenever in an {\sc mvdm} or Jeffrey divergence distance computation one or both of a pair of values occur fewer than {\tt n} times, Overlap is used rather than the {\sc mvdm} metric. The default value for {\tt n} is 1 (no switching). \item the {\tt -a 3} or {\tt -a IB2} switch invokes the {\sc ib2} algorithm. This algorithm expects to have the {\tt -b} switch set. \item the {\tt -b n} option sets the number ($n$) of lines counting from the top of the training set file, which form the bootstrap set of memorized instances to which {\sc ib2} will start adding instances incrementally. \item the {\tt -a 4} or {\tt -a TRIBL2} switch invokes the {\sc tribl2} algorithm. \item the {\tt -C n} switch (default: {\tt n} set to 10) restricts the number of concurrent connections to a TiMBL server (cf. the {\tt -S} switch). \item the {\tt +v/-v} option has {\tt cm} as a new optional argument; it returns the confusion matrix, obtained after testing, between predicted and actual classes in the test data. \end{itemize} \item The ``programmer's reference'' or API section has been separated from this manual. This new API, describing the underlying structure of TiMBL, is available as a separate document in the TiMBL software distribution. \item Two bugs relating to a type of sparse data problem have been resolved. The first involved leave-one-out experiments on data sets with features that have values that occur only once in the training data. The second bug occurred with the use of the {\tt -F Binary} option with the same type of data. \item {\bf [4.1]} Exemplar weights are stored in the TiMBL-tree. \item {\bf [4.1]} The core representation of TiMBL-trees has been modified, causing no changes at the surface except that the {\sc tribl} variant uses less memory. \item {\bf [4.2]} Feature value and class information in the internal TiMBL tree is hashed, by default, except with binary features. Hashing can be explicitly set on or off through the flag {\tt +H} or {\tt -H}. \item {\bf [4.2]} The discretization of numeric features, used for computing feature weights, has changed from linear binning between minimum and maximum values, to equal-content binning. \item {\bf [4.2]} Tie resolution between equal class distributions in the nearest neighbors set is resolved by first expanding the $k$ by one value. If the tie persists after the enlargement of the nearest neighbor set, the original tie resolution method is applied. \item {\bf [4.3]} Internal changes in the code (with no effect on learning and classification functionality) have been implemented with respect to namespaces. \item {\bf [4.3]} A progress marker (one dot per 10 seconds) in computationally intensive operations on the internal representation of the instance base (e.g. pruning {\sc IGTree}s) is added in TiMBL's screen output. \item A number of bugs have been fixed, notably to handle erroneous input more robustly. \end{itemize} \section{From version 2.0 to 3.0} \begin{itemize} \item Server functionality. Apart from the standard processing of test items from a file, alternatively you can now specify a portnumber with {\tt -S portnumber} to open a socket and send commands for classification of test patterns or change of parameters to it. A sample client program is included in the distribution. This allows fast response times when small amounts of test material are presented at various intervals. It also opens the possibility of having large numbers of ``classification agents'' cooperate in real time, or of classication of the same data with different parameters. \item Leave-one-out testing. To get an estimate of the classification error, without setting aside part of one's data as a test set, one can now test by ``leave-one-out'' ({\tt -t leave\_one\_out}), in effect testing on every case once, while training on the rest of the cases, without completely re-initializing the classifier for every test case. \item Support for sparse binary features. For tasks with large numbers of sparse binary features, TiMBL now allows for an input format which lists only the ``active'' features, avoiding the listing of the many (zero-valued) features for each case. This format is described in Section~\ref{binaryformat}. \item Additional feature weighting metrics. We have added chi-squared and shared variance measures as weighting schemes. These weighting metrics are sometimes more robust to large numbers of feature values and other forms of data sparseness. \item Different metrics (Overlap, {\sc mvdm} or Numeric) can be applied to different features. \item The command line interface has slightly been cleaned up, and re-organized: \begin{itemize} \item The {\tt -m metricnumber} switch to choose metrics has been replaced by the use of a specification string following {\tt -m}. E.g.~you can specify to use {\sc mvdm} as the default metric, but use Overlap on features 5-7,9, Numeric on feature 1, and ignore feature 10 ({\tt -m M:O5-7,9:N1:I10}). \item All of the output needed for analysing the matching of nearest neighbors has been moved to the verbosity setting. \item Verbosity levels and some other options can be switched on {\tt +v} and off {\tt -v}, even between different classification actions. \item Because of the large amount of verbosity levels, the {\tt +v} option takes mnemonic abbreviations as arguments instead of numeric verbosity levels. Although the old (numeric) format is still supported, it's use is not encouraged as it will disappear in future versions. \end{itemize} \item Because of significant optimizations in the nearest neighbor search, the default is no longer to use inverted indexes. These can however still be turned on by using the {\tt +-} switch on the command line. \item You can now choose the output filename or have it generated by TiMBL on the basis of the test filename and the parameters. \item You can use TiMBL in a pipeline of commands by specifying '-' as either input, output or both. \item Several problems with the display of nearest neighbors in the output have been fixed. \item The API has been adapted a bit to allow more practical use of it. \end{itemize} \section{From version 1.0 to 2.0} \begin{itemize} \item We have added a new algorithm: {\sc tribl}, a hybrid between the fast {\sc IGTree} algorithm and real nearest neighbor search (for more details, see~\ref{tribl}, or~\namecite{Daelemans+97d}). This algorithm is invoked with the {\tt -a 2} switch and requires the specification of a so-called {\sc tribl}-offset, the feature where {\sc IGTree} stops and case bases are stored under the leaves of the constructed tree. \item Support for numeric features. Although the package has retained its focus on discrete features, it can now also process numeric features, scale them, and compute feature weights on them. You specify which features are numeric with the {\tt -N} option on the command line. \item The organization of the code is much more object-oriented than in version 1.0. %The main benefit of this is that: \item A Memory-Based Learning API is made available. You can define Memory-Based classification objects in your own C++ programs and access all of the functionality of TiMBL by linking to the TiMBL library. \item It has become easier to examine the way decisions are made from nearest neighbors, because several verbosity-levels allow you to dump similarity values ({\tt -D}), distributions ({\tt -v 16}), and nearest neighbor sets ({\tt -v 32}) to the output file. The {\tt -d} option for writing the distributions no longer exists. \item Better support for the manipulation of {\sc mvdm} matrices. Using the {\tt -U} and {\tt -u} options it is now possible to respectively save and read back value difference matrices (see Section~\ref{mvdmformat}). \item Both ``pre-stored'' and ``regular'' {\sc mvdm} experiments now generate filenames with ``{\tt mvd}'' in the suffix. This used to be ``{\tt pvd}'' and ``{\tt mvd}'' respectively. \item a number of minor bugs have been fixed. \end{itemize} \chapter{Quick-start Tutorial} \label{tutorial} This quick-start tutorial is meant to get you started with TiMBL right away. We discuss how to format the data of a task to serve as training examples, which choices can be made during the construction of the classifier, how various choices can be evaluated in terms of their generalization accuracy, and various other practical issues. The reader who is interested in more background information on TiMBL implementation issues and a formal description of Memory-Based Learning, is advised to read Chapter~\ref{algorithms}. Memory-Based Learning ({\sc mbl}) is based on the idea that intelligent behavior can be obtained by analogical reasoning, rather than by the application of abstract {\em mental rules} as in rule induction and rule-based processing. In particular, {\sc mbl} is founded in the hypothesis that the extrapolation of behavior from stored representations of earlier experience to new situations, based on the similarity of the old and the new situation, is of key importance. {\sc mbl} algorithms take a set of examples (fixed-length patterns of feature-values and their associated class) as input, and produce a {\em classifier} which can classify new, previously unseen, input patterns. Although TiMBL was designed with linguistic classification tasks in mind, it can in principle be applied to any kind of classification task with symbolic or numeric features and discrete (non-continuous) classes for which training data is available. As an example task for this tutorial we go through the application of TiMBL to the prediction of Dutch diminutive suffixes. The necessary data sets are included in the TiMBL distribution, so you can replicate the examples given below on your own system. \section{Data} The operation of TiMBL will be illustrated below by means of a real natural language processing task: prediction of the diminutive suffix form in Dutch~\cite{Daelemans+97b}. In Dutch, a noun can receive a diminutive suffix to indicate {\em small size} literally or metaphorically attributed to the referent of the noun; e.g. {\em mannetje} means {\em little man}. Diminutives are formed by a productive morphological rule which attaches a form of the Germanic suffix {\em -tje} to the singular base form of a noun. The suffix shows variation in its form (Table \ref{variation}). The task we consider here is to predict which suffix form is chosen for previously unseen nouns on the basis of their form. \begin{table}[ht] \begin{center} \begin{tabular}{l|l|l} Noun & Form & Suffix \\ \noalign{\smallskip} \hline \noalign{\smallskip} huis (house) & huisje & {\em -je} \\ man (man) & mannetje & {\em -etje\/} \\ raam (window) & raampje & {\em -pje\/} \\ woning (house) & woninkje & {\em -kje\/} \\ baan (job) & baantje & {\em -tje\/} \\ \end{tabular} \caption{Allomorphic variation in Dutch diminutives.}\label{variation} \end{center} \end{table} For these experiments, we collect a representation of nouns in terms of their syllable structure as training material\footnote{These words were collected form the {\sc celex} lexical database~\cite{Baayen+93}.}. For each of the last three syllables of the noun, four different features are collected: whether the syllable is stressed or not (values - or +), the string of consonants before the vocalic part of the syllable (i.e. its onset), its vocalic part (nucleus), and its post-vocalic part (coda). Whenever a feature value is not present (e.g. a syllable does not have an onset, or the noun has less than three syllables), the value `=' is used. The class to be predicted is either E ({\em -etje}), T ({\em -tje}), J ({\em -je}), K ({\em -kje}), or P ({\em -pje}). Some examples are given below (the word in the rightmost column is only provided for convenience and is not used). The values of the syllabic content features are given in phonetic notation. \begin{table}[ht] \begin{center} \begin{tabular}{cccccccccccc|l|l|l} + & b & i & = & - & z & @ & = & - & m & A & nt & J & {\em biezenmand} \\ = & = & = & = & = & = & = & = & + & b & I & x & E & {\em big}\\ = & = & = & = & + & b & K & = & - & b & a & n & T & {\em bijbaan}\\ = & = & = & = & + & b & K & = & - & b & @ & l & T & {\em bijbel}\\ \end{tabular} \end{center} \end{table} Our goal is to use TiMBL in order to train a classifier that can predict the class of new, previously unseen words as correctly as possible, given a set of training examples that are described by the features given above. Because the basis of classification in TiMBL is the storage of all training examples in memory, a test of the classifier's accuracy must be done on a separate test set. We will call these datasets {\tt dimin.train} and {\tt dimin.test}, respectively. The training set {\tt dimin.train} contains 2999 words and the test set contains 950 words, none of which are present in the training set. Although a single train/test partition suffices here for the purposes of explanation, it does not factor out the bias of choosing this particular split. Unless the test set is sufficiently large, a more reliable generalization accuracy measurement is used in real experiments, e.g.~10-fold cross-validation~\cite{Weiss+91}. This means that 10 separate experiments are performed, and in each ``fold'' 90\% of the data is used for training and 10\% for testing, in such a way that each instance is used as a test item exactly once. Another reliable way of testing the real error of a classifier is leave-one-out~\cite{Weiss+91}. In this approach, every data item in turn is selected once as a test item, and the classifier is trained on all remaining items. Accuracy of the classifier is then the number of data items correctly predicted. With the option {\tt -t leave\_one\_out}, this testing methodology is used by TiMBL. We will use this option in the tutorial on the file {\tt dimin.data}, the union of {\tt dimin.train} and {\tt dimin.test}. \section{Using TiMBL} Different formats are allowed for training and test data files. TiMBL is able to guess the type of format in most cases. We will use comma-separated values here, with the class as the final value. This format is called C4.5 format in TiMBL because it is the same as that used in Quinlan's well-known C4.5 program for learning decision trees~\cite{Quinlan93}. See Section~\ref{fileformats} for more information about this and other file formats. An experiment is started by executing TiMBL with the two files ({\tt dimin.train} and {\tt dimin.test}) as arguments (``$>$'' is the command line prompt): {\footnotesize \begin{verbatim} > timbl -f dimin.train -t dimin.test \end{verbatim} } Upon completion, a new file has been created with name {\small\tt dimin.test.IB1.O.gr.k1.out}, which is identical to the input test file except that an extra comma-separated column is added with the class predicted by TiMBL. The name of the file provides information about the {\sc mbl} algorithms and metrics used in the experiment (the default values in this case). We will describe these shortly. Apart from the result file, information about the operation of the algorithm is also sent to the standard output. It is therefore advisable to redirect the output to a file in order to make a log of the results. {\footnotesize \begin{verbatim} > timbl -f dimin.train -t dimin.test > dimin-exp1 \end{verbatim} } The defaults used in this case work reasonably well for most problems. We will now provide a point by point explanation of what goes on in the output. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} TiMBL 6.4.2 (c) ILK 1998 - 2011. Tilburg Memory Based Learner Induction of Linguistic Knowledge Research Group, Tilburg University CLiPS Computational Linguistics Group, University of Antwerp Mon Apr 23 15:43:26 2012 Examine datafile 'dimin.train' gave the following results: Number of Features: 12 InputFormat : C4.5 \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} TiMBL has detected 12 features and the C4.5 input format (comma-separated features, class at the end). %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Phase 1: Reading Datafile: dimin.train Start: 0 @ Mon Oct 19 21:30:00 2009 Finished: 2999 @ Mon Oct 19 21:30:00 2009 Calculating Entropy Mon Oct 19 21:30:00 2009 Lines of data : 2999 DB Entropy : 1.6178929 Number of Classes : 5 Feats Vals InfoGain GainRatio 1 3 0.030971064 0.024891536 2 50 0.060860038 0.027552191 3 19 0.039562857 0.018676787 4 37 0.052541227 0.052620750 5 3 0.074523225 0.047699231 6 61 0.10604433 0.024471911 7 20 0.12348668 0.034953203 8 69 0.097198760 0.043983864 9 2 0.045752381 0.046816705 10 64 0.21388759 0.042844587 11 18 0.66970458 0.18507018 12 43 1.2780762 0.32537181 Feature Permutation based on GainRatio/Values : < 9, 5, 11, 1, 12, 7, 4, 3, 10, 8, 2, 6 > \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} Phase 1 is the training data analysis phase. Time stamps for start and end of analysis are provided. Some preliminary analysis of the training data is done: number of training items, number of classes, entropy of the training data. For each feature, the number of values, and four variants of an information-theoretic measure of feature relevance are given. These are used both for memory organization during training and for feature relevance weighting during testing (see Chapter~\ref{algorithms}). Finally, an ordering (permutation) of the features is given. This ordering is used for building the tree-index to the case-base. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Phase 2: Learning from Datafile: dimin.train Start: 0 @ Mon Oct 19 21:30:00 2009 Finished: 2999 @ Mon Oct 19 21:30:00 2009 Size of InstanceBase = 19231 Nodes, (769240 bytes), 49.77 % compression Examine datafile 'dimin.test' gave the following results: Number of Features: 12 InputFormat : C4.5 \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} Phase 2 is the learning phase: all training items are stored in an efficient way in memory for use during testing. Again timing information (real time) is provided, as well as information about the size of the data structure representing the stored examples and the amount of compression achieved. %\vspace{0.5cm} %\rule{\textwidth}{0.5mm} {\footnotesize \begin{verbatim} Starting to test, Testfile: dimin.test Writing output in: dimin.test.IB1.O.gr.k1.out Algorithm : IB1 Global metric : Overlap Deviant Feature Metrics:(none) Weighting : GainRatio Feature 1 : 0.024891535617620 Feature 2 : 0.027552191321752 Feature 3 : 0.018676787182524 Feature 4 : 0.052620750282779 Feature 5 : 0.047699230752236 Feature 6 : 0.024471910753751 Feature 7 : 0.034953203413051 Feature 8 : 0.043983864437713 Feature 9 : 0.046816704745507 Feature 10 : 0.042844587034556 Feature 11 : 0.185070180760327 Feature 12 : 0.325371814230901 Tested: 1 @ Fri Dec 24 20:27:07 2010 Tested: 2 @ Fri Dec 24 20:27:07 2010 Tested: 3 @ Fri Dec 24 20:27:07 2010 Tested: 4 @ Fri Dec 24 20:27:07 2010 Tested: 5 @ Fri Dec 24 20:27:07 2010 Tested: 6 @ Fri Dec 24 20:27:07 2010 Tested: 7 @ Fri Dec 24 20:27:07 2010 Tested: 8 @ Fri Dec 24 20:27:07 2010 Tested: 9 @ Fri Dec 24 20:27:07 2010 Tested: 10 @ Fri Dec 24 20:27:07 2010 Tested: 100 @ Fri Dec 24 20:27:07 2010 Ready: 950 @ Fri Dec 24 20:27:07 2010 Seconds taken: 0.0678 (14003.54 p/s) overall accuracy: 0.967368 (919/950), of which 39 exact matches There were 5 ties of which 4 (80.00%) were correctly resolved \end{verbatim} } %\rule{\textwidth}{0.5mm} %\vspace{0.5cm} In Phase 3, the trained classifier is applied to the test set. Because we have not specified which algorithm to use, the default settings are used ({\sc ib1} with information-theoretic feature weighting). This algorithm computes the similarity between a test item and each training item in terms of {\em weighted overlap}: the total difference between two patterns is the sum of the relevance weights of those features which are not equal. The class for the test item is decided on the basis of the least distant item(s) in memory. To compute relevance, Gain Ratio is used (an information-theoretic measure, see Section~\ref{infogain}). Time stamps indicate the progress of the testing phase. Finally, accuracy on the test set is logged, and the number of exact matches\footnote{An exact match in this experiment can occur when two different nouns have the same feature-value representation.} and ties (two or more classes are equally frequent in the nearest neighbor set). In this experiment, the diminutive suffix form of 96.7\% of the new words was correctly predicted. Train and test set overlap in 39 items, and the algorithm had to break five ties, four of which were broken correctly. The meaning of the output file names can be explained now:\\ {\tt dimin.test.IB1.O.gr.k1.out} means output file ({\tt .out}) for {\tt dimin.test} with algorithm {\sc mbl} (={\sc ib1}), similarity computed as {\em weighted overlap} ({\tt .O}), relevance weights computed with {\em gain ratio} ({\tt .gr}), and number of most similar memory patterns on which the output class was based equal to 1 ({\tt .k1}). \section{Algorithms and metrics} A precise discussion of the different algorithms and metrics implemented in TiMBL is given in Chapter~\ref{algorithms}. We will discuss the effect of the most important ones on our data set. A first choice in algorithms is between using {\sc ib1} and {\sc igtree}. In the trade-off between generalization accuracy and efficiency, {\sc ib1} usually, but not always, leads to more accuracy at the cost of more memory and slower computation, whereas {\sc igtree} is a fast heuristic approximation of {\sc ib1}, but sometimes less accurate. The {\sc IGTree} algorithm is used when {\tt -a 1} is given on the command line, whereas the {\sc ib1} algorithm used above (the default) would have been specified explicitly by {\tt -a 0}. {\footnotesize \begin{verbatim} > timbl -a1 -f dimin.train -t dimin.test \end{verbatim}} We see that {\sc IGTree} performs only slightly worse (96.6\%) than {\sc ib1} (96.8\%) for this train-test partitioning of the data --- it uses less memory and is faster, however. When using the {\sc ib1} algorithm, there is a choice of metrics for influencing the definition of similarity. With {\em weighted overlap}, each feature is assigned a weight, determining its relevance in solving the task. With the {\em modified value difference metric} ({\sc mvdm}), each pair of values of a particular feature is assigned a value difference. The intuition here is that in our diminutive problem, for example, the codas $n$ and $m$ should be regarded as being more similar than $n$ and $p$. These pair-wise differences are computed for each pair of values in each feature (see Section~\ref{mvdm}). Selection between weighted overlap and {\sc mvdm} is done by means of the {\tt -mM} parameter. The following selects {\sc mvdm}, whereas {\tt -mO} ({\em weighted overlap}) is the default. {\footnotesize \begin{verbatim} > timbl -mM -f dimin.train -t dimin.test \end{verbatim} } Especially when using {\sc mvdm}, but also in other cases, it may be useful to extrapolate not just from the most similar example in memory, which is the default, but from several. This can be achieved by using the $-k$ parameter followed by the wanted number of nearest neighbors. E.g., the following applies {\sc ib1} with the {\sc mvdm} metric, with extrapolation from the 5 nearest neighbors. {\footnotesize \begin{verbatim} > timbl -mM -k5 -f dimin.train -t dimin.test \end{verbatim} } Whenever more than one nearest neighbor is taken into account for extrapolation, it may be useful to weigh the influence of the neighbors on the final decision as a function of their distance from the test item. Several possible implementations of this distance function are provided. E.g., the following provides inverse distance: {\footnotesize \begin{verbatim} > timbl -mM -k5 -dID -f dimin.train -t dimin.test \end{verbatim} } Within the {\sc ib1} {\em weighted overlap}\/ option, the default feature weighting method is gain ratio. Other feature relevance weighting methods are available as well. By setting the parameter {\tt -w} to 0, an {\em unweighted overlap}\/ definition of similarity is created where each feature is considered equally relevant. In that case, similarity reduces to the number of equal values in the same position in the two patterns being compared. As an alternative weighting, users can provide their own weights by using the {\tt -w} parameter with a filename in which the feature weights are stored (see Section~\ref{weightformat} for a description of the format of the weights file). \begin{table} \begin{center} \begin{tabular}{l|rrrr} & no weight & gain & information & chi \\ & (overlap) & ratio & gain & squared \\ \noalign{\smallskip} \hline \noalign{\smallskip} Overlap, $-k1$ & 86.4 & 96.7 & 96.6 & 96.6 \\ Overlap, $-k3$ & 73.1 & 96.4 & 96.8 & 96.9 \\ Overlap, $-k5$ & 62.6 & 95.4 & 96.1 & 96.1 \\ \hline \noalign{\smallskip} {\sc mvdm}, $-k1$ & 95.8 & 96.3 & 96.1 & 96.2 \\ {\sc mvdm}, $-k3$ & 97.4 & 97.6 & 97.6 & 97.6 \\ {\sc mvdm}, $-k5$ & {\bf 97.8} & 97.7 & 97.7 & 97.7 \\ \hline \noalign{\smallskip} \end{tabular} \caption{Some results for diminutive prediction.} \label{diminresults} \end{center} \end{table} Table \ref{diminresults} shows a small matrix indicating the effect of distance metric (Overlap versus {\sc mvdm}) and weighting method choice on generalization accuracy, using the same training and test set as before, and increasing $k$ from 1 to 3 and 5. While increasing $k$ leads to a deterioration of generalization accuracy with the Overlap function, it leads to improvements with {\sc mvdm}. Another clear contrast is that the absence of feature weighting leads to the lowest scores with the Overlap function, and the highest score with {\sc mvdm} and $k=5$. Given that TiMBL offers several more hyperparameters than only $k$, the distance metric, and the feature weighting metric, it should be obvious that even with a single training and test set experiment, a large experimental matrix can be explored. Unfortunately, the location of the cell with the highest number in this matrix cannot be predicted upfront. It is therefore useful to try out a large set of reasonable combinations of options by cross-validation on the training data to achieve best results with {\sc mbl} \cite{VandenBosch04b}. The option {\tt -t @f} where {\tt f} is the name of a file, allows you to predefine various combinations of options to be tested and test them without having the training stages repeated each time. See Chapter \ref{commandline}. \section{More options} Several input and output options exist to make life easier while experimenting. See Chapter~\ref{commandline} for a detailed description of these options. One especially useful option for testing linguistic hypotheses is the ignore option, which allows you to skip certain features when computing similarity. E.g. if we want to test the hypothesis that only the rime (nucleus and coda) and the stress of the last syllable are actually relevant in determining the form of the diminutive suffix, we can execute the following with the previously best parameter settings to disregard all but the fourth-last and the last two features. As a result we get an accuracy of 97.1\%. {\footnotesize \begin{verbatim} > timbl -mM:I1-8,10 -k5 -w0 -f dimin.train -t dimin.test \end{verbatim} } The {\tt +/-v} (verbosity) option allows you to control the amount of information that is generated in the output, ranging from nearly nothing ({\tt +v s}) to a lot ({\tt +v as+cs+di+db+n+k}). Specific verbosity settings exist for dumping option settings ({\tt +v o}), feature relevance weights (default), value-class conditional probabilities ({\tt +v p}), exact matches ({\tt +v e}), distributions ({\tt +v db}), a confusion matrix ({\tt +v cm}), advanced statistics besides accuracy: micro-average and macro-average F-score and AUC ({\tt +v as}), per-class advanced statistics ({\tt +v cs}), the nearest neighbors on which decision are based ({\tt +v n}), just the class distributions per $k$-nearest distance per classified instance ({\tt +v k}), or the distances to the nearest neighbor ({\tt +v di}). E.g. the following command results in an output file with distributions. {\footnotesize \begin{verbatim} > timbl +v db -f dimin.train -t dimin.test \end{verbatim} } The resulting output file {\tt dimin.test.IB1.O.gr.k1.out} contains lines like the following. {\footnotesize \begin{verbatim} +,t,L,=,-,m,@,=,-,l,I,N,E,E { E 1.00000 } =,=,=,=,=,=,=,=,+,pr,O,p,J,J { E 3.00000, J 12.0000 } =,=,=,=,=,=,=,=,+,w,e,t,J,J { J 2.00000 } =,=,=,=,+,t,L,n,-,h,L,s,J,J { J 1.00000 } =,=,=,=,=,=,=,=,+,t,L,n,T,T { T 1.00000 } =,=,=,=,=,=,=,=,+,z,o,m,P,P { P 3.00000 } +,d,a,=,-,m,@,s,-,kr,A,ns,J,J { J 1.00000 } =,=,=,=,+,=,a,rd,-,m,A,n,E,E { E 2.00000 } =,=,=,=,=,=,=,=,+,f,M,n,T,T { T 43.0000, E 20.0000 } -,d,u,=,-,k,@,=,-,m,A,nt,J,J { J 1.00000 } \end{verbatim} } This information can e.g. be used to assign a certainty to a decision of the classifier, or to make available a second-best back-off option. Another verbosity option, {\tt +v di}, displays the distance to the nearest neighbor: {\footnotesize \begin{verbatim} > timbl +v di -f dimin.train -t dimin.test +,l,a,=,-,d,@,=,-,k,A,st,J,J 0.070701 -,s,i,=,-,f,E,r,-,st,O,k,J,J 0.000000 =,=,=,=,=,=,=,=,+,sp,a,n,T,T 0.042845 =,=,=,=,=,=,=,=,+,st,o,t,J,J 0.042845 =,=,=,=,+,sp,a,r,-,b,u,k,J,J 0.024472 +,h,I,N,-,k,@,l,-,bl,O,k,J,J 0.147489 -,m,e,=,-,d,A,l,+,j,O,n,E,E 0.182421 -,sn,u,=,-,p,@,=,+,r,K,=,T,T 0.046229 =,=,=,=,=,=,=,=,+,sp,A,N,E,E 0.042845 +,k,a,=,-,k,@,=,-,n,E,st,J,J 0.114685 \end{verbatim} } This can be used to study how very similar instances (low distance) and less similar patterns (higher distance) are used in the process of generalization. The listing of nearest neighbors is useful for the analysis of the behavior of a classifier. It can be used to interpret why particular decisions or errors occur. {\footnotesize \begin{verbatim} > timbl +v n+k -mM -k3 -w0 -f dimin.train -t dimin.test +,m,I,=,-,d,A,G,-,d,},t,J,J { J 3.00000 } # k=1, 1 Neighbor(s) at distance: 0.99179269134432 # +,p,a,=,-,t,@,rs,-,f,A,t,{ J 1.00000 } # k=2, 1 Neighbor(s) at distance: 0.99458957262696 # +,h,o,=,-,n,@,G,-,b,A,k,{ J 1.00000 } # k=3, 1 Neighbor(s) at distance: 1.0088291749842 # +,h,E,r,-,d,@,rs,-,t,A,s,{ J 1.00000 } -,t,@,=,-,l,|,=,-,G,@,n,T,T { T 3.00000 } # k=1, 1 Neighbor(s) at distance: 0.33024081383366 # -,x,@,=,+,h,|,=,-,G,@,n,{ T 1.00000 } # k=2, 1 Neighbor(s) at distance: 0.49144604610567 # -,d,@,r,-,w,a,=,-,G,@,n,{ T 1.00000 } # k=3, 1 Neighbor(s) at distance: 0.56944572926932 # -,st,@,=,-,l,I,=,-,N,@,=,{ T 1.00000 } \end{verbatim} } A confusion matrix, printed when the {\tt +v cm} option is selected, can bring to light specific errors of the classifier that would not be apparent from the overall accuracy. Applied to the diminutive data, the following confusion matrix is computed and printed: {\footnotesize \begin{verbatim} > timbl +v cm -f dimin.train -t dimin.test Confusion Matrix: T E J P K ----------------------------------- T | 453 0 2 0 0 E | 0 87 4 1 8 J | 1 5 346 0 0 P | 0 3 0 24 0 K | 0 7 0 0 9 -*- | 0 0 0 0 0 \end{verbatim} } The confusion matrix associates the class predicted by TiMBL (vertically) with the real class of the test items given (horizontally). All cells outside the diagonal contain errors of one class being mistaken for another. For example, the K class ({\em -kje}) is mispredicted seven times as class E ({\em -etje}). (The bottom line, labeled with {\tt -*-}, would contain aggregate counts of classes occuring in the test data that did not occur in the training data. In the diminutive data this does not occur.) In general, a confusion matrix allows a more fine-grained analysis of experimental results and better experimental designs (some parameter settings may work for some classes but not for others, or some may improve recall, and others precision, e.g.). From such a matrix, not only accuracy can be derived, but also a number of additional metrics that have become popular in machine learning, information retrieval, and subsequently also in computational linguistics: {\em recall}, {\em precision}, and their harmonic mean {\em F-score}, as well as {\em true positive rate}, {\em false positive rate}, and their joint measure {\em AUC} in ROC space. The details of these advanced statistics are given in Section~\ref{advancedstats}. They can be reported by TiMBL using the {\tt +v as} and {\tt +v cs} verbosity options: {\footnotesize \begin{verbatim} > timbl +v as+cs -f dimin.train -t dimin.test Scores per Value Class: class | TP FP TN FN precision recall(TPR) FPR F-score AUC T | 453 1 494 2 0.99780 0.99560 0.00202 0.99670 0.99679 E | 87 15 835 13 0.85294 0.87000 0.01765 0.86139 0.92618 J | 346 6 592 6 0.98295 0.98295 0.01003 0.98295 0.98646 P | 24 1 922 3 0.96000 0.88889 0.00108 0.92308 0.94390 K | 9 8 926 7 0.52941 0.56250 0.00857 0.54545 0.77697 F-Score beta=1, microav: 0.967160 F-Score beta=1, macroav: 0.861914 AUC, microav: 0.980138 AUC, macroav: 0.926060 overall accuracy: 0.967368 (919/950), of which 39 exact matches There were 5 ties of which 4 (80.00%) were correctly resolved \end{verbatim} } We hope that this tutorial has made it clear that, once you have coded your data in fixed-length feature-value patterns, it should be relatively straightforward to get the first results using TiMBL. You can then experiment with different metrics and algorithms to try and further improve your results. \chapter{Memory-based learning algorithms} \label{algorithms} TiMBL is a program implementing several memory-based learning algorithms. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. The main differences among the algorithms incorporated in TiMBL lie in: \begin{itemize} \item The definition of {\em similarity}, \item The way the instances are stored in memory, and \item The way the search through memory is conducted. \end{itemize} In this chapter, various choices for these issues are described. We start in Section~\ref{mbl} with a formal description of the basic memory-based learning algorithm, i.e.~a nearest neighbor search. We then introduce different distance metrics, such as Information Gain weighting, which allows us to deal with features of differing importance, and the Modified Value Difference metric, which allows us to make a graded guess of the match between two different symbolic values, and describe the standard versus three distance-weighted versions of the class voting mechanism of the nearest neighbor classifier. In Section~\ref{indexing}, we give a description of various algorithmic optimizations for nearest neighbor search. Sections~\ref{igtree} to~\ref{ib2} describe three variants of the standard nearest neighbor classifier implemented within TiMBL, that optimize some intrinsic property of the standard algorithm. First, in Section~\ref{igtree}, we describe {\sc IGTree}, which replaces the exact nearest neighbor search with a very fast heuristic that exploits the difference in importance between features. Second, in Section~\ref{tribl}, we describe the {\sc tribl} algorithm, which is a hybrid between {\sc IGTree} and nearest neighbor search. Third, Section~\ref{ib2} describes the {\sc ib2} algorithm, which incrementally and selectively adds instances to memory during learning. The chapter is concluded by Section~\ref{furtherreading}, which provides an overview of further reading into theory and applications of memory-based learning to natural language processing tasks. \section{Memory-based learning} \label{mbl} Memory-based learning is founded on the hypothesis that performance in cognitive tasks is based on reasoning on the basis of similarity of new situations to {\em stored representations of earlier experiences}, rather than on the application of {\em mental rules}\/ abstracted from earlier experiences (as in rule induction and rule-based processing). The approach has surfaced in different contexts using a variety of alternative names such as similarity-based, example-based, exemplar-based, analogical, case-based, in\-stance-based, and lazy learning~\cite{Stanfill+86,Aha+91,Cost+93,Kolodner93,Aha97a}. Historically, memory-based learning algorithms are descendants of the $k$-nearest neighbor (henceforth $k$-{\sc nn}) algorithm \cite{Cover+67,Devijver+82,Aha+91}. An {\sc mbl} system, visualized schematically in Figure~\ref{mbl-method}, contains two components: a {\em learning component}\/ which is memory-based (from which {\sc mbl} borrows its name), and a {\em performance component}\/ which is similarity-based. The learning component of {\sc mbl} is memory-based as it involves adding training instances to memory (the {\em instance base} or case base); it is sometimes referred to as `lazy' as memory storage is done without abstraction or restructuring. An instance consists of a fixed-length vector of $n$ feature-value pairs, and an information field containing the classification of that particular feature-value vector. In the performance component of an {\sc mbl} system, the product of the learning component is used as a basis for mapping input to output; this usually takes the form of performing classification. During classification, a previously unseen test example is presented to the system. The similarity between the new instance $X$ and all examples $Y$ in memory is computed using some {\em distance metric} $\Delta(X,Y)$. The extrapolation is done by assigning the most frequent category within the found set of most similar example(s) (the $k$-nearest neighbors) as the category of the new test example. In case of a tie among categories, a tie breaking resolution method is used. This method is described in subsection~\ref{tiebreaking}. \begin{figure}[htb] \begin{center} \leavevmode \epsfxsize=8cm \epsffile{mble-method.eps} \caption{General architecture of an {\sc mbl} system. } \label{mbl-method} \end{center} \end{figure} \subsection{The Overlap metric} \label{overlap} The most basic metric that works for patterns with symbolic features is the {\bf Overlap metric}\footnote{This metric is also referred to as Hamming distance, Manhattan metric, city-block distance, or L1 metric.} given in Equations~\ref{distance} and~\ref{overlapeq}; where $\Delta(X,Y)$ is the distance between instances $X$ and $Y$, represented by $n$ features, and $\delta$ is the distance per feature. The distance between two patterns is simply the sum of the differences between the features. The $k$-{\sc nn} algorithm with this metric is called {\sc ib1} \cite{Aha+91}. \begin{equation} \Delta(X,Y) = \sum_{i=1}^{n} \delta(x_{i},y_{i}) \label{distance} \end{equation} where: \begin{equation} \delta(x_{i}, y_{i}) = \left\{ \begin{array}{ll} abs(\frac{x_{i}-y_{i}}{max_{i}-min_{i}}) & \mbox{if numeric, else}\\ 0 & \mbox{if $x_{i} = y_{i}$}\\ 1 & \mbox{if $x_{i} \neq y_{i}$}\\ \end{array} \right. \label{overlapeq} \end{equation} The major difference with the {\sc ib1} algorithm originally proposed by \cite{Aha+91}, is that in our version the value of $k$ refers to $k$-nearest {\em distances}\/ rather than $k$-nearest examples. With $k=1$, for instance, TiMBL's nearest neighbor set can contain several instances that are equally distant to the test instance. Arguably, our $k$-NN kernel could therefore be called $k$-nearest distances classification. Another difference with the original {\sc ib1} as well as with other implementations such as $k$-NN in the {\sc weka} machine learning toolkit \cite{Witten+99} is the way in which ties are resolved in choosing the majority category among the set of nearest neighbors. Since this method is independent of the distance function we discuss this issue separately in subsection~\ref{tiebreaking}. \paragraph{Variations on Overlap: Levenshtein and Dice coefficient metrics} The Overlap metric is all-or-nothing. For measuring the similarity between numeric or atomically symbolic values this may suffice, but there are cases (such as in natural language processing) in which string-valued feature values occur that can mismatch with other string values in a meaningfully graded way. For example, the value pair ``bathe'' and ``bathes'' only differs in one letter; counting them as more similar than ``bathe'' and ``rumour'', for example, may be useful for the classification task at hand. We implemented two additional metrics, Levenshtein distance and the Dice coefficient, that each provide a graded similarity score between pairs of strings. {\bf Levenshtein} distance is a classic {\em edit distance}\/ metric \cite{Levenshtein66} that counts the number of insertions, deletions, and substitutions to transform the one string into the other. In our (dynamic programming) implementation the three operations count equally heavily. The {\bf Dice} coefficient computes the overlap between the occurrences of character bigrams in two strings as in Equation~\ref{dice}, where $n_{x_{i} \cap y{i}}$ is the number of character bigrams (uniquely) occuring both in string value $x_{i}$ and in string value $y_{i}$ (and where $i$ is the index of the feature as introduced in Equation~\ref{distance})\footnote{Strings of length one are not handled by Dice; we back off to Overlap in these cases.}. The equation subtracts the similarity from 1, because we assume $\delta$ to produce a distance, not a similarity. \begin{equation} \delta(x_{i}, y_{i}) = 1 - \frac{2 n_{x_{i} \cap y_{i}}}{n_{x_{i}} + n_{y_{i}}} \label{dice} \end{equation} \subsection{Information-gain and gain ratio feature weighting} \label{infogain} The distance metric in Equation~\ref{overlapeq} straightforwardly counts the number of (mis)matching feature-values in both patterns. In the absence of information about feature relevance, this is a reasonable choice. Otherwise, we can add domain knowledge bias to weight or select different features (see e.g.~\namecite{Cardie96} for an application of linguistic bias in a language processing task), or look at the behavior of features in the set of examples used for training. We can compute statistics about the relevance of features by looking at which features are good predictors of the class labels. Information Theory gives us a useful tool for measuring feature relevance in this way~\cite{Quinlan86,Quinlan93}. {\bf Information Gain} (IG) weighting looks at each feature in isolation, and measures how much information it contributes to our knowledge of the correct class label. The Information Gain of feature $i$ is measured by computing the difference in uncertainty (i.e.\ entropy) between the situations without and with knowledge of the value of that feature (Equation~\ref{IGgain}). \begin{equation} w_{i} = H(C) - \sum_{v \in V_{i}} P(v) \times H(C|v) \label{IGgain} \end{equation} Where $C$ is the set of class labels, $H(C) = - \sum_{c \in C} P(c) \log_{2} P(c)$ is the entropy of the class labels, $V_{i}$ is the set of values for feature $i$, and $H(C|v)$ is the conditional entropy of the subset of the training examples that have value $v$ on feature $i$. The probabilities are estimated from relative frequencies in the training set. For numeric features, an intermediate step needs to be taken to apply the symbol-based computation of IG. All real values of a numeric features are temporarily discretized into a number (the default is 20) of intervals. Instances are ranked on their real value, and then spread evenly over the intervals; each interval contains the same number of instances (i.e., by default, $1/20$th of the total amount of instances). Instances in each of these intervals are then used in the IG computation as all having the same unordered, symbolic value per group. Note again that this discretization is only temporary; it is not used in the computation of the distance metric. It is important to realize that the IG weight is really a probability-weighted average of the informativity of the different values of the feature. On the one hand, this pre-empts the consideration of values with low frequency but high informativity. Such values ``disappear'' in the average. On the other hand, this also makes the IG weight very robust to estimation problems. Each parameter (weight) is estimated on the whole data set. Information Gain, however, tends to overestimate the relevance of features with large numbers of values. Imagine a data set of hospital patients, where one of the available features is a unique ``patient ID number''. This feature will have very high Information Gain, but it does not give any generalization to new instances. To normalize Information Gain for features with different numbers of values, Quinlan~\cite{Quinlan93} has introduced a normalized version, called {\bf Gain Ratio}, which is Information Gain divided by $si(i)$ (split info), the entropy of the feature-values (Equation~\ref{splitinfo}). \begin{equation} w_{i} = \frac{H(C) - \sum_{v \in V_{i}} P(v) \times H(C|v)}{si(i)} \label{IGgainratio} \end{equation} \begin{equation} si(i) = - \sum_{v \in V_{i}} P(v) \log_{2} P(v) \label{splitinfo} \end{equation} The resulting Gain Ratio values can then be used as weights $w_{f}$ in the weighted distance metric (Equation~\ref{distancew})\footnote{In a generic use IG refers both to Information Gain and to Gain Ratio throughout this manual. In specifying parameters for the software, the distinction between both needs to be made, because they often result in different behavior.}. The $k$-{\sc nn} algorithm with this metric is called {\sc ib1-ig} \cite{Daelemans+92b}. \begin{equation} \Delta(X,Y) = \sum_{i=1}^{n}\ w_{i} \ \delta(x_{i},y_{i}) \label{distancew} \end{equation} The possibility of automatically determining the relevance of features implies that many different and possibly irrelevant features can be added to the feature set. This is a very convenient methodology if domain knowledge does not constrain the choice enough beforehand, or if we wish to measure the importance of various information sources experimentally. However, because IG values are computed for each feature independently, this is not necessarily the best strategy. Sometimes better results can be obtained by leaving features out than by letting them in with a low weight. Very redundant features can also be challenging for {\sc ib1-ig}, because IG will overestimate their joint relevance. Imagine an informative feature which is duplicated. This results in an overestimation of IG weight by a factor two, and can lead to accuracy loss, because the doubled feature will dominate the distance metric. \subsection{Chi-squared and shared variance feature weighting} \label{chisquared} Unfortunately, as~\namecite{White+94} have shown, the Gain Ratio measure still has an unwanted bias towards features with more values. The reason for this is that the Gain Ratio statistic is not corrected for the number of degrees of freedom of the contingency table of classes and values. \namecite{White+94} proposed a feature selection measure based on the chi-squared statistic, as values of this statistic can be compared across conditions with different numbers of degrees of freedom. The \chisq statistic is computed from the same contingency table as the Information Gain measure by the following formula (Equation~\ref{chisq-eq}). \begin{equation} \chi^{2} = \sum_{i} \sum_{j} \frac{(E_{ij} - O_{ij})^{2}} {E_{ij}} \label{chisq-eq} \end{equation} where $O_{ij}$ is the observed number of cases with value $v_{i}$ in class $c_{j}$, i.e.~$O_{ij} = n_{ij}$, and $E_{ij}$ is the expected number of cases which should be in cell ($v_{i}$, $c_{j}$) in the contingency table, if the null hypothesis (of no predictive association between feature and class) is true (Equation~\ref{chisq-expect-eq}). Let $n_{.j}$ denote the marginal for class $j$ (i.e.~the sum over column $j$ of the table), $n_{i.}$ the marginal for value $i$, and $n_{..}$ the total number of cases (i.e.~the sum of all the cells of the contingency table). \begin{equation} E_{ij} = \frac{n_{.j} n_{i.}}{n_{..}} \label{chisq-expect-eq} \end{equation} The \chisq statistic is well approximated by the chi-square distribution with $\nu = (m-1)(n-1)$ degrees of freedom, where $m$ is the number of values and $n$ is the number of classes. We can then either use the \chisq values as feature weights in Equation~\ref{distancew}, or we can explicitly correct for the degrees of freedom by using the {\bf Shared Variance} measure (Equation~\ref{shared-variance-eq}). \begin{equation} SV_{i} = \frac{ \chi^2_{i}}{N \times ( min(|C|,|V_{i}|)-1 ) } \label{shared-variance-eq} \end{equation} Where $|C|$ and $|V_{i}|$ are the number of classes and the number of values of feature $i$, respectively, and $N$ is the number of instances\footnote{Note that with two classes, the shared variance weights of all features are simply divided by $N$, and will not be different from \chisq weights.}. We will refer to these variations of {\sc mbl} as {\sc ib1-\chisq} and {\sc ib1-sv}. One should keep in mind, that the correspondence to the chi-square distribution generally becomes poor if the expected frequencies in the contingency table cells become small. A common recommendation is that the \chisq test cannot be trusted when more than $20\%$ of the expected frequencies are less than $5$, or any are less than $1$. Chi-squared and shared variance weights of {\em numeric}\/ features are computed via a discretization preprocessing step (also used with computing IG and GR weights). Values are first discretized into a number (20 by default) of equally-spaced intervals between the minimum and maximum values of the feature. These groups are then used as discrete values in computing chi-squared and shared variance weights. \subsection{Modified value difference, Jensen-Shannon divergence, and Jeffrey divergence metrics} \label{mvdm} The choice of representation for instances in {\sc mbl} remains the key factor determining the strength of the approach. The features and categories in NLP tasks are usually represented by symbolic labels. The metrics that have been described so far, i.e.~Overlap and IG Overlap, are limited to counting exact matches between feature values. This means that all values of a feature are seen as equally dissimilar. However, if we think of an imaginary task in e.g.~the phonetic domain, we might want to use the information that 'b' and 'p' are more similar than 'b' and 'a'. For this purpose a metric was defined by \namecite{Stanfill+86} and further refined by \namecite{Cost+93}. It is called the (Modified) Value Difference Metric ({\sc mvdm}; Equation~\ref{MVDMeq}), and it is a method to determine the similarity of the values of a feature by looking at co-occurrence of values with target classes. For the distance between two values $v_{1},\ v_{2}$ of a feature, we compute the difference of the conditional distribution of the classes $C_{i}$ for these values. \begin{equation} \delta(v_{1}, v_{2}) = \sum_{i=1}^{n} \left| P(C_{i}|v_{1}) - P(C_{i}|v_{2}) \right| \label{MVDMeq} \end{equation} For computational efficiency, all pairwise $\delta(v_{1}, v_{2})$ values can be precomputed before the actual nearest neighbor search starts. Although the {\sc mvdm} metric does not explicitly compute feature relevance, an implicit feature weighting effect is present. If features are very informative, their conditional class probabilities will often be skewed towards a particular class. This implies that on average the $\delta(v_{1}, v_{2})$ will be large. For uninformative features, on the other hand, the conditional class probabilities will tend to be closer to the overall class distribution, so that on average the $\delta(v_{1}, v_{2})$ will be very small. {\sc mvdm} differs considerably from Overlap based metrics in its composition of the nearest neighbor sets. Overlap causes an abundance of ties in nearest neighbor position. For example, if the nearest neighbor is at a distance of one mismatch from the test instance, then the nearest neighbor set will contain the entire partition of the training set that matches all the other features but contains {\em any} value for the mismatching feature (see~\namecite{Zavrel+97} for a more detailed discussion). With the {\sc mvdm} metric, however, the nearest neighbor set will either contain patterns which have the value with the lowest $\delta(v_{1}, v_{2})$ in the mismatching position, or {\sc mvdm} will select a totally different nearest neighbor which has less exactly matching features, but a smaller distance in the mismatching features. In sum, this means that the nearest neighbor set is usually much smaller for {\sc mvdm} at the same value of $k$. In NLP tasks we have found it useful to experiment with values of $k$ larger than one for {\sc mvdm}, because this re-introduces some of the beneficial smoothing effects associated with large nearest neighbor sets. One cautionary note about this metric is connected with data sparsity. In many practical applications we are confronted with a very limited set of examples, with values occuring only a few times or once in the whole data set. If two such values occur with the same class, {\sc mvdm} will regard them as identical, and if they occur with two different classes their distance will be maximal. In cases of such extreme behaviour on the basis of low-frequency evidence, it may be safer to back off to the Overlap metric, where only an exact value match yields zero distance. TiMBL offers this back-off from {\sc mvdm} to Overlap through a frequency threshold, that switches from the {\sc mvdm} to the Overlap metric when one or both of a pair of matched values occurs fewer times in the learning material than this threshold. Jensen-Shannon divergence and Jeffrey divergence are offered as related, but more complex alternatives to {\sc mvdm}. They are both statistical dissimilarity metrics that can be used to compute the distance between class distributions of two values of the same feature. Functionally they are quite similar to {\sc mvdm} as well as to Kullback-Leibler (KL) divergence; Jeffrey divergence and Jensen-Shannon divergence are both symmetric versions of KL divergence, and symmetry is what we need for estimating value difference. First, Jeffrey divergence is best known for its application as a distance function in unsupervised vector space models, e.g. in image retrieval, where it is applied to histogram vectors. While {\sc mvdm} computes a straightforward geometrical distance between two class distribution vectors, Jeffrey divergence introduces a logarithm term, as seen in Equation~\ref{jd}. Jeffrey divergence is a symmetric variant of Kullback-Leibner distance; the $m$ term given in Equation~\ref{jdm} is used for this purpose. \begin{equation} \delta(v_{1}, v_{2}) = \sum_{i=1}^{n} ( P(C_{i}|v_{1}) log \frac{P(C_{i}|v_{1})}{m} + P(C_{i}|v_{2}) log \frac{P(C_{i}|v_{2})}{m} ) \label{jd} \end{equation} \begin{equation} m = \frac{P(C_{i}|v_{1}) + P(C{i}|v_{2})}{2} \label{jdm} \end{equation} Compared to {\sc mvdm}, Jeffrey divergence assigns relatively larger distances to value pairs of which the class distributions are more orthogonal. In other words, it assigns more prominence to zero probabilities, which in the case of sparse data (e.g, with Zipfian distributions of values) are generally better estimations than non-zero probabilities. This makes Jeffrey divergence in principle more robust than {\sc mvdm} with respect to sparse data. As with {\sc mvdm}, TiMBL offers an optional frequency-thresholded back-off from Jeffrey and Jensen-Shannon divergence to the Overlap metric to further remedy some negative effects due to data sparseness. \subsection{Dot-product and cosine metrics} \label{dotproduct} When features have numeric or binary values, TiMBL can also compute the distance between two instances via the dot product (or inner product) of their feature-value vectors. The dot product (which is higher with better matches) is subsequently inversed to a distance by subtracting it from the maximum dot product attainable, i.e. that on an exact match. In Equation~\ref{doteq} this maximal dot product is referred to as $dot_{max}$. \begin{equation} \label{doteq} \Delta(X,Y) = dot_{max} - \sum_{i=1}^{n} w_{i} x_{i} y_{i} \end{equation} As with the other distance metrics incorporated in TiMBL, we include the feature weight $w_{i}$ in the metric. When no weighting is set ({\tt -w 0}), all weights are set to $1.0$, and equation~\ref{doteq} reduces to the normal unweighted dot product. The dot-product metric is typically used with binary vectors or sparse vectors in general. When either $x_{i}$ or $y_{i}$ has a zero value, that value pair is not counted in the dot product. Consequently, the significant deviation from the Overlap metric is that matching values that both have a zero value do not count here, whereas they count as much as any other value match in the Overlap metric. A commonly used variant of the dot product metric, e.g. in information retrieval, is the cosine metric, which corrects for large differences in the length of the instance vectors. The cosine metric divides the dot product metric by the product of the length of the two vectors. As with the dot product, TiMBL converts the cosine metric similarity to a distance by subtracting it from a $cos_{max}$ term that is larger than the maximal cosine similarity, as given in Equation~\ref{coseq}. Again, feature weighting is included in the formula: \begin{equation} \label{coseq} \Delta(X,Y) = cos_{max} - \frac{\sum_{i=1}^{n} w_{i} x_{i} y_{i}}{\sqrt{\sum_{i=1}^{n} w_{i} x_{i}^2 \sum_{i=1}^{n} w_{i} y_{i}^2}} \end{equation} Due to its internal tree structure, TiMBL is not particularly suited to handle feature vectors with thousands or more features. Many features cause very deep and usually very unbalanced trees, from which retrieval can be rather inefficient (especially when there is little variance in the feature weights). Other internal data structures such as inverted indices are typically more suited to these types of vector spaces. For now, inverted indices are not implemented in TiMBL. \subsection{Distance-weighted class voting} \label{distweightvote} The most straightforward method for letting the $k$ nearest neighbors vote on the class of a new case is the {\em majority voting} method, in which the vote of each neighbor receives equal weight, and the class with the highest number of votes is chosen (or in case of a tie, some tie resolution is performed, cf. Subsection~\ref{tiebreaking}). We can see the voting process of the $k$-NN classifier as an attempt to make an optimal class decision, given an estimate of the conditional class probabilities in a local region of the data space. The radius of this region is determined by the distance of the $k$-furthest neighbor. Sometimes, if $k$ is small, and the data is very sparse, or the class labels are noisy, the ``local'' estimate is very unreliable. As it turns out in experimental work, using a larger value of $k$ can often lead to higher accuracy. The reason for this is that in densely populated regions, with larger $k$ the local estimates become more reliable, because they are "smoother". However, when the majority voting method is used, smoothing can easily become oversmoothing in sparser regions of the same data set. The reason for this is that the radius of the $k$-NN region can become extended far beyond the local neighborhood of the query point, but the far neighbors will receive equal influence as the close neighbors. This can result in classification errors that could easily have been avoided if the measure of influence would somehow be correlated with the measure of similarity. To remedy this, we have implemented three types of distance weighted voting functions in TiMBL. A voting rule in which the votes of different members of the nearest neighbor set are weighted by a function of their distance to the query, was first proposed by Dudani~\shortcite{Dudani76}. In this scheme, henceforth referred to as IL (for inverse-linear), a neighbor with smaller distance is weighted more heavily than one with a greater distance: the nearest neighbor gets a weight of 1, the furthest neighbor a weight of 0 and the other weights are scaled linearly to the interval in between (\namecite{Dudani76}, Equation~\ref{dudani_eq}.). \begin{equation} \label{dudani_eq} w_{j}= \left \{ \begin{array}{ll} \frac{d_{k} - d_{j}}{d_{k} - d_{1}} & \mbox{if $d_{k} \not= d_{1}$ } \\ 1 & \mbox{if $d_{k} = d_{1}$}\\ \end{array} \right. \end{equation} Where $d_{j}$ is the distance to the query of the $j$'th nearest neighbor, $d_{1}$ the distance of the nearest neighbor, and $d_{k}$ of the furthest ($k$'th) neighbor. Dudani (\namecite{Dudani76}, eq. 2 and 3) further proposed the {\em inverse distance weight} (henceforth ID). In Equation~\ref{inverseweight} a small constant is usually added to the denominator to avoid division by zero~\cite{Wettschereck94}. \begin{equation} \label{inverseweight} w_{j}= \left \{ \begin{array}{ll} \frac{1}{d_{j} + \epsilon} \\ \end{array} \right. \end{equation} Another weighting function considered here is based on the work of \namecite{Shepard87}, who argues for a universal perceptual law which states that the relevance of a previous stimulus for the generalization to a new stimulus is an exponentially decreasing function of its distance in a psychological space (henceforth ED). This gives the weighed voting function of Equation~\ref{expdecayweight}, where $\alpha$ and $\beta$ are constants determining the slope and the power of the exponential decay function. \begin{equation} \label{expdecayweight} w_{j}= e^{-\alpha d_{j}^\beta} \end{equation} Note that in Equations~\ref{inverseweight} and ~\ref{expdecayweight} the weight of the nearest and furthest neighbors and the slope between them depend on their absolute distance to the query. This assumes that the relationship between absolute distance and the relevance gradient is fixed over different datasets. This assumption is generally false; even within the same dataset, different feature weighting metrics can cause very different absolute distances. Figure~\ref{dist-weight-fig} visualises a part of the curves of ID and ED, the latter with a few varied settings of $\alpha$ and $\beta$. Generally, both distance weighting functions assign highly differing weights for close neighbors, and less differing weights for more distant neighbors. ID assigns very high votes (distance weights) to nearest neighbors at distances approaching 0.0 - in effect it assigns absolute preference to exact matches. In contrast, all ED variants have a vote of 1.0 for exact matches, and have a shallower curve than the ID curve for higher distances. Higher values of $\alpha$ in the ED function assign relatively higher weights to exact matches. When $\beta$ is set to larger values than $1.0$, the ED curve becomes bell-shaped, effectively assigning relatively less different weights between exact-matching neighbors and near-exact matching instances. \begin{figure}[htb] \begin{center} \leavevmode \epsfxsize=0.8\columnwidth \epsffile{distanceweight-ided.eps} \caption{Visualisation of the Inverse Distance weighting function (IL) and three variants of the Exponential Decay distance weighting function (ED) varying settings of $\alpha$ (1) and $\beta$ (b). } \label{dist-weight-fig} \end{center} \end{figure} Following Dudani's proposal, the benefits of weighted voting for $k$-NN have been discussed widely, e.g. \cite{Bailey+78,Morin+81,MacLeod+87}, but mostly from an analytical perspective. With the popularity of Instance-Based Learning applications, these issues have gained a more practical importance. In his thesis on $k$-NN classifiers, \namecite{Wettschereck94} cites Dudani, but proceeds to work with Equation~\ref{inverseweight}. He tested this function on a large amount of datasets and found weak evidence for performance increase over majority voting. An empirical comparison of the discussed weighted voting methods in~\cite{Zavrel97} has shown that weighted voting indeed often outperforms unweighted voting, and that Dudani's original method (Equation~\ref{dudani_eq}) mostly outperforms the other two methods. From that set of experiments, it also seems that Dudani's method shows its optimal performance at much larger values of $k$ than the other voting methods. \subsection{Tie breaking} \label{tiebreaking} Thus far we have described the last step of $k$-NN classification as taking the majority category among the set of nearest neighbors, where their vote is either unweighted or weighted by their distance (subsection~\ref{distweightvote}). Especially in case of unweighted voting, ties can occur; e.g.\ of a set of ten nearest neighbors, five vote for class $A$, and the other five for $B$. The procedure for breaking this tie in the $k$-NN classifier in TiMBL is as follows. First, the value of the $k$ parameter is incremented by $1$, and the additional nearest neighbors at this new $k$th distance are added to the current nearest neighbor set ($k$ is subsequently reset to its user-specified value). If the tie in the class distribution persists, then the class label is selected with the highest overall occurrence in the training set. If that is also equal, then the first class is taken that was encountered when reading the training instance file. Optionally, TiMBL can be set to avoid ties by making a {\em random}\/ choice of a classification from a class distribution in a nearest-neighbor set, weighted by the distribution of the classes in the set. \subsection{Exemplar weighting} \label{exemplar} Exemplar weighting in memory-based learning captures the intuition that some instances are better (more reliable, more typical, more regular) nearest neighbors than others. Classification accuracy could benefit from giving these instances some priority in the $k$-NN classification process. This idea has been explored in the context of on the one hand classification \cite{Salzberg90,Zhang92}, and on the other hand editing bad instances from memory \cite{Aha+91}. \namecite{Salzberg90}, as a classic example, uses {\em class-prediction strength}: the ratio of the number of times the instance type is a nearest neighbor of another instance with the same class and the number of times that the instance type is the nearest neighbor of another instance type regardless of the class. Another example is {\em typicality}\/ as used by \namecite{Zhang92}. Exemplar weights could in principle be used either as weights in the class voting (as distance weights, cf. Subsection~\ref{distweightvote}), or as weights in the distance metric (eq.~\ref{distancew}). TiMBL supports only the latter type, and in this respect exemplar weighting is not an intrinsic part of TiMBL. TiMBL does not compute exemplar weighting metrics itself, but only allows users to specify preprocessed exemplar weights with the {\tt -s} input option. Subsequently, when the distance between a test instance and a memory instance is computed, TiMBL uses the memory instance's weight as follows, where $\Delta^{E}(X,Y)$ is the exemplar-weighted distance between instances $X$ and $Y$, and $ew_{X}$ is the exemplar weight of memory instance $X$: \begin{equation} \Delta^{E}(X,Y) = \frac{\Delta(X,Y)}{ew_{X} + \epsilon} \label{exweight} \end{equation} $\epsilon$ is the smallest non-zero number, and is used to avoid division by zero. Exemplar weights approaching zero yield very large distances; relatively higher values yield relatively smaller distances. Note that when a training instance occurs more than once in a training set, TiMBL expects it to have the same example weight with all occurrences; TiMBL cannot handle different example weights for the same instance type. TiMBL produces a warning ({\em Warning: deviating exemplar weight in line \#$\ldots$}), and uses the first weight found for the instance. \section{Indexing optimizations} \label{indexing} The discussion of the algorithm and the metrics in the section above is based on a naive implementation of nearest neighbor search: a flat array of instances which is searched from beginning to end while computing the similarity of the test instance with each training instance. Such an implementation, unfortunately, reveals the flip side of the lazy learning coin. Although learning is very cheap: just storing the instances in memory, the computational price of classification can become very high for large data sets. The computational cost is proportional to $N$, the number of instances in the training set, times $f$, the number of features. In our current implementation of {\sc ib1} we use tree-based indexing to alleviate these costs. \subsection{Tree-based indexing} The tree-based memory indexing operation replaces the flat array by a tree structure. Instances are stored in the tree as paths from a root node to a leaf, the arcs of the path are the consecutive feature-values, and the leaf node contains a {\em distribution}\/ of classes, i.e.~a count of how many times which class occurs with this pattern of feature-values. % (see Figure~\ref{example2}). Due to this storage structure, instances with identical feature-values are collapsed into a single path, and only their separate class information needs to be stored in the distribution at the leaf node. Many different {\bf tokens} of a particular {\bf instance type} share one path from the root to a leaf node. Moreover, instances which share a prefix of feature-values, also share a partial path. This reduces storage space (although at the cost of some book-keeping overhead) and has two implications for nearest neighbor search efficiency. %\begin{figure}[htb] % \begin{center} % \leavevmode % \epsfxsize=0.8\columnwidth % \epsffile{example2.eps}\ % \caption{A tree-structured storage of the instance % base from figure~\ref{example1}. An exact match for % the test is in this case directly found by a top down % traversal of the tree (grey path). If there is no % exact match, all paths are interpreted as instances % and the distances are computed. The order of the % features in this tree is based on Gain Ratio. % } % \label{example2} % \end{center} %\end{figure} First, the tree can be searched top-down very quickly for {\em exact matches}. When $k=1$, an exact match ($\Delta(X,Y)=0$) can never be beaten, so then it is possible to omit any further distance computations. The shortcut is built into TiMBL, but by default it is not used with $k>1$. TiMBL does, however, offer the possibility to use the shortcut at any value of $k$, with the command line switch ({\tt +x}. Using it can speed up classification radically for some types of data, but with $k>1$, the shortcut is not guaranteed to give the same performance (for better or for worse) as classification without it. Second, the distance computation for the nearest neighbor search can re-use partial results for paths which share prefixes. This re-use of partial results is in the direction from the root to the leaves of the tree. When we have proceeded to a certain level of the tree, we know how much similarity (Equation~\ref{overlapeq}) can still contribute to the overall distance (Equation~\ref{distance}), and discard whole branches of the tree which will never be able to rise above the partial similarity of the current least similar nearest neighbor. By doing the search depth first\footnote{Suggested by Gert Durieux.}, the similarity threshold quickly gets initialized to a good value, so that large parts of the search space can be pruned\footnote{With the special command line setting {\tt --silly=true} this tree search shortcut is switched off; as the name of the setting suggests, this is not recommended, except for explicit speed comparisons.}. Disregarding this last constraint on search, the number of feature-value comparisons is equal to the number of arcs in the tree. Thus if we can find an ordering of the features which produces more overlap between partial paths, and hence a smaller tree, we can gain both space and time improvements. An ordering which was found to produce small trees for many of our NLP data sets is Gain Ratio divided by the number of feature-values (this is the default setting). Through the {\tt --Treeorder=} command line switch, however, the user is allowed to experiment with different orderings. Note that different orderings may only affect classification speed, not the actual classifications. \section{IGTree} \label{igtree} Using Information Gain rather than unweighted Overlap distance to define similarity in {\sc ib1} improves its performance on several {\sc nlp} tasks \cite{Daelemans+92b,VandenBosch+93,VandenBosch97}. The positive effect of Information Gain on performance prompted us to develop an alternative approach in which the instance memory is restructured in such a way that it contains the same information as before, but in a compressed decision tree structure. We call this algorithm {\sc IGTree}~\cite{Daelemans+97} %(see Figure~\ref{example3} %for an illustration). In this structure, similar to the tree-structured instance base described above, instances are stored as paths of connected nodes which contain classification information. Nodes are connected via arcs denoting feature values. Information Gain is used to determine the order in which instance feature-values are added as arcs to the tree. The reasoning behind this compression is that when the computation of information gain points to one feature clearly being the most important in classification, search can be restricted to matching a test instance to those memory instances that have the same feature-value as the test instance at that feature. Instead of indexing all memory instances only once on this feature, the instance memory can then be optimized further by examining the second most important feature, followed by the third most important feature, etc. Again, compression is obtained as similar instances share partial paths. %\begin{figure}[htb] % \begin{center} % \leavevmode % \epsfxsize=0.7\columnwidth % \epsffile{example3.eps} % \caption{A pruned {\sc IGTree} for the instance base % of Figure~\ref{example1}. The classification for % the test instance is found by top down search of the % tree, and returning the class label (default) of the % node after the last matching feature-value (arc). Note % that this tree is essentially a compressed version of % the tree in Figure~\ref{example2}. % } % \label{example3} % \end{center} %\end{figure} Because {\sc IGTree} makes a heuristic approximation of nearest neighbor search by a top down traversal of the tree in the order of feature relevance, we no longer need to store all the paths. The idea is that it is not necessary to fully store those feature-values of the instance that have lower Information Gain than those features which already fully disambiguate the instance classification. Apart from compressing all training instances in the tree structure, the {\sc IGTree} algorithm also stores with each non-terminal node information concerning the {\em most probable} or {\em default} classification given the path thus far, according to the bookkeeping information maintained by the tree construction algorithm. This extra information is essential when processing unknown test instances. Processing an unknown input involves traversing the tree (i.e., matching all feature-values of the test instance with arcs in the order of the overall feature Information Gain), and either retrieving a classification when a leaf is reached (i.e., an exact match was found), or using the default classification on the last matching non-terminal node if an exact match fails. In sum, it can be said that in the trade-off between computation during learning and computation during classification, the {\sc igtree} approach chooses to invest more time in organizing the instance base using Information Gain and compression, to obtain simplified and faster processing during classification, as compared to {\sc ib1} and {\sc ib1-ig}. The generalization accuracy of {\sc IGTree} is usually comparable to that of {\sc ib1-ig}; often slightly worse, but sometimes even better. The two causes for {\sc IGTree}'s surprisingly good accuracies attained with dramatically faster classification are that (i) most 'unseen' instances contain large parts that fully match stored parts of training instances, and (ii) the probabilistic information stored at non-terminal nodes (i.e., the default classifications) still produces strong `best guesses' when exact matching fails. The difference between the top-down traversal of the tree and precise nearest neighbor search becomes more pronounced when the differences in informativity between features are small. In such a case a slightly different weighting would have produced a switch in the ordering and a completely different tree. The result can be a considerable change in classification outcomes, and hence also in accuracy. However, we have found in our work on NLP datasets that when the goal is to obtain a very fast classifier for processing large amounts of text, the tradeoff between a somewhat lower accuracy against stellar speed increases can be very attractive. It should be noted that by design, {\sc IGTree} is not suited for numeric features, as it does not use some type of discretization. If present in data, numbers will simply be treated as literal strings by {\sc IGTree}. Moreover, one should realize that the success of {\sc igtree} is determined by a good judgement of feature relevance ordering. Hence {\sc IGTree} is not to be used with e.g. ``no weights'' ({\tt -w 0}). Also, setting the {\tt -k} parameter obviously has no effect on {\sc IGTree} performance. \section{The TRIBL and TRIBL2 hybrids} \label{tribl} The application of {\sc IGTree} on a number of common machine-learning datasets suggested that it is not applicable to problems where the relevance of the predictive features cannot be ordered in a straightforward way, e.g.~if the differences in Information Gain are only very small. In those cases, {\sc ib1-ig} or even {\sc ib1} tend to perform significantly better than {\sc IGTree}. For this reason we have designed {\sc tribl} \cite{Daelemans+97d} and {\sc tribl2} as hybrid combinations of {\sc IGTree} and {\sc ib1}. Both algorithms aim to exploit the trade-off between (i) optimization of search speed (as in {\sc IGTree}), and (ii) maximal generalization accuracy. They do that by splitting the classification of new instances into a quick decision-tree ({\sc IGTree}) traversal based on the first (most important and most class-disambiguating) features, followed by a slow but relatively accurate $k$-NN ({\sc ib1}) classification based on the remaining less important features. The difference between {\sc tribl} and {\sc tribl2} is that the former algorithm fixes the point in the feature ordering where {\sc IGTree} is succeeded by {\sc ib1}, while {\sc tribl2} determines this switching point automatically per classification. We briefly describe both variants. For {\sc tribl}, a parameter ({\tt -q}) determines the switching point in the feature ordering from {\sc IGTree} to {\sc ib1}. A heuristic that we have used with some success is based on {\em average feature information gain}; when the Information Gain of a feature exceeds the average Information Gain of all features $+$ one standard deviation of the average, then the feature is used for constructing an {\sc IGTree}, including the computation of defaults on nodes. When the Information Gain of a feature is below this threshold, and the node is still ambiguous, tree construction halts and the leaf nodes at that point represent case bases containing subsets of the original training set. During search, the normal {\sc IGTree} search algorithm is used, until the case-base nodes are reached, in which case regular {\sc ib1} nearest neighbor search is used on this sub-case-base. {\sc tribl2} does not employ a fixed switching point. Rather, during the classification of an instance it continues to use {\sc IGTree} as long as it finds matching feature values in the weighting-governed feature ordering. Only when it finds a mismatch it reverts to {\sc ib1} classification on all remaining features. The reasoning behind this mismatch-based switching is that it offers a fairly optimal minimalisation of the use of {\sc ib1}; it is only invoked when mismatching occurs, which is the typical point in which {\sc ib1} can improve on decision-tree-style classification, which does not consider the other potentially matching features in the ordering \cite{Daelemans+99}. A final note: as with {\sc IGTree}, it does not make sense to use {\sc tribl} and {\sc tribl2} without feature weighting, so do not combine {\sc tribl} or {\sc tribl2} with ({\tt -w 0}). \section{IB2: Incremental editing} \label{ib2} In memory-based learning it seems sensible to keep any instance in memory that plays a (potentially) positive role in the correct classification of other instances. Alternatively, when it plays no role at all, or when it is disruptive for classification, it may be a good idea to discard, or {\em edit} it from memory. On top of not harming or even improving generalization performance, the editing of instances from memory could also alleviate the practical processing burden of the $k$-NN classifier kernel, since it would have less instances to compare new instances to. This potential double pay-off spawned a distinct line of work on editing in the $k$-NN classifier quite early \namecite{Hart68} and \namecite{Wilson72}. TiMBL offers an implementation of one particular editing algorithm called {\sc ib2} \cite{Aha+91}, an extension to the basic {\sc ib1} algorithm introduced in the same article. {\sc ib2} implements an incremental editing strategy. Starting from a seed memory filled with a certain (usually small) number of labeled training instances, {\sc ib2} adds instances incrementally to memory only when they are {\em misclassified}\/ by the $k$-NN classifier on the basis of the instances in memory at that point. These instances are added, since they are assumed to be representatives of a part of the complete instance space in which they themselves and potentially more nearest-neighbor instances have a particular class different from the class of neigboring instances already in memory. The economical idea behind {\sc ib2} is that this way typically only instances on the boundaries of such areas are stored, and not the insides of the areas; the classification of instances that would be positioned well inside such areas is assumed to be safeguarded by the memorized boundary instances surrounding it. Although the {\sc ib2} may optimize storage considerably, its strategy to store all misclassified instances incrementally makes {\sc ib2} sensitive to noise \cite{Aha+91}. It is also yet unclear what the effect is of the size of the seed. \section{Advanced evaluation metrics} \label{advancedstats} Aside from accuracy (the percentage of correctly classified test instances), TiMBL offers some more evaluation metrics that have become common in information retrieval and machine learning in general, namely precision, recall, and F-score, and ROC-space (with dimensions true positive rate and false positive rate), and AUC. We describe these metrics in more detail here. \begin{figure} \begin{center} \epsfig{file=pos-neg.eps, width=0.5\textwidth} \vspace*{-0.5cm} \end{center} \caption{Class-specific confusion matrix containing the basic counts used in the advanced performance metrics. \label{confmat}} \end{figure} Figure~\ref{confmat} displays the general confusion matrix\index{confusion matrix} for one class $C$, splitting all classifications on a test set into four cells. The TP or true positives cell contains a count of examples that have class $C$ and are predicted to have this class correctly by the classifier. The FP or false positives cell contains a count of examples of a different class that the classifier incorrectly classified as $C$. The FN or false negatives cell contains examples of class $C$ for which the classifier predicted a different class label than $C$. On the basis of these four numbers and the total number of positive examples $P=TP+FN$ and negative examples $N=FP+TN$, we can compute the following performance measures: \begin{description} \item[Precision]\index{precision} $= \frac{TP}{TP+FP}$, or the proportional number of times the classifier has correctly made the decision that some instance has class $C$. \item[Recall or True Positive Rate (TPR)]\index{TPR}\index{recall}\index{true positive rate} $= \frac{TP}{P}$, or the proportional number of times an example with class $C$ in the test data has indeed been classified as class $C$ by the classifier. \item[False Positive Rate (FPR)]\index{FPR}\index{false positive rate} $= \frac{FP}{N}$, or the proportional number of times an example with a different class than $C$ in the test data has been classified as class $C$ by the classifier. \item[F-score]\index{F-score} $= \frac{2 \times precision \times recall}{precision + recall}$, or the harmonic mean\index{harmonic mean} of precision and recall \cite{VanRijsbergen79}, is a commonly used metric to summarize precision and recall in one measure. The left part of Figure~\ref{spaces} shows F-score isolines in the two-dimensional space of recall (x-axis) and precision (y-axis). The curvature of the isolines is caused by the harmonic aspect of the formula (in contrast, the normal mean has straight isolines orthogonal to the $x=y$ diagonal), which penalizes large differences between precision and recall. The isolines could be likened to height isolines in a map, where the peak of the hill is at the upper right corner of the space. \item [AUC]\index{AUC}\index{area under the curve} or {\em area under the curve}\/ in the so-called ROC\index{ROC space} or {\em receiver operator characteristics}\/\index{receiver operator characteristics} space \cite{Egan75,Swets+00}, is the surface of the grey area in the right graph of Figure~\ref{spaces}. The ROC space is defined by the two dimensions FPR (false positive rate, x-axis) and TPR (true positive rate, or recall, y-axis). The difference with F-score is that it does not make use of the statistically unreliable precision metric; rather, it takes into account all cells of the matrix in Figure~\ref{confmat} including the TN (true negative) cell (for a more detailed description and arguments for using ROC analysis, cf. \cite{Fawcett04}). Its ``peak'' is in the upper left corner, at a FPR of zero and a TPR of 1. Rather than using the harmonic mean, it is common to report on the AUC, area under the classifier's TPR-FPR curve, where in the case of a discrete-output classifier such as {\sc TiMBL} this can be taken to mean the two lines connecting the experiment's TPR and FPR to the $(0,0)$ coordinate and the $(1,1)$ coordinate, respectively; the AUC is then the grey area between these points and coordinate $(1,0)$. \end{description} While these advanced statistics can be computed per class, they can also be averaged to produce a single outcome for a full test set. Common methods for averaging F-scores and AUC scores are micro-averaging and macro-averaging. In micro-averaging, each class' F-score or AUC is weighted proportionally to the frequency of the class in the test set. A macro-average sums the F-scores or AUCs and divides the total by the number of classes in the training set. In computing these averages, TiMBL bases itself on the classes in the training set. When a class does not re-occur in test material, it can have no recall, but it can have precision, hence it is always incorporated in averages. A class that occurs in test material but not in training material can never be predicted correctly, and is never included in averages. \begin{figure} \begin{center} \begin{minipage}[t]{0.53\textwidth} \epsfig{file=fspace.eps, width=\textwidth} \end{minipage}\hfill \begin{minipage}[t]{0.47\textwidth} \epsfig{file=roc-auc.eps, width=\textwidth} \end{minipage} \end{center} \caption{Precision--recall space with F-score isolines (left), and ROC space with an experimental outcome marked by the dot, and the outcome's AUC, the shaded surface between the dot and coordinates $(0,0)$, $(1,0)$, and $(1,1)$ (right). \label{spaces}} \end{figure} \section{Applications of TiMBL} \label{furtherreading} This section provides a brief historical overview of work that has used TiMBL as a tool. Much of this work concerns tasks in natural language processing, but TiMBL has been applied in other domains as well. For historical background predating the 1990s, see \cite{Daelemans+05}. \subsubsection{Algorithmic development} As the original user and developer groups, the Tilburg and Antwerp groups have published a number of papers and articles containing descriptions of the algorithms and specialised metrics collected in TiMBL, usually demonstrating their functioning using NLP tasks. The {\sc ib1-ig} algorithm was first introduced in \cite{Daelemans+92b} in the context of a comparison of memory-based approaches with error-back\-propagation learning for a hyphenation task. Predecessor versions of {\sc IGTree} can be found in \cite{Daelemans+93c,VandenBosch+93} where they are applied to grapheme-to-phoneme conversion. See \cite{Daelemans+97} for a description and review of {\sc IGTree} and {\sc ib1-ig}. {\sc tribl} is described in \cite{Daelemans+97d}. Experiments with distance-weighted class voting are described in \cite{Zavrel97}. Aspects of using binary-valued (unpacked multi-valued) features are discussed in \cite{VandenBosch+00}. \namecite{Raaijmakers00} describes an extension of TiMBL with error-correcting output codes. \namecite{Hendrickx+04} report on an experiment to import maximum-entropy matrices to replace {\sc mvdm} matrices \cite{Hendrickx+04}, improving over the maximum-entropy classifier. Comparisons between memory-based learning and editing variants are reported in \cite{VandenBosch99,Daelemans+99}. A hybrid of TiMBL and the {\sc ripper} rule-induction algorithm \cite{Cohen95} is described in \cite{VandenBosch00,VandenBosch04}. Using TiMBL as a classifier combination method is discussed in \cite{Halteren+01}. \namecite{VandenBosch04} presents a search algorithm to find optimal combinations of parameter settings automatically, given a labeled training set of examples, showing large gains of the default settings (also of other machine learning algorithms). Parallelization of TiMBL, through splitting either the training set or the test set in $n$ pieces in shared-memory multi-processor architectures, is explored in \cite{VandenBosch+07b}. \subsubsection{Applications in morpho-phonology} The memory-based algorithms implemented in the TiMBL package have been targeted to a large range of Natural Language Processing tasks. Examples of applications in the {\bf morpho-phonological} area are hyphenation and syllabification \cite{Daelemans+92b}; classifiying phonemes in speech \cite{Kocsor+00}; assignment of word stress \cite{Daelemans+94}; grapheme-to-phoneme conversion, \cite{VandenBosch+93,Daelemans+96,Canisius+06}; diminutive formation \cite{Daelemans+98a}; and morphological analysis \cite{VandenBosch+96,VandenBosch+99,Canisius+06}. Although these examples are applied mostly to Germanic languages (English, Dutch, and German), applications to other languages with more complicated writing systems or morphologies, or with limited resources, have also been presented: for example, letter-phoneme conversion in Scottish Gaelic \cite{Wolters+97}, morphological analysis of Arabic \cite{Marsi+05}, or diacritic restoration in languages with a diacritic-rich writing system \cite{Mihalcea02,DePauw+07}. \subsubsection{Linguistic and psycholinguistic modelling} Whereas most work using TiMBL has been oriented towards natural language engineering applications, the {\bf linguistic} and {\bf psycholinguistic} relevance of memory-based learning is another focus of research in Antwerp, Tilburg and elsewhere. Work in this area has been done on stress assignment in Dutch simplex words \cite{Daelemans+94,Gillis+00} and English compounds \cite{Plag+07}, reading aloud \cite{VandenBosch+00b}, phonological bootstrapping \cite{Durieux+00}, the prediction of linking morphemes in Dutch \cite{Krott+01}, morphology \cite{Eddington00,Eddington03}, and the Dutch plural inflection \cite{Keuleers+07}. \namecite{Vandekerckhove+13} use TiMBL to develop a language bigram model and use this to explain behavior of patients with a specific impairment that prohibits them to assess adjective orderings as normal or less felicitous. A comparison to other analogical methods for linguistics is provided in \cite{Daelemans+97f,Daelemans02}. \namecite{VandenBosch+13b} offer links to work in psychology on models of human memory, in particular to episodic memory and fast memory access with global matching models \cite{Clark+96}. \subsubsection{Applications in syntax and semantics} At the {\bf syntactic} sentence level TiMBL has been applied to Part-of-Speech tagging \cite{Daelemans+96b,Zavrel+99,Halteren+01}; PP-attachment \cite{Zavrel+97b}; subcategorization \cite{Buchholz98}; phrase chunking \cite{Veenstra98,Sang+99}; shallow parsing of English \cite{Daelemans+99a,Buchholz+99,Yeh00} and Arabic \cite{Azmi+10}; clause identification \cite{Orasan00,Sang01}; detecting the scope of negation markers and hedge cues \cite{Morante+08c,Morante+09c}; sentence-boundary detection \cite{Stevenson+00}; and, beyond the sentence level, to co-reference resolution \cite{Preiss02,Mitkov+02,Hoste05,Klenner+08,Wunsch+09,Zhekova+11}. Memory-based learning has been integrated as a classifier engine in more complex dependency parsing systems \cite{Nivre+04,Sagae+05,Canisius+06b}, and dependency parsing in combination with semantic role labeling \cite{Morante+09b}. \subsubsection{Other applications} TiMBL has been involved in several subtasks of {\bf natural language generation}: predicting the order of prenominal adjectives for generation \cite{Malouf00}, article generation \cite{Minnen+00}; preposition generation \cite{Lee+08b}; generating referring expressions \cite{Hendrickx+08}; and ranking paraphrases of noun compounds \cite{Wubben10}. Memory-based learning has been applied succesfully to {\bf lexical semantics}, in particular to word sense disambiguation \cite{Veenstra+00,Stevenson+99,Kokkinakis00,Mihalcea02,Hoste+02,DeCadt+04}, but also in other lexical semantic tasks such as determining noun countability \cite{Baldwin+03}, animacy \cite{Orasan+01}, semantic relations within noun compounds \cite{Kim+06b,Nastase+06}, and cross-linguistic word sense disambigation, a subtask of machine translation \cite{VanGompel10,VanGompel+13,VanGompel+14}. As a tool for {\bf text mining}, TiMBL has been used for named-entity recognition \cite{Buchholz+00,Hendrickx+03,DeMeulder+03,Sporleder+06b,Leveling+06,VandenBosch+13c}, information extraction \cite{Zavrel+00b,Zavrel+03,Ahn06}, event extraction \cite{Morante+09d}, text classification \cite{Spitters00}, question classification \cite{Garcia+06,Dridan+07}, spam filtering \cite{Androutsopoulos+00}, and authorship attribution \cite{Luyckx+08,Kestemont+09}. In the field of {\bf discourse and dialogue}, TiMBL has been used for dialogue act classification and shallow semantic analysis of speech-recognised utterances \cite{Gustafson+99,Krahmer+01,VandenBosch+01,Lendvai+02a,Lendvai+03}, in disfluency detection in transcribed spontaneous speech \cite{Lendvai+03c}, in classifying ellipsis in dialogue \cite{Fernandez+04}, and in classifying errors in answers to reading comprehension tasks \cite{Bailey+08}. Relations to {\bf statistical language modeling}, in particular the interesting equivalence relations with back-off smoothing in probabilistic classifiers, are discussed in \cite{Zavrel+97}. Relations between classification-based word prediction and statistical language modeling are identified in \cite{VandenBosch05,VandenBosch06,Stehouwer+09b}. Next-word prediction with TiMBL has been specifically applied to {\bf word completion} \cite{VandenBosch+08,VandenBosch11,Verberne+12,Stoop+14}. TiMBL has been shown to be a useful {\bf error detector} and corrector, such as in the context of error detection in textual databases \cite{Sporleder+06}, in detecting dependency parsing annotation errors \cite{Dickinson09}, in confusible disambiguation \cite{Stehouwer+09}, and in preposition and determiner errors \cite{VandenBosch+12,VandenBosch+13}. In {\bf machine translation}, $k$-nearest neighbor classification offers a conceptual bridge between example-based machine translation (EBMT) and statistical MT. Pure memory-based approaches are described in \cite{VandenBosch+07,Canisius+09,VandenBosch+09,VanGompel+09}; hybrids with statistical machine translation are presented in \cite{Stroppa+07,Haque+09,Haque+10}; sub-sentential paraphrasing using pivot translations is described in \cite{Max09}. Although most of the applications of TiMBL are in the natural language processing and computational linguistics areas, it is occasionally used in other related domains as well. Examples are the classification of newborns' cries \cite{Feier+14}, scaffolded learning by robots \cite{Saunders+06}, or the detection of clinically important micro-organisms through an electronic nose \cite{Moens+06}. \subsubsection{Dissertations and special issue} The first dissertation-length study devoted to the approach was \cite{VandenBosch97}, in which the approach is compared to alternative learning methods for NLP tasks related to English word pronunciation (stress assignment, syllabification, morphological analysis, alignment, grapheme-to-phoneme conversion). TiMBL is also central in the Ph.D. theses of \namecite{Buchholz02}, \namecite{Lendvai04}, \namecite{Hendrickx05}, \namecite{Hoste05}, \namecite{Keuleers08}, \namecite{Canisius09}, and \namecite{Vandekerckhove13}. In 1999 a special issue of the {\em Journal for Experimental and Theoretical Artificial Intelligence} (Vol.~11(3), edited by Walter Daelemans) was devoted to Memory-Based Language Processing. The introduction to this special issue discusses the inspiration sources and alternative developments related to the memory-based approach taken in TiMBL \cite{Daelemans99b}. \ \\ {\it All Tilburg/Antwerp papers referred to in this section, as well as more recent papers, are available in electronic form from the {\sc ILK} home page: {\tt http://ilk.uvt.nl} and the {\sc CLiPS} home page: \\ {\tt http://www.clips.ua.ac.be/}.} \chapter{Software usage and options} \label{reference} \section{Command line options} \label{commandline} The user interacts with TiMBL through the use of command line arguments. When you have installed TiMBL successfully, and you type {\tt timbl} at the command line without any further arguments, it will print an overview of the most basic command line options. {\footnotesize \begin{verbatim} TiMBL 6.4.2 (c) ILK 1998 - 2012. Tilburg Memory Based Learner Induction of Linguistic Knowledge Research Group, Tilburg University CLiPS Computational Linguistics Group, University of Antwerp Mon Apr 23 15:43:26 2012 usage: timbl -f data-file {-t test-file} or see: timbl -h for all possible options \end{verbatim} } If you are satisfied with all of the default settings, you can proceed with just these basics: \begin{description} \item {\tt -f } : supplies the name of the file with the training items. \item {\tt -t } : supplies the name of the file with the test items. \item {\tt -h} : prints a glossary of all available command line options. \end{description} The presence of a training file will make TiMBL pass through the first two phases of its cycle. In the first phase it examines the contents of the training file, and computes a number of statistics on it (feature weights etc.). In the second phase the instances from the training file are stored in memory. If no test file is specified, the program exits, possibly writing some of the results of learning to files (see below). If there is a test file, the selected classifier, trained on the present training data, is applied to it, and the results are written to a file the name of which is a combination of the name of the test file and a code representing the chosen algorithm settings. TiMBL then reports the percentage of correctly classified test items. The default settings for the classification phase are: a Memory-Based Learner, with Gain Ratio feature weighting, with $k=1$, and with optimizations for speedy search. If you need to change the settings, because you want to use a different type of classifier, or because you need to make a trade-off between speed and memory-use, then you can use the options that are shown using {\tt -h}. The sections below provide a reference to the use of these command line arguments, and they are roughly ordered by the type of action that the option has effect on. Note that some options (listed with ``{\tt +/-}'') can be turned on ({\tt +}) or off ({\tt -}). \subsection{Algorithm and metric selection} \begin{description} \item {\tt -a or } : determines the classification algorithm. Possible values are: \begin{description} \item {\tt 0} or {\tt IB1} -- the {\sc ib1} ($k$-NN) algorithm (default). See Sections~\ref{mbl} and~\ref{indexing}. \item {\tt 1} or {\tt IGTREE} -- {\sc IGTree}, decision-tree-based optimization. See Section~\ref{igtree}. \item {\tt 2} or {\tt TRIBL} -- {\sc tribl}, a hybrid of {\sc ib1} and {\sc IGTree}. See Section~\ref{tribl}. \item {\tt 3} or {\tt IB2} -- {\sc ib2}, incremental edited memory-based learning. See Section~\ref{ib2}. \item {\tt 4} or {\tt TRIBL2} -- {\sc tribl2}, a non-parameteric version of {\sc tribl}. See Section~\ref{tribl}. \end{description} \item {\tt -m } : determines which distance metrics are used for each feature. The format of this string is as follows:\\ {\tt GlobalMetric:MetricRange:MetricRange}\\ Where {\tt GlobalMetric} is used for alle features except for the ones that are assigned other metrics by following the restrictions given by {\tt :MetricRange}. A range can be written using comma's for lists, and hyphens for intervals. The metric code can be one of the following nine: \begin{itemize} \item {\tt O} -- Overlap (default; see Subsection~\ref{overlap}) \item {\tt M} -- Modified value difference ({\sc mvdm}; see Subsection~\ref{mvdm}) \item {\tt J} -- Jeffrey divergence (see Subsection~\ref{mvdm}) \item {\tt S} -- Jensen-Shannon divergence (see Subsection~\ref{mvdm}) \item {\tt D} -- Dot product (see Subsection~\ref{dotproduct}) \item {\tt C} -- Cosine distance (see Subsection~\ref{dotproduct}) \item {\tt N} -- Numeric (for numeric features; see Subsection~\ref{overlap}) \item {\tt E} -- Euclidean distance (for numeric features; see Subsection~\ref{overlap}) \item {\tt L} -- Levenshtein (see Subsection~\ref{overlap}) \item {\tt DC} -- Dice coefficient (see Subsection~\ref{overlap}) \item {\tt I} -- Ignore (ignore specified features) \end{itemize} For example, {\tt -mO:N3:I2,5-7} sets the global metric to overlap, declares the third feature to be numeric, and ignores features 2 and 5, 6, and 7. Ignore {\em can}\/ be the global metric; it must be followed by a {\tt MetricRange} string with metric {\tt O}, {\tt M}, {\tt J}, {\tt D}, or {\tt N} specifying in the range which features are {\em not}\/ ignored. \item {\tt -w } : chooses between feature-weighting possibilities. The weights are used in the metric of {\sc ib1} and in the ordering of the {\sc IGTree}. Possible values are: \begin{description} \item {\tt -w0} or {\tt -w nw} -- No weighting, i.e. all features have the same importance (weight = 1). \item {\tt -w1} or {\tt -w gr} -- Gain Ratio weighting (default). See section~\ref{infogain}. \item {\tt -w2} or {\tt -w ig} -- Information Gain weighting. See section~\ref{infogain}. \item {\tt -w3} or {\tt -w x2} -- Chi-squared ($\chi^2$) weighting. See section~\ref{chisquared}. \item {\tt -w4} or {\tt -w sv} -- Shared Variance weighting. See section~\ref{chisquared}. \item {\tt -w5} or {\tt -w sd} -- Standard Deviation weighting. \item n=$<$filename$>$:$<$number$>$ or n=$<$filename$>$ -- Instead of the five weight settings above we can supply a filename to the {\tt -w} option. This causes TiMBL to read this file and use its contents as weights. If only $<$filename$>$ is given as an argument, the file is supposed to contain one list of feature weights for all features. The $<$filename$>$:$<$number$>$ option assumes that a weights file generated by TiMBL with the {\tt -W} option (and possibly edited by the user) is read back in; the number refers to one of the five numbers above. See section~\ref{weightformat} for a description of the format of weights files. \end{description} \item {\tt -k } : number of nearest neighbors used for extrapolation. Only applicable in conjunction with {\sc ib1} ({\tt -a 0}), {\sc tribl} ({\tt -a 2}), {\sc tribl2} ({\tt -a 4}) and {\sc ib2} ({\tt -a 3}). The default is 1. Especially with the {\sc mvdm} metric it is often useful to determine a good value larger than 1 for this parameter (usually an odd number, to avoid ties). Note that due to ties (instances with exactly the same similarity to the test instance) the number of instances used to extrapolate might in fact be much larger than this parameter. \item {\tt -d } : The type of class voting weights that are used for extrapolation from the nearest neighbor set. {\tt val} can be one of: \begin{itemize} \item {\tt Z} : normal majority voting; all neighbors have equal weight (default). \item {\tt ID} : Inverse Distance weighting. See Section~\ref{distweightvote}, Equation~\ref{inverseweight}. \item {\tt IL} : Inverse Linear weighting. See Section~\ref{distweightvote}, Equation~\ref{dudani_eq}. \item {\tt ED::} : Exponential Decay weighting with decay parameters {\tt a} ($\alpha$) and {\tt b} ($\beta$). No spaces are allowed in the string. Parameter {\tt b} can be left unspecified: {\tt ED:} assumes $\beta=1$. The syntax used in previous TiMBL versions ({\tt ED}) is still supported but deprecated. See Section~\ref{distweightvote}, Equation~\ref{expdecayweight}. \end{itemize} \item {\tt -L } : frequency threshold for switching from the {\sc mvdm} or Jeffrey Divergence to the Overlap distance metric. The default is 1 (never switch). When in a pair of matched values one or both values occur less frequently than {\tt n} times in the learning material, TiMBL switches from {\sc mvdm} or Jeffrey Divergence to Overlap. Higher values of {\tt n} force TiMBL to use the Overlap metric more. Only applicable in conjunction with the {\sc mvdm} ({\tt -mM}) and Jeffrey divergence ({\tt -mJ}) or Jensen-Shannon divergence ({\tt -mS}) distance metrics. \item {\tt -b } : determines n ($\geq 1$), the number of instances, to be taken from the top of the training file, to act as the bootstrap set of memorized instances before {\sc ib2} starts adding new instances. Only applicable in conjunction with {\sc ib2} ({\tt -a 3}). \item {\tt -q } : {\tt n} is the {\sc tribl} offset, the index number of the feature (counting from 1) after which {\sc tribl} should switch from {\sc IGTree} to {\sc ib1}. Only applicable in conjunction with {\sc tribl} ({\tt -a 2}). \item {\tt -R } : Resolve ties in the classifier randomly, using a random generator with seed n. {\tt -R } causes the classification to be based on a random pick (with seed n) of a category according to the probability distribution in the nearest neighbor set. By default, {\tt -R} is not used, but rather the deterministic tie resolution scheme described in Subsection~\ref{overlap}. \item {\tt -t $<$@file$>$} : If the filename given after {\tt -t} starts with '{\tt @}', TiMBL will read commands for testing from {\tt file}. This file should contain one set of instructions per line. On each line new values can be set for the following command line options: {\tt -B -d -e -k -L -M -o -p -Q -R -t -u +/-v -w +/-x +/-\%}. It is compulsory that each line in {\tt file} contains a {\tt -t } argument to specify the name of the test file. \item {\tt -t } : the string {\tt } is the literal name of the file with the test items. \item {\tt -t leave\_one\_out} : No test file is read, but testing is done on each pattern of the training file, by treating each pattern of the training file in turn as a test case (and the whole remainder of the file as training cases). Only applicable in conjunction with {\sc ib1} ({\tt -a0}). \item {\tt -t cross\_validate} : An $n$-fold cross-validation experiment is performed on the basis of $n$ files (e.g. $1/n$ partitionings of an original data file). The names of these $n$ files need to be in a text file (one name per line) which is given as argument of {\tt -f}. In each fold $f=1 \ldots n$, file number $f$ is taken as test set, and the remaining $n-1$ files are concatenated to form the training set. Only applicable in conjunction with {\sc ib1} ({\tt -a0}). \item {\tt -T } : The $n$th column in the training set of labeled examples contains the label to be predicted, while all other columns represent the input features. By default, the last column is assumed to contain the class labels. \end{description} \subsection{Input options} \begin{description} \item {\tt -f } : the string {\tt } is the literal name of the file with the training items, or (in conjunction with {\tt -t cross\_validate}, the file containing the names of the cross-validation files. \item {\tt -F } : Force TiMBL to interpret the training and test file as a specific data format. Possible values for this parameter are: {\tt Compact, C4.5, ARFF, Columns, Sparse, Binary} (case-insensitive). The default is that TiMBL guesses the format from the contents of the first line of the data file. ARFF is not automatically detected. See section~\ref{dataformats} for description of the data formats and the guessing rules. The {\tt Compact} format cannot be used with numeric features. \item {\tt -l } : Feature length. Only applicable with the Compact data format; {\tt } is the number of characters used for each feature-value and category symbol. \item {\tt -i } : Skip the first two training phases: instead of processing a training file, read a previously saved (see {\tt -I} option) instance-base or {\sc IGTree} from the file {\tt treefile}. See section~\ref{treeformat} for the format of this file. \item {\tt --matrixin=} : Read value distance metrics (such as {\sc mvdm} or Jeffrey divergence matrices written to file with {\tt --matrixout=} : Replace the automatically computed value-class probability matrix with the matrices provided in this file. \item {\tt -P } : Specify a path to read the data files from. This path is ignored if the name of the data file already contains path information. \item {\tt -s} : Use the whitespace-delimited exemplar weights, given after each training instance in the training file {\tt }, during classification. {\tt } may contain exemplar weights, but they are not used in classification. If the test file does not have an exemplar weights column, you must specify {\tt -s1}. Exemplar weights can also be ignored (in both training and test files) by specifying {\tt -s0}. Does not work simultaneously with {\tt --occurrences}. \item {\tt --occurrences=\{train|test|both\}} :Use the whitepace-delimited integers, given after each instances in a training or test file (or both when training and test actions are invoked at once), as representing the number of occurrences that instance should be counted as. By default these counts are not given, in which case TiMBL counts every instance as a single token. Does not work simultaneously with {\tt -s}. \end{description} \subsection{Output options} \begin{description} \item {\tt -I } : After phase two of learning, save the resulting tree-based representation of the instance-base or {\sc igtree} in a file. This file can later be read back in using the {\tt -i} option (see above). For {\sc IGTree} this also automatically saves the current weights into {\tt treefile.wgt} unless this is overridden by {\tt -W}. See section~\ref{treeformat} for a description of the resulting file's format. \item {\tt --matrixout=} : Store calculated {\sc mvdm} or Jeffrey divergence distance metrics in file {\tt filename}. \item {\tt -X } : instead of the proprietary file format written with the {\tt -I} switch, {\tt -X} writes the TiMBL tree into an XML tree in {\tt }. This XML file cannot be read back into TiMBL. \item {\tt -W } : Save the currently used feature-weights in a file. \item {\tt -U } : Write the automatically computed value-class probability matrix to this file. \item {\tt -n } : Save the feature-value and target category symbols in a C4.5 style ``names file'' with the name {\tt }. Take caution of the fact that TiMBL does not mind creating a file with ',' '.' '$|$' and ':' values in features; C4.5 will produce errors on this. \item {\tt -p } : Indicate progress during training and testing after every n processed patterns. The default setting is 100,000. \item {\tt -e } : During testing, compute and print an estimate on how long it will take to classify n test patterns. Off by default. \item {\tt +/-v } : Verbosity Level; determines how much information is written to the output during a run. Unless indicated otherwise, this information is written to standard error. The use of {\tt +} turns a given verbosity level {\bf on}, whereas {\tt -} turns it {\bf off} (only useable in non-commandline contexts, such as client/server communication or {\tt -t @testcommandfile}). This parameter can take on the following values (case-insensitive): \begin{description} \item {\tt s} : work silently (turns off all set verbosity levels). \item {\tt o} : show all options set. \item {\tt b}: show node/branch count and branching factor. \item {\tt f} : show calculated feature weights. (default) \item {\tt p} : show {\sc mvdm} matrices. \item {\tt e} : show exact matches. \item {\tt as} : show overall advanced statistics (micro and macro averages of F-score and AUC). \item {\tt cm} : show confusion matrix between actual and predicted classes. \item {\tt cs} : show per-class statistics (precision, recall, true positive rate, false positive rate, F-score, AUC). \item {\tt di} : add the distance of the nearest neighbor to the output file. \item {\tt db} : add class distribution in the nearest neighbor set to the output file. \item {\tt md} : add matching depth and node type (N for non-ending node, L for leaf) to output file. \item {\tt k} : add a summary of class distribution information of all nearest neighbors to the output file (sets {\tt -x}) \item {\tt n} : add nearest neigbors to the output file (sets {\tt -x}) \end{description} You may combine levels using '{\tt +}' e.g. {\tt +v p+db} or {\tt -v o+di}. \item {\tt -G } : Normalize class distributions generated by {\tt +v db}. \begin{description} \item {\tt 0 (zero)} : Normalize distributions so that they add up to 1.0 \item {\tt 1:} : Smooth by adding floating-point $f$ to all class votes (e.g. {\tt -G1:1} performs add-one smoothing). \end{description} \item {\tt --Beam=} : Limit the number of returned classes and class votes returned by {\tt +v db} to $n$. Default is infinity (no limit). \item {\tt +/- \%} : Write the percentage of correctly classified test instances, the number of correctly classified instances, and the total number of classified instances (one number per line, three lines in total) to a file with the same name as the output file, but with the suffix ``{\tt .\%}''. \item {\tt -o $<$filename$>$} : Write the test output to filename. Useful for different runs with the same settings on the same testfile, where the default output file name would normally be the same. \item {\tt -O $<$path$>$} : Write all output to the path given here. The default is to write all output to the directory where the test file is located. \item {\tt -V} : Show the TiMBL version number. \end{description} \subsection{Internal representation options} \begin{description} \item {\tt -N } : (maximum) number of features. Obligatory for Sparse and Binary formats. When larger than a pre-defined constant (default 2500), N needs to be set explicitly for all algorithms. \item {\tt +/-x} : turns the shortcut search for exact matches on or off in {\sc ib1} (and {\sc ib2}, {\sc tribl}, and {\sc tribl2}). The default is off ({\tt -x}). Turning it on makes {\sc ib1} generally faster, but with $k>1$ the shortcut produces different results from a genuine $k$ nearest neighbors search, since absolute preference is given to the exact match. \item {\tt -M } : Set the maximum number of nearest neighbors printed using the {\tt +vn} verbosity option. By default this is set to 500, but when you are interested in the contents of really large nearest neighbor sets (which is possible with large $k$ or large data sets with few features), {\tt n} can be increased up to 100,000. \item {\tt +/-H} : Turn on/off hashing of feature values and class labels in TiMBL trees. Hashing is done by default, but with short (e.g. one-character) feature values and/or classes less memory is used when hashing is set off. \item {\tt +/-D} : Store class distributions on all nodes in the TiMBL tree. Default is off ({\tt -D}). Setting {\tt +D} costs memory, and is only necessary when using IGTree in combination with the {\tt +v db} verbosity setting (which prints class distributions in the classifier output). \item {\tt -B } : Number of bins used for discretizing numeric data (only used for computing feature weights). \item {\tt -c } : Clipping (threshold) frequency for prestoring {\sc mvdm} matrices. Cells in the matrix are only stored if both feature values occur more than {\tt } times. \item {\tt --clones=} : Classify a test set using $n$ processors in parallel. \item {\tt --Treeorder=} : Set the ordering of the TiMBL tree (with {\sc ib1} and {\sc ib2}), i.e., rank the features according to the metric identified by {\tt }. The default ordering is {\tt G/V} (according to gain ratio divided by the number of values), but some orderings may produce faster classification. Note that different orderings do {\em not}\/ change the classification behavior of {\sc ib1} and {\sc ib2}. {\tt } can take the following values: \begin{description} \item {\tt DO} : no ordering (the ordering of the features in the data file is taken) \item {\tt GRO} : gain ratio (eq.~\ref{IGgainratio}) \item {\tt IGO} : information gain (eq.~\ref{IGgain}) \item {\tt 1/V} : $1/V$, where $V$ is the number of values \item {\tt G/V} : gain ratio divided by the number of values \item {\tt I/V} : information gain divided by the number of values \item {\tt X2O} : \chisq \ (eq.~\ref{chisq-eq}) \item {\tt X/V} : \chisq \ divided by the number of values \item {\tt SVO} : shared variance (eq.~\ref{shared-variance-eq}) \item {\tt S/V} : shared variance divided by the number of values \item {\tt GxE} : gain ratio $\times si$, where $si$ is the split info of the feature (eq.~\ref{splitinfo}) \item {\tt IxE} : information gain $\times si$ \item {\tt 1/S} : $1/si$ \end{description} \end{description} \subsection{Hidden options} The commandline interface to TiMBL contains several hidden options that have been built in over time for particular reasons. Some have survived over time, and although their use is not for the faint-hearted, some may offer interesting functionalities. A small list of disclosed hidden options follows. \begin{description} \item {\tt --sloppy=\{true|false\}}: in combination with leave-one-out (LOO) testing, this option turns off all weight recomputation. By default, leaving out one training example out causes all feature weights, value-class matrices, and derived metrics such as {\sc mvdm} to be recomputed, because strictly the example-specific statistics should be absent when it is held out and classified. {\tt --sloppy} skips this, causing a significant speedup, and usually slightly better LOO scores. Use only if your experimental method allows it. Default value is {\tt false}. \item {\tt --silly=\{true|false\}}: set to {\tt true}, switches off the optimized nearest-neighbor search in {\sc ib1} and {\sc tribl}. This causes TiMBL to compare all feature values of a test instance to full paths in the TiMBL tree. This causes TiMBL to slow down dramatically on most datasets. Setting is available to enable testing the effect of optimized search. Default value is {\tt false}. \item{\tt --Diversify}: modifies all features weights by subtracting the smallest weight (plus $\epsilon$) from all weights. The smallest weight thus becomes $\epsilon$. This modification ``diversifies'' the feature weights, and was introduced to enhance the effect of {\sc Dimbl}, the multi-CPU variant of TiMBL\footnote{For {\sc Dimbl}, see \url{http://ilk.uvt.nl/dimbl}}. \end{description} \section{File formats} \label{fileformats} This section describes the format of the input and output files used by TiMBL. Where possible, the format is illustrated using the classical ``objects'' data set, which consists of 12 instances of 5 different everyday objects (nut, screw, key, pen, scissors), described by 3 discrete features (size, shape, and number of holes). \subsection{Data files} \label{dataformats} The training and test sets for the learner consist of descriptions of instances in terms of a fixed number of feature-values. TiMBL supports a number of different formats for the instances, but they all have in common that the files should contain one instance per line. The number of instances is determined automatically, and the format of each instance is inferred from the format of the first line in the training set. The last feature of the instance is assumed to be the target category\footnote{unless the {\tt -s} or {\tt --occurrences} options are used, which both assume a final column with numbers; the class is then the before-last feature.}. Should the guess of the format by TiMBL turn out to be wrong, you can force it to interpret the data as a particular format by using the {\tt -F} option. Note that TiMBL, by default, will interpret features as having {\em symbolic, discrete values}. Unless you specify explicitly that certain features are numeric, using the {\tt -m} option, TiMBL will interpret numbers as just another string of characters. If a feature is numeric, its values will be scaled to the interval [0,1] for purposes of distance computation (see Equation~\ref{overlapeq}). The computation of feature weights will be based on a discretization of the feature. Once TiMBL has determined the input format, it will skip and complain about all lines in the input which do not respect this format (e.g.~have a different number of feature-values with respect to that format). During testing, TiMBL writes the classifications of the test set to an output file. The format of this output file is by default the same as the input format, with the addition of the predicted category being appended after the correct category. If we turn on higher levels of verbosity, the output files will also contain distributions, distances and nearest neighbor sets. \subsubsection{Column format} \label{comlumnformat} The {\bf column format} uses white space as the separator between features. White space is defined as a sequence of one or more spaces or tab characters. Every instance of white space is interpreted as a feature separator, so it is not possible to have feature-values containing white space. The column format is auto-detected when an instance of white space is detected on the first line before a comma has been encountered. The example data set looks like this in the column format: \begin{footnotesize} \begin{verbatim} small compact 1 nut small long none screw small long 1 key small compact 1 nut large long 1 key small compact none screw small compact 1 nut large long none pen large long 2 scissors large long 1 pen large other 2 scissors small other 2 key \end{verbatim} \end{footnotesize} \subsubsection{C4.5 format} \label{c45format} This format is a derivative of the format that is used by the well-known C4.5 decision tree learning program~\cite{Quinlan93}. The separator between the features is a comma, and the category (viz. the last feature on the line) is followed by a period (although this is not mandatory: TiMBL is robust to missing periods)\footnote{The periods after the category are not reproduced in the output}. White space within the line is taken literally, so the pattern {\tt a,\ b\ c,d} will be interpreted as {\tt `a',`\ b\ c',`d'}. An exception is the class label, which should not contain any whitespace. When using this format, especially with linguistic data sets or with data sets containing floating point numbers, one should take special care that commas do not occur as feature values and that periods do not occur within the category. Note that TiMBL's C4.5 format does not require a so called {\em namesfile}. However, TiMBL can produce such a file for C4.5 with the {\tt -n} option. The C4.5 format is auto-detected when a comma is detected on the first line before any white space has been encountered. The example data set looks like this in the C4.5 format: \begin{footnotesize} \begin{verbatim} small,compact,1,nut. small,long,none,screw. small,long,1,key. small,compact,1,nut. large,long,1,key. small,compact,none,screw. small,compact,1,nut. large,long,none,pen. large,long,2,scissors. large,long,1,pen. large,other,2,scissors. small,other,2,key. \end{verbatim} \end{footnotesize} \subsubsection{ARFF format} \label{arffformat} ARFF is a format that is used by the WEKA machine learning workbench~\cite{Garner95,Witten+99}\footnote{WEKA is available from the Waikato University Department of Computer Science, \url{http://www.cs.waikato.ac.nz/~ml/weka}}. Although TiMBL at present does not entirely follow the ARFF specification, it still tries to do as well as it can in reading this format. The ARFF format is not autodetected, and needs to be specified on the commanline with {\tt -F ARFF}. In ARFF data, the actual data are preceded by a information on feature types, feature names, and names of values in case of symbolic features. TiMBL ignores all of these lines, and starts reading data from after the {\tt @data} statement until the end of the file. Feature-values are supposed to be separated by commas; white space is deleted entirely, so the pattern {\tt a, b c,d} will be interpreted as {\tt `a',`bc',`d'}. There should be no whitespace in class labels. % We hope to include better support for the ARFF format %in future releases. \begin{footnotesize} \begin{verbatim} % There are 4 attributes. % There are 12 instances. % Attribute information: Ints Reals Enum Miss % 'size' 0 0 12 0 % 'shape' 0 0 12 0 % 'n_holes' 9 0 3 0 % 'class.' 0 0 12 0 @relation 'example.data' @attribute 'size' { small, large} @attribute 'shape' { compact, long, other} @attribute 'n_holes' { 1, none, 2} @attribute 'class.' { nut., screw., key., pen., scissors.} @data small,compact,1,nut. small,long,none,screw. small,long,1,key. small,compact,1,nut. large,long,1,key. small,compact,none,screw. small,compact,1,nut. large,long,none,pen. large,long,2,scissors. large,long,1,pen. large,other,2,scissors. small,other,2,key. \end{verbatim} \end{footnotesize} \subsubsection{Compact format} \label{compactformat} The compact format is especially useful when dealing with very large data files. Because this format does not use any feature separators, file size is reduced considerably in some cases. The price of this is that all features and class labels must be of equal length (in characters) and TiMBL needs to know beforehand what this length is. You must tell TiMBL by using the {\tt -l} option. The compact format is auto-detected when neither of the other formats applies. The same example data set might look like this in the column format with two characters per feature: \begin{footnotesize} \begin{verbatim} smco1_nu smlonosc smlo1_ke smco1_nu lalo1_ke smconosc smco1_nu lalonope lalo2_sc lalo1_pe laot2_sc smot2_ke \end{verbatim} \end{footnotesize} \subsubsection{Sparse format} The sparse format is relevant for data with features of which a significant portion of the values is $0.0$ (numeric), $0$ (binary), or some ``null'' symbolic value. Storing only the non-null values typically takes less space on disk. Consider, for example, a data set in text classification with 10,000 features each representing the tf*idf weight of a term. It would be uneconomical to store instances as long lines of \begin{footnotesize} \begin{verbatim} 0.02, 0.0, 0.0, 0.0, 0.54, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... , 0.01,sports \end{verbatim} \end{footnotesize} The Sparse format allows to store such an instance as \begin{footnotesize} \begin{verbatim} (1,0.02)(5,0.54)...(10000,0.01)sports \end{verbatim} \end{footnotesize} That is, a sequence of ($,$) expressions between parentheses each indicating that the feature number $index$ has value $value$, with the class label at the end, directly following the last parenthesis. The feature index is assumed to start at 1. In case of symbolic values, whitespace included in the parentheses are considered significant (i.e., part of the values). A case with only null values can be represented as either `{\tt class} or {\tt ,class}. This option must be specified by the user ({\tt -F Sparse}); it is not guessed from the data. It must also be accompanied by a user declaration of the number of features ({\tt -N }). \subsubsection{Sparse Binary format} \label{binaryformat} The sparse binary format, a simplified version of the Sparse format, is especially useful when dealing with large numbers of two-valued (binary) features, of which each case only has a very few active ones, such as e.g. in text categorization. Thus instead of representing a case as: \begin{footnotesize} \begin{verbatim} 1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,small. \end{verbatim} \end{footnotesize} We can represent it as: \begin{footnotesize} \begin{verbatim} 1,8,11,12,18,small. \end{verbatim} \end{footnotesize} This format allows one to specify only the index numbers of the active features (indices start at one), while implicitly assuming that the value for all the remaining features is zero. Because each case has a different number of active features, we must specify in some other way what the actual number of features is. This must be done using the {\tt -N} option. As the format is very similar to numeric features, it must always be user-declared using {\tt -F Binary}. The last feature of a line is always interpreted as being the category string. A case with only zeroes can be represented as either `{\tt class} or {\tt ,class}. \subsection{Weight files} \label{weightformat} The feature weights used for computing similarities and for the internal organization of the memory base can be saved to a file {\tt -W}. These files can be read back into TiMBL with {\tt -w $<$filename$>$:$<$weight number$>$}, where the weight number refers to the five options in TiMBL. It is also possible to change these files manually before reading them in -- and additionally it is also possible to write a file from scratch and read this into TiMBL. This allows the experimenter to handcraft feature weights. The generic format for the weights file is as follows. The weights file may contain comments on lines that start with a \# character. The other lines contain the number of the feature followed by its numeric weight. An example of such a file is provided below. The numbering of the weights starts with 1. In this example, the data set has three features. \begin{footnotesize} \begin{verbatim} # DB Entropy: 2.29248 # Classes: 5 # Lines of data: 12 # Fea. Weight 1 0.765709 2 0.614222 3 0.73584 \end{verbatim} \end{footnotesize} Weight files written by TiMBL are of the same format, but write all weights in a concatenation, separated by \# lines that carry the abbreviated name of the weight (nw, gr, ig, x2, sv). The following example illustrates this format (which can be edited manually, as long as the same number of lines is kept): \begin{footnotesize} \begin{verbatim} # DB Entropy: 1.61789 # Classes: 5 # Lines of data: 2999 # nw # Fea. Weight 1 1 2 1 3 1 # # gr # Fea. Weight 1 0.0428445870345557 2 0.185070180760327 3 0.325371814230901 # # ig # Fea. Weight 1 0.213887591411729 2 0.669704582861074 3 1.27807624584789 # # sv # Fea. Weight 1 0.0762436694064095 2 0.233998145488354 3 0.596896311429044 # # x2 # Fea. Weight 1 914.619058199289 2 2807.0417532783 3 7160.36815190281 # \end{verbatim} \end{footnotesize} \subsection{Value difference files} \label{mvdmformat} Using the {\sc mvdm} metric, it can sometimes be interesting to inspect the matrix of conditional class probabilities from Equation~\ref{MVDMeq}. By using the {\tt -U} option, we can write the computed matrix to a file. This way we can see which values are considered to be similar by the metric. For each feature a row vector is given for each value, of the conditional probabilities of all the classes (columns) given that value. \begin{footnotesize} \begin{verbatim} targets A, B, C, D, E. feature # 1 Matrix: small 0.429 0.286 0.286 0.000 0.000 large 0.000 0.000 0.200 0.400 0.400 feature # 2 Matrix: compact 0.750 0.250 0.000 0.000 0.000 long 0.000 0.167 0.333 0.333 0.167 other 0.000 0.000 0.500 0.000 0.500 feature # 3 Matrix: 1 0.500 0.000 0.333 0.167 0.000 none 0.000 0.667 0.000 0.333 0.000 2 0.000 0.000 0.333 0.000 0.667 \end{verbatim} \end{footnotesize} As long as this format is observed, the file can be modified (manually or by substituting other vector-based representations for the values), and the new matrix can be read in and used with the {\sc mvdm} metric with the {\tt -u} option. \subsection{Tree files} \label{treeformat} Although the learning phase in TiMBL is relatively fast, it can be useful to store the internal representation of the data set both for later usage and for faster subsequent learning. In TiMBL, the data set is stored internally in a tree structure (see Section~\ref{indexing}). When using {\sc ib1}, this tree representation contains all the training cases as full paths in the tree. When using {\sc IGTree}, unambiguous paths in the tree are pruned before it is used for classification or written to file; on the same data, {\sc IGTree} trees are usually considerably smaller than {\sc ib1} trees. In either tree type, the arcs represent feature values and nodes contain class distribution information. The features are in the same order throughout the tree. This order is either determined by memory-size considerations in {\sc ib1}, or by feature relevance in {\sc IGTree}. It can explicitly be manipulated using the {\tt -T} option. We strongly advise to refrain from manually editing the tree file. However, the syntax of the tree file is as follows. First a header consisting of information about the status of the tree, the feature-ordering (the permutation from the order in the data file to the order in the tree), and the presence of numeric features is provided\footnote{Although in this header each line starts with '\#', these lines cannot be seen as comment lines.}. Subsequently, unless hashing has been set off explicitly ({\tt -H}), a legenda is given of numeric hash codes for the class names (one unique integer per class) and feature value names (one unique integer per value). Subsequently, the tree's nodes and arcs are given in a proprietary non-indented bracket notation. Starting from the root node, each node is denoted by an opening parenthesis ``('', followed by an integer coding the default class. After this, there is the class distribution list, within curly braces ``\{ \}'', containing a non-empty list of category codes followed by integer counts. After this comes an optional comma-separated list of arcs to child nodes, within ``[ ]'' brackets. An arc is labeled with a coded feature value. The node that the arc leads to again has a class distribution, and any number of child nodes pointed to by arcs. The {\sc ib1} tree constructed from our example data set looks as follows: \begin{footnotesize} \begin{verbatim} # Status: complete # Permutation: < 1, 3, 2 > # Numeric: . # Version 4 (Hashed) # Classes 1 nut 2 screw 3 key 4 pen 5 scissors Features 1 small 2 compact 3 1 4 long 5 none 6 large 7 2 8 other (1{ 1 3, 2 2, 3 3, 4 2, 5 2 }[1(1[3(1[2(1{ 1 3 }) ,4(3{ 3 1 }) ] ) ,5(2[2(2{ 2 1 }) ,4(2{ 2 1 }) ] ) ,7(3[8(3{ 3 1 }) ] ) ] ) ,6(4[3(3[4(3{ 3 1, 4 1 }) ] ) ,5(4[4(4{ 4 1 }) ] ) ,7(5[4(5{ 5 1 }) ,8(5{ 5 1 }) ] ) ] ) ] ) \end{verbatim} \end{footnotesize} The corresponding compressed {\sc IGTree} version is considerably smaller. \begin{footnotesize} \begin{verbatim} # Status: pruned # Permutation: < 1, 3, 2 > # Numeric: . # Version 4 (Hashed) # Classes 1 nut 2 screw 3 key 4 pen 5 scissors Features 1 small 2 compact 3 1 4 long 5 none 6 large 7 2 8 other (1{ 1 3, 2 2, 3 3, 4 2, 5 2 }[1(1{ 1 3, 2 2, 3 2 }[3(1{ 1 3, 3 1 }[4(3{ 3 1 }) ] ) ,5(2{ 2 2 }) ,7(3{ 3 1 }) ] ) ,6(4{ 3 1, 4 2, 5 2 }[3(3{ 3 1, 4 1 }) ,7(5{ 5 2 }) ] ) ] ) \end{verbatim} \end{footnotesize} TiMBL tree files generated by versions 1.0 to 3.0 of TiMBL, which do not contain hashed class and value names, are no longer recognized in current TiMBL versions. Backward compatibility to trees generated by versions 1.0 to 3.0 is preserved in TiMBL version 4 up to release 4.3.1. \clearpage \bibliographystyle{fullname} \bibliography{ilk} \end{document} LanguageMachines-timbl-642727d/docs/texfiles/distanceweight-ided.eps000077500000000000000000000354521451477526200255150ustar00rootroot00000000000000%!PS-Adobe-2.0 EPSF-2.0 %%Title: distanceweight-ided.eps %%Creator: gnuplot 3.7 patchlevel 2 %%CreationDate: Fri Sep 12 14:55:25 2003 %%DocumentFonts: (atend) %%BoundingBox: 50 50 410 302 %%Orientation: Portrait %%EndComments /gnudict 256 dict def gnudict begin /Color false def /Solid false def /gnulinewidth 5.000 def /userlinewidth gnulinewidth def /vshift -73 def /dl {10 mul} def /hpt_ 31.5 def /vpt_ 31.5 def /hpt hpt_ def /vpt vpt_ def /M {moveto} bind def /L {lineto} bind def /R {rmoveto} bind def /V {rlineto} bind def /vpt2 vpt 2 mul def /hpt2 hpt 2 mul def /Lshow { currentpoint stroke M 0 vshift R show } def /Rshow { currentpoint stroke M dup 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/UP { dup vpt_ mul /vpt exch def hpt_ mul /hpt exch def /hpt2 hpt 2 mul def /vpt2 vpt 2 mul def } def /DL { Color {setrgbcolor Solid {pop []} if 0 setdash } {pop pop pop 0 setgray Solid {pop []} if 0 setdash} ifelse } def /BL { stroke userlinewidth 2 mul setlinewidth Rounded { 1 setlinejoin 1 setlinecap } if } def /AL { stroke userlinewidth 2 div setlinewidth Rounded { 1 setlinejoin 1 setlinecap } if } def /UL { dup gnulinewidth mul /userlinewidth exch def dup 1 lt {pop 1} if 10 mul /udl exch def } def /PL { stroke userlinewidth setlinewidth Rounded { 1 setlinejoin 1 setlinecap } if } def /LTb { BL [] 0 0 0 DL } def /LTa { AL [1 udl mul 2 udl mul] 0 setdash 0 0 0 setrgbcolor } def /LT0 { PL [] 1 0 0 DL } def /LT1 { PL [4 dl 2 dl] 0 1 0 DL } def /LT2 { PL [2 dl 3 dl] 0 0 1 DL } def /LT3 { PL [1 dl 1.5 dl] 1 0 1 DL } def /LT4 { PL [5 dl 2 dl 1 dl 2 dl] 0 1 1 DL } def /LT5 { PL [4 dl 3 dl 1 dl 3 dl] 1 1 0 DL } def /LT6 { PL [2 dl 2 dl 2 dl 4 dl] 0 0 0 DL } def /LT7 { PL [2 dl 2 dl 2 dl 2 dl 2 dl 4 dl] 1 0.3 0 DL } def /LT8 { PL [2 dl 2 dl 2 dl 2 dl 2 dl 2 dl 2 dl 4 dl] 0.5 0.5 0.5 DL } def /Pnt { stroke [] 0 setdash gsave 1 setlinecap M 0 0 V stroke grestore } def /Dia { stroke [] 0 setdash 2 copy vpt add M hpt neg vpt neg V hpt vpt neg V hpt vpt V hpt neg vpt V closepath stroke Pnt } def /Pls { stroke [] 0 setdash vpt sub M 0 vpt2 V currentpoint stroke M hpt neg vpt neg R hpt2 0 V stroke } def /Box { stroke [] 0 setdash 2 copy exch hpt sub exch vpt add M 0 vpt2 neg V hpt2 0 V 0 vpt2 V hpt2 neg 0 V closepath stroke Pnt } def /Crs { stroke [] 0 setdash exch hpt sub exch vpt add M hpt2 vpt2 neg V currentpoint stroke M hpt2 neg 0 R hpt2 vpt2 V stroke } def /TriU { stroke [] 0 setdash 2 copy vpt 1.12 mul add M hpt neg vpt -1.62 mul V hpt 2 mul 0 V hpt neg vpt 1.62 mul V closepath stroke Pnt } def /Star { 2 copy Pls Crs } def /BoxF { stroke [] 0 setdash exch hpt sub exch vpt add M 0 vpt2 neg V hpt2 0 V 0 vpt2 V hpt2 neg 0 V closepath fill } def /TriUF { stroke [] 0 setdash vpt 1.12 mul add M hpt neg vpt -1.62 mul V hpt 2 mul 0 V hpt neg vpt 1.62 mul V closepath fill } def /TriD { stroke [] 0 setdash 2 copy vpt 1.12 mul sub M hpt neg vpt 1.62 mul V hpt 2 mul 0 V hpt neg vpt -1.62 mul V closepath stroke Pnt } def /TriDF { stroke [] 0 setdash vpt 1.12 mul sub M hpt neg vpt 1.62 mul V hpt 2 mul 0 V hpt neg vpt -1.62 mul V closepath fill} def /DiaF { stroke [] 0 setdash vpt add M hpt neg vpt neg V hpt vpt neg V hpt vpt V hpt neg vpt V closepath fill } def /Pent { stroke [] 0 setdash 2 copy gsave translate 0 hpt M 4 {72 rotate 0 hpt L} repeat closepath stroke grestore Pnt } def /PentF { stroke [] 0 setdash gsave translate 0 hpt M 4 {72 rotate 0 hpt L} repeat closepath fill grestore } def /Circle { stroke [] 0 setdash 2 copy hpt 0 360 arc stroke Pnt } def /CircleF { stroke [] 0 setdash hpt 0 360 arc fill } def /C0 { BL [] 0 setdash 2 copy moveto vpt 90 450 arc } bind def /C1 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 90 arc closepath fill vpt 0 360 arc closepath } bind def /C2 { BL [] 0 setdash 2 copy moveto 2 copy vpt 90 180 arc closepath fill vpt 0 360 arc closepath } bind def /C3 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 180 arc closepath fill vpt 0 360 arc closepath } bind def /C4 { BL [] 0 setdash 2 copy moveto 2 copy vpt 180 270 arc closepath fill vpt 0 360 arc closepath } bind def /C5 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 90 arc 2 copy moveto 2 copy vpt 180 270 arc closepath fill vpt 0 360 arc } bind def /C6 { BL [] 0 setdash 2 copy moveto 2 copy vpt 90 270 arc closepath fill vpt 0 360 arc closepath } bind def /C7 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 270 arc closepath fill vpt 0 360 arc closepath } bind def /C8 { BL [] 0 setdash 2 copy moveto 2 copy vpt 270 360 arc closepath fill vpt 0 360 arc closepath } bind def /C9 { BL [] 0 setdash 2 copy moveto 2 copy vpt 270 450 arc closepath fill vpt 0 360 arc closepath } bind def /C10 { BL [] 0 setdash 2 copy 2 copy moveto vpt 270 360 arc closepath fill 2 copy moveto 2 copy vpt 90 180 arc closepath fill vpt 0 360 arc closepath } bind def /C11 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 180 arc closepath fill 2 copy moveto 2 copy vpt 270 360 arc closepath fill vpt 0 360 arc closepath } bind def /C12 { BL [] 0 setdash 2 copy moveto 2 copy vpt 180 360 arc closepath fill vpt 0 360 arc closepath } bind def /C13 { BL [] 0 setdash 2 copy moveto 2 copy vpt 0 90 arc closepath fill 2 copy moveto 2 copy vpt 180 360 arc closepath fill vpt 0 360 arc closepath } bind def /C14 { BL [] 0 setdash 2 copy moveto 2 copy vpt 90 360 arc closepath fill vpt 0 360 arc } bind def /C15 { BL [] 0 setdash 2 copy vpt 0 360 arc closepath fill vpt 0 360 arc closepath } bind def /Rec { newpath 4 2 roll moveto 1 index 0 rlineto 0 exch rlineto neg 0 rlineto closepath } bind def /Square { dup Rec } bind def /Bsquare { vpt sub exch vpt sub exch vpt2 Square } bind def /S0 { BL [] 0 setdash 2 copy moveto 0 vpt rlineto BL Bsquare } bind def /S1 { BL [] 0 setdash 2 copy vpt Square fill Bsquare } bind def /S2 { BL [] 0 setdash 2 copy exch vpt sub exch vpt Square fill Bsquare } bind def /S3 { BL [] 0 setdash 2 copy exch vpt sub exch vpt2 vpt Rec fill Bsquare } bind def /S4 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt Square fill Bsquare } bind def /S5 { BL [] 0 setdash 2 copy 2 copy vpt Square fill exch vpt sub exch vpt sub vpt Square fill Bsquare } bind def /S6 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt vpt2 Rec fill Bsquare } bind def /S7 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt vpt2 Rec fill 2 copy vpt Square fill Bsquare } bind def /S8 { BL [] 0 setdash 2 copy vpt sub vpt Square fill Bsquare } bind def /S9 { BL [] 0 setdash 2 copy vpt sub vpt vpt2 Rec fill Bsquare } bind def /S10 { BL [] 0 setdash 2 copy vpt sub vpt Square fill 2 copy exch vpt sub exch vpt Square fill Bsquare } bind def /S11 { BL [] 0 setdash 2 copy vpt sub vpt Square fill 2 copy exch vpt sub exch vpt2 vpt Rec fill Bsquare } bind def /S12 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt2 vpt Rec fill Bsquare } bind def /S13 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt2 vpt Rec fill 2 copy vpt Square fill Bsquare } bind def /S14 { BL [] 0 setdash 2 copy exch vpt sub exch vpt sub vpt2 vpt Rec fill 2 copy exch vpt sub exch vpt Square fill Bsquare } bind def /S15 { BL [] 0 setdash 2 copy Bsquare fill Bsquare } bind def /D0 { gsave translate 45 rotate 0 0 S0 stroke grestore } bind def /D1 { gsave translate 45 rotate 0 0 S1 stroke grestore } bind def /D2 { gsave translate 45 rotate 0 0 S2 stroke grestore } bind def /D3 { gsave translate 45 rotate 0 0 S3 stroke grestore } bind def /D4 { gsave translate 45 rotate 0 0 S4 stroke grestore } bind def /D5 { gsave translate 45 rotate 0 0 S5 stroke grestore } bind def /D6 { gsave translate 45 rotate 0 0 S6 stroke grestore } bind def /D7 { gsave translate 45 rotate 0 0 S7 stroke grestore } bind def /D8 { gsave translate 45 rotate 0 0 S8 stroke grestore } bind def /D9 { gsave translate 45 rotate 0 0 S9 stroke grestore } bind def /D10 { gsave translate 45 rotate 0 0 S10 stroke grestore } bind def /D11 { gsave translate 45 rotate 0 0 S11 stroke grestore } bind def /D12 { gsave translate 45 rotate 0 0 S12 stroke grestore } bind def /D13 { gsave translate 45 rotate 0 0 S13 stroke grestore } bind def /D14 { gsave translate 45 rotate 0 0 S14 stroke grestore } bind def /D15 { gsave translate 45 rotate 0 0 S15 stroke grestore } bind def /DiaE { stroke [] 0 setdash vpt add M hpt neg vpt neg V hpt vpt neg V hpt vpt V hpt neg vpt V closepath stroke } def /BoxE { stroke [] 0 setdash exch hpt sub exch vpt add M 0 vpt2 neg V hpt2 0 V 0 vpt2 V hpt2 neg 0 V closepath stroke } def /TriUE { stroke [] 0 setdash vpt 1.12 mul add M hpt neg vpt -1.62 mul V hpt 2 mul 0 V hpt neg vpt 1.62 mul V closepath stroke } def /TriDE { stroke [] 0 setdash vpt 1.12 mul sub M hpt neg vpt 1.62 mul V hpt 2 mul 0 V hpt neg vpt -1.62 mul V closepath stroke } def /PentE { stroke [] 0 setdash gsave translate 0 hpt M 4 {72 rotate 0 hpt L} repeat closepath stroke grestore } def /CircE { stroke [] 0 setdash hpt 0 360 arc stroke } def /Opaque { gsave closepath 1 setgray fill grestore 0 setgray closepath } def /DiaW { stroke [] 0 setdash vpt add M hpt neg vpt neg V hpt vpt neg V hpt vpt V hpt neg vpt V Opaque stroke } def /BoxW { stroke [] 0 setdash exch hpt sub exch vpt add M 0 vpt2 neg V hpt2 0 V 0 vpt2 V hpt2 neg 0 V Opaque stroke } def /TriUW { stroke [] 0 setdash vpt 1.12 mul add M hpt neg vpt -1.62 mul V hpt 2 mul 0 V hpt neg vpt 1.62 mul V Opaque stroke } def /TriDW { stroke [] 0 setdash vpt 1.12 mul sub M hpt neg vpt 1.62 mul V hpt 2 mul 0 V hpt neg vpt -1.62 mul V Opaque stroke } def /PentW { stroke [] 0 setdash gsave translate 0 hpt M 4 {72 rotate 0 hpt L} repeat Opaque stroke grestore } def /CircW { stroke [] 0 setdash hpt 0 360 arc Opaque stroke } def /BoxFill { gsave Rec 1 setgray fill grestore } def /BoxColFill { gsave Rec /Fillden exch def currentrgbcolor /ColB exch def /ColG exch def 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Shieber %%% Mon Mar 30 17:23:36 EST 1992 %%% Modification of BibTeX style file "aaai-named" by Peter %%% Patel-Schneider %%% Intended for use with the style file "fullname.sty" % BibTeX `aaai-named' style file for BibTeX version 0.99c, LaTeX version 2.09 % This version was made by modifying the master file made by % Oren Patashnik (PATASHNIK@SCORE.STANFORD.EDU) % Copyright (C) 1985, all rights reserved. % Modifications Copyright 1988, Peter F. Patel-Schneider % Copying of this file is authorized only if either % (1) you make absolutely no changes to your copy, including name, or % (2) if you do make changes, you name it something other than % btxbst.doc, plain.bst, unsrt.bst, alpha.bst, and abbrv.bst. % This restriction helps ensure that all standard styles are identical. % There are undoubtably bugs in this style. If you make bug fixes, % improvements, etc. please let me know. My e-mail address is: % pfps@spar.slb.com % Citation format: [author-last-name, year] % [author-last-name and author-last-name, year] % [author-last-name {\em et al.}, year] % % Reference list ordering: alphabetical by author or whatever passes % for author in the absence of one. % % This BibTeX style has support for short (year only) citations. This % is done by having the citations actually look like % \citename{name-info, }year % The LaTeX style has to have the following % \let\@internalcite\cite % \def\cite{\def\citename##1{##1}\@internalcite} % \def\shortcite{\def\citename##1{}\@internalcite} % \def\@biblabel#1{\def\citename##1{##1}[#1]\hfill} % which makes \shortcite the macro for short citations. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Changes made by SMS for thesis style % no emphasis on "et al." % "Ph.D." includes periods (not "PhD") % moved year to immediately after author's name % Changes made by SMS for compatability with MIT Press style % inverted first and last names for first author %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ENTRY { address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year } {} { label extra.label sort.label } INTEGERS { output.state before.all mid.sentence after.sentence after.block } FUNCTION {init.state.consts} { #0 'before.all := #1 'mid.sentence := #2 'after.sentence := #3 'after.block := } STRINGS { s t } FUNCTION {output.nonnull} { 's := output.state mid.sentence = { ", " * write$ } { output.state after.block = { add.period$ write$ newline$ "\newblock " write$ } { output.state before.all = 'write$ { add.period$ " " * write$ } if$ } if$ mid.sentence 'output.state := } if$ s } FUNCTION {output} { duplicate$ empty$ 'pop$ 'output.nonnull if$ } FUNCTION {output.check} { 't := duplicate$ empty$ { pop$ "empty " t * " in " * cite$ * warning$ } 'output.nonnull if$ } FUNCTION {output.bibitem} { newline$ "\bibitem[" write$ label write$ "]{" write$ cite$ write$ "}" write$ newline$ "" before.all 'output.state := } FUNCTION {fin.entry} { add.period$ write$ newline$ } FUNCTION {new.block} { output.state before.all = 'skip$ { after.block 'output.state := } if$ } FUNCTION {new.sentence} { output.state after.block = 'skip$ { output.state before.all = 'skip$ { after.sentence 'output.state := } if$ } if$ } FUNCTION {not} { { #0 } { #1 } if$ } FUNCTION {and} { 'skip$ { pop$ #0 } if$ } FUNCTION {or} { { pop$ #1 } 'skip$ if$ } FUNCTION {new.block.checka} { empty$ 'skip$ 'new.block if$ } FUNCTION {new.block.checkb} { empty$ swap$ empty$ and 'skip$ 'new.block if$ } FUNCTION {new.sentence.checka} { empty$ 'skip$ 'new.sentence if$ } FUNCTION {new.sentence.checkb} { empty$ swap$ empty$ and 'skip$ 'new.sentence if$ } FUNCTION {field.or.null} { duplicate$ empty$ { pop$ "" } 'skip$ if$ } FUNCTION {emphasize} { duplicate$ empty$ { pop$ "" } { "{\em " swap$ * "}" * } if$ } INTEGERS { nameptr namesleft numnames } %%% This function reverses the first name in the author list, e.g., %%% Dewey, John, Thomas Cheatham, and Elias Howe. %%% For editor names, no reversal is appropriate, so %%% format.names.unreversed is used. FUNCTION {format.names} { 's := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr nameptr #1 > { "{ff~}{vv~}{ll}{, jj}" } { "{vv~}{ll}{, jj}{, ff}" } if$ format.name$ 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ t "others" = { " et~al." * } { " and " * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {format.authors} { author empty$ { "" } { author format.names } if$ } FUNCTION {format.names.unreversed} { 's := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ t "others" = { " et~al." * } { " and " * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {format.editors} { editor empty$ { "" } { editor format.names editor num.names$ #1 > { ", editors" * } { ", editor" * } if$ } if$ } FUNCTION {format.editors.unreversed} { editor empty$ { "" } { editor format.names.unreversed editor num.names$ #1 > { ", editors" * } { ", editor" * } if$ } if$ } FUNCTION {format.title} { title empty$ { "" } { title "t" change.case$ } if$ } FUNCTION {n.dashify} { 't := "" { t empty$ not } { t #1 #1 substring$ "-" = { t #1 #2 substring$ "--" = not { "--" * t #2 global.max$ substring$ 't := } { { t #1 #1 substring$ "-" = } { "-" * t #2 global.max$ substring$ 't := } while$ } if$ } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } if$ } while$ } FUNCTION {format.date} { year empty$ { month empty$ { "" } { "there's a month but no year in " cite$ * warning$ month } if$ } { month empty$ { "" } { month } if$ } if$ } FUNCTION {format.btitle} { title emphasize } FUNCTION {tie.or.space.connect} { duplicate$ text.length$ #3 < { "~" } { " " } if$ swap$ * * } FUNCTION {either.or.check} { empty$ 'pop$ { "can't use both " swap$ * " fields in " * cite$ * warning$ } if$ } FUNCTION {format.bvolume} { volume empty$ { "" } { "volume" volume tie.or.space.connect series empty$ 'skip$ { " of " * series emphasize * } if$ "volume and number" number either.or.check } if$ } FUNCTION {format.number.series} { volume empty$ { number empty$ { series field.or.null } { output.state mid.sentence = { "number" } { "Number" } if$ number tie.or.space.connect series empty$ { "there's a number but no series in " cite$ * warning$ } { " in " * series * } if$ } if$ } { "" } if$ } FUNCTION {format.edition} { edition empty$ { "" } { output.state mid.sentence = { edition "l" change.case$ " edition" * } { edition "t" change.case$ " edition" * } if$ } if$ } INTEGERS { multiresult } FUNCTION {multi.page.check} { 't := #0 'multiresult := { multiresult not t empty$ not and } { t #1 #1 substring$ duplicate$ "-" = swap$ duplicate$ "," = swap$ "+" = or or { #1 'multiresult := } { t #2 global.max$ substring$ 't := } if$ } while$ multiresult } FUNCTION {format.pages} { pages empty$ { "" } { pages multi.page.check { "pages" pages n.dashify tie.or.space.connect } { "page" pages tie.or.space.connect } if$ } if$ } FUNCTION {format.year.label} { year extra.label * } FUNCTION {format.vol.num.pages} { volume field.or.null number empty$ 'skip$ { "(" number * ")" * * volume empty$ { "there's a number but no volume in " cite$ * warning$ } 'skip$ if$ } if$ pages empty$ 'skip$ { duplicate$ empty$ { pop$ format.pages } { ":" * pages n.dashify * } if$ } if$ } FUNCTION {format.chapter.pages} { chapter empty$ 'format.pages { type empty$ { "chapter" } { type "l" change.case$ } if$ chapter tie.or.space.connect pages empty$ 'skip$ { ", " * format.pages * } if$ } if$ } FUNCTION {format.in.ed.booktitle} { booktitle empty$ { "" } { editor empty$ { "In " booktitle emphasize * } { "In " format.editors.unreversed * ", " * booktitle emphasize * } if$ } if$ } FUNCTION {empty.misc.check} { author empty$ title empty$ howpublished empty$ month empty$ year empty$ note empty$ and and and and and key empty$ not and { "all relevant fields are empty in " cite$ * warning$ } 'skip$ if$ } FUNCTION {format.thesis.type} { type empty$ 'skip$ { pop$ type "t" change.case$ } if$ } FUNCTION {format.tr.number} { type empty$ { "Technical Report" } 'type if$ number empty$ { "t" change.case$ } { number tie.or.space.connect } if$ } FUNCTION {format.article.crossref} { key empty$ { journal empty$ { "need key or journal for " cite$ * " to crossref " * crossref * warning$ "" } { "In {\em " journal * "\/}" * } if$ } { "In " key * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.crossref.editor} { editor #1 "{vv~}{ll}" format.name$ editor num.names$ duplicate$ #2 > { pop$ " et~al." * } { #2 < 'skip$ { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = { " et~al." * } { " and " * editor #2 "{vv~}{ll}" format.name$ * } if$ } if$ } if$ } FUNCTION {format.book.crossref} { volume empty$ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ "In " } { "Volume" volume tie.or.space.connect " of " * } if$ editor empty$ editor field.or.null author field.or.null = or { key empty$ { series empty$ { "need editor, key, or series for " cite$ * " to crossref " * crossref * warning$ "" * } { "{\em " * series * "\/}" * } if$ } { key * } if$ } { format.crossref.editor * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.incoll.inproc.crossref} { editor empty$ editor field.or.null author field.or.null = or { key empty$ { booktitle empty$ { "need editor, key, or booktitle for " cite$ * " to crossref " * crossref * warning$ "" } { "In {\em " booktitle * "\/}" * } if$ } { "In " key * } if$ } { "In " format.crossref.editor * } if$ " \cite{" * crossref * "}" * } FUNCTION {article} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block crossref missing$ { journal emphasize "journal" output.check format.vol.num.pages output format.date output } { format.article.crossref output.nonnull format.pages output } if$ new.block note output fin.entry } FUNCTION {book} { output.bibitem author empty$ { format.editors "author and editor" output.check } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ new.block format.year.label "year" output.check new.block format.btitle "title" output.check crossref missing$ { format.bvolume output new.block format.number.series output new.sentence publisher "publisher" output.check address output } { new.block format.book.crossref output.nonnull } if$ format.edition output format.date output new.block note output fin.entry } FUNCTION {booklet} { output.bibitem format.authors output new.block format.year.label "year" output.check new.block format.title "title" output.check howpublished address new.block.checkb howpublished output address output format.date output new.block note output fin.entry } FUNCTION {inbook} { output.bibitem author empty$ { format.editors "author and editor" output.check } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ format.year.label "year" output.check new.block format.btitle "title" output.check crossref missing$ { format.bvolume output format.chapter.pages "chapter and pages" output.check new.block format.number.series output new.sentence publisher "publisher" output.check address output } { format.chapter.pages "chapter and pages" output.check new.block format.book.crossref output.nonnull } if$ format.edition output format.date output new.block note output fin.entry } FUNCTION {incollection} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output new.sentence publisher "publisher" output.check address output format.edition output format.date output format.chapter.pages output } { format.incoll.inproc.crossref output.nonnull format.chapter.pages output } if$ new.block note output fin.entry } FUNCTION {inproceedings} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output format.pages output address empty$ { organization publisher new.sentence.checkb organization output publisher output format.date output } { address output.nonnull format.date output new.sentence organization output publisher output } if$ } { format.incoll.inproc.crossref output.nonnull format.pages output } if$ new.block note output fin.entry } FUNCTION {conference} { inproceedings } FUNCTION {manual} { output.bibitem author empty$ { organization empty$ 'skip$ { organization output.nonnull address output } if$ } { format.authors output.nonnull } if$ format.year.label "year" output.check new.block new.block format.btitle "title" output.check author empty$ { organization empty$ { address new.block.checka address output } 'skip$ if$ } { organization address new.block.checkb organization output address output } if$ format.edition output format.date output new.block note output fin.entry } FUNCTION {mastersthesis} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block "Master's thesis" format.thesis.type output.nonnull school "school" output.check address output format.date output new.block note output fin.entry } FUNCTION {misc} { output.bibitem format.authors output new.block format.year.label output new.block title howpublished new.block.checkb format.title output howpublished new.block.checka howpublished output format.date output new.block note output fin.entry empty.misc.check } FUNCTION {phdthesis} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.btitle "title" output.check new.block "{Ph.D.} thesis" format.thesis.type output.nonnull school "school" output.check address output format.date output new.block note output fin.entry } FUNCTION {proceedings} { output.bibitem editor empty$ { organization output } { format.editors output.nonnull } if$ new.block format.year.label "year" output.check new.block format.btitle "title" output.check format.bvolume output format.number.series output address empty$ { editor empty$ { publisher new.sentence.checka } { organization publisher new.sentence.checkb organization output } if$ publisher output format.date output } { address output.nonnull format.date output new.sentence editor empty$ 'skip$ { organization output } if$ publisher output } if$ new.block note output fin.entry } FUNCTION {techreport} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block format.tr.number output.nonnull institution "institution" output.check address output format.date output new.block note output fin.entry } FUNCTION {unpublished} { output.bibitem format.authors "author" output.check new.block format.year.label "year" output.check new.block format.title "title" output.check new.block note "note" output.check format.date output fin.entry } FUNCTION {default.type} { misc } MACRO {jan} {"January"} MACRO {feb} {"February"} MACRO {mar} {"March"} MACRO {apr} {"April"} MACRO {may} {"May"} MACRO {jun} {"June"} MACRO {jul} {"July"} MACRO {aug} {"August"} MACRO {sep} {"September"} MACRO {oct} {"October"} MACRO {nov} {"November"} MACRO {dec} {"December"} MACRO {acmcs} {"ACM Computing Surveys"} MACRO {acta} {"Acta Informatica"} MACRO {cacm} {"Communications of the ACM"} MACRO {ibmjrd} {"IBM Journal of Research and Development"} MACRO {ibmsj} {"IBM Systems Journal"} MACRO {ieeese} {"IEEE Transactions on Software Engineering"} MACRO {ieeetc} {"IEEE Transactions on Computers"} MACRO {ieeetcad} {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} MACRO {ipl} {"Information Processing Letters"} MACRO {jacm} {"Journal of the ACM"} MACRO {jcss} {"Journal of Computer and System Sciences"} MACRO {scp} {"Science of Computer Programming"} MACRO {sicomp} {"SIAM Journal on Computing"} MACRO {tocs} {"ACM Transactions on Computer Systems"} MACRO {tods} {"ACM Transactions on Database Systems"} MACRO {tog} {"ACM Transactions on Graphics"} MACRO {toms} {"ACM Transactions on Mathematical Software"} MACRO {toois} {"ACM Transactions on Office Information Systems"} MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} MACRO {tcs} {"Theoretical Computer Science"} READ FUNCTION {sortify} { purify$ "l" change.case$ } INTEGERS { len } FUNCTION {chop.word} { 's := 'len := s #1 len substring$ = { s len #1 + global.max$ substring$ } 's if$ } INTEGERS { et.al.char.used } FUNCTION {initialize.et.al.char.used} { #0 'et.al.char.used := } EXECUTE {initialize.et.al.char.used} FUNCTION {format.lab.names} { 's := s num.names$ 'numnames := numnames #1 = { s #1 "{vv }{ll}" format.name$ } { numnames #2 = { s #1 "{vv }{ll }and " format.name$ s #2 "{vv }{ll}" format.name$ * } { numnames #3 = { s #1 "{vv }{ll}, " format.name$ s #2 "{vv }{ll}, and " format.name$ * s #3 "{vv }{ll}" format.name$ * } { s #1 "{vv }{ll }\bgroup et al.\egroup " format.name$ } if$ } if$ } if$ } FUNCTION {author.key.label} { author empty$ { key empty$ { cite$ #1 #3 substring$ } { key #3 text.prefix$ } if$ } { author format.lab.names } if$ } FUNCTION {author.editor.key.label} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } { key #3 text.prefix$ } if$ } { editor format.lab.names } if$ } { author format.lab.names } if$ } FUNCTION {author.key.organization.label} { author empty$ { key empty$ { organization empty$ { cite$ #1 #3 substring$ } { "The " #4 organization chop.word #3 text.prefix$ } if$ } { key #3 text.prefix$ } if$ } { author format.lab.names } if$ } FUNCTION {editor.key.organization.label} { editor empty$ { key empty$ { organization empty$ { cite$ #1 #3 substring$ } { "The " #4 organization chop.word #3 text.prefix$ } if$ } { key #3 text.prefix$ } if$ } { editor format.lab.names } if$ } FUNCTION {calc.label} { type$ "book" = type$ "inbook" = or 'author.editor.key.label { type$ "proceedings" = 'editor.key.organization.label { type$ "manual" = 'author.key.organization.label 'author.key.label if$ } if$ } if$ duplicate$ "\protect\citename{" swap$ * "}" * year field.or.null purify$ * 'label := year field.or.null purify$ * sortify 'sort.label := } FUNCTION {sort.format.names} { 's := #1 'nameptr := "" s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { nameptr #1 > { " " * } 'skip$ if$ s nameptr "{vv{ } }{ll{ }}{ ff{ }}{ jj{ }}" format.name$ 't := nameptr numnames = t "others" = and { "et al" * } { t sortify * } if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {sort.format.title} { 't := "A " #2 "An " #3 "The " #4 t chop.word chop.word chop.word sortify #1 global.max$ substring$ } FUNCTION {author.sort} { author empty$ { key empty$ { "to sort, need author or key in " cite$ * warning$ "" } { key sortify } if$ } { author sort.format.names } if$ } FUNCTION {author.editor.sort} { author empty$ { editor empty$ { key empty$ { "to sort, need author, editor, or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } { author sort.format.names } if$ } FUNCTION {author.organization.sort} { author empty$ { organization empty$ { key empty$ { "to sort, need author, organization, or key in " cite$ * warning$ "" } { key sortify } if$ } { "The " #4 organization chop.word sortify } if$ } { author sort.format.names } if$ } FUNCTION {editor.organization.sort} { editor empty$ { organization empty$ { key empty$ { "to sort, need editor, organization, or key in " cite$ * warning$ "" } { key sortify } if$ } { "The " #4 organization chop.word sortify } if$ } { editor sort.format.names } if$ } FUNCTION {presort} { " " calc.label sort.label pop$ type$ "book" = type$ "inbook" = or 'author.editor.sort { type$ "proceedings" = 'editor.organization.sort { type$ "manual" = 'author.organization.sort 'author.sort if$ } if$ } if$ * " " * year field.or.null sortify * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {presort} SORT STRINGS { longest.label last.sort.label next.extra } INTEGERS { longest.label.width last.extra.num } FUNCTION {initialize.longest.label} { "" 'longest.label := #0 int.to.chr$ 'last.sort.label := "" 'next.extra := #0 'longest.label.width := #0 'last.extra.num := } FUNCTION {forward.pass} { last.sort.label sort.label = { last.extra.num #1 + 'last.extra.num := last.extra.num int.to.chr$ 'extra.label := } { "a" chr.to.int$ 'last.extra.num := "" 'extra.label := sort.label 'last.sort.label := } if$ } FUNCTION {reverse.pass} { next.extra "b" = { "a" 'extra.label := } 'skip$ if$ label extra.label * 'label := label width$ longest.label.width > { label 'longest.label := label width$ 'longest.label.width := } 'skip$ if$ extra.label 'next.extra := } EXECUTE {initialize.longest.label} ITERATE {forward.pass} REVERSE {reverse.pass} FUNCTION {begin.bib} { et.al.char.used { "\newcommand{\etalchar}[1]{$^{#1}$}" write$ newline$ } 'skip$ if$ preamble$ empty$ 'skip$ { preamble$ write$ newline$ } if$ "\begin{thebibliography}{" "}" * write$ newline$ } EXECUTE {begin.bib} EXECUTE {init.state.consts} ITERATE {call.type$} FUNCTION {end.bib} { newline$ "\end{thebibliography}" write$ newline$ } EXECUTE {end.bib} LanguageMachines-timbl-642727d/docs/texfiles/fullname.sty000077500000000000000000000042621451477526200234360ustar00rootroot00000000000000%%% fullname.sty %%% %%% Stuart M. Shieber %%% Mon Mar 30 17:23:36 EST 1992 %%% Modifications to the citation macros intended to be used with the %%% fullname.bst style. %%% Some material taken from Peter Patel-Schneider's AAAI style for %%% use in conjunction with AAAI-named bibliography style. %%% %%% Citation forms: %%% %%% Macro Output format %%% ----------- ----------------------------------------- %%% \cite: (Dewey, 1988) %%% (Dewey, 1988, page 15) %%% (Dewey, 1988; Cheatham, 1987; Howe, 1903) %%% \shortcite: (1988) %%% (1988, page 15) %%% \namecite: Dewey (1988) %%% Dewey (1988, page 15) % don't box citations, add space between multiple citations, separate with ; \def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi \def\@citea{}\@cite{\@for\@citeb:=#2\do {\@citea\def\@citea{; }\@ifundefined {b@\@citeb}{{\bf ?}\@warning {Citation `\@citeb' on page \thepage \space undefined}}% {\csname b@\@citeb\endcsname}}}{#1}} % Allow short (name-less) citations, when used in % conjunction with a bibliography style that creates labels like % \citename{, } \let\@internalcite\cite \def\cite{\def\citename##1{##1, }(\@internalcite} \def\shortcite{\def\citename##1{}(\@internalcite} \def\namecite{\def\citename##1{##1 (}\@internalcite} \def\@biblabel#1{\def\citename##1{##1}[#1]\hfill} %%% More changes made by SMS (originals in latex.tex) % Use parentheses instead of square brackets in the text. \def\@cite#1#2{{#1\if@tempswa , #2\fi})} % Don't put a label in the bibliography at all. Just use the unlabeled format % instead. \def\thebibliography#1{\section*{References\@mkboth {References}{References}}\list {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} \setlength{\itemindent}{-\parindent}} \def\newblock{\hskip .11em plus .33em minus -.07em} \sloppy\clubpenalty4000\widowpenalty4000 \sfcode`\.=1000\relax} \let\endthebibliography=\endlist \def\@lbibitem[#1]#2{\item[]\if@filesw { \def\protect##1{\string ##1\space}\immediate \write\@auxout{\string\bibcite{#2}{#1}}\fi\ignorespaces}} \def\@bibitem#1{\item\if@filesw \immediate\write\@auxout {\string\bibcite{#1}{\the\c@enumi}}\fi\ignorespaces} LanguageMachines-timbl-642727d/docs/texfiles/ilk.bib000077500000000000000000030747641451477526200223500ustar00rootroot00000000000000%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Antal at 2015-06-08 12:07:42 +0200 %% Saved with string encoding Unicode (UTF-8) @string{aaai = {Proceedings of AAAI}} @string{ac = {{IEEE} Transactions on Automatic Control}} @string{acl = {Proceedings of the Annual Meeting of the ACL}} @string{acl00 = {Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics}} @string{acl01 = {Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics}} @string{acl02 = {Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics}} @string{acl03 = {Proceedings of the 41th Annual Meeting of the Association for Computational Linguistics}} @string{acl09 = {Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics}} @string{acl10 = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics}} @string{acl87 = {Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics}} @string{acl88 = {Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics}} @string{acl89 = {Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics}} @string{acl90 = {Proceedings of the 28th Annual Meeting of the Association for Computational Linguistics}} @string{acl91 = {Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics}} @string{acl92 = {Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics}} @string{acl93 = {Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics}} @string{acl94 = {Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics}} @string{acl95 = {Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics}} @string{acl96 = {Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics}} @string{acl99 = {Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics}} @string{ai = {Artificial Intelligence}} @string{anlp = {Proceedings of ACL Conference on Applied Natural Language Processing}} @string{arpa = {Proceedings of the ARPA Workshop on Human Language Technology}} @string{benjamin = {Benjamin/Cummings Publishing Company, Inc.}} @string{cacm = {Communications of the ACM}} @string{cl = {Computational Linguistics}} @string{coling = {Proceedings of COLING}} @string{coling/acl98 = {Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, {Montr\'{e}al}, Quebec, Canada}} @string{coling00 = {Proceedings of the 18th International Conference on Computational Linguistics, COLING-2000}} @string{coling88 = {Proceedings of the 12th International Conference on Computational Linguistics, COLING-88}} @string{coling90 = {Proceedings of the 13th International Conference on Computational Linguistics, COLING-90}} @string{coling92 = {Proceedings of the 14th International Conference on Computational Linguistics, COLING-92}} @string{coling94 = {Proceedings of the 15th International Conference on Computational Linguistics, COLING-94}} @string{coling96 = {Proceedings of the 16th International Conference on Computational Linguistics, COLING-96, Copenhagen, Denmark}} @string{conll = {Computational Natural Language Learning}} @string{csl = {Computer Speech and Language}} @string{cup = {Cambridge University Press}} @string{cvpr = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}} @string{darpa = {Proceedings of the DARPA Workshop on Speech and Natural Language}} @string{eacl/acl97 = {Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Annual Meeting of the European Chapter of the Association for Computational Linguistics}} @string{ecml = {Proceedings of the European Conference on Machine Learning}} @string{focs = {IEEE Symposium of Foundation of Computer Science}} @string{icassp = {Proceedings of ICASSP}} @string{iccv = {Proceedings of the International Conference on Computer Vision}} @string{icml = {Proceedings of the International Conference on Machine Learning}} @string{icra = {Proceedings of the International Conference on Robotics and Automation}} @string{ijcai = {Proceedings of the International Joint Conference on Artificial Intelligence}} @string{ijcnn = {Proceedings of the International Joint Conference on Neural Networks}} @string{ijcv = {International Journal of Computer Vision}} @string{infctrl = {Information and Control}} @string{ipw = {Proceedings of the International Parsing Workshop}} @string{iros = {Proceedings of the {IEEE/RSJ} International Workshop on Intelligent Robots and Systems}} @string{it = {IEEE Transactions on information theory}} @string{jacm = {Journal of the ACM}} @string{jnle = {Journal for Natural Language Engineering}} @string{mit = {The MIT Press}} @string{ml = {Machine Learning}} @string{r&a = {{IEEE} Journal of Robotics and Automation}} @string{sigir = {Proceedings of International Conference on Research and Development in Information Retrieval, SIGIR}} @string{sijad = {SIAM Journal of Algebraic and Discrete Methods}} @string{sijc = {SIAM Journal of Computing}} @string{smc = {{IEEE} Transactions on Systems, Man, and Cybernetics}} @string{stoc = {ACM Symposium of the Theory of Computing}} @string{tassp = {IEEE Transactions on Acoustics, speech, and Signal Processing}} @string{tcs = {Theoretical Computer Science}} @string{tmi = {Proceedings of the International Conference on Theoretical and Methodological Issues in Machine Translation}} @string{uwoed = {Proceedings of the Annual Conference of the UW Center for the New OED and Text Research}} @string{wvlc95 = {Proceedings of the 3rd ACL/SIGDAT Workshop on Very Large Corpora, Cambridge, Massachusetts, USA}} @string{wvlc96 = {Proceedings of the 4th ACL/SIGDAT Workshop on Very Large Corpora, Copenhagen, Denmark}} @string{wvlc97 = {Proceedings of the 5th ACL/SIGDAT Workshop on Very Large Corpora, Beijing, China, and Hong Kong}} @string{wvlc98 = {Proceedings of the 6th ACL/SIGDAT Workshop on Very Large Corpora, {Montr'{e}al}, Quebec, Canada}} @inproceedings{Brousseau+95, Author = {J. 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Meder and A. {Van den Bosch}}, Date-Added = {2015-05-02 12:30:22 +0000}, Date-Modified = {2015-05-02 12:31:44 +0000}, Journal = {Folklore}, Keywords = {motifs, folktales, tunes and tales}, Number = {1}, Pages = {37--52}, Title = {MOMFER: A search engine of Thompson's Motif-Index of Folk Literature}, Volume = {126}, Year = {2015}} @inproceedings{Koolen+15, Author = {M. Koolen and T. Bogers and A. {Van Den Bosch} and J. Kamps}, Bibsource = {dblp computer science bibliography, http://dblp.org}, Biburl = {http://dblp.uni-trier.de/rec/bib/conf/ecir/KoolenBBK15}, Booktitle = {Advances in Information Retrieval - 37th European Conference on {IR} Research, {ECIR} 2015, Vienna, Austria, March 29 - April 2, 2015. 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Daelemans}, Date-Added = {2014-07-15 11:17:35 +0000}, Date-Modified = {2014-07-15 11:28:06 +0000}, Journal = {Journal of Neurolinguistics}, Number = {1}, Pages = {46--72}, Title = {Selective impairment of adjective order constraints as overeager abstraction: An elaboration on Kemmerer et al.(2009)}, Volume = {26}, Year = {2013}} @inproceedings{Underwood+13, Author = {T. Underwood and M. L. Black and L. Auvil and B. Capitanu}, Booktitle = {Proceedings of the 2013 IEEE International Conference on Big Data}, Date-Added = {2014-06-22 15:01:13 +0000}, Date-Modified = {2014-06-22 15:02:10 +0000}, Organization = {IEEE}, Pages = {95--103}, Title = {Mapping mutable genres in structurally complex volumes}, Year = {2013}} @article{Brin+98, Author = {S. Brin and L. Page}, Date-Added = {2014-06-20 12:49:10 +0000}, Date-Modified = {2014-06-20 12:49:36 +0000}, Journal = {Computer networks and ISDN systems}, Number = {1}, Pages = {107--117}, Title = {The anatomy of a large-scale hypertextual Web search engine}, Volume = {30}, Year = {1998}} @article{Briun, Date-Added = {2014-06-20 12:48:21 +0000}, Date-Modified = {2014-06-20 12:49:05 +0000}} @inproceedings{Hall+08, Address = {Stroudsburg, PA, USA}, Author = {D. Hall and D. Jurafsky and C. Manning}, Booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing}, Date-Added = {2014-06-20 12:45:53 +0000}, Date-Modified = {2014-06-20 12:46:51 +0000}, Pages = {363--371}, Publisher = {ACL}, Title = {Studying the history of ideas using topic models}, Year = {2008}} @inproceedings{Byrne+10, Address = {Williamsburg, VA}, Author = {K. Byrne and E. Klein}, Booktitle = {Proceedings of Computer Applications and Quantitative Methods in Archaeology}, Date-Added = {2014-06-20 12:42:42 +0000}, Date-Modified = {2014-06-20 12:44:31 +0000}, Title = {Automatic Extraction of Archaeological Events from Text}, Year = {2010}} @book{Cornelius95, Address = {New York, NY}, Author = {R. Cornelius}, Date-Added = {2014-06-20 12:39:21 +0000}, Date-Modified = {2014-06-21 08:04:54 +0000}, Edition = {1st}, Publisher = {Simon \& Schuster}, Title = {A bridge too far}, Year = {1995}} @book{Gerritsen+06, Address = {Groesbeek, the Netherlands}, Author = {S. Gerritsen and W. Lenders}, Date-Added = {2014-06-20 12:35:28 +0000}, Date-Modified = {2014-06-20 12:36:35 +0000}, Publisher = {Nationaal Bevrijdingsmuseum 1944-1945}, Title = {Verhalen Die Blijven: Beleefde Geschiedenis in de Grensregio}, Year = {2006}} @book{Stacey67, Author = {C. P. Stacey}, Date-Added = {2014-06-20 11:07:56 +0000}, Date-Modified = {2014-06-20 20:48:37 +0000}, Publisher = {R. Duhamel, Queen's Printer}, Title = {Official History of the {C}anadian {A}rmy in the {S}econd {W}orld {W}ar: The victory campaign: The operations in {N}orthwest {E}urope, 1944--45}, Year = {1967}} @book{Kershaw90, Author = {R. Kershaw}, Date-Added = {2014-06-20 11:04:47 +0000}, Date-Modified = {2014-06-20 20:49:18 +0000}, Publisher = {Crowood}, Title = {It never snows in {S}eptember: The {G}erman view of {M}arket-{G}arden and the {B}attle of {A}rnhem, {S}eptember 1944}, Year = {1990}} @book{Bennett08, Address = {Great Britain}, Author = {D. Bennett}, Date-Added = {2014-06-20 11:01:30 +0000}, Date-Modified = {2014-06-21 08:01:49 +0000}, Publisher = {Casemate}, Title = {Magnificent disaster: The Failure of {M}arket {G}arden, the {A}rnhem {O}peration, {S}eptember 1944}, Year = {2008}} @book{Henke96, Author = {K. D. Henke}, Date-Added = {2014-06-19 22:10:56 +0000}, Date-Modified = {2014-06-21 08:01:02 +0000}, Publisher = {Oldenbourg Verlag}, Title = {Die amerikanische Besetzung Deutschlands}, Year = {1996}} @unpublished{McCallum02, Author = {Andrew Kachites McCallum}, Date-Added = {2014-06-19 22:00:11 +0000}, Date-Modified = {2014-06-19 22:00:18 +0000}, Note = {http://mallet.cs.umass.edu}, Title = {MALLET: A Machine Learning for Language Toolkit}, Year = 2002} @book{Jockers13, Author = {M. L. Jockers}, Date-Added = {2014-06-19 21:54:35 +0000}, Date-Modified = {2014-06-19 21:54:55 +0000}, Publisher = {University of Illinois Press}, Title = {Macroanalysis: Digital methods and literary history}, Year = {2013}} @article{Sporleder10, Author = {C. Sporleder}, Date-Added = {2014-06-19 21:27:15 +0000}, Date-Modified = {2014-06-19 21:28:11 +0000}, Journal = {Language and Linguistics Compass}, Number = {9}, Pages = {750--768}, Title = {Natural Language Processing for Cultural Heritage Domains}, Volume = {4}, Year = {2010}} @incollection{Duering14, Address = {Wiesbaden}, Author = {M. D\"{u}ring}, Booktitle = {Visualisierung Sozialer Netzwerke}, Date-Added = {2014-06-19 21:18:57 +0000}, Date-Modified = {2014-06-19 21:22:30 +0000}, Editor = {R. Haussling}, Publisher = {VS Verlag f{\"u}r Sozialwissenschaften}, Title = {Netzwerkvisualisierungen in den {G}eschichtswissenschaften zwischen explorativer {Q}uellenanalyse und der {S}uggestionskraft des {B}ildes}, Year = {2014}} @inproceedings{Wubben+14, Address = {Reykjavik, Iceland}, Author = {S. Wubben and A. {Van Den Bosch} and E. Krahmer}, Booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, Date = {26-31}, Date-Added = {2014-05-28 07:29:29 +0000}, Date-Modified = {2015-05-02 12:34:38 +0000}, Isbn = {978-2-9517408-8-4}, Language = {english}, Month = {May}, Publisher = {European Language Resources Association (ELRA)}, Title = {Creating and Using Large Monolingual Parallel Corpora for Sentential Paraphrase Generation}, Year = {2014}} @inproceedings{Hurriyetoglu+14, Address = {Gothenburg, Sweden}, Author = {A. H\"{u}rriyeto\u{g}lu and N. Oostdijk and A. {Van den Bosch}}, Booktitle = {Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)}, Date-Added = {2014-04-02 10:02:40 +0000}, Date-Modified = {2015-05-02 12:34:06 +0000}, Month = {April}, Pages = {8--16}, Publisher = {Association for Computational Linguistics}, Title = {Estimating Time to Event from Tweets Using Temporal Expressions}, Url = {http://www.aclweb.org/anthology/W14-1302}, Year = {2014}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W14-1302}} @inproceedings{Stoop+14, Address = {Gothenburg, Sweden}, Author = {W. Stoop and A. {Van den Bosch}}, Booktitle = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics}, Date-Added = {2014-04-02 10:02:02 +0000}, Date-Modified = {2015-02-26 23:02:23 +0000}, Month = {April}, Pages = {318--327}, Publisher = {Association for Computational Linguistics}, Title = {Using idiolects and sociolects to improve word prediction}, Url = {http://www.aclweb.org/anthology/E14-1034}, Year = {2014}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/E14-1034}} @inproceedings{Kunneman+14, Address = {Gothenburg, Sweden}, Author = {F. Kunneman and C. Liebrecht and A. {Van den Bosch}}, Booktitle = {Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)}, Date-Added = {2014-04-02 10:00:22 +0000}, Date-Modified = {2015-05-02 12:35:01 +0000}, Month = {April}, Pages = {26--34}, Publisher = {Association for Computational Linguistics}, Title = {The (Un)Predictability of Emotional Hashtags in Twitter}, Url = {http://www.aclweb.org/anthology/W14-1304}, Year = {2014}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W14-1304}} @inproceedings{lee2008correcting, Author = {Lee, John and Seneff, Stephanie}, Booktitle = {ACL}, Date-Added = {2014-03-12 22:20:11 +0000}, Date-Modified = {2014-03-12 22:20:11 +0000}, Pages = {174--182}, Title = {Correcting Misuse of Verb Forms.}, Year = {2008}} @inproceedings{Foster+09, Author = {J. Foster and {\O}. Andersen}, Booktitle = {Proceedings of the Fourth Workshop on Innovative Use of {NLP} for Building Educational Applications}, Date-Added = {2014-03-12 22:09:46 +0000}, Date-Modified = {2014-03-12 22:11:12 +0000}, Organization = {Association for Computational Linguistics}, Pages = {82--90}, Title = {GenERRate: generating errors for use in grammatical error detection}, Year = {2009}} @inproceedings{Sjobergh+05, Author = {J. Sj{\"o}bergh and O. Knutsson}, Booktitle = {Proceeding of the International Conference Recent Advances in Natural Language Processing}, Date-Added = {2014-03-12 22:05:14 +0000}, Date-Modified = {2014-03-12 22:06:50 +0000}, Title = {Faking errors to avoid making errors: Very weakly supervised learning for error detection in writing}, Year = {2005}} @inproceedings{Rozovskaya+11, Author = {A. Rozovskaya and D. Roth}, Booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1}, Date-Added = {2014-03-12 22:01:21 +0000}, Date-Modified = {2014-03-12 22:02:05 +0000}, Organization = {Association for Computational Linguistics}, Pages = {924--933}, Title = {Algorithm selection and model adaptation for {ESL} correction tasks}, Year = {2011}} @inproceedings{Langlais+00, Author = {P. Langlais and G. Foster and G. Lapalme}, Booktitle = {Proceedings of the 2000 NAACL-ANLP Workshop on Embedded machine translation systems-Volume 5}, Date-Added = {2014-02-12 21:39:52 +0000}, Date-Modified = {2014-02-12 21:40:27 +0000}, Organization = {Association for Computational Linguistics}, Pages = {46--51}, Title = {TransType: A computer-aided translation typing system}, Year = {2000}} @article{Goodman01, Author = {Goodman, J.}, Date-Added = {2014-02-12 21:37:26 +0000}, Date-Modified = {2014-02-12 21:37:47 +0000}, Journal = {Computer Speech \& Language}, Number = {4}, Pages = {403--434}, Publisher = {Elsevier}, Title = {A bit of progress in language modeling}, Volume = {15}, Year = {2001}} @article{Ehsan+13, Author = {Ehsan, N. and Faili, H.}, Date-Added = {2014-01-05 18:41:28 +0000}, Date-Modified = {2014-01-05 18:41:56 +0000}, Journal = {Software: Practice and Experience}, Number = {2}, Pages = {187--206}, Title = {Grammatical and context-sensitive error correction using a statistical machine translation framework}, Volume = {43}, Year = {2013}} @inproceedings{Brockett+06, Author = {Brockett, C. and Dolan, W. B. and Gamon, M.}, Booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics}, Date-Added = {2014-01-05 18:39:43 +0000}, Date-Modified = {2014-01-05 18:40:07 +0000}, Organization = {Association for Computational Linguistics}, Pages = {249--256}, Title = {Correcting ESL errors using phrasal SMT techniques}, Year = {2006}} @inproceedings{Yuan+13, Address = {Sofia, Bulgaria}, Author = {Yuan, Z. and Felice, M.}, Booktitle = {Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task}, Date-Added = {2014-01-05 18:37:05 +0000}, Date-Modified = {2014-01-05 18:40:22 +0000}, Month = {August}, Pages = {52--61}, Publisher = {Association for Computational Linguistics}, Title = {Constrained Grammatical Error Correction using Statistical Machine Translation}, Url = {http://www.aclweb.org/anthology/W13-3607}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W13-3607}} @inproceedings{Ng+13, Address = {Sofia, Bulgaria}, Author = {Ng, H. T. and Wu, S. M. and Wu, Y. and Hadiwinoto, C. and Tetreault, J.}, Booktitle = {Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task}, Date-Added = {2014-01-05 18:19:01 +0000}, Date-Modified = {2014-01-06 20:33:42 +0000}, Month = {August}, Pages = {1--12}, Publisher = {Association for Computational Linguistics}, Title = {The {CoNLL}-2013 Shared Task on Grammatical Error Correction}, Url = {http://www.aclweb.org/anthology/W13-3601}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W13-3601}} @inproceedings{Karsdorp+12b, Address = {Lisbon, Portugal}, Author = {F. Karsdorp and P. {Van Kranenburg} and T. Meder and A. {Van den Bosch}}, Booktitle = {Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2)}, Date-Added = {2013-12-21 16:30:42 +0000}, Date-Modified = {2013-12-21 16:32:46 +0000}, Editor = {F. Mambrini, M. Passarotti, and C. Sporleder}, Pages = {39--50}, Publisher = {Edi\,{c}\n{o}es Colibri}, Title = {Casting a spell: Identification and ranking of actors in folktales}, Year = {2012}} @article{VandenBosch+12c, Author = {A. {Van den Bosch} and R. Morante and S. Canisius}, Date-Added = {2013-12-21 16:27:49 +0000}, Date-Modified = {2013-12-21 16:29:03 +0000}, Journal = {Computational Linguistics in the Netherlands Journal}, Pages = {97--117}, Title = {Joint learning of dependency parsing and semantic role labeling}, Volume = {2}, Year = {2013}, Bdsk-Url-1 = {http://www.clinjournal.org/sites/default/files/6VanDenBosch2012_0.pdf}} @article{Berendsen+13, Author = {R. Berendsen and M. {De Rijke} and K. Balog and T. Bogers and A. {Van den Bosch}}, Date-Added = {2013-12-21 16:25:41 +0000}, Date-Modified = {2013-12-21 16:26:50 +0000}, Journal = {Journal of the American Society for Information Science and Technology,}, Number = {10}, Pages = {2024--2044}, Title = {On the assessment of expertise profiles}, Volume = {64}, Year = {2013}, Bdsk-Url-1 = {http://onlinelibrary.wiley.com/doi/10.1002/asi.22908/pdf}} @inproceedings{Karsdorp+13, Address = {Nijmegen, The Netherlands}, Author = {F. Karsdorp and A. {Van den Bosch}}, Booktitle = {Proceedings of the 22 Annual Belgian-Dutch Conference on Machine Learning}, Date-Added = {2013-12-21 16:24:08 +0000}, Date-Modified = {2013-12-21 16:24:58 +0000}, Pages = {41--49}, Title = {Identifying motifs in folktales using topic models}, Year = {2013}} @inproceedings{VandenBosch+13c, Address = {Rio de Janeiro}, Author = {A. {Van den Bosch} and T. Bogers}, Booktitle = {\#MSM2013 Workshop Concept Extraction Challenge Proceedings,}, Date-Added = {2013-12-21 16:22:15 +0000}, Date-Modified = {2014-07-15 11:54:02 +0000}, Editor = {A. Cano and M. Rowe and M. Stankovic and A.-S. Dadzie}, Pages = {40--43}, Title = {Memory-based named entity recognition in tweets}, Year = {2013}} @inproceedings{Tops+13, Address = {Delft, The Netherlands}, Author = {H. Tops and A. {Van den Bosch} and F. Kunneman}, Booktitle = {Proceedings of the 25th Benelux Artificial Intelligence Conference}, Date-Added = {2013-12-21 16:20:22 +0000}, Date-Modified = {2013-12-21 16:21:56 +0000}, Editor = {K. Hindriks, M. de Weerdt, B. van Riemsdijk, and M. Warnier}, Title = {Predicting time-to-event from Twitter messages Predicting time-to-event from Twitter messages Predicting time-to-event from Twitter messages}, Year = {2013}, Bdsk-Url-1 = {http://bnaic2013.tudelft.nl/proceedings/papers/paper_45.pdf}} @article{Sang+13, Abstract = {As data sets keep growing, computational linguists are experiencing more big data problems: challenging demands on storage and processing caused by very large data sets. An example of this is dealing with social media data: including metadata, the messages of the social media site Twitter in 2012 comprise more than 250 terabytes of structured text. Handling data volumes like this requires parallel computing architectures with appropriate software tools. In this paper we present our experiences in working with such a big data set, a collection of two billion Dutch tweets. We show how we collected and stored the data. Next we deal with searching in the data using the Hadoop framework and visualizing search results. In order to determine the usefulness of this tweet analysis resource, we have performed three case studies based on the data: relating word frequency to real-life events, finding words related to a topic, and gathering information about conversations. The three case studies are presented in this paper. Access to this current and expanding tweet data set is offered via the website twiqs.nl.}, Attachments = {http://clinjournal.org/sites/default/files/08-TjongKimSang-vandenBosch-CLIN2013.pdf}, Author = {E. {Tjong Kim Sang} and A. {Van den Bosch}}, Date-Added = {2013-12-21 16:16:17 +0000}, Date-Modified = {2013-12-21 16:18:12 +0000}, Issn = {2211-4009}, Journal = {Computational Linguistics in the Netherlands Journal}, Month = {12/2013}, Pages = {121-134}, Title = {Dealing with big data: The case of Twitter}, Volume = {3}, Year = {2013}, Bdsk-Url-1 = {http://clinjournal.org/sites/default/files/08-TjongKimSang-vandenBosch-CLIN2013.pdf}} @inproceedings{Hendrickx+13, Address = {Sofia, Bulgaria}, Author = {I. Hendrickx and M. D{\"u}ring and K. Zervanou and A. {Van Den Bosch}}, Booktitle = {Proceedings of the Third Workshop on Annotation of Corpora for Research in the Humanities (ACRH-3)}, Date-Added = {2013-12-16 12:57:13 +0000}, Date-Modified = {2013-12-16 12:59:00 +0000}, Isbn = {978-954-91700-5-4}, Keywords = {isher, strikes, iish, ehumanities, digital humanities, computational history}, Month = {12th December 2013}, Opteditor = {F. Mambrini, M. Passarotti and C. Sporleder}, Pages = {25--36}, Publisher = {The Institute of Information and Communication Technologies, Bulgarian Academy of Scienes}, Title = {Searching and Finding Strikes in the {N}ew {Y}ork {T}imes}, Year = {2013}} @article{Bresnan+08, Author = {J. Bresnan and J. Hay}, Date-Added = {2013-12-08 23:02:27 +0000}, Date-Modified = {2013-12-08 23:03:18 +0000}, Journal = {Lingua}, Number = {2}, Pages = {245--259}, Title = {Gradient grammar: An effect of animacy on the syntax of {\it give} in {N}ew {Z}ealand and {A}merican {E}nglish}, Volume = {118}, Year = {2008}} @book{Milroy+03, Author = {L. Milroy and M. Gordon}, Date-Added = {2013-12-08 22:44:41 +0000}, Date-Modified = {2013-12-08 22:45:25 +0000}, Publisher = {Wiley}, Title = {Sociolinguistics: Method and interpretation}, Year = {2003}} @url{Davies13, Author = {M. Davies}, Date-Added = {2013-12-08 21:10:19 +0000}, Date-Modified = {2013-12-08 23:16:59 +0000}, Lastchecked = {December 8, 2013}, Title = {{Corpus of Global Web-Based English}: 1.9 billion words from speakers in 20 countries}, Urldate = {http://corpus2.byu.edu/glowbe/}, Year = {2013--}} @article{Campbell+01, Author = {A. Campbell and M. Tomasello}, Date-Added = {2013-11-30 21:36:52 +0000}, Date-Modified = {2013-11-30 21:37:20 +0000}, Journal = {Applied Psycholinguistics}, Number = {2}, Pages = {253--267}, Title = {The acquisition of English dative constructions}, Volume = {22}, Year = {2001}} @book{Harrell01, Author = {F. E. Harrell}, Date-Added = {2013-11-29 19:14:34 +0000}, Date-Modified = {2013-11-30 21:40:57 +0000}, Publisher = {Springer}, Title = {Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis}, Year = {2001}} @article{Mollin09, Author = {S. Mollin}, Date-Added = {2013-11-27 08:23:26 +0000}, Date-Modified = {2013-11-27 08:23:37 +0000}, Journal = {Journal of Corpus Linguistics}, Pages = {367--392}, Title = {``I entirely understand'' is a {B}lairism: The methodology of identifying idiolectal collocations}, Volume = {14 (3)}, Year = {2009}} @article{Louwerse04, Author = {M. M. Louwerse}, Date-Added = {2013-11-27 08:22:42 +0000}, Date-Modified = {2013-11-27 08:22:50 +0000}, Journal = {Computers and the Humanities}, Number = {2}, Pages = {207--221}, Title = {Semantic Variation in Idiolect and Sociolect: Corpus Linguistic Evidence from Literary Texts}, Volume = {38}, Year = {2004}} @book{MacWhinney00, Address = {Mahwah, NJ}, Author = {B. MacWhinney}, Date-Added = {2013-11-27 06:53:09 +0000}, Date-Modified = {2013-11-27 06:55:05 +0000}, Publisher = {Lawrence Erlbaum}, Series = {The {CHILDES} project: Tools for analyzing talk}, Title = {The database}, Volume = {2}, Year = {2000}} @incollection{Bresnan+07, Address = {Amsterdam, The Netherlands}, Author = {J. Bresnan and A. Cueni and T. Nikitina and R. H. Baayen}, Booktitle = {Cognitive foundations of interpretation}, Date-Added = {2013-11-09 21:56:02 +0000}, Date-Modified = {2013-11-09 21:57:57 +0000}, Editor = {G. Bouma and I. Kr\"{a}mer and J. Zwarts}, Keywords = {dative alternation}, Pages = {69--94}, Publisher = {Royal Netherlands Academy of Arts and Sciences}, Title = {Predicting the dative alternation}, Year = {2007}} @article{DeMarneffe+12, Author = {M.-C. {De Marneffe} and S. Grimm and I. Arnon and S. Kirby and J. Bresnan}, Date-Added = {2013-11-09 21:39:25 +0000}, Date-Modified = {2013-11-30 21:48:12 +0000}, Journal = {Language and Cognitive Processes}, Keywords = {dative alternation; CHILDES}, Number = {1}, Pages = {25--61}, Title = {A statistical model of the grammatical choices in child production of dative sentences}, Volume = {27}, Year = {2012}} @inproceedings{Strik+02, Address = {Denver, CO.}, Author = {H. Strik and W. Daelemans and D. Binnenpoorte and J. Sturm and F. {De Vriend} and C. Cucchiarini}, Booktitle = {Proceedings of ICSLP}, Date-Added = {2013-11-04 11:22:52 +0000}, Date-Modified = {2013-12-15 13:11:31 +0000}, Pages = {1549--1552}, Title = {Dutch {HLT} resources: from {BLARK} to priority lists}, Year = {2002}} @inproceedings{Krauwer03, Author = {S. Krauwer}, Booktitle = {Proceedings of the International Workshop on Speech and Computer (SPECOM-2003)}, Date-Added = {2013-11-04 11:16:51 +0000}, Date-Modified = {2013-12-15 13:12:53 +0000}, Pages = {8--15}, Title = {The {B}asic {L}anguage {R}esource {K}it ({BLARK}) as the first milestone for the Language Resources Roadmap}, Year = {2003}} @article{Bouma+01, Author = {G. Bouma and G. {Van Noord} R. and Malouf}, Date-Added = {2013-10-06 09:02:11 +0000}, Date-Modified = {2013-10-06 09:02:48 +0000}, Journal = {Language and Computers}, Number = {1}, Pages = {45--59}, Publisher = {Rodopi}, Title = {Alpino: Wide-coverage computational analysis of Dutch}, Volume = {37}, Year = {2001}} @book{Haeseryn+97, Address = {Groningen, The Netherlands}, Author = {W. Haeseryn and K. Romijn and G. Geerts and J. {De Rooij} and M. {Van der Toorn}}, Date-Added = {2013-08-18 19:55:03 +0000}, Date-Modified = {2013-08-18 20:05:47 +0000}, Keywords = {Dutch syntax}, Publisher = {Martinus Nijhoff}, Title = {{Algemene Nederlandse Spraakkunst}}, Volume = {2}, Year = {1997}} @techreport{Burnard00, Address = {Oxford, UK}, Author = {L. Burnard}, Date-Added = {2013-08-13 15:02:13 +0000}, Date-Modified = {2015-02-26 23:02:53 +0000}, Institution = {Oxford University}, Publisher = {Oxford University Computing Services}, Title = {Reference Guide for the British National Corpus (World Edition)}, Year = {2000}} @article{VandenBosch+13b, Author = {A. {Van den Bosch} and W. Daelemans}, Date-Added = {2013-08-07 20:10:18 +0000}, Date-Modified = {2013-11-30 21:47:51 +0000}, Journal = {Language and Speech}, Number = {3}, Pages = {308--326}, Title = {Implicit Schemata and Categories in Memory-based Language Processing}, Volume = {56}, Year = {2013}} @article{VanderWouden09, Author = {T. {Van der Wouden}}, Date-Added = {2013-08-07 08:54:10 +0000}, Date-Modified = {2013-08-07 09:25:16 +0000}, Journal = {Nederlandse Taalkunde}, Number = {14}, Pages = {300-306}, Title = {Er staat op de laatste plaats van het middenveld een onbepaalde zelfstandignaamwoordgroep}, Year = {2009}} @article{Grondelaers+02, Author = {S. Grondelaers and M. Brysbaert and D. Speelman and D. Geeraerts}, Date-Added = {2013-08-07 08:18:09 +0000}, Date-Modified = {2013-08-07 08:19:55 +0000}, Journal = {{Gramma/TTT Tijdschrift voor Taalkunde}}, Keywords = {syntactic variation}, Pages = {1--22}, Title = {Er als accessibility marker: On- en offline evidentie voor een procedurele duiding van presentatieve zinnen}, Volume = {9}, Year = {2002}} @article{Grondelaers+09, Author = {S. Grondelaers and D. Speelman and D. Drieghe and M. Brysbaert and D. Geeraerts}, Date-Added = {2013-08-07 08:14:37 +0000}, Date-Modified = {2013-08-07 08:15:44 +0000}, Journal = {Acta psychologica}, Keywords = {syntactic variation}, Number = {2}, Pages = {153--160}, Publisher = {Elsevier}, Title = {Introducing a new entity into discourse: Comprehension and production evidence for the status of Dutch {\it er} ``there'' as a higher-level expectancy monitor}, Volume = {130}, Year = {2009}} @phdthesis{Theijssen12, Author = {D. Theijssen}, Date-Added = {2013-08-07 08:08:22 +0000}, Date-Modified = {2013-08-07 08:09:23 +0000}, Keywords = {dative alternation}, Month = {June}, School = {Radboud University Nijmegen}, Title = {Making choices: Modelling the English dative alternation}, Year = {2012}} @incollection{Grondelaers+08, Address = {Berlin}, Author = {S. Grondelaers and D. Speelman and D. Geeraerts}, Booktitle = {Cognitive socionlinguistics: Language variation, cultural models, social systems}, Date-Added = {2013-08-07 07:55:11 +0000}, Date-Modified = {2013-08-07 07:57:27 +0000}, Editor = {G. Kristiansen and R. Dirven}, Keywords = {syntactic variation, corpus linguistics, sociolinguistics}, Pages = {153-2004}, Publisher = {Mouton de Gruyter}, Title = {National variation in the use of {\it er} "there": Regional and diachronic constraints on cognitive explanations}, Year = {2008}} @incollection{Grondelaers+07, Address = {Amsterdam/Philadelphia}, Author = {S. Grondelaers and D. Geeraerts and D. Speelman}, Booktitle = {Methods in cognitive linguistics}, Date-Added = {2013-08-07 07:52:29 +0000}, Date-Modified = {2013-08-07 07:54:47 +0000}, Editor = {M. Gonzales-Marques and I. Mittelberg and S. Coulson and M.J. Spivey}, Keywords = {corpus linguistics, syntactic variation}, Pages = {149-169}, Publisher = {John Benjamins}, Title = {A case for cognitive corpus linguistics}, Year = {2007}} @inproceedings{Wubben+13, Address = {Sofia, Bulgaria}, Author = {S. Wubben and E. Krahmer and A. {Van den Bosch}}, Booktitle = {Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities}, Date-Added = {2013-08-06 10:12:56 +0000}, Date-Modified = {2013-08-07 07:50:49 +0000}, Keywords = {vi, language transformation, machine translation, Middle Dutch}, Month = {August}, Pages = {11--19}, Publisher = {Association for Computational Linguistics}, Title = {Using character overlap to improve language transformation}, Url = {http://www.aclweb.org/anthology/W13-2702}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W13-2702}} @inproceedings{VandenBosch+13, Address = {Sofia, Bulgaria}, Author = {A. {Van den Bosch} and P. Berck}, Booktitle = {Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task}, Date-Added = {2013-08-06 10:12:07 +0000}, Date-Modified = {2013-08-07 07:49:53 +0000}, Keywords = {vi, spelling correction, grammatical error correction, memory-based language modeling}, Month = {August}, Pages = {102--108}, Publisher = {Association for Computational Linguistics}, Title = {Memory-based grammatical error correction}, Url = {http://www.aclweb.org/anthology/W13-3614}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W13-3614}} @inproceedings{Liebrecht+13, Address = {Atlanta, Georgia}, Author = {C. Liebrecht and F. Kunneman and A. {Van den Bosch}}, Booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}, Date-Added = {2013-06-17 19:19:18 +0000}, Date-Modified = {2013-09-30 22:08:43 +0000}, Month = {June}, Pages = {29--37}, Publisher = {Association for Computational Linguistics}, Title = {The perfect solution for detecting sarcasm in tweets \#not}, Url = {http://www.aclweb.org/anthology/W13-1605}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W13-1605}} @inproceedings{VanGompel+13, Address = {Atlanta, Georgia, USA}, Author = {M. {Van Gompel} and A. {Van den Bosch}}, Booktitle = {Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)}, Date-Added = {2013-06-17 19:17:11 +0000}, Date-Modified = {2013-06-17 19:17:43 +0000}, Month = {June}, Pages = {183--187}, Publisher = {Association for Computational Linguistics}, Title = {WSD2: Parameter optimisation for Memory-based Cross-Lingual Word-Sense Disambiguation}, Url = {http://www.aclweb.org/anthology/S13-2033}, Year = {2013}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/S13-2033}} @inproceedings{Hurriyetoglu+13, Author = {A. H\"urriyetoglu and F. Kunneman and A. {Van den Bosch}}, Crossref = {DIR-2013}, Date-Added = {2013-05-27 21:47:57 +0000}, Date-Modified = {2013-05-27 21:49:55 +0000}, Keywords = {adnext twitter}, Pages = {20--23}, Title = {Estimating the Time between Twitter Messages and Future Events}, Url = {http://ceur-ws.org/Vol-986/paper_23.pdf}, Bdsk-Url-1 = {http://ceur-ws.org/Vol-986/paper_23.pdf}} @inproceedings{Sanders+13, Author = {E. Sanders and A. {Van den Bosch}}, Crossref = {DIR-2013}, Date-Added = {2013-05-27 21:45:27 +0000}, Date-Modified = {2013-05-27 21:46:34 +0000}, Keywords = {twitter, elections, adnext, politics}, Pages = {68--71}, Title = {Relating Political Party Mentions on Twitter with Polls and Election Results}, Url = {http://ceur-ws.org/Vol-986/paper_9.pdf}, Bdsk-Url-1 = {http://ceur-ws.org/Vol-986/paper_9.pdf}} @proceedings{Eickhoff+13, Address = {Aachen}, Booktitle = {Proceedings of the 13th Dutch-Belgian Workshop on Information Retrieval (DIR)}, Date-Added = {2013-05-27 21:44:36 +0000}, Date-Modified = {2013-05-27 21:45:00 +0000}, Editor = {C. Eickhoff and A. P. de Vries}, Eventdate = {2013-04-26}, Issn = {1613-0073}, Number = 986, Series = {CEUR Workshop Proceedings}, Title = {Proceedings of the 13th Dutch-Belgian Workshop on Information Retrieval (DIR)}, Url = {http://ceur-ws.org/Vol-986/}, Venue = {Delft, The Netherlands}, Year = 2013, Bdsk-Url-1 = {http://ceur-ws.org/Vol-986/}} @inproceedings{Steinberger+11, Author = {Steinberger, Ralf and Pouliquen, Bruno and Kabadjov, Mijail and Belyaeva, Jenya and van der Goot, Erik}, Booktitle = {Proceedings of the 8th International Conference `Recent Advances in Natural Language Processing}, Date-Added = {2013-03-20 00:40:57 +0000}, Date-Modified = {2013-03-20 00:41:05 +0000}, Pages = {104--110}, Title = {JRC-Names: A freely available, highly multilingual named entity resource}, Year = {2011}} @inproceedings{Li+12, Author = {Li, Chenliang and Weng, Jianshu and He, Qi and Yao, Yuxia and Datta, Anwitaman and Sun, Aixin and Lee, Bu-Sung}, Booktitle = {Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval}, Date-Added = {2013-03-20 00:11:51 +0000}, Date-Modified = {2013-03-20 00:11:59 +0000}, Organization = {ACM}, Pages = {721--730}, Title = {Twiner: Named entity recognition in targeted twitter stream}, Year = {2012}} @inproceedings{Ritter+11, Author = {Ritter, Alan and Clark, Sam and Etzioni, Oren and others}, Booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing}, Date-Added = {2013-03-20 00:11:04 +0000}, Date-Modified = {2013-03-20 00:11:13 +0000}, Organization = {Association for Computational Linguistics}, Pages = {1524--1534}, Title = {Named entity recognition in tweets: an experimental study}, Year = {2011}} @article{Liu+13, Author = {Liu, Xiaohua and Wei, Furu and Zhang, Shaodian and Zhou, Ming}, Date-Added = {2013-03-20 00:09:51 +0000}, Date-Modified = {2013-03-20 00:10:08 +0000}, Journal = {ACM Transactions on Intelligent Systems and Technology (TIST)}, Number = {1}, Pages = {3}, Publisher = {ACM}, Title = {Named entity recognition for tweets}, Volume = {4}, Year = {2013}} @article{Attardo00, Author = {S. Attardo}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of Pragmatics}, Number = {6}, Pages = {793--826}, Title = {Irony as relevant inappropriateness}, Volume = {32}, Year = {2000}} @article{Attardo00b, Author = {S. Attardo}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {RASK}, Pages = {3--20}, Title = {Irony markers and functions: Towards a goal-oriented theory of irony and its processing}, Volume = {12}, Year = {2000}} @incollection{Attardo07, Address = {New York, NY}, Author = {S. Attardo}, Booktitle = {Irony in language and thought: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {135--170}, Publisher = {Lawrence Erlbaum}, Title = {Irony as relevant inappropriateness}, Year = {2007}} @article{Attardo+03, Author = {Attardo, S. and Eisterhold, J. and Hay, J. and Poggi, I.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Humor}, Number = {2}, Pages = {243--260}, Title = {Visual markers of irony and sarcasm}, Volume = {16}, Year = {2003}} @incollection{Brown80, Address = {Washington, DC}, Author = {Brown, R. L.}, Booktitle = {Language use and the uses of language}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Shuy and A. Shnukal}, Pages = {111--127}, Publisher = {Georgetown University Press}, Title = {The pragmatics of verbal irony}, Year = {1980}} @article{Bryant+05, Author = {Bryant, G. A. and Tree, J. E. Fox}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Language and Speech}, Number = {3}, Pages = {257--277}, Publisher = {SAGE Publications}, Title = {Is there an ironic tone of voice?}, Volume = {48}, Year = {2005}} @book{Burgers10, Address = {Nijmegen, The Netherlands}, Author = {Burgers, C. F.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Publisher = {Ipskamp}, Title = {Verbal irony: Use and effects in written discourse}, Year = {2010}} @article{Colston+04, Author = {Colston, H. L. and Lee, S. Y.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Metaphor and Symbol}, Number = {4}, Pages = {289--306}, Title = {Gender differences in verbal irony use}, Volume = {19}, Year = {2004}} @article{Ducharme94, Author = {Ducharme, L. J.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Symbolic interaction}, Number = {1}, Pages = {51--62}, Title = {Sarcasm and interactional politics}, Volume = {17}, Year = {1994}} @article{Gibbs86, Author = {Gibbs, R. W.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of Experimental Psychology: General}, Number = {1}, Pages = {3}, Title = {On the psycholinguistics of sarcasm}, Volume = {115}, Year = {1986}} @incollection{Gibbs07, Address = {New York, NY}, Author = {R. W. Gibbs}, Booktitle = {Irony in language and thougt: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {173--200}, Publisher = {Lawrence Erlbaum}, Title = {On the psycholinguistics of sarcasm}, Year = {2007}} @incollection{Gibbs07b, Address = {New York, NY}, Author = {R. W. Gibbs}, Booktitle = {Irony in language and thougt: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {339--360}, Publisher = {Lawrence Erlbaum}, Title = {Irony in talk among friends}, Year = {2007}} @incollection{Gibbs+07, Address = {New York, NY}, Author = {R. W. Gibbs and H. Colston}, Booktitle = {Irony in language and thougt: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {581--595}, Publisher = {Lawrence Erlbaum}, Title = {Irony as persuasive communication}, Year = {2007}} @incollection{Gibbs+05, Address = {New York, NY}, Author = {R. W. Gibbs and C. Izett}, Booktitle = {Figurative language comprehension: Social and cultural influences}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {H. Colston and A. Katz}, Pages = {131--151}, Publisher = {Lawrence Erlbaum}, Title = {Irony as persuasive communication}, Year = {2005}} @article{Gibbs+91, Author = {Gibbs, R. W. and O'Brien, J.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of pragmatics}, Number = {6}, Pages = {523--530}, Title = {Psychological aspects of irony understanding}, Volume = {16}, Year = {1991}} @article{Giora95, Author = {Giora, R.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Discourse processes}, Number = {2}, Pages = {239--264}, Title = {On irony and negation}, Volume = {19}, Year = {1995}} @book{Giora03, Author = {Giora, R.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Publisher = {Oxford University Press}, Title = {On our mind: Salience, context, and figurative language}, Year = {2003}} @incollection{Grice75, Address = {New York, NY}, Author = {H. Grice}, Booktitle = {Speech acts: Syntax and semantics}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {P. Cole and J. 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Uchida}, Pages = {257--270}, Publisher = {John Benjamins}, Title = {Irony from a cognitive perspective}, Year = {1998}} @book{Jahandarie99, Author = {Jahandarie, K.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Publisher = {Greenwood Publishing Group}, Title = {Spoken and written discourse: A multi-disciplinary perspective}, Year = {1999}} @article{Katz+04, Author = {Katz, A. N. and Blasko, D. G. and Kazmerski, V. A.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Current Directions in Psychological Science}, Number = {5}, Pages = {186--189}, Title = {Saying what you don't mean social influences on sarcastic language processing}, Volume = {13}, Year = {2004}} @article{Kotthoff03, Author = {Kotthoff, H.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of Pragmatics}, Number = {9}, Pages = {1387--1411}, Title = {Responding to irony in different contexts: On cognition in conversation}, Volume = {35}, Year = {2003}} @article{Kreuz+93, Author = {Kreuz, R. J. and Roberts, R. M.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Poetics}, Number = {1}, Pages = {151--169}, Title = {The empirical study of figurative language in literature}, Volume = {22}, Year = {1993}} @article{Kreuz+95, Author = {Kreuz, R. J. and Roberts, R. M.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Metaphor and symbol}, Number = {1}, Pages = {21--31}, Title = {Two cues for verbal irony: Hyperbole and the ironic tone of voice}, Volume = {10}, Year = {1995}} @incollection{Kreuz+96, Address = {Norwood, NJ}, Author = {Kreuz, R. and Roberts, R. and Johnson, B. and Bertus, E.}, Booktitle = {Empirical approaches to literature and aesthetics}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. Kreuz and M. MacNealy}, Pages = {83--97}, Publisher = {Ablex}, Title = {Figurative language occurrence and co-occurrence in contemporary literature}, Year = {1996}} @incollection{Kumon-Nakamura+07, Address = {New York, NY}, Author = {Kumon-Nakamura, S. and Glucksberg, S. and Brown, M.}, Booktitle = {Irony in language and thougt: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {57--95}, Publisher = {Lawrence Erlbaum}, Title = {How about another piece of pie: The allusional pretense theory of discourse irony}, Year = {2007}} @article{Leigh94, Author = {Leigh, J. H}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of Advertising}, Pages = {17--33}, Title = {The use of figures of speech in print ad headlines}, Year = {1994}} @article{Livnat04, Author = {Livnat, Zohar}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Pragmatics \& Cognition}, Number = {1}, Pages = {57--70}, Title = {On verbal irony, meta-linguistic knowledge and echoic interpretation}, Volume = {12}, Year = {2004}} @book{Mizzau84, Address = {Milan, Italy}, Author = {M. Mizzau}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Publisher = {Feltrinelli}, Title = {L'ironia: la contraddizione consentita}, Year = {1984}} @book{Muecke69, Author = {Muecke, D. 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Katz}, Pages = {131--151}, Publisher = {Lawrence Erlbaum}, Title = {Social factors in the interpretation of verbal irony: The role of speaker and listener characteristics}, Year = {2005}} @article{Reyes+12, Author = {Reyes, A. and Rosso, P. and Veale, T.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Language Resources and Evaluation}, Pages = {1--30}, Title = {A multidimensional approach for detecting irony in Twitter}, Year = {2012}} @article{Rockwell03, Author = {Rockwell, P.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Perceptual and motor skills}, Number = {1}, Pages = {251--256}, Title = {Empathy and the expression and recognition of sarcasm by close relations or strangers}, Volume = {97}, Year = {2003}} @article{Rockwell07, Author = {Rockwell, P.}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Journal of psycholinguistic research}, Number = {5}, Pages = {361--369}, Title = {Vocal features of conversational sarcasm: A comparison of methods}, Volume = {36}, Year = {2007}} @incollection{Seto98, Address = {Amsterdam, The Netherlands}, Author = {Seto, K.-i.}, Booktitle = {Relevance theory: Applications and implications}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. Carson and S. Uchida}, Pages = {239--255}, Publisher = {John Benjamins}, Title = {On non-echoic irony}, Year = {1998}} @book{Sperber+95, Address = {Oxford, UK}, Author = {D. Sperber and D. Wilson}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Edition = {2nd}, Publisher = {Blackwell Publishers}, Title = {Relevance: Communication and cognition}, Year = {1995}} @incollection{Srinarawat05, Address = {Amsterdam, The Netherlands}, Author = {Srinarawat, D.}, Booktitle = {Broadening the horizon of linguistic politeness}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. Lakoff and S. Ide}, Pages = {175--193}, Publisher = {John Benjamins}, Title = {Indirectness as a politeness strategy of {T}hai speakers}, Year = {2005}} @inproceedings{Tsur+10, Author = {Tsur, Oren and Davidov, Dmitry and Rappoport, Ari}, Booktitle = {Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Pages = {162--169}, Title = {ICWSM--A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews}, Year = {2010}} @article{VanMulken+12, Author = {M. {Van Mulken} and P. J. Schellens}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Journal = {Tijdschrift voor taalbeheersing}, Number = {1}, Pages = {26--53}, Title = {Over loodzware bassen en wapperende broekspijpen. Gebruik en perceptie van taalintensiverende stijlmiddelen}, Volume = {34}, Year = {2012}} @incollection{Wilson+07, Address = {New York, NY}, Author = {D. Wilson and D. Sperber}, Booktitle = {Irony in language and thought: A cognitive science reader}, Date-Added = {2013-03-11 19:50:15 +0000}, Date-Modified = {2013-03-11 19:50:15 +0000}, Editor = {R. W. Gibbs and R. W. Gibbs Jr. and H. Colston}, Pages = {35--55}, Publisher = {Lawrence Erlbaum}, Title = {On verbal irony}, Year = {2007}} @book{Paul20, Address = {Halle, Germany}, Author = {H. Paul}, Date-Added = {2012-11-29 20:22:12 +0000}, Date-Modified = {2012-11-29 21:00:31 +0000}, Edition = {Fifth}, Keywords = {neogrammarians, linguistics}, Publisher = {Max Niemeyer}, Title = {Prinzipien der Sprachgeschichte}, Year = {1920}} @article{EuropeanParliamentCouncil01, Author = {{The European Parliament and the Council}}, Date-Added = {2012-11-28 20:13:30 +0000}, Date-Modified = {2012-11-28 20:20:24 +0000}, Journal = {Official Journal of the European Union}, Keywords = {European Union}, Month = {May}, Pages = {43---48}, Title = {{Regulation (EC) No 1049/2001 of the European Parliament and of the Council regarding public access to European Parliament, Council and Commission documents}}, Volume = {145}, Year = {2001}} @book{Sinninghe33, Address = {Zutphen, The Netherlands}, Author = {J.R.W. Sinninghe}, Date-Added = {2012-11-18 12:53:16 +0000}, Date-Modified = {2012-11-18 12:53:44 +0000}, Publisher = {Thieme}, Title = {Noord-Brabantsch sagenboek}, Year = {1933}} @book{Dijkstra+84, Address = {Groningen, The Netherlands}, Author = {T. Dijkstra and G. Kempen}, Date-Added = {2012-11-18 12:49:06 +0000}, Date-Modified = {2012-11-18 12:50:17 +0000}, Publisher = {Wolters-Noordhoff}, Title = {Taal in uitvoering}, Year = {1984}} @book{Odijk12, Author = {J. Odijk}, Date-Added = {2012-11-18 12:11:56 +0000}, Date-Modified = {2012-11-18 12:17:05 +0000}, Isbn = {978-3-642-25977-7}, Note = {Available online at \url{http://www.meta-net.eu/whitepapers}}, Publisher = {Springer}, Series = {META-NET White Paper Series}, Title = {{Het Nederlands in het Digitale Tijdperk -- The Dutch Language in the Digital Age}}, Year = {2012}} @article{Bannard+08, Author = {C. Bannard and D. Matthews}, Date-Added = {2012-11-18 12:06:25 +0000}, Date-Modified = {2012-11-18 12:09:30 +0000}, Journal = {Psychological Science}, Keywords = {psycholinguistics, frequency effects, ngrams}, Number = {3}, Pages = {241--248}, Title = {Stored word sequences in language learning: The effect of familiarity on children's repetition of four-word combinations}, Volume = {19}, Year = {2008}} @book{VanPamelen12, Author = {F. {Van Pamelen}}, Date-Added = {2012-11-18 11:58:28 +0000}, Date-Modified = {2012-11-18 12:16:34 +0000}, Publisher = {De Fontein}, Title = {De zin van de ommezijde}, Year = {2012}} @article{VandeCamp+12, Author = {M. {Van de Camp} and A. {Van den Bosch}}, Date-Added = {2012-10-28 10:32:25 +0000}, Date-Modified = {2012-10-28 10:33:42 +0000}, Journal = {Decision Support Systems}, Keywords = {hitime, cls, lst, social networks, social history}, Number = {4}, Pages = {761--769}, Title = {The socialist network}, Volume = {53}, Year = {2012}} @proceedings{VandenBosch+12b, Address = {New Brunswick, NJ}, Date-Added = {2012-10-28 10:30:32 +0000}, Date-Modified = {2012-10-28 10:31:48 +0000}, Editor = {A. {Van den Bosch} and H. Shatkay}, Keywords = {amicus, dssd, scientific discourse}, Publisher = {ACL}, Title = {Proceedings of the ACL 2012 workshop on Detecting Structure in Scholarly Discourse (DSSD-2012)}, Year = {2012}} @proceedings{Zervanou+12, Address = {New Brunswick, NJ}, Date-Added = {2012-10-28 10:29:01 +0000}, Date-Modified = {2012-10-28 10:30:27 +0000}, Editor = {K. Zervanou and A. {Van den Bosch}}, Keywords = {latech, isher, cls, lst}, Publisher = {ACL}, Title = {Proceedings of the EACL 2012 workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH-2012)}, Year = {2012}} @inproceedings{Verberne+12, Address = {New Brunswick, NJ}, Author = {S. Verberne and A. {Van den Bosch} and H. Strik and L. Boves}, Booktitle = {Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics}, Date-Added = {2012-10-28 10:27:13 +0000}, Date-Modified = {2012-10-28 10:28:54 +0000}, Keywords = {cls, lst, predictive text entry, text completion, wopr}, Pages = {561--569}, Publisher = {ACL}, Title = {The effect of domain and text type on text prediction quality}, Year = {2012}} @incollection{Mos+12, Address = {Berlin, Germany}, Author = {M. Mos and A. {Van den Bosch} and P. Berck}, Booktitle = {Frequency Effects in Language Learning and Processing}, Date-Added = {2012-10-28 10:24:35 +0000}, Date-Modified = {2012-10-28 10:35:08 +0000}, Editor = {S. Gries and D. Divjak}, Keywords = {vici, cls, lst, language modeling, human language processing, perplexity}, Pages = {207--240}, Publisher = {Mouton De Gruyter}, Title = {The predictive value of word-level perplexity in human sentence processing: A case study on fixed adjective--preposition constructions in Dutch}, Volume = {1}, Year = {2012}} @inproceedings{Karsdorp+12, Address = {Istanbul, Turkey}, Author = {F. Karsdorp and P. {Van Kranenburg} and T. Meder and D. Trieschnigg and A. {Van den Bosch}}, Booktitle = {Proceedings of the 2012 Computational Models of Narrative Workshop}, Date-Added = {2012-10-28 10:22:34 +0000}, Date-Modified = {2012-10-28 10:24:19 +0000}, Keywords = {tunes and tales, cls, lst, folktales, motifs}, Pages = {22--26}, Title = {In search of an appropriate abstraction level for motif annotations}, Year = {2012}} @inproceedings{Vossen+12, Address = {Istanbul, Turkey}, Author = {P. Vossen and A. G\"{o}r\"{o}g and R. Izquierdo and A. {Van den Bosch}}, Booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation}, Date-Added = {2012-10-28 10:20:48 +0000}, Date-Modified = {2012-10-28 10:22:21 +0000}, Keywords = {dutchsemcor, cls, lst}, Pages = {584--589}, Title = {{DutchSemCor}: Targeting the ideal sense-tagged corpus}, Year = {2012}} @inproceedings{VandenBosch+12, Address = {New Brunswick, NJ}, Author = {A. {Van den Bosch} and P. Berck}, Booktitle = {Proceedings of the 7th Workshop on the Innovative Use of NLP for Building Educational Applications}, Date-Added = {2012-10-28 10:18:21 +0000}, Date-Modified = {2012-10-28 10:35:37 +0000}, Keywords = {vici, cls, lst, grammatical error correction}, Pages = {289--294}, Publisher = {ACL}, Title = {Memory-based text correction for preposition and determiner errors}, Year = {2012}} @inproceedings{Wubben+12, Address = {New Brunswick, NJ}, Author = {S. Wubben and E. Krahmer and A. {Van den Bosch}}, Booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics}, Date-Added = {2012-10-28 10:16:52 +0000}, Date-Modified = {2012-10-28 10:20:37 +0000}, Keywords = {ilk, memphix, sentence simplification, monolingual machine translation}, Organization = {ACL}, Pages = {1015--1024.}, Title = {Sentence simplification by monolingual machine translation}, Year = {2012}} @inproceedings{Kunneman+12, Address = {Maastricht, The Netherlands}, Author = {F. Kunneman and A. {Van den Bosch}}, Booktitle = {Proceedings of the 24th Benelux Conference on Artficial Intelligence}, Date-Added = {2012-10-28 10:13:40 +0000}, Date-Modified = {2012-10-28 10:15:33 +0000}, Editor = {N. Roos and M. Winands and J. Uiterwijk}, Keywords = {cls, lst, adnext}, Pages = {147--154}, Title = {Leveraging unscheduled event prediction through mining scheduled event tweets}, Year = {2012}} @techreport{Asur+10, Author = {Asur, S. and Huberman, B. A.}, Date-Added = {2012-06-14 21:22:10 +0000}, Date-Modified = {2012-06-15 07:52:48 +0000}, Institution = {HP Labs}, Title = {{Predicting the Future with Social Media}}, Year = {2010}} @inproceedings{Ritterman+09, Author = {Ritterman, J. and Osborne, M. and Klein, E.}, Booktitle = {1st International Workshop on Mining Social Media}, Date-Added = {2012-06-14 21:21:44 +0000}, Date-Modified = {2012-06-14 21:21:44 +0000}, Title = {{Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic}}, Year = {2009}} @inproceedings{Radinsky+12, Author = {Radinsky, K. and Davidovich, S. and Markovitch, S.}, Booktitle = {Proceedings of the 21st International Conference on the World Wide Web}, Date-Added = {2012-06-14 21:20:46 +0000}, Date-Modified = {2012-06-14 21:21:30 +0000}, Title = {{Learning causality for news event prediction}}, Year = {2012}} @inproceedings{Lanagan+11, Author = {Lanagan, J. and Smeaton, A. F.}, Booktitle = {Fifth International AAAI Conference on Weblogs and Social Media}, Citeulike-Article-Id = {10745556}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Posted-At = {2012-06-05 22:06:11}, Priority = {0}, Title = {{Using Twitter to Detect and Tag Important Events in Live Sports}}, Year = {2011}} @inproceedings{Sajnani+11, Author = {Sajnani, H. and Javanmardi, S. and McDonald, David W. and Lopes, Cristina V.}, Booktitle = {Analyzing Microtext: Papers from the 2011 AAAI Workshop}, Citeulike-Article-Id = {10745543}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Posted-At = {2012-06-05 22:02:36}, Priority = {2}, Title = {{Multi-Label Classification of Short Text: A Study on Wikipedia Barnstars}}, Year = {2011}} @inproceedings{Abel+12, Author = {Abel, F. and Hauff, C. and Houben, G. and Tao, K. and Stronkman, R.}, Booktitle = {Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT 2012}, Citeulike-Article-Id = {10745539}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Posted-At = {2012-06-05 21:59:30}, Priority = {2}, Title = {{Semantics + Filtering + Search = Twitcident, Exploring Information in Social Web Streams}}, Year = {2012}} @inproceedings{Kapanipathi+11, Author = {Kapanipathi, P. and Thomas, C. and Mendes, Pablo N. and Sheth, A.}, Booktitle = {Proceedings of Manufacturing \& Service Operations Management (MSOM), 2011}, Citeulike-Article-Id = {10745537}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Posted-At = {2012-06-05 21:55:17}, Priority = {2}, Title = {{Continuous Semantics: Dynamically Following Events}}, Year = {2011}} @inproceedings{Becker+11, Author = {Becker, H. and Chen, F. and Iter, D. and Naaman, M. and Gravano, L.}, Booktitle = {Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM '11)}, Citeulike-Article-Id = {10745535}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-14 19:20:14 +0000}, Posted-At = {2012-06-05 21:49:45}, Priority = {2}, Title = {{Automatic Identification and Presentation of Twitter Content for Planned Events}}, Year = {2011}} @inproceedings{Choudhury+11, Author = {Choudhury, S. and Breslin, John G.}, Booktitle = {Proceedings of the ESWC2011 Workshop on Making Sense of Microposts}, Citeulike-Article-Id = {10745510}, Citeulike-Linkout-0 = {http://oro.open.ac.uk/32460/}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-14 19:21:43 +0000}, Day = {30}, Month = may, Posted-At = {2012-06-05 21:32:23}, Priority = {2}, Title = {{Extracting Semantic Entities and Events from Sports Tweets}}, Url = {http://oro.open.ac.uk/32460/}, Year = {2011}, Bdsk-Url-1 = {http://oro.open.ac.uk/32460/}} @inproceedings{Sriram+10, Abstract = {{In microblogging services such as Twitter, the users may become overwhelmed by the raw data. One solution to this problem is the classification of short text messages. As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations. To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text. The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages.}}, Address = {New York, NY, USA}, Author = {Sriram, Bharath and Fuhry, Dave and Demir, Engin and Ferhatosmanoglu, Hakan and Demirbas, Murat}, Booktitle = {Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval}, Citeulike-Article-Id = {7573100}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1835643}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1835449.1835643}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Doi = {10.1145/1835449.1835643}, Isbn = {978-1-4503-0153-4}, Location = {Geneva, Switzerland}, Pages = {841--842}, Posted-At = {2012-06-05 21:27:20}, Priority = {0}, Publisher = {ACM}, Series = {SIGIR '10}, Title = {{Short text classification in twitter to improve information filtering}}, Url = {http://dx.doi.org/10.1145/1835449.1835643}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1835449.1835643}} @inproceedings{Sankaranarayanan+09, Abstract = {{Twitter is an electronic medium that allows a large user populace to communicate with each other simultaneously. Inherent to Twitter is an asymmetrical relationship between friends and followers that provides an interesting social network like structure among the users of Twitter. Twitter messages, called tweets, are restricted to 140 characters and thus are usually very focused. We investigate the use of Twitter to build a news processing system, called TwitterStand, from Twitter tweets. The idea is to capture tweets that correspond to late breaking news. The result is analogous to a distributed news wire service. The difference is that the identities of the contributors/reporters are not known in advance and there may be many of them. Furthermore, tweets are not sent according to a schedule: they occur as news is happening, and tend to be noisy while usually arriving at a high throughput rate. Some of the issues addressed include removing the noise, determining tweet clusters of interest bearing in mind that the methods must be online, and determining the relevant locations associated with the tweets.}}, Address = {New York, NY, USA}, Author = {Sankaranarayanan, Jagan and Samet, Hanan and Teitler, Benjamin E. and Lieberman, Michael D. and Sperling, Jon}, Booktitle = {Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, Citeulike-Article-Id = {6369732}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1653771.1653781}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1653771.1653781}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Doi = {10.1145/1653771.1653781}, Isbn = {978-1-60558-649-6}, Keywords = {twitter}, Location = {Seattle, Washington}, Pages = {42--51}, Posted-At = {2012-06-05 21:23:11}, Priority = {2}, Publisher = {ACM}, Series = {GIS '09}, Title = {{TwitterStand: news in tweets}}, Url = {http://dx.doi.org/10.1145/1653771.1653781}, Year = {2009}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1653771.1653781}} @inproceedings{Jackoway+11, Abstract = {{Twitter presents a source of information that cannot easily be obtained anywhere else. However, though many posts on Twitter reveal up-to-the-minute information about events in the world or interesting sentiments, far more posts are of no interest to the general audience. A method to determine which Twitter users are posting reliable information and which posts are interesting is presented. Using this information a search through a large, online news corpus is conducted to discover future events before they occur along with information about the location of the event. These events can be identified with a high degree of accuracy by verifying that an event found in one news article is found in other similar news articles, since any event interesting to a general audience will likely have more than one news story written about it. Twitter posts near the time of the event can then be identified as interesting if they match the event in terms of keywords or location. This method enables the discovery of interesting posts about current and future events and helps in the identification of reliable users.}}, Address = {New York, NY, USA}, Author = {Jackoway, Alan and Samet, Hanan and Sankaranarayanan, Jagan}, Booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks}, Citeulike-Article-Id = {10001986}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=2063224}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/2063212.2063224}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-14 19:18:36 +0000}, Doi = {10.1145/2063212.2063224}, Isbn = {978-1-4503-1033-8}, Keywords = {event\_prediction, twitter}, Location = {Chicago, Illinois}, Pages = {25--32}, Posted-At = {2012-06-05 21:22:18}, Priority = {0}, Publisher = {ACM}, Series = {LBSN '11}, Title = {{Identification of live news events using Twitter}}, Url = {http://dx.doi.org/10.1145/2063212.2063224}, Year = {2011}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/2063212.2063224}} @article{Phuvipadawat+10, Abstract = {{Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called '' Hotstream'' based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.}}, Address = {Los Alamitos, CA, USA}, Author = {Phuvipadawat, Swit and Murata, Tsuyoshi}, Booktitle = {Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on}, Citeulike-Article-Id = {9760493}, Citeulike-Linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.205}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.205}, Citeulike-Linkout-2 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=5616930}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Doi = {10.1109/WI-IAT.2010.205}, Isbn = {978-0-7695-4191-4}, Journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, Month = aug, Pages = {120--123}, Posted-At = {2012-06-05 21:19:16}, Priority = {2}, Publisher = {IEEE Computer Society}, Title = {{Breaking News Detection and Tracking in Twitter}}, Url = {http://dx.doi.org/10.1109/WI-IAT.2010.205}, Volume = {3}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.205}} @inproceedings{Finin+2010, Abstract = {{We describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd-Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk to collect judgements on the quality of "word clouds."}}, Address = {Stroudsburg, PA, USA}, Author = {Finin, Tim and Murnane, Will and Karandikar, Anand and Keller, Nicholas and Martineau, Justin and Dredze, Mark}, Booktitle = {Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk}, Citeulike-Article-Id = {8825082}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1866709}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Location = {Los Angeles, California}, Pages = {80--88}, Posted-At = {2012-02-21 11:08:18}, Priority = {3}, Publisher = {Association for Computational Linguistics}, Series = {CSLDAMT '10}, Title = {{Annotating named entities in Twitter data with crowdsourcing}}, Url = {http://portal.acm.org/citation.cfm?id=1866709}, Year = {2010}, Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1866709}} @inproceedings{Treeratpituk+06, Abstract = {{Government agencies must often quickly organize and analyze large amounts of textual information, for example comments received as part of notice and comment rulemaking. Hierarchical organization is popular because it represents information at different levels of detail and is convenient for interactive browsing. Good hierarchical clustering algorithms are available, but there are few good solutions for automatically labeling the nodes in a cluster hierarchy.This paper presents a simple algorithm that automatically assigns labels to hierarchical clusters. The algorithm evaluates candidate labels using information from the cluster, the parent cluster, and corpus statistics. A trainable threshold enables the algorithm to assign just a few high-quality labels to each cluster. Experiments with Open Directory Project (ODP) hierarchies indicate that the algorithm creates cluster labels that are similar to labels created by ODP editors.}}, Address = {New York, NY, USA}, Author = {Treeratpituk, Pucktada and Callan, Jamie}, Booktitle = {Proceedings of the 2006 international conference on Digital government research}, Citeulike-Article-Id = {8569756}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1146650}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1146598.1146650}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Doi = {10.1145/1146598.1146650}, Location = {San Diego, California}, Pages = {167--176}, Posted-At = {2012-01-18 11:21:03}, Priority = {2}, Publisher = {ACM}, Series = {dg.o '06}, Title = {{Automatically labeling hierarchical clusters}}, Url = {http://dx.doi.org/10.1145/1146598.1146650}, Year = {2006}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1146598.1146650}} @inproceedings{Kumaran+05, Abstract = {{New Event Detection (NED) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. We compare two stories by finding three cosine similarities based on names, topics and the full text. These additional comparisons suggest treating the NED problem as a binary classification problem with the comparison scores serving as features. The classifier models we learned show statistically significant improvement over the baseline vector space model system on all the collections we tested, including the latest TDT5 collection.The presence of automatic speech recognizer (ASR) output of broadcast news in news streams can reduce performance and render our named entity recognition based approaches ineffective. We provide a solution to this problem achieving statistically significant improvements.}}, Address = {Stroudsburg, PA, USA}, Author = {Kumaran, Giridhar and Allan, James}, Booktitle = {Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing}, Citeulike-Article-Id = {3486907}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1220575.1220591}, Citeulike-Linkout-1 = {http://dx.doi.org/10.3115/1220575.1220591}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:51 +0000}, Doi = {10.3115/1220575.1220591}, Location = {Vancouver, British Columbia, Canada}, Pages = {121--128}, Posted-At = {2012-01-10 14:15:09}, Priority = {0}, Publisher = {Association for Computational Linguistics}, Series = {HLT '05}, Title = {{Using names and topics for new event detection}}, Url = {http://dx.doi.org/10.3115/1220575.1220591}, Year = {2005}, Bdsk-Url-1 = {http://dx.doi.org/10.3115/1220575.1220591}} @inproceedings{Petrovic+10, Abstract = {{With the recent rise in popularity and size of social media, there is a growing need for systems that can extract useful information from this amount of data. We address the problem of detecting new events from a stream of Twitter posts. To make event detection feasible on web-scale corpora, we present an algorithm based on locality-sensitive hashing which is able overcome the limitations of traditional approaches, while maintaining competitive results. In particular, a comparison with a state-of-the-art system on the first story detection task shows that we achieve over an order of magnitude speedup in processing time, while retaining comparable performance. Event detection experiments on a collection of 160 million Twitter posts show that celebrity deaths are the fastest spreading news on Twitter.}}, Address = {Stroudsburg, PA, USA}, Author = {Petrovi\'{c}, Sa\v{s}a and Osborne, Miles and Lavrenko, Victor}, Booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, Citeulike-Article-Id = {8882414}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1858020}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:49:17 +0000}, Isbn = {1-932432-65-5}, Keywords = {first\_story\_detection, locality\_sensitive\_hashing, tdt, twitter}, Location = {Los Angeles, California}, Pages = {181--189}, Posted-At = {2012-01-06 13:47:39}, Priority = {0}, Publisher = {Association for Computational Linguistics}, Series = {HLT '10}, Title = {{Streaming first story detection with application to Twitter}}, Url = {http://portal.acm.org/citation.cfm?id=1858020}, Year = {2010}, Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1858020}} @mastersthesis{Kunneman11, Author = {F. Kunneman}, Citeulike-Article-Id = {10200587}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-10-28 10:16:30 +0000}, Institution = {Radboud University Nijmegen}, Keywords = {machine\_learning, text\_cohesion, topic\_clustering, topic\_segmentation}, Posted-At = {2012-01-05 13:30:23}, Priority = {0}, Title = {The automatic topic segmentation and topic tracking of television programs}} @article{Moon96, Abstract = {{A common task in signal processing is the estimation of the parameters of a probability distribution function. Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in noise. In many parameter estimation problems the situation is more complicated because direct access to the data necessary to estimate the parameters is impossible, or some of the data are missing. Such difficulties arise when an outcome is a result of an accumulation of simpler outcomes, or when outcomes are clumped together, for example, in a binning or histogram operation. There may also be data dropouts or clustering in such a way that the number of underlying data points is unknown (censoring and/or truncation). The EM (expectation-maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation. The EM algorithm is presented at a level suitable for signal processing practitioners who have had some exposure to estimation theory}}, Author = {Moon, T. K.}, Citeulike-Article-Id = {989971}, Citeulike-Linkout-0 = {http://dx.doi.org/10.1109/79.543975}, Citeulike-Linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=543975}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:16 +0000}, Doi = {10.1109/79.543975}, Institution = {Electr. \& Comput. Eng. Dept., Utah State Univ., Logan, UT}, Issn = {10535888}, Journal = {IEEE Signal Processing Magazine}, Keywords = {expectation-maximization}, Month = nov, Number = {6}, Pages = {47--60}, Posted-At = {2012-01-02 13:22:36}, Priority = {3}, Publisher = {IEEE}, Title = {{The expectation-maximization algorithm}}, Url = {http://dx.doi.org/10.1109/79.543975}, Volume = {13}, Year = {1996}, Bdsk-Url-1 = {http://dx.doi.org/10.1109/79.543975}} @inproceedings{Sakaki+10, Abstract = {{Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96\% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.}}, Address = {New York, NY, USA}, Author = {Sakaki, Takeshi and Okazaki, Makoto and Matsuo, Yutaka}, Booktitle = {Proceedings of the 19th international conference on World wide web}, Citeulike-Article-Id = {7097817}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1772690.1772777}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1772690.1772777}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:46:57 +0000}, Doi = {10.1145/1772690.1772777}, Isbn = {978-1-60558-799-8}, Location = {Raleigh, North Carolina, USA}, Pages = {851--860}, Posted-At = {2012-01-02 13:20:54}, Priority = {0}, Publisher = {ACM}, Series = {WWW '10}, Title = {{Earthquake shakes Twitter users: real-time event detection by social sensors}}, Url = {http://dx.doi.org/10.1145/1772690.1772777}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1772690.1772777}} @inproceedings{Cataldi+10, Abstract = {{Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a variety of purposes, including daily conversations, URLs sharing and information news. Considering its world-wide distributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impressive short response time, has the principal advantage. In this paper we recognize this primary role of Twitter and we propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. First, we extract the contents (set of terms) of the tweets and model the term life cycle according to a novel aging theory intended to mine the emerging ones. A term can be defined as emerging if it frequently occurs in the specified time interval and it was relatively rare in the past. Moreover, considering that the importance of a content also depends on its source, we analyze the social relationships in the network with the well-known Page Rank algorithm in order to determine the authority of the users. Finally, we leverage a navigable topic graph which connects the emerging terms with other semantically related keywords, allowing the detection of the emerging topics, under user-specified time constraints. We provide different case studies which show the validity of the proposed approach.}}, Address = {New York, NY, USA}, Author = {Cataldi, Mario and Di Caro, Luigi and Schifanella, Claudio}, Booktitle = {Proceedings of the Tenth International Workshop on Multimedia Data Mining}, Citeulike-Article-Id = {8500602}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1814249}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1814245.1814249}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:48:00 +0000}, Doi = {10.1145/1814245.1814249}, Isbn = {978-1-4503-0220-3}, Keywords = {aging\_theory, first\_story\_detection, new\_event\_detection, social\_network, text\_analysis, topic\_detection, twitter}, Location = {Washington, D.C.}, Posted-At = {2012-01-02 13:19:55}, Priority = {0}, Publisher = {ACM}, Series = {MDMKDD '10}, Title = {{Emerging topic detection on Twitter based on temporal and social terms evaluation}}, Url = {http://dx.doi.org/10.1145/1814245.1814249}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1814245.1814249}} @inproceedings{Benhardus10, Author = {Benhardus, J.}, Citeulike-Article-Id = {10191863}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:45:33 +0000}, Journal = {UCCS REU fore Artificial Intelligence, Natural Language Processing and Information Retrieval, Final Report}, Keywords = {natural\_language\_processing, trend\_detection, twitter}, Posted-At = {2012-01-02 13:17:59}, Priority = {0}, Title = {{Streaming Trend Detection in Twitter}}, Year = {2010}} @inproceedings{Bethard+06, Abstract = {{Complex tasks like question answering need to be able to identify events in text and the relations among those events. We show that this event identification task and a related task, identifying the semantic class of these events, can both be formulated as classification problems in a word-chunking paradigm. We introduce a variety of linguistically motivated features for this task and then train a system that is able to identify events with a precision of 82\% and a recall of 71\%. We then show a variety of analyses of this model, and their implications for the event identification task.}}, Address = {Stroudsburg, PA, USA}, Author = {S. Bethard and J. H. Martin}, Booktitle = {Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing}, Citeulike-Article-Id = {10191852}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1610098}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2013-08-07 09:27:42 +0000}, Isbn = {1-932432-73-6}, Keywords = {event\_detection, semantic\_class\_event}, Location = {Sydney, Australia}, Pages = {146--154}, Posted-At = {2012-01-02 13:04:00}, Priority = {0}, Publisher = {Association for Computational Linguistics}, Series = {EMNLP '06}, Title = {{Identification of event mentions and their semantic class}}, Url = {http://portal.acm.org/citation.cfm?id=1610098}, Year = {2006}, Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1610098}} @inproceedings{Mei+05, Abstract = {{Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.}}, Address = {New York, NY, USA}, Author = {Mei, Qiaozhu and Zhai, ChengXiang}, Booktitle = {Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining}, Citeulike-Article-Id = {1720074}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1081870.1081895}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1081870.1081895}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:23 +0000}, Doi = {10.1145/1081870.1081895}, Isbn = {1-59593-135-X}, Keywords = {clustering, evolutionary\_theme\_patterns, temporal\_text\_mining}, Location = {Chicago, Illinois, USA}, Pages = {198--207}, Posted-At = {2012-01-02 13:01:52}, Priority = {0}, Publisher = {ACM}, Series = {KDD '05}, Title = {{Discovering evolutionary theme patterns from text: an exploration of temporal text mining}}, Url = {http://dx.doi.org/10.1145/1081870.1081895}, Year = {2005}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1081870.1081895}} @inproceedings{Singh+10, Abstract = {{Events are the fundamental abstractions to study the dynamic world. We believe that the next generation of web (i.e. event-web), will focus on interconnections between events as they occur across space and time [3]. In fact we argue that the real value of large volumes of microblog data being created daily lies in its inherent spatio-temporality, and its correlation with the real-world events. In this context, we studied the structural properties of a corpus of 5,835,237 Twitter microblogs, and found it to exhibit Power laws across space and time, much like those exhibited by events in multiple domains. The properties studied over microblogs on different topics can be applied to study relationships between related events, as well as data organization for event-based, real-time, and location-aware applications.}}, Address = {New York, NY, USA}, Author = {Singh, Vivek K. and Jain, Ramesh}, Booktitle = {Proceedings of the 19th international conference on World wide web}, Citeulike-Article-Id = {10191848}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1772865}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1772690.1772865}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:02 +0000}, Doi = {10.1145/1772690.1772865}, Isbn = {978-1-60558-799-8}, Keywords = {event-web, pagerank, twitter, zipfs\_law}, Location = {Raleigh, North Carolina, USA}, Pages = {1183--1184}, Posted-At = {2012-01-02 13:00:12}, Priority = {0}, Publisher = {ACM}, Series = {WWW '10}, Title = {{Structural analysis of the emerging event-web}}, Url = {http://dx.doi.org/10.1145/1772690.1772865}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1772690.1772865}} @inproceedings{Bethard+08, Author = {Bethard, S. and Corvey, W. and Klingenstein, S. and Martin, James H.}, Citeulike-Article-Id = {10191845}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:46:11 +0000}, Journal = {LREC-2008}, Keywords = {causal\_structure, corpus, time\_expressions}, Posted-At = {2012-01-02 12:58:49}, Priority = {0}, Title = {{Building a Corpus of Temporal-Causal Structure}}, Year = {2008}} @article{Bethard+08b, Author = {Bethard, S. and Martin, James H. and Klingenstein, Sara K.}, Citeulike-Article-Id = {10191824}, Citeulike-Linkout-0 = {http://clear.colorado.edu/\~{}bethard/documents/bethard\_2007\_ijsc\_temporal.pdf}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:46:20 +0000}, Day = {12}, Journal = {International Journal of Semantic Computing}, Keywords = {machine\_learning, temporal\_relations, timelines}, Month = oct, Posted-At = {2012-01-02 12:52:42}, Priority = {0}, Title = {{Finding Temporal Structure in Text: Machine Learning of Syntactic Temporal Relations}}, Url = {http://clear.colorado.edu/\~{}bethard/documents/bethard\_2007\_ijsc\_temporal.pdf}, Year = {2008}, Bdsk-Url-1 = {http://clear.colorado.edu/%5C~%7B%7Dbethard/documents/bethard%5C_2007%5C_ijsc%5C_temporal.pdf}} @inproceedings{Li+05, Abstract = {{Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.}}, Address = {New York, NY, USA}, Author = {Li, Zhiwei and Wang, Bin and Li, Mingjing and Ma, Wei Y.}, Booktitle = {Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, Citeulike-Article-Id = {319837}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1076034.1076055}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1076034.1076055}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Doi = {10.1145/1076034.1076055}, Isbn = {1-59593-034-5}, Keywords = {news\_event\_detection, probabilistics}, Location = {Salvador, Brazil}, Pages = {106--113}, Posted-At = {2012-01-02 11:52:15}, Priority = {0}, Publisher = {ACM}, Series = {SIGIR '05}, Title = {{A probabilistic model for retrospective news event detection}}, Url = {http://dx.doi.org/10.1145/1076034.1076055}, Year = {2005}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1076034.1076055}} @inproceedings{Kumaran+04, Abstract = {{New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.}}, Address = {New York, NY, USA}, Author = {Kumaran, Giridhar and Allan, James}, Booktitle = {Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval}, Citeulike-Article-Id = {1219863}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1009044}, Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1008992.1009044}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:44 +0000}, Doi = {10.1145/1008992.1009044}, Isbn = {1-58113-881-4}, Keywords = {cosine\_similarity, named\_entity\_detection, new\_event\_detection, text\_classification}, Location = {Sheffield, United Kingdom}, Pages = {297--304}, Posted-At = {2012-01-02 11:50:07}, Priority = {0}, Publisher = {ACM}, Series = {SIGIR '04}, Title = {{Text classification and named entities for new event detection}}, Url = {http://dx.doi.org/10.1145/1008992.1009044}, Year = {2004}, Bdsk-Url-1 = {http://dx.doi.org/10.1145/1008992.1009044}} @inproceedings{Mazur+10, Abstract = {{The reliable extraction of knowledge from text requires an appropriate treatment of the time at which reported events take place. Unfortunately, there are very few annotated data sets that support the development of techniques for event time-stamping and tracking the progression of time through a narrative. In this paper, we present a new corpus of temporally-rich documents sourced from English Wikipedia, which we have annotated with TIMEX2 tags. The corpus contains around 120000 tokens, and 2600 TIMEX2 expressions, thus comparing favourably in size to other existing corpora used in these areas. We describe the preparation of the corpus, and compare the profile of the data with other existing temporally annotated corpora. We also report the results obtained when we use DANTE, our temporal expression tagger, to process this corpus, and point to where further work is required. The corpus is publicly available for research purposes.}}, Address = {Stroudsburg, PA, USA}, Author = {Mazur, Pawet and Dale, Robert}, Booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing}, Citeulike-Article-Id = {10191724}, Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1870747}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:47:33 +0000}, Keywords = {corpus, time\_expressions, wikipedia}, Location = {Cambridge, Massachusetts}, Pages = {913--922}, Posted-At = {2012-01-02 11:46:10}, Priority = {0}, Publisher = {Association for Computational Linguistics}, Series = {EMNLP '10}, Title = {{WikiWars: a new corpus for research on temporal expressions}}, Url = {http://portal.acm.org/citation.cfm?id=1870747}, Year = {2010}, Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1870747}} @article{Anderson07, Author = {Anderson, P.}, Citeulike-Article-Id = {10191227}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:45:24 +0000}, Journal = {JISC Technology and Standards Watch}, Keywords = {20, web}, Month = feb, Posted-At = {2012-01-02 08:25:09}, Priority = {0}, Title = {{What is Web 2.0? Ideas, technologies and implications for education}}, Year = {2007}} @book{Tan+05, Author = {Tan, P. N. and Steinbach, M. and Kumar, V.}, Citeulike-Article-Id = {10191210}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Publisher = {Addison-Wesley}, Title = {{Introduction to Data Mining}}, Year = {2005}} @article{Brock+08, Author = {Brock, G. and Pihur, V. and Datta, S. and Datta, S.}, Citeulike-Article-Id = {10191209}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {J {S}tat {S}oftw}, Keywords = {*file-import-12-01-02}, Pages = {1--22}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {{clValid: an R package for cluster validation}}, Volume = {25}, Year = {2008}} @article{Shriberg+01, Author = {Shriberg, E. and T\"{u}r, G. and Hakkani-T\"{u}r, D. and Stolcke, A.}, Citeulike-Article-Id = {10191208}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {Computational {L}inguistics}, Keywords = {*file-import-12-01-02}, Pages = {31--57}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {{Integrating prosodic and lexical cues for automatic topic segmentation}}, Volume = {27}, Year = {2001}} @incollection{Karlgren+01, Author = {Karlgren, J. and Sahlgren, M.}, Booktitle = {Foundations of real-world understanding}, Citeulike-Article-Id = {10191207}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Editor = {Uesaka, Y. and Kanerva, P. and Asoh, H.}, Keywords = {*file-import-12-01-02}, Pages = {294--308}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {{From words to understanding}}, Year = {2001}} @article{Meila+01, Author = {Meil\`{a}, M. and Heckerman, D.}, Citeulike-Article-Id = {10191206}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {Machine {L}earning}, Keywords = {*file-import-12-01-02}, Pages = {9--29}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {An {E}xperimental {C}omparison of {M}odel-{B}ased {C}lustering {M}ethods}, Volume = {42}, Year = {2001}} @book{Manning+08, Author = {Manning, C. and Raghavan, P. and Sch\"{u}tze, H.}, Citeulike-Article-Id = {10191205}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Publisher = {Cambridge {U}niversity {P}ress}, Title = {Introduction to {I}nformation {R}etrieval}, Year = {2008}} @article{Jain+99, Author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.}, Citeulike-Article-Id = {10191204}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {A{C}{M} {C}omputing {S}urveys}, Keywords = {*file-import-12-01-02}, Number = {3}, Pages = {264--323}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {Data {C}lustering: {A} {R}eview}, Volume = {31}, Year = {1999}} @techreport{Steinbach+00b, Author = {Steinbach, M. and Karypis, G. and Kumar, V.}, Citeulike-Article-Id = {10191203}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:50:25 +0000}, Keywords = {*file-import-12-01-02}, Number = {34}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {{A comparison of document clustering techniques}}, Year = {2000}} @article{Amaral+07, Author = {Amaral, R. and Meinedo, H. and Caseiro, D. and Trancoso, I. and Neto, J.}, Citeulike-Article-Id = {10191202}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {E{U}{R}{A}{S}{I}{P} {J}ournal on {A}dvances in {S}ignal {P}rocessing}, Keywords = {*file-import-12-01-02}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {A {P}rototype {S}ystem for {S}elective {D}issemination of {B}roadcast {N}ews in {E}uropean {P}ortuguese}, Year = {2007}} @inproceedings{Rosenberg+06, Author = {Rosenberg, A. and Hirschberg, J.}, Booktitle = {HLT-NAACL}, Citeulike-Article-Id = {10191201}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Pages = {125--128}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {{Story segmentation of broadcast news in english, mandarin and arabic}}, Year = {2006}} @book{Huijbregts08, Author = {Huijbregts, M.}, Citeulike-Article-Id = {10191199}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Posted-At = {2012-01-02 08:15:28}, Priority = {0}, Title = {Segmentation, {D}iarization and {S}peech {T}ranscription: {S}urprise {D}ata {U}nraveled}, Year = {2008}} @article{Hearst91, Author = {Hearst, M. A.}, Citeulike-Article-Id = {10191198}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {Computational Linguistics}, Keywords = {*file-import-12-01-02}, Number = {1}, Pages = {33--64}, Posted-At = {2012-01-02 08:15:27}, Priority = {0}, Title = {{TexTiling: Segmenting text into multi-paragraph suptopic passages}}, Volume = {23}, Year = {1991}} @inproceedings{Reynar99, Author = {Reynar, J. C.}, Booktitle = {Proceedings of the 37th Annual Meeting of the ACL}, Citeulike-Article-Id = {10191197}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Pages = {357--364}, Posted-At = {2012-01-02 08:15:27}, Priority = {0}, Title = {{Statistical Models for Topic Segmentation}}, Year = {1999}} @article{Shahnaz+06, Author = {Shahnaz, F. and Berry, M. W. and Pauca, V. P. and Plemmons, R. J.}, Citeulike-Article-Id = {10191196}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Journal = {Information Processing \& Management}, Keywords = {*file-import-12-01-02}, Number = {2}, Pages = {373--386}, Posted-At = {2012-01-02 08:15:27}, Priority = {0}, Title = {{Document clustering using nonnegative matrix factorization}}, Volume = {42}, Year = {2006}} @inproceedings{Xu+03, Author = {Xu, W. and Liu, X. and Gong, Yihong}, Booktitle = {Proceedings of SIGIR'03}, Citeulike-Article-Id = {10191195}, Date-Added = {2012-06-06 06:43:32 +0000}, Date-Modified = {2012-06-06 06:43:32 +0000}, Keywords = {*file-import-12-01-02}, Pages = {267--273}, Posted-At = {2012-01-02 08:15:27}, Priority = {0}, Title = {{Document Clustering Based On Non-Negative Matrix Factorization}}, Year = {2003}} @article{Hammouda+04, Author = {Hammouda, K. M. and Kamel, M. 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Oostdijk}, Booktitle = {Proceedings of LREC-2000, Athens}, Date-Added = {2012-03-27 18:43:06 +0000}, Date-Modified = {2012-03-27 18:43:33 +0000}, Pages = {887--894}, Title = {{The spoken Dutch corpus: overview and first evaluation}}, Volume = {2}, Year = {2000}} @article{MacKenzie+02, Date-Added = {2012-01-15 16:56:35 +0000}, Date-Modified = {2012-01-15 16:56:54 +0000}} @article{VandenBosch11, Author = {A. {Van den Bosch}}, Date-Added = {2012-01-15 15:58:00 +0000}, Date-Modified = {2012-10-28 10:34:14 +0000}, Journal = {Computational Linguistics in the Netherlands Journal}, Keywords = {vici, ilk, word completion, predictive text entry}, Pages = {79--94}, Title = {Effects of context and recency in scaled word completion}, Volume = {1}, Year = {2011}} @book{Sporleder+11, Address = {Berlin}, Date-Added = {2011-11-08 12:12:04 +0000}, Date-Modified = {2011-11-08 12:13:23 +0000}, Editor = {C. Sporleder and A. {Van den Bosch} and K. 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Daelemans}, Booktitle = {Interactive multi-modal question answering}, Date-Added = {2011-11-08 12:08:45 +0000}, Date-Modified = {2011-11-08 12:10:29 +0000}, Editor = {A. {Van den Bosch} and G. {Bouma}}, Keywords = {imix, vici, ilk, constraint satisfaction inference, named-entity recognition, information extraction}, Publisher = {Springer Verlag}, Series = {Theory and Applications of Natural Language Processing: Edited Volumes}, Title = {Constraint satisfaction inference for entity recognition}, Year = {2011}} @article{Haque+11, Author = {R. Haque and S. Kumar Naskar and A. {Van den Bosch} and A. Way}, Date-Added = {2011-11-08 11:42:40 +0000}, Date-Modified = {2011-11-08 11:45:03 +0000}, Journal = {Machine Translation}, Keywords = {ilk, vici, mt, machine translation, IGTree}, Month = {September}, Number = {3}, Pages = {239--285}, Title = {Integrating source-language context into phrase-based statistical machine translation}, Volume = {25}, Year = {2011}} @inproceedings{Ordelman+01, Author = {R. Ordelman and A. {Van Hessen} and F. {De Jong}}, Booktitle = {Proceedings of Eurospeech 2001}, Date-Added = {2011-10-17 21:26:52 +0000}, Date-Modified = {2011-10-17 21:27:57 +0000}, Title = {Lexicon optimization for Dutch speech recognition in spoken document retrieval}, Year = {2001}} @incollection{Daelemans+10b, Address = {Oxford, UK}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Handbook of Computational Linguistics and Natural Language Processing}, Date-Added = {2011-09-11 14:57:52 +0000}, Date-Modified = {2011-09-11 14:59:49 +0000}, Editor = {A. Clark and C. 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Mac Namee and I. {Van der Sluis}}, Keywords = {memphix, vici, ilk, paraphrasing, statistical machine translation}, Pages = {203--207}, Title = {Paraphrase generation as monolingual translation: Data and evaluation}, Year = {2010}, Bdsk-Url-1 = {http://ilk.uvt.nl/~swubben/publications/INLG2010.pdf}} @inproceedings{VandenBosch+10, Address = {Menlo Park, CA}, Author = {A. {Van den Bosch} and P. Nauts and N. Eckhardt}, Booktitle = {Commonsense Knowledge: Papers from the AAAI Fall Symposium}, Date-Added = {2011-09-11 14:49:50 +0000}, Date-Modified = {2011-09-11 14:52:09 +0000}, Editor = {C. Havasi and D. Lenat and B. {Van Durme}}, Keywords = {ilk, vici, open mind, common sense, child development}, Pages = {114--119}, Publisher = {AAAI Press}, Title = {A kid's {O}pen {M}ind {C}ommon {S}ense}, Year = {2010}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/FS10-02-026.pdf}} @inproceedings{Zhekova+11, Address = {Portland, Oregon, USA}, Author = {D. Zhekova and S. 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Way}, Booktitle = {AMTA 2010: The Ninth Conference of the Association for Machine Translation in the Americas, Proceedings}, Date-Added = {2011-09-10 09:55:25 +0000}, Date-Modified = {2012-10-28 10:34:47 +0000}, Keywords = {ilk, vici, supertagging, statistical machine translation, igtree}, Pages = {210--219}, Title = {Supertags as source language context in hierarchical phrase-based {SMT}}, Year = {2010}} @inproceedings{Wubben10, Address = {Uppsala, Sweden}, Author = {S. Wubben}, Booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation}, Date-Added = {2011-09-10 09:47:11 +0000}, Date-Modified = {2011-09-10 09:48:48 +0000}, Keywords = {ilk, paraphrasing, memphix}, Pages = {260--263}, Title = {UvT: Memory-based pairwise ranking of paraphrasing verbs}, Year = {2010}} @inproceedings{VandeCamp+11, Address = {Portland, Oregon}, Author = {M. {Van de Camp} and A. {Van den Bosch}}, Booktitle = {Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)}, Date-Added = {2011-06-28 10:50:51 +0200}, Date-Modified = {2011-06-28 10:51:19 +0200}, Month = {June}, Pages = {61--69}, Publisher = {Association for Computational Linguistics}, Title = {A Link to the Past: Constructing Historical Social Networks}, Url = {http://www.aclweb.org/anthology/W11-1708}, Year = {2011}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W11-1708}} @inproceedings{Wubben+11b, Address = {Portland, Oregon}, Author = {S. Wubben and E. Marsi and A. {Van den Bosch} and E. Krahmer}, Booktitle = {Proceedings of the Workshop on Monolingual Text-To-Text Generation}, Date-Added = {2011-06-28 10:50:02 +0200}, Date-Modified = {2011-09-11 14:54:06 +0000}, Keywords = {ilk, vici, memphix, paraphrasing, DAESO}, Month = {June}, Pages = {27--33}, Publisher = {Association for Computational Linguistics}, Title = {Comparing Phrase-based and Syntax-based Paraphrase Generation}, Url = {http://www.aclweb.org/anthology/W11-1604}, Year = {2011}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W11-1604}} @inproceedings{Zervanou+11, Address = {Portland, OR, USA}, Author = {K. Zervanou and I. Korkontzelos and A. {Van den Bosch} and S. Ananiadou}, Booktitle = {Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities}, Date-Added = {2011-06-28 10:49:27 +0200}, Date-Modified = {2011-06-28 10:52:19 +0200}, Month = {June}, Pages = {44--53}, Publisher = {Association for Computational Linguistics}, Title = {Enrichment and Structuring of Archival Description Metadata}, Url = {http://www.aclweb.org/anthology/W11-1507}, Year = {2011}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/W11-1507}} @article{Morris+92, Author = {C. Morris and A. Newell and L. Booth and I. Ricketts and J. 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Wubben}, Date-Added = {2011-03-06 17:56:43 +0100}, Date-Modified = {2012-10-28 10:35:45 +0000}, Institution = {Tilburg University}, Keywords = {ilk, vici, open mind common sense, semantic relatedness}, Number = {08-01}, Title = {Using free link structure to calculate semantic relatedness}, Type = {ILK Research Group Technical Report Series}, Year = {2008}} @techreport{Bogers+07b, Author = {T. Bogers and K. Balog}, Date-Added = {2011-03-06 17:55:18 +0100}, Date-Modified = {2011-03-06 17:56:30 +0100}, Institution = {Tilburg University}, Keywords = {ilk, apropos, expert search, uvt, tilburg}, Number = {07-06}, Title = {UvT Expert Collection documentation}, Type = {ILK Research Group Technical Report Series}, Year = {2007}} @techreport{Tesink07, Author = {S. 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Arntzen and E. {Van Nieukerken}}, Date-Added = {2011-03-06 13:21:52 +0100}, Date-Modified = {2011-03-06 13:23:37 +0100}, Institution = {Tilburg University}, Keywords = {ilk, mitch, natural history, reptiles, amphibians, taxonomy}, Number = {06-01}, Title = {Cleaning and enriching research data on reptiles and amphibians: The MITCH pilot project and "nulmeting"}, Type = {ILK Research Group Technical Report Series}, Year = {2006}} @techreport{Divina+06, Author = {F. Divina and P. Vogt}, Date-Added = {2011-03-06 13:20:43 +0100}, Date-Modified = {2011-03-06 13:21:30 +0100}, Institution = {Tilburg University}, Keywords = {ilk, language evolution}, Number = {06-02}, Title = {Modelling language evolution in a complex ecological environment}, Type = {ILK Research Group Technical Report Series}, Year = {2006}} @techreport{Morante06, Author = {R. Morante}, Date-Added = {2011-03-06 13:19:23 +0100}, Date-Modified = {2011-03-06 13:20:22 +0100}, Institution = {Tilburg University}, Keywords = {ilk, semantic role labeling}, Number = {06-03}, Title = {Semantic role annotation in the Cast3LB-CoNLL-SemRol corpus}, Type = {ILK Research Group Technical Report Series}, Year = {2006}} @techreport{Geertzen03, Author = {J. Geertzen}, Date-Added = {2011-03-06 13:15:32 +0100}, Date-Modified = {2011-03-06 13:18:55 +0100}, Institution = {Tilburg University}, Keywords = {ilk, suffix trees}, Number = {03-11}, Title = {String alignment in grammatical induction: What suffix trees can do}, Type = {ILK Research Group Technical Report Series}, Year = {2003}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/ilk0311.pdf}} @inproceedings{VanErp+10, Address = {Nijmegen, The Netherlands}, Author = {M. {Van Erp} and S. Hunt}, Booktitle = {Proceedings of the 10th Dutch-Belgian Information Retrieval Workshop}, Date-Added = {2011-02-27 12:08:56 +0100}, Date-Modified = {2011-02-27 12:13:17 +0100}, Keywords = {ilk, hitime, mitch, natural history}, Pages = {31--38}, Publisher = {Radboud University Nijmegen}, Title = {Knowledge-driven information retrieval for natural history}, Year = {2010}, Bdsk-Url-1 = {http://www.ru.nl/publish/pages/544689/proceedings_dir2010.pdf}} @inproceedings{Zervanou10, Address = {Uppsala, Sweden}, Author = {K. Zervanou}, Booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation}, Date-Added = {2011-02-27 11:22:17 +0100}, Date-Modified = {2011-02-27 11:23:15 +0100}, Keywords = {ilk, hitime, term extraction, keyphrase extraction}, Month = {July}, Pages = {194--197}, Publisher = {Association for Computational Linguistics}, Title = {UvT: The UvT Term Extraction System in the Keyphrase Extraction Task}, Url = {http://www.aclweb.org/anthology/S10-1042}, Year = {2010}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/S10-1042}} @inproceedings{headdeniii+09, Address = {Boulder, Colorado}, Author = {Headden III, William P. and Johnson, Mark and McClosky, David}, Booktitle = {Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, Date-Added = {2011-02-20 15:44:06 +0100}, Date-Modified = {2011-02-20 15:44:22 +0100}, Month = {June}, Pages = {101--109}, Publisher = {Association for Computational Linguistics}, Title = {Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing}, Url = {http://www.aclweb.org/anthology/N/N09/N09-1012}, Year = {2009}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/N/N09/N09-1012}} @inproceedings{Gao+03, Acmid = {1075162}, Address = {Stroudsburg, PA, USA}, Author = {Gao, J. and Suzuki, H.}, Booktitle = {Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1}, Date-Added = {2011-02-20 15:34:39 +0100}, Date-Modified = {2011-02-20 15:34:53 +0100}, Doi = {http://dx.doi.org/10.3115/1075096.1075162}, Location = {Sapporo, Japan}, Numpages = {8}, Pages = {521--528}, Publisher = {Association for Computational Linguistics}, Series = {ACL '03}, Title = {Unsupervised learning of dependency structure for language modeling}, Url = {http://dx.doi.org/10.3115/1075096.1075162}, Year = {2003}, Bdsk-Url-1 = {http://dx.doi.org/10.3115/1075096.1075162}} @inproceedings{Spitkovsky+09, Author = {Spitkovsky, V. I. and Alshawi, H. and Jurafsky, D.}, Booktitle = {NIPS: Grammar Induction, Representation of Language and Language Learning}, Date-Added = {2011-02-20 13:36:28 +0100}, Date-Modified = {2011-06-19 23:06:46 +0200}, Pages = {1--10}, Title = {{B}aby {S}teps: How ``{L}ess is {M}ore'' in Unsupervised Dependency Parsing}, Year = {2009}} @inproceedings{Gorla+07, Acmid = {1619931}, Address = {Vancouver, British Columbia, Canada}, Author = {J. Gorla and A. Goyal and R. 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Westerhout}, Keywords = {ilk, memphix, vici, paraphrasing, headlines, clin}, Pages = {169--183}, Publisher = {LOT}, Title = {Paraphrasing headlines by machine translation}, Year = {2011}} @inproceedings{VanGompel+11, Address = {Utrecht, the Netherlands}, Author = {M. {Van Gompel} and A. {Van den Bosch} and P. Berck}, Booktitle = {Computational Linguistics in the Netherlands 2010: Selected Papers from the Twentieth CLIN Meeting}, Date-Added = {2011-02-19 13:13:10 +0100}, Date-Modified = {2011-09-11 14:54:36 +0000}, Editor = {T. Markus and P. Monachesi and E. Westerhout}, Keywords = {vici, pbmbmt, mbmt, clin, ilk}, Pages = {45--58}, Publisher = {LOT}, Title = {Extending memory-based machine translation to phrases}, Year = {2011}} @article{Bogers+11, Author = {T. Bogers and A. {Van den Bosch}}, Date-Added = {2011-02-09 09:35:19 +0100}, Date-Modified = {2013-08-07 07:50:07 +0000}, Journal = {International Journal of Electronic Commerce}, Keywords = {apropos, ilk, recommender systems, vici, social bookmarking, web 2.0}, Month = {Spring}, Number = {3}, Pages = {33-75}, Title = {Fusing Recommendations for Social Bookmarking Websites}, Volume = {15}, Year = {2011}} @inproceedings{Luyckx+08, Address = {Manchester, UK}, Author = {K. Luyckx and W. Daelemans}, Booktitle = {Proceedings of the Twenty-Second International Conference on Computational Linguistics (COLING 2008)}, Date-Added = {2011-02-07 21:09:28 +0100}, Date-Modified = {2012-01-15 20:23:08 +0000}, Keywords = {authorship attribution, authorship verification, TiMBL, clips}, Pages = {513--520}, Publisher = {Coling 2008 Organizing Committee}, Title = {Authorship Attribution and Verification with Many Authors and Limited Data}, Year = {2008}} @inproceedings{Morante+09d, Author = {Morante, R. and Van Asch, V. and Daelemans, W.}, Booktitle = {Proceedings of the Workshop on BioNLP: Shared Task}, Date-Added = {2011-02-07 21:03:30 +0100}, Date-Modified = {2011-02-07 21:04:12 +0100}, Keywords = {TiMBL, event extraction}, Organization = {Association for Computational Linguistics}, Pages = {59--67}, Title = {{A memory-based learning approach to event extraction in biomedical texts}}, Year = {2009}} @inproceedings{Dickinson09, Author = {Dickinson, M.}, Booktitle = {Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics}, Date-Added = {2011-02-07 21:00:08 +0100}, Date-Modified = {2011-02-07 21:00:39 +0100}, Keywords = {TiMBL, error correction, dependency parsing}, Organization = {Association for Computational Linguistics}, Pages = {193--201}, Title = {{Correcting dependency annotation errors}}, Year = {2009}} @conference{Kestemont+09, Author = {M. Kestemont and K. {Van Dalen-Oskam}}, Booktitle = {Proceedings of the Twenty-first Benelux Conference on Artificial Intelligence (BNAIC 2009)}, Date-Added = {2011-02-07 20:52:54 +0100}, Date-Modified = {2015-05-31 13:04:40 +0000}, Issn = {1568-7805}, Keywords = {TiMBL, authorship attribution, author discrimination}, Pages = {121--128}, Title = {{Predicting the past: memory-based copyist and author discrimination in medieval epics}}, Volume = {21}, Year = {2009}} @inproceedings{Hendrickx+08, Author = {Hendrickx, I. and Daelemans, W. and Luyckx, K. and Morante, R. and Van Asch, V.}, Booktitle = {Proceedings of the Fifth International Natural Language Generation Conference}, Date-Added = {2011-02-07 20:44:56 +0100}, Date-Modified = {2011-02-07 20:45:35 +0100}, Keywords = {TiMBL, referential expressions, natural language generation}, Organization = {Association for Computational Linguistics}, Pages = {194--195}, Title = {{CNTS: Memory-based learning of generating repeated references}}, Year = {2008}} @inproceedings{Max09, Author = {Max, A.}, Booktitle = {Proceedings of the 2009 Workshop on Applied Textual Inference}, Date-Added = {2011-02-07 20:41:13 +0100}, Date-Modified = {2011-02-07 20:42:03 +0100}, Keywords = {TiMBL, paraphrasing, pivot translation}, Organization = {Association for Computational Linguistics}, Pages = {18--26}, Title = {{Sub-sentential paraphrasing by contextual pivot translation}}, Year = {2009}} @conference{Morante+09c, Author = {Morante, R. and Daelemans, W.}, Booktitle = {Proceedings of the Workshop on BioNLP}, Date-Added = {2011-02-07 19:43:16 +0100}, Date-Modified = {2011-02-07 19:43:51 +0100}, Keywords = {hedging, TiMBL}, Organization = {Association for Computational Linguistics}, Pages = {28--36}, Title = {{Learning the scope of hedge cues in biomedical texts}}, Year = {2009}} @article{Plag+07, Author = {Plag, I. and Kunter, G. and Lappe, S.}, Date-Added = {2011-02-07 19:23:04 +0100}, Date-Modified = {2011-02-07 19:23:44 +0100}, Issn = {1613-7027}, Journal = {Corpus Linguistics and Lingustic Theory}, Keywords = {timbl, analogical modeling, compounding, stress assignment, English}, Number = {2}, Pages = {199--232}, Publisher = {Citeseer}, Title = {{Testing hypotheses about compound stress assignment in English: a corpus-based investigation}}, Volume = {3}, Year = {2007}} @techreport{DeClerq+10, Author = {Orph{\'e}e De Clerq and Martin Reynaert}, Date-Added = {2011-02-03 20:38:45 +0100}, Date-Modified = {2011-02-03 20:42:08 +0100}, Institution = {LT3 Research Group -- Hogeschool Gent}, Keywords = {ilk, lt3, sonar, corpus, IPR}, Number = {LT3 10-02}, Title = {{S}o{N}a{R} {A}cquisition {M}anual Version 1.0}, Url = {http://lt3.hogent.be/en/publications/}, Year = {2010}, Bdsk-Url-1 = {http://lt3.hogent.be/en/publications/}} @inproceedings{Reynaert+10, Address = {Valletta, Malta}, Author = {M. Reynaert and N. Oostdijk and O. {De Clercq} and H. {Van den Heuvel} and F. {De Jong}}, Booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, Date = {19-21}, Date-Added = {2011-02-03 20:36:39 +0100}, Date-Modified = {2012-11-28 22:30:07 +0000}, Isbn = {2-9517408-6-7}, Keywords = {ilk, corpus, corpus cleanup, IPR, sonar}, Language = {english}, Month = {May}, Publisher = {European Language Resources Association (ELRA)}, Title = {Balancing {SoNaR}: {IPR} versus Processing Issues in a 500-Million-Word Written {D}utch Reference Corpus}, Year = {2010}} @inproceedings{vanZaanen00, Author = {{Van Zaanen}, Menno}, Booktitle = {Proceedings of the 18th International Conference on Computational Linguistics ({COLING}); Saarbr{\"u}cken, Germany}, Date-Added = {2011-01-27 01:11:05 +0100}, Date-Modified = {2011-01-27 01:20:36 +0100}, Month = {jul # {~31--} # aug # {~4}}, Organization = {Association for Computational Linguistics}, Pages = {961--967}, Title = {{ABL}: {A}lignment-{B}ased {L}earning}, Year = {2000}} @inproceedings{Farkas+10, Annote = {URL: http://www.aclweb.org/anthology/W10-3001}, Author = {Farkas, Rich\'{a}rd and Vincze, Veronika and M\'{o}ra, Gy\"{o}rgy and Csirik, J\'{a}nos and Szarvas, Gy\"{o}rgy}, Booktitle = {Proceedings of the Fourteenth Conference on Computational Natural Language Learning}, Date-Added = {2011-01-26 21:38:19 +0100}, Date-Modified = {2011-01-29 16:44:08 +0100}, Month = {July}, Pages = {1--12}, Publisher = {Association for Computational Linguistics}, Title = {The {CoNLL}-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text}, Year = {2010}} @inproceedings{McDonald+07, Author = {McDonald, R. and Nivre, J.}, Booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning}, Date-Added = {2011-01-25 00:33:25 +0100}, Date-Modified = {2011-01-25 00:35:18 +0100}, Month = {June}, Organization = {Association for Computational Linguistics}, Pages = {pp. 122--131}, Title = {Characterizing the Errors of Data-Driven Dependency Parsing Models}, Year = {2007}} @inproceedings{Nivre+08, Address = {Columbus, Ohio}, Author = {J. 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Warnock and B. Wendroff}, Booktitle = {ICCA Journal}, Date-Added = {2010-12-17 14:12:41 +0100}, Date-Modified = {2011-01-13 19:18:59 +0100}, Pages = {10--13}, Title = {Search Tables in Computer Chess}, Volume = {11--1}, Year = {1988}} @inproceedings{Zobrist70, Author = {A. L. Zobrist}, Booktitle = {ICCA Journal (republished)}, Date-Added = {2010-12-17 14:06:38 +0100}, Date-Modified = {2011-01-13 19:19:19 +0100}, Pages = {69--73}, Title = {A New Hashing Method With Application For Game Playing}, Volume = {13--2}, Year = {1970}} @article{Daelemans+94, Author = {W. Daelemans and S. Gillis and G. Durieux}, Date-Added = {2010-12-08 23:33:52 +0100}, Date-Modified = {2010-12-08 23:50:06 +0100}, Journal = {Computational Linguistics}, Keywords = {ilk, stress assignment, Dutch}, Number = {3}, Pages = {421--451}, Title = {The acquisition of stress: a data-oriented approach.}, Volume = {20}, Year = {1994}} @conference{Post+09, Author = {M. Post and D. 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Snider}, Date-Added = {2010-12-08 13:39:14 +0100}, Date-Modified = {2010-12-08 13:40:36 +0100}, Journal = {Journal of Memory and Language}, Number = {1}, Pages = {67--82}, Title = {More than words: Frequency effects for multi-word phrases}, Volume = {62}, Year = {2010}} @book{Goldberg06, Author = {Goldberg, A.E.}, Date-Added = {2010-12-08 13:13:05 +0100}, Date-Modified = {2010-12-08 13:13:31 +0100}, Isbn = {0199268525}, Publisher = {Oxford University Press, USA}, Title = {Constructions at work: The nature of generalization in language}, Year = {2006}} @book{Bod+03, Date-Added = {2010-12-03 23:15:16 +0100}, Date-Modified = {2010-12-03 23:16:34 +0100}, Editor = {R. Bod and R. Scha and K. Sima'an}, Publisher = {CSLI Publications}, Series = {Studies in Computational Linguistics}, Title = {Data-oriented Parsing}, Year = {2003}} @incollection{Krott+02, Address = {Amsterdam, The Netherlands}, Author = {A. Krott and R. Schreuder and H. Baayen}, Booktitle = {Analogical modeling: An exemplar-based approach to language}, Date-Added = {2010-12-03 23:12:18 +0100}, Date-Modified = {2010-12-03 23:13:45 +0100}, Editor = {R. Skousen and D. Lonsdale and D.B. Parkinson}, Publisher = {John Benjamins}, Title = {Analogical hierarchy: Exemplar-based modeling of linkers in {D}utch noun--noun compounds}, Year = {2002}} @incollection{Eddington02, Address = {Amsterdam, The Netherlands}, Author = {D. Eddington}, Booktitle = {Analogical modeling: An exemplar-based approach to language}, Date-Added = {2010-12-03 23:10:21 +0100}, Date-Modified = {2010-12-03 23:11:51 +0100}, Editor = {R. Skousen and D. Lonsdale and D.B. Parkinson}, Publisher = {John Benjamins}, Title = {A comparison of two analogical models: Tilburg memory-based learner versus analogical modeling}, Year = {2002}} @inproceedings{Langlais+09, Address = {Athens, Greece}, Author = {P. Langlais and F. Yvon and P. Zweigenbaum}, Booktitle = {Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL-09)}, Date-Added = {2010-12-03 23:08:02 +0100}, Date-Modified = {2010-12-03 23:09:39 +0100}, Pages = {487--495}, Title = {Improvements in analogical learning: Application to translating multi-terms of the medical domain}, Year = {2009}} @article{Younger67, Author = {D.H. Younger}, Citeulike-Article-Id = {5914327}, Journal = {Information and Control}, Keywords = {parsing}, Number = {2}, Pages = {189--208}, Posted-At = {2009-10-09 06:23:37}, Priority = {2}, Title = {{Recognition and parsing of context-free languages in time $n^{3}$}}, Volume = {10}, Year = {1967}} @article{Yvon+07, Author = {F. Yvon and N. 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L\"{u}deling and M. Kyt\"{o}}, Keywords = {ilk, vici, corpus linguistics, machine learning, corpus annotation}, Pages = {855--872}, Publisher = {Walter de Gruyter}, Title = {Machine Learning}, Volume = {2}, Year = {2009}} @book{Baase+00, Address = {Boston}, Author = {S. Baase and A. Van Gelder}, Date-Added = {2010-11-11 20:41:07 +0100}, Date-Modified = {2010-11-11 20:41:48 +0100}, Publisher = {Addison Wesley Longman}, Title = {Computer algorithms}, Year = {2000}} @misc{vanZaanen10, Author = {M.M. {Van Zaanen}}, Date-Added = {2010-11-04 13:47:02 +0100}, Date-Modified = {2010-12-20 21:59:32 +0100}, Howpublished = {\href{http://ilk.uvt.nl/~menno/research/software/suffixtree}{http://ilk.uvt.nl/~menno/research/software/suffixtree}}, Keywords = {suffix trees}, Title = {Suffix Tree Package}, Year = {2010}} @inproceedings{Stehouwer+10c, Author = {H. Stehouwer and M. {Van Zaanen}}, Booktitle = {Proceedings of Computational Linguistics---Applications, 2010}, Date-Added = {2010-11-04 11:28:01 +0100}, Date-Modified = {2010-11-04 12:09:34 +0100}, Keywords = {ilk, vici, suffix arrays, suffix trees, ngrams}, Organization = {International Multiconference on Computer Science and Information Technology}, Pages = {151--158}, Title = {Finding patterns in strings using suffixarrays}, Year = {2010}} @article{Reynaert10, Affiliation = {Tilburg Centre for Cognition and Communication, Tilburg University, Kamer D 342, P.O. Box 90153, 5000 LE Tilburg, Netherlands}, Author = {Reynaert, Martin}, Date-Added = {2010-11-04 10:53:38 +0100}, Date-Modified = {2010-11-04 10:54:25 +0100}, Issn = {1433-2833}, Journal = {International Journal on Document Analysis and Recognition}, Keywords = {ilk, vici, corpus cleanup, spelling correction, spelling variation, ticcl}, Note = {10.1007/s10032-010-0133-5}, Pages = {1-15}, Publisher = {Springer Berlin / Heidelberg}, Title = {Character confusion versus focus word-based correction of spelling and OCR variants in corpora}, Url = {http://dx.doi.org/10.1007/s10032-010-0133-5}, Year = {2010}, Bdsk-Url-1 = {http://dx.doi.org/10.1007/s10032-010-0133-5}} @inproceedings{Stehouwer+10b, Author = {H. Stehouwer and M. {Van Zaanen}}, Booktitle = {Proceedings of the 22nd Benelux Conference on Artificial Intelligence (BNAIC)}, Date-Added = {2010-11-02 21:09:48 +0100}, Date-Modified = {2011-01-26 17:19:59 +0100}, Keywords = {ilk, vici, suffix arrays, language modeling}, Title = {Using suffix arrays as language models: Scaling the $n$-gram}, Year = {2010}} @incollection{Stehouwer+10, Author = {H. Stehouwer and M. {Van Zaanen}}, Booktitle = {Grammatical Inference: Theoretical Results and Applications}, Date-Added = {2010-11-02 21:07:59 +0100}, Date-Modified = {2010-11-02 21:11:17 +0100}, Editor = {Sempere, Jos{\'e} and Garc{\'\i}a, Pedro}, Keywords = {ilk, vici, suffix arrays, language modeling}, Pages = {305--308}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Title = {Enhanced suffix arrays as language models: Virtual $k$-testable languages}, Volume = {6339}, Year = {2010}} @incollection{Katrenko+10, Author = {S. Katrenko and M. {Van Zaanen}}, Booktitle = {Grammatical Inference: Theoretical Results and Applications}, Date-Added = {2010-11-02 21:06:20 +0100}, Date-Modified = {2010-11-02 21:07:00 +0100}, Editor = {Sempere, Jos{\'e} and Garc{\'\i}a, Pedro}, Keywords = {ilk, vici, grammar induction}, Pages = {293-296}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Title = {Rademacher Complexity and Grammar Induction Algorithms: What It May (Not) Tell Us}, Volume = {6339}, Year = {2010}} @incollection{VanZaanen+10b, Author = {M. {Van Zaanen} and T. Gaustad}, Booktitle = {Grammatical Inference: Theoretical Results and Applications}, Date-Added = {2010-11-02 21:05:38 +0100}, Date-Modified = {2010-11-02 21:07:29 +0100}, Editor = {Sempere, Jos{\'e} and Garc{\'\i}a, Pedro}, Keywords = {ilk, vici, grammar induction}, Pages = {245--257}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Title = {Grammatical Inference as Class Discrimination}, Volume = {6339}, Year = {2010}} @incollection{VanZaanen+10, Author = {M. {Van Zaanen} and Pieter Kanters}, Booktitle = {11th International Society for Music Information Retrieval Conference (ISMIR 2010)}, Date-Added = {2010-11-02 21:04:19 +0100}, Date-Modified = {2010-11-02 21:07:48 +0100}, Keywords = {ilk, vici, grammar induction, mood}, Pages = {75--80}, Title = {Automatic Mood Classification using tf*idf based on Lyrics}, Year = {2010}} @article{Hirst+05, Author = {G. Hirst and A. Budanitsky}, Date-Added = {2010-11-01 22:23:13 +0100}, Date-Modified = {2010-11-01 22:24:56 +0100}, Journal = {Natural Language Engineering}, Keywords = {spelling correction, trigrams}, Pages = {87--111}, Title = {Correcting real-word spelling errors by restoring lexical cohesion}, Volume = {11(1)}, Year = {2005}} @book{Mitton96, Address = {Harlow, Essex, UK}, Annote = {Online here: http://eprints.bbk.ac.uk/469/ at the time of writing (1-11-2010)}, Author = {R. Mitton}, Date-Added = {2010-11-01 21:51:15 +0100}, Date-Modified = {2011-06-19 23:01:09 +0200}, Publisher = {Longman}, Title = {English Spelling and the Computer}, Year = {1996}} @inproceedings{VandenHoven+10, Address = {Vienna, Austria}, Author = {M. {Van den Hoven} and A. {Van den Bosch} and K. Zervanou}, Booktitle = {Proceedings of the First International AMICUS Workshop on Automated Motif Discovery in Cultural Heritage and Scientific Communication Texts}, Date-Added = {2010-10-31 11:26:30 +0100}, Date-Modified = {2011-01-16 21:52:58 +0100}, Editor = {S. Dar\'{a}nyi and P. Lendvai}, Keywords = {hitime, ilk, social history, iish, iisg, strikes}, Pages = {20--28}, Title = {Beyond reported history: Strikes that never happened}, Year = {2010}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/Amicus10-vdh-vdb-z.pdf}} @techreport{Dake03, Author = {J. Dake}, Date-Added = {2010-10-06 14:43:23 +0200}, Date-Modified = {2010-10-06 14:45:28 +0200}, Institution = {Tilburg University}, Keywords = {ilk, master's thesis, timbl, igtree, tribl, tribl2, vi}, Number = {03-02}, Title = {Explorations of the speed-accuracy trade-off in memory-based learning algorithms}, Type = {ILK Research Group Technical Report Series}, Year = {2003}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/ilk0302.pdf}} @inproceedings{DeRijke+10, Author = {M. {De Rijke} and K. Balog and T. Bogers and A. {Van den Bosch}}, Booktitle = {CLEF 2010: Conference on Multilingual and Multimodal Information Access Evaluation}, Date-Added = {2010-09-28 10:39:58 +0200}, Date-Modified = {2010-09-28 10:54:46 +0200}, Keywords = {entity profiling, recommender systems, apropos, ilk}, Pages = {94--99}, Title = {On the Evaluation of Entity Profiles}, Year = {2010}, Bdsk-Url-1 = {http://bit.ly/9awTeq}} @inproceedings{Zens+04, Author = {R. Zens and H. Ney}, Booktitle = {Proceedings of the 41st Annual Meeting on Association for Computational Linguistics}, Date-Added = {2010-08-24 23:10:18 +0200}, Date-Modified = {2010-08-24 23:11:44 +0200}, Organization = {Association for Computational Linguistics}, Pages = {144---151}, Title = {comparative study on reordering constraints in statistical machine translation}, Year = {2003}} @inproceedings{Al-Onaizan+06, Author = {Al-Onaizan, Y. and Papineni, K.}, Booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics}, Date-Added = {2010-08-24 22:35:57 +0200}, Date-Modified = {2010-08-24 22:37:05 +0200}, Organization = {Association for Computational Linguistics}, Pages = {529--536}, Title = {Distortion models for statistical machine translation}, Year = {2006}} @inproceedings{Che+09, Address = {Boulder, Colorado}, Author = {W. Che and Z. Li and Y. Li and Y. Guo and B. Qin and T. 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John}, Date-Added = {2010-06-25 21:09:28 +0200}, Date-Modified = {2010-06-25 21:10:11 +0200}, Institution = {Carnegie Mellon University}, Number = {CMU-HCII-94-106}, Title = {The {GOMS} Family of Analysis Techniques: Tools for Design and Evaluation}, Year = {1994}} @inproceedings{Lee+08b, Author = {J. Lee and O. Knutsson}, Booktitle = {Proceedings of CICLing 2008, 9th International Conference on Intelligent Text Processing and Computational Linguistics}, Date-Added = {2010-06-08 23:04:39 +0200}, Date-Modified = {2010-08-02 11:41:38 +0200}, Editor = {A. Gelbukh}, Number = {4919}, Pages = {643--654}, Publisher = {Springer Verlag}, Series = {Lecture Notes in Computer Science}, Title = {The Role of PP Attachment in Preposition Generation}, Year = {2008}} @book{Inhelder+58, Address = {New York, NY}, Author = {B. Inhelder and J. 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Wu}, Month = {July}, Pages = {293--300}, Publisher = {Association for Computational Linguistics}, Title = {Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users}, Year = 2004} @article{Cuenod+95, Author = {C. Cuenod and S. Bookheimer and L. Hertz-Pannier and T. Zeffiro and W. Theodore and D. Le bihan}, Date-Modified = {2008-07-23 16:57:06 +0200}, Journal = {{Neurology}}, Keywords = {fMRI, neurosciences, human language processing}, Pages = {1821--}, Title = {{Functional MRI during word generation, using conventional equipment: A potential tool for \dots}}, Volume = 45, Year = 1995} @article{Cutler+86, Author = {A. Cutler and J. Mehler and D. Norris and J. Segui}, Journal = {Journal of Memory and Language}, Pages = {385--400}, Title = {The syllable's differing role in segmentation of {F}rench and {E}nglish}, Volume = 25, Year = 1986} @article{Cutler+88, Author = {A. Cutler and D. 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Daciuk and G. {Van Noord}}, Booktitle = {CIAA '01: Revised Papers from the 6th International Conference on Implementation and Application of Automata}, Date-Modified = {2010-09-20 00:14:17 +0200}, Isbn = {3-540-00400-9}, Pages = {65--73}, Publisher = {Springer-Verlag}, Title = {Finite Automata for Compact Representation of Language Models in {NLP}}, Year = {2002}} @techreport{Daelemans+00, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:21:45 +0100}, Institution = {ILK, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK-0001}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 3.0, Reference Manual}, Year = 2000} @incollection{Daelemans+01, Address = {Dordrecht}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Data-driven techniques in speech synthesis}, Date-Modified = {2011-01-29 16:41:59 +0100}, Editor = {R. Damper}, Keywords = {ilk, grapheme-phoneme conversion, speech synthesis}, Pages = {149--172}, Publisher = {Kluwer Academic Publishers}, Title = {{TreeTalk}: Memory-Based Word Phonemisation}, Year = 2001} @techreport{Daelemans+01a, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:21:04 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK Technical Report 01-04}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 4.0, Reference Guide}, Year = 2001} @inproceedings{Daelemans+01b, Author = {W. Daelemans and V. Hoste}, Booktitle = {Proceedings of the Third International Conference on Language Resources and Evaluation}, Date-Modified = {2010-01-02 21:20:52 +0100}, Keywords = {ilk}, Pages = {to appear}, Title = {Evaluation of Machine Learning Methods for Natural Language Processing Tasks}, Year = 2002} @techreport{Daelemans+02, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:20:45 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK 02-10}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 4.3, Reference Guide}, Year = 2002} @inproceedings{Daelemans+02b, Address = {Las Palmas, Gran Canaria}, Author = {W. Daelemans and V. Hoste}, Booktitle = {Proceedings of the Third International Conference on Language Resources and Evaluation}, Date-Modified = {2010-01-02 21:20:33 +0100}, Keywords = {ilk}, Pages = {755--760}, Title = {Evaluation of Machine Learning Methods for Natural Language Processing Tasks}, Year = 2002} @techreport{Daelemans+03, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:20:21 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK 03-10}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 5.0, Reference Guide}, Year = 2003} @inproceedings{Daelemans+03b, Archive = {Iris, web}, Author = {W. Daelemans and V. Hoste and F. {De Meulder} and B. Naudts}, Booktitle = {Proceedings of the 14th European Conference on Machine Learning ( ECML-2003)}, Date-Modified = {2010-01-02 21:20:15 +0100}, Keywords = {ilk, feature selection}, Pages = {84-95}, Source = {Iris}, Title = {Combined optimization of feature selection and algorithm param eter interaction in machine learning of language}, Year = 2003, Bdsk-Url-1 = {http://www.cnts.ua.ac.be/~walter/papers/2003/dhdn03.pdf}} @techreport{Daelemans+03c, Author = {W. Daelemans and J. Zavrel and A. {Van den Bosch} and K. {Van der Sloot}}, Date-Modified = {2010-01-02 21:20:02 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, MBT, part-of-speech tagging, memory-based learning}, Number = {ILK 03-13}, Title = {{MBT}: Memory Based Tagger, version 2.0, Reference Guide}, Year = 2003} @techreport{Daelemans+03d, Author = {W. Daelemans and J. Zavrel and A. {Van den Bosch} and K. {Van der Sloot}}, Date-Modified = {2011-01-29 16:42:13 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, MBT, memory-based learning, part-of-speech tagging}, Number = {ILK 03-13}, Title = {{MBT}: Memory Based Tagger, version 2.0, Reference Guide}, Year = 2003} @techreport{Daelemans+04, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:19:51 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK 04-02}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 5.1.0, Reference Guide}, Year = 2004} @inproceedings{Daelemans+04b, Author = {W. Daelemans and A. H\"othker and E. {Tjong Kim Sang}}, Booktitle = {Proceedings of the 4th International Conference on Language Resources and Evaluation}, Date-Added = {2009-11-15 14:33:14 +0100}, Date-Modified = {2010-09-14 22:11:13 +0200}, Keywords = {clips, subtitling}, Pages = {1045--1048}, Title = {Automatic Sentence Simplification for Subtitling in {Dutch} and {English}}, Year = {2004}} @book{Daelemans+05, Address = {Cambridge, UK}, Author = {W. Daelemans and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:19:23 +0100}, Keywords = {ilk, memory-based learning, memory-based language processing}, Publisher = {Cambridge University Press}, Title = {Memory-based language processing}, Year = 2005} @techreport{Daelemans+10, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-03-29 10:26:50 +0200}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK 10-01}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 6.3, Reference Guide}, Year = 2010} @techreport{Daelemans+07b, Author = {W. Daelemans and J. Zavrel and A. {Van den Bosch} and K. {Van der Sloot}}, Date-Modified = {2010-09-18 14:40:48 +0200}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, memory-based learning, POS tagging, memory-based tagging, part-of-speech tagging}, Number = {ILK 07-04}, Title = {{MBT}: Memory Based Tagger, version 3.0, Reference Guide}, Year = 2007} @incollection{Daelemans+07c, Address = {Ghent, Belgium}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Tussen taal, spelling en onderwijs: Essays bij het emeritaat van Frans Daems.}, Date-Added = {2010-01-03 01:25:23 +0100}, Date-Modified = {2010-01-03 01:26:59 +0100}, Editor = {D. Sandra and R. Rymenans and P. Cuvelier and P. {Van Petegem}}, Keywords = {ilk, homophones, confusible disambiguation, phonology, spelling, Dutch}, Pages = {199--210}, Publisher = {Academia Press}, Title = {Dat gebeurd mei niet: Computationele modellen voor verwarbare homofonen}, Year = {2007}, Bdsk-Url-1 = {http://www.cnts.ua.ac.be/~walter/papers/2007/db07.pdf}} @inproceedings{Daelemans+92, Address = {Amsterdam, The Netherlands}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Artificial Neural Networks 2}, Date-Modified = {2011-06-21 18:25:29 +0200}, Editor = {I. Aleksander and J. Taylor}, Keywords = {ilk, hyphenation}, Pages = {1647--1650}, Publisher = {North-Holland}, Title = {A neural network for hyphenation}, Volume = 2, Year = 1992} @inproceedings{Daelemans+92b, Address = {Enschede}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Proceedings of TWLT3: Connectionism and Natural Language Processing}, Date-Modified = {2010-01-02 21:18:38 +0100}, Editor = {M. F. J. Drossaers and A. Nijholt}, Keywords = {ilk, syllabification}, Organization = {Twente University}, Pages = {27--37}, Title = {Generalisation Performance of Backpropagation Learning on a Syllabification Task}, Year = 1992} @article{Daelemans+92c, Author = {W. Daelemans and K. {De Smedt} and G. Gazdar}, Date-Modified = {2010-01-02 21:18:21 +0100}, Journal = CL, Keywords = {inheritance}, Number = 2, Pages = {205--218}, Title = {Inheritance in natural language processing}, Volume = 18, Year = 1992} @incollection{Daelemans+93, Author = {W. Daelemans and S. Gillis and G. Durieux and A. {Van den Bosch}}, Booktitle = {Computational phonology}, Date-Modified = {2010-01-02 21:18:06 +0100}, Editor = {T. M. Ellison and J. M. Scobbie}, Keywords = {ilk, stress assignment}, Pages = {157--178}, Publisher = {Edinburgh, UK: Centre for Cognitive Sciences}, Series = {Edinburgh Working Papers in Cognitive Science}, Title = {Learnability and markedness in data-driven acquisition of stress}, Volume = 8, Year = 1993} @inproceedings{Daelemans+93b, Author = {W. Daelemans and A. {Van den Bosch} and S. Gillis and G. Durieux}, Booktitle = {Proceedings of ECML workshop on ML techniques for Text Analysis, Vienna, 1993}, Date-Modified = {2010-01-02 21:17:46 +0100}, Keywords = {ilk, stress assignment}, Title = {A data-driven approach to stress acquisition}, Year = 1993} @inproceedings{Daelemans+93c, Address = {Berlin}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Proceedings of Eurospeech '93}, Date-Modified = {2011-01-29 16:42:38 +0100}, Keywords = {ilk, grapheme-phoneme conversion}, Pages = {1459--1466}, Publisher = {T.U. Berlin}, Title = {{TabTalk}: reusability in data-oriented grapheme-to-phoneme conversion}, Year = 1993} @inproceedings{Daelemans+94a, Author = {W. Daelemans and S. Gillis and G. Durieux}, Booktitle = {Proceedings of the First NeMLaP Conference, Manchester, UK}, Date-Modified = {2010-01-02 21:17:24 +0100}, Keywords = {ilk, memory-based language processing, analogical modeling}, Title = {Skousen's analogical modeling algorithm: A comparison with lazy learning}, Year = 1994} @inproceedings{Daelemans+94c, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Proceedings of the Second {{\sc esca/ieee}} Workshop on Speech Synthesis, New York}, Date-Modified = {2010-01-02 21:17:11 +0100}, Keywords = {ilk, grapheme-phoneme conversion}, Organization = {{{\sc esca/ieee}}}, Pages = {199--203}, Title = {A language-independent, data-oriented architecture for grapheme-to-phoneme conversion}, Year = 1994} @inproceedings{Daelemans+94d, Address = {Rotterdam}, Author = {W. Daelemans and S. Gillis and G. Durieux}, Booktitle = {Proceedings of the 4th {B}elgian-{D}utch Conference on Machine Learning}, Date-Modified = {2010-01-02 21:17:02 +0100}, Editor = {J. C. Bioch and S. H. Nienhuys-Cheng}, Keywords = {ilk, analogical modeling}, Pages = {302--310}, Publisher = {Erasmus University}, Title = {Skousen's analogical modeling algorithm}, Year = 1994} @incollection{Daelemans+96, Address = {Berlin}, Author = {W. Daelemans and A. {Van den Bosch}}, Booktitle = {Progress in Speech Processing}, Date-Modified = {2010-01-02 21:16:54 +0100}, Editor = {J. P. H. {Van Santen} and R. W. Sproat and J. P. Olive and J. Hirschberg}, Keywords = {ilk, grapheme-phoneme conversion}, Pages = {77--89}, Publisher = {Springer-Verlag}, Title = {Language-independent data-oriented grapheme-to-phoneme conversion}, Year = 1996} @inproceedings{Daelemans+96b, Author = {W. Daelemans and J. Zavrel and P. Berck and S. Gillis}, Booktitle = {Proceedings of the Fourth Workshop on Very Large Corpora}, Date-Modified = {2010-01-02 21:16:43 +0100}, Editor = {E. Ejerhed and I. Dagan}, Keywords = {ilk, MBT, part-of-speech tagging, memory-based learning}, Organization = {ACL SIGDAT}, Pages = {14--27}, Title = {{MBT}: A Memory-Based Part of Speech Tagger Generator}, Year = 1996} @inproceedings{Daelemans+96c, Author = {W. Daelemans and P. Berck and S. Gillis}, Booktitle = COLING96, Date-Modified = {2010-01-02 21:16:37 +0100}, Keywords = {ilk, phonology}, Organization = {Center for Sprogteknologi}, Pages = {95--100}, Title = {Unsupervised discovery of phonological categories through supervised learning of morphological rules}, Year = 1996} @inproceedings{Daelemans+96d, Address = {The Netherlands}, Author = {W. Daelemans and J. Zavrel and P. Berck}, Booktitle = {Informatiewetenschap 1996, Wetenschappelijke bij\-drage aan de Vierde Interdisciplinaire Onderzoeksconferentie Informatiewetenchap}, Date-Modified = {2010-09-20 00:15:32 +0200}, Editor = {K. {Van der Meer}}, Keywords = {ilk, part-of-speech tagging, MBT, memory-based learning, memory-based language processing}, Organization = {TU Delft}, Pages = {33--40}, Title = {Part-of-Speech Tagging for {D}utch with {MBT}, a Memory-based Tagger Generator}, Year = 1996} @article{Daelemans+97, Author = {W. Daelemans and A. {Van den Bosch} and A. Weijters}, Date-Modified = {2010-01-02 21:16:19 +0100}, Journal = {Artificial Intelligence Review}, Keywords = {ilk, IGTree}, Pages = {407--423}, Title = {{IGT}ree: using trees for compression and classification in lazy learning algorithms}, Volume = 11, Year = 1997} @article{Daelemans+97b, Author = {W. Daelemans and P. Berck and S. Gillis}, Date-Modified = {2010-01-02 21:16:00 +0100}, Journal = {Folia Linguistica}, Keywords = {ilk, diminutive inflection, Dutch}, Number = {1--2}, Pages = {57--75}, Title = {Data mining as a method for linguistic analysis: Dutch diminutives}, Volume = {XXXI}, Year = 1997} @incollection{Daelemans+97c, Address = {London}, Author = {W. Daelemans and S. Gillis and G. Durieux}, Booktitle = {New Methods in Language Processing}, Date-Modified = {2010-01-02 21:15:39 +0100}, Editor = {D. Jones and H. Somers}, Keywords = {ilk, analogical modeling, memory-based language processing}, Pages = {3--15}, Publisher = {UCL Press}, Title = {Skousen's Analogical Modeling Algorithm: A comparison with Lazy Learning}, Year = 1997} @proceedings{Daelemans+97d, Address = {Prague, Czech Republic}, Date-Modified = {2010-01-02 21:15:20 +0100}, Editor = {W. Daelemans and A. Weijters and A. {Van den Bosch}}, Keywords = {ilk}, Publisher = {University of Economics}, Title = {Workshop Notes of the ECML/MLnet familiarisation workshop on Empirical learning of natural language processing tasks}, Year = 1997} @inproceedings{Daelemans+97f, Address = {Berlin}, Author = {W. Daelemans and A. Weijters and A. {Van den Bosch}}, Booktitle = {Machine Learning: Proceedings of ECML-97}, Date-Modified = {2010-01-02 21:15:15 +0100}, Editor = {M. {Van Someren} and G. Widmer}, Keywords = {ilk}, Number = 1224, Pages = {337--344}, Publisher = {Springer-Verlag}, Series = {Lecture Notes in Artificial Intelligence}, Title = {Empirical learning of natural language processing tasks}, Year = 1997} @inproceedings{Daelemans+97g, Address = {Prague, Czech Republic}, Author = {W. Daelemans and A. {Van den Bosch} and J. Zavrel}, Booktitle = {Poster Papers of the Ninth European Conference on Machine Learning}, Date-Modified = {2010-01-02 21:15:07 +0100}, Editor = {M. {Van Someren} and G. Widmer}, Keywords = {ilk, IGTree}, Pages = {29--38}, Publisher = {University of Economics}, Title = {A feature-relevance heuristic for indexing and compressing large case bases}, Year = 1997} @techreport{Daelemans+98, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:14:56 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL}, Number = {ILK 98-03}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 1.0, Reference Manualilburg {M}emory {B}ased {L}earner, version 1.0, Reference Manual}, Year = 1998} @techreport{Daelemans+98a, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:14:27 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL}, Number = {ILK 98-03}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 1.0, Reference Manual}, Year = 1998} @inproceedings{Daelemans+98b, Author = {W. Daelemans and A. {Van den Bosch} and J. Zavrel and J. Veenstra and S. Buchholz and G. J. Busser}, Booktitle = {Proceedings of {ELSNET} in {W}onderland, March, 1998}, Date-Modified = {2010-01-02 21:14:06 +0100}, Keywords = {ilk, memory-based language processing}, Pages = {105--113}, Publisher = {ELSNET}, Title = {Rapid development of {NLP} modules with {M}emory-{B}ased {L}earning}, Year = 1998} @article{Daelemans+99, Author = {W. Daelemans and A. {Van den Bosch} and J. Zavrel}, Date-Modified = {2010-09-14 12:52:41 +0200}, Journal = {Machine Learning, Special issue on Natural Language Learning}, Keywords = {ilk, memory-based language processing, lazy learning, decision trees}, Pages = {11--41}, Title = {Forgetting exceptions is harmful in language learning}, Volume = 34, Year = 1999} @inproceedings{Daelemans+99a, Address = {Bergen, Norway}, Author = {W. Daelemans and S. Buchholz and J. Veenstra}, Booktitle = {Proceedings of CoNLL}, Date-Modified = {2010-01-02 21:13:50 +0100}, Keywords = {ilk, shallow parsing}, Title = {Memory-based shallow parsing}, Year = 1999} @techreport{Daelemans+99b, Author = {W. Daelemans and J. Zavrel and K. {Van der Sloot} and A. {Van den Bosch}}, Date-Modified = {2010-01-02 21:13:12 +0100}, Institution = {ILK Research Group, Tilburg University}, Keywords = {ilk, TiMBL, memory-based learning}, Number = {ILK 99-01}, Title = {{TiMBL}: Tilburg Memory Based Learner, version 2.0, Reference Manual}, Year = 1999} @inproceedings{Daelemans+99c, Address = {Bergen, Norway}, Author = {W. Daelemans and S. Buchholz and J. Veenstra}, Booktitle = {Proceedings of CoNLL}, Date-Modified = {2010-01-02 21:12:32 +0100}, Keywords = {ilk, shallow parsing}, Title = {Memory-based shallow parsing}, Year = 1999} @incollection{Daelemans02, Address = {Amsterdam, The Netherlands}, Author = {W. Daelemans}, Booktitle = {Analogical Modeling}, Date-Modified = {2010-01-02 21:12:06 +0100}, Editor = {R. Skousen and D. Lonsdale and D.B. Parkinson}, Keywords = {ilk, analogical modeling, memory-based language processing}, Publisher = {John Benjamins}, Title = {A comparison of analogical modeling to memory-based language processing}, Year = 2002} @phdthesis{Daelemans87, Author = {W. Daelemans}, Date-Modified = {2010-01-02 19:59:41 +0100}, Keywords = {morphology}, School = {Katholieke Universiteit Leuven}, Title = {Studies in language technology: An object-oriented model of morphophonological aspects of {D}utch}, Year = 1987} @inproceedings{Daelemans88, Author = {W. Daelemans}, Booktitle = COLING88, Date-Modified = {2011-01-29 16:43:02 +0100}, Keywords = {grapheme-phoneme conversion, Dutch}, Pages = {133--138}, Title = {{GRAFON}: A grapheme-to-phoneme system for {D}utch}, Year = 1988} @incollection{Daelemans88b, Address = {Leuven}, Author = {W. Daelemans}, Booktitle = {Worlds behind Words}, Date-Modified = {2010-01-02 19:59:18 +0100}, Editor = {F.J. Heyvaert and F. Steurs}, Keywords = {hyphenation}, Pages = {347-364}, Publisher = {Leuven University Press}, Title = {Automatic hyphenation: linguistics versus engineering}, Year = 1988} @incollection{Daelemans89, Address = {Leuven}, Author = {W. Daelemans}, Booktitle = {Worlds behind Words}, Date-Modified = {2010-01-02 19:59:09 +0100}, Editor = {F. J. Heyvaert and F. Steurs}, Keywords = {hyphenation}, Pages = {347--364}, Publisher = {Leuven University Press}, Title = {Automatic hyphenation: Linguistics versus engineering}, Year = 1989} @incollection{Daelemans95, Address = {Berlin}, Author = {W. Daelemans}, Booktitle = {Machine Translation and the Lexicon}, Date-Modified = {2010-01-02 19:59:01 +0100}, Editor = {P. Steffens}, Keywords = {ilk, lexicon}, Pages = {85--98}, Publisher = {Springer-Verlag}, Series = {Lecture Notes in Artificial Intelligence}, Title = {Memory-based lexical acquisition and processing}, Volume = 898, Year = 1995} @incollection{Daelemans96, Address = {Tilburg}, Author = {W. 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Dorr and Doug Jones}, Booktitle = COLING96, Date-Modified = {2009-09-06 20:35:34 +0200}, Keywords = {wsd, word sense disambiguation}, Pages = {322--327}, Title = {Role of Word Sense Disambiguation in Lexical Acquisition: Predicting Semantics from Syntactic Clues}, Year = 1996} @inproceedings{Dougherty+95, Author = {Dougherty, J. and Kohavi, R. and Sahami, M}, Booktitle = {Proceedings of the International Conference on Machine Learning}, Title = {Supervised and unsupervised discretization of continuous features}, Year = 1995} @article{Dresher+90, Author = {E. Dresher and J. Kaye}, Journal = {Cognition}, Number = 2, Pages = {137--195}, Title = {A computational learning model for metrical phonology}, Volume = 32, Year = 1990} @inproceedings{Dridan+07, Address = {Melbourne, Australia}, Author = {R. Dridan and T. Baldwin}, Booktitle = {Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics}, Date-Modified = {2009-11-14 18:51:47 +0100}, Keywords = {question answering}, Pages = {333--341}, Title = {What to classify and how: Experiments in question classification for Japanese}, Year = 2007} @article{Dronkers96, Author = {N.F. Dronkers}, Journal = {Nature}, Title = {A new brain region for speech: The insula and articulatory planning}, Year = 1996} @inproceedings{Duboue+01, Address = {Toulouse, France}, Author = {Pablo A. Duboue and Kathleen R. McKeown}, Booktitle = {Proceedings of 39th Annual Meeting of the Association for Computational Linguistics}, Doi = {10.3115/1073012.1073035}, Month = {July}, Pages = {172--179}, Publisher = {Association for Computational Linguistics}, Title = {Empirically Estimating Order Constraints for Content Planning in Generation}, Url = {http://www.aclweb.org/anthology/P01-1023}, Year = {2001}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/P01-1023}, Bdsk-Url-2 = {http://dx.doi.org/10.3115/1073012.1073035}} @inproceedings{Dudani76, Author = {Dudani, S.A.}, Booktitle = {IEEE Transactions on Systems, Man, and Cybernetics}, Pages = {325--327}, Title = {The Distance-Weighted $k$-Nearest Neighbor Rule}, Volume = {SMC-6}, Year = 1976} @inproceedings{Duffy86, Author = {Gavan Duffy}, Booktitle = AAAI, Title = {Categorical disambiguation}, Year = 1986} @article{Dunlop+00, Author = {M. D. Dunlop and A. Crossan}, Date-Modified = {2010-09-18 14:37:41 +0200}, Journal = {Personal Technologies}, Keywords = {predictive text entry}, Number = 2, Title = {Predictive Text Entry Methods for Mobile Phones}, Volume = 4, Year = 2000} @incollection{Durieux+00, Address = {Amsterdam, The Netherlands}, Author = {G. Durieux and S. Gillis}, Booktitle = {Approaches to bootstrapping: {P}honological, syntactic and neurophysiological aspects of early language acquisition}, Date-Modified = {2011-06-21 18:25:40 +0200}, Editor = {B. H\"ohle and J. Weissenborn}, Pages = {189-232}, Publisher = {Benjamins}, Title = {Predicting grammatical classes from phonological cues: An empirical test}, Year = 2000} @misc{Durieux+98, Author = {G. Durieux and W. Daelemans and S. Gillis}, Title = {Paper read at the Corsendonk Round Table}, Year = 1998} @inproceedings{Dutriaux+91, Address = {San Mateo, CA}, Author = {A. Dutriaux and D. Zipser}, Booktitle = {Connectionist Models: Proceedings of the 1990 Summer School}, Editor = {D.S. Touretzky and J.L. Elman and T.J. Sejnowski and G.E. Hinton}, Publisher = {Morgan Kaufmann}, Title = {Unsupervised Discovery of Speech Segments Using Recurrent Networks}, Year = 1991} @article{Earley70, Author = {Earley, J.}, Date-Added = {2009-11-15 00:26:06 +0100}, Date-Modified = {2009-11-15 00:26:19 +0100}, Journal = {Communications of the ACM}, Number = {2}, Pages = {94--102}, Title = {An efficient context-free parsing algorithm}, Volume = {13}, Year = {1970}} @inproceedings{Eckle+96, Author = {Judith Eckle and Ulrich Heid}, Booktitle = {Proceedings of the 4th International Conference on Computational Lexicography, COMPLEX '96, Budapest, Hungary}, Title = {Extracting raw material for a German subcategorization lexicon from newspaper text}, Year = 1996} @unpublished{Eckle+98, Author = {Judith Eckle-Kohler}, Month = {August}, Note = {to appear in: Proceedings of the Eighth Euralex International Congress, University of Li\`{e}ge, Belgium}, Title = {Methods for quality assurance in semi-automatic lexicon acquisition from corpora}, Year = 1998} @misc{Eckle+??, Author = {Judith Eckle and Ulrich Heid}, Title = {Extracting raw material for a German subcategorization lexicon from newspaper text}} @article{Eddington00, Author = {David Eddington}, Journal = {Lingua}, Pages = {281--298}, Title = {Analogy and the dual-route model of morphology}, Volume = 110, Year = 2000} @article{Eddington03, Author = {David Eddington}, Journal = {Lingua}, Pages = {849--871}, Title = {Issues in modeling language processing analogically}, Volume = 114, Year = 2003} @article{Edmonds67, Author = {Edmonds, J.}, Date-Added = {2009-11-15 00:26:25 +0100}, Date-Modified = {2009-11-15 00:26:36 +0100}, Journal = {Journal of Research of the National Bureau of Standards}, Pages = {233--240}, Title = {Optimum branchings}, Volume = {71}, Year = {1967}} @article{Efron+87, Author = {Bradley Efron and Ronald Thisted}, Journal = {Biometrika}, Pages = {445-455}, Title = {Did Shakespeare write a newly discovered poem?}, Volume = 74, Year = 1987} @book{Egan75, Address = {New York, NY}, Author = {J. P. Egan}, Publisher = {Academic Press}, Series = {Series in Cognition and Perception}, Title = {Signal detection theory and {ROC} analysis}, Year = 1975} @inproceedings{Egedi+94, Author = {Dania Egedi and Patrick MArtin}, Booktitle = {Proceedings of the International Workshop on Sharable Natural Language Resources, Nara, Japan}, Month = {August}, Pages = {123--130}, Title = {A Freely Avaiable Syntactic Lexicon for English}, Year = 1994} @inproceedings{Eijk93, Author = {P. {Van der Eijk}}, Booktitle = {EACL}, Pages = {113--119}, Title = {Automating the acquisition of bilingual terminology}, Year = 1993} @incollection{Eisner00, Address = {Norwell, MA, USA}, Author = {Eisner, J.}, Booktitle = {Advances in Probabilistic and Other Parsing Technologies}, Date-Added = {2009-11-15 00:26:45 +0100}, Date-Modified = {2009-11-15 00:26:59 +0100}, Editor = {H. Bunt and A. Nijholt}, Pages = {29--62}, Publisher = {Kluwer Academic Publishers}, Title = {Bilexical grammars and their cubic-time parsing algorithms}, Year = {2000}} @book{Eisner96, Author = {J. Eisner}, Publisher = {Technical Report IRCS-96-11, Institute for Research in Cognitive Science, University of Pennsylvania}, Title = {An Empirical Comparison of Probability Models for Dependency Grammar}, Year = 1996} @techreport{Ejerhed+92, Author = {E. Ejerhed and G. Kallgren and O. Wennstedt and M. Astrom}, Institution = {Department of General Linguistics, University of Umea}, Title = {The Linguistic Annotation System of the Stockholm-Umea Project}, Year = 1992} @inproceedings{Elisseeff+02, Author = {Elisseeff, A. and Weston, J.}, Booktitle = {Advances in Neural Information Processing Systems}, Date-Added = {2009-11-15 00:27:06 +0100}, Date-Modified = {2009-11-15 00:27:20 +0100}, Pages = {681--688}, Title = {A Kernel Method for Multi-Labelled Classification}, Volume = {14}, Year = {2002}} @phdthesis{Ellison93, Author = {T. M. Ellison}, School = {University of Western Australia}, Title = {Machine learning of phonological structure}, Year = 1993} @article{Elman+88, Author = {J. L. Elman and D. Zipser}, Journal = {Journal of the Acoustical Society of America}, Pages = {1615--1625}, Title = {Learning the Hidden Structure of Speech}, Volume = 83, Year = 1988} @article{Elman90, Author = {J. Elman}, Journal = {Cognitive Science}, Pages = {179--211}, Title = {Finding Structure in Time}, Volume = 14, Year = 1990} @article{Elman90b, Author = {Elman, J.}, Journal = {{Cognition}}, Number = 2, Title = {{Learning and development in neural networks: the importance of starting small}}, Volume = 14, Year = 1990} @article{Elman93, Author = {Elman, J.}, Journal = {{Cognitive Science}}, Pages = {71--99}, Title = {{Finding structure in time}}, Volume = 48, Year = 1993} @inproceedings{Elworthy00, Author = {D. Elworthy}, Booktitle = {Proceedings of TREC-9}, Organization = {NIST}, Title = {Question Answering Using a Large NLP System}, Year = 2001} @inproceedings{Elworthy94, Author = {David Elworthy}, Booktitle = ANLP, Pages = {53--58}, Title = {Does {Baum-Welch} re-estimation improve taggers?}, Year = 1994} @inproceedings{Ernst-Gerlach+06, Abstract = {In this paper, we describe a new approach for retrieval in texts with non-standard spelling, which is important for historic texts in English or German. For this purpose, we present a new algorithm for generating search term variants in ancient orthography. By applying a spell checker on a corpus of historic texts, we generate a list of candidate terms for which the contemporary spellings have to be assigned manually. Then our algorithm produces a set of probabilistic rules. These probabilities can be considered for ranking in the retrieval stage. An experimental comparison shows that our approach outperforms competing methods.}, Author = {Andrea Ernst-Gerlach and Norbert Fuhr}, Booktitle = {28th European Conference on Information Retrieval Research ({ECIR} 2006)}, Date-Added = {2010-01-29 15:09:33 +0100}, Date-Modified = {2010-01-29 15:09:39 +0100}, Publisher = {Springer}, Title = {Generating Search Term Variants for Text Collections with Historic Spellings}, Year = 2006} @inproceedings{Ernst-Gerlach+07, Address = {New York, NY, USA}, Author = {Andrea Ernst-Gerlach and Norbert Fuhr}, Booktitle = {JCDL '07: Proceedings of the 2007 conference on Digital libraries}, Date-Added = {2010-01-29 15:09:47 +0100}, Date-Modified = {2010-01-29 15:09:53 +0100}, Doi = {http://doi.acm.org/10.1145/1255175.1255242}, Isbn = {978-1-59593-644-8}, Location = {Vancouver, BC, Canada}, Pages = {333--341}, Publisher = {ACM Press}, Title = {Retrieval in text collections with historic spelling using linguistic and spelling variants}, Year = {2007}, Bdsk-Url-1 = {http://doi.acm.org/10.1145/1255175.1255242}} @inproceedings{Escudero+00, Author = {Escudero, G. and Marquez, L. and Rigau, G.}, Booktitle = {European Conference on Machine Learning}, Date-Modified = {2009-09-06 20:35:43 +0200}, Keywords = {wsd, word sense disambiguation}, Pages = {129-141}, Title = {Boosting Applied to Word Sense Disambiguation}, Year = 2000} @inproceedings{Essen+93, Author = {Ute Essen and Volker Steinbiss}, Booktitle = {Proceedings of ICASSP}, Organization = {IEEE}, Pages = {161--164}, Title = {Coocurrence Smoothing for Stochastic Language Modeling}, Volume = 1, Year = 1993} @inproceedings{Essen92, Author = {Ute Essen and Volker Steinbiss}, Booktitle = {Proceedings of {ICASSP}}, Pages = {161--164}, Publisher = {{IEEE}}, Title = {Cooccurrence Smoothing for Stochastic Language Modeling}, Volume = {I}, Year = 1992} @book{Estes94, Address = {New York}, Author = {W. K. Estes}, Date-Modified = {2010-02-14 23:07:58 +0100}, Publisher = {Oxford University Press}, Series = {Oxford Psychology Series}, Title = {Classification and cognition}, Volume = 22, Year = 1994} @article{Evans+96, Author = {Roger Evans and Gerald Gazdar}, Journal = {Computational Linguistics}, Number = 2, Pages = {167--216}, Title = {{DATR}: A Language for Lexical Knowledge Representation}, Volume = 22, Year = 1996} @article{Evans01, Author = {R. Evans}, Journal = {Journal of Literary and Linguistic Computing}, Number = 1, Pages = {45--57}, Title = {Applying Machine Learning Toward an Automatic Classification of {\it It}}, Volume = 16, Year = 2001} @inproceedings{Even-Zohar+00, Address = {New Brunswick, NJ}, Author = {Y. Even-Zohar and D. Roth}, Booktitle = {Proceedings of the First North-American Conference on Computational Linguistics}, Pages = {124--131}, Publisher = {ACL}, Title = {A classification approach to word prediction}, Year = 2000} @inproceedings{Even-Zohar+99, Address = {Bar-Ilan, Israel}, Author = {Y. Even-Zohar and D. Roth and D. Zelenko}, Booktitle = {BISFAI99}, Title = {Word Clustering via Classification}, Year = 1999} @techreport{Fahlman+90, Author = {S. E. Fahlman and C. Lebi{{\`{e}}}re}, Institution = {School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA}, Number = {CMU-CS-90-100}, Title = {The Cascade-correlation Learning Architecture}, Year = 1990} @techreport{Fahlman88, Author = {S. E. Fahlman}, Institution = {Carnegie--Mellon University}, Number = {CMU-CS-88-162}, Title = {An empirical study of learning speed in back-propagation networks}, Year = 1988} @inproceedings{Fahlman88b, Address = {San Mateo, CA}, Author = {S. E. Fahlman}, Booktitle = {Proceedings of the 1988 Connectionist Summer School}, Pages = {xx-xx}, Publisher = {Morgan Kaufmann}, Title = {Faster-learning variations on Back-propagation: An empirical study}, Year = 1988} @book{Fano61, Address = {Cambridge, MA}, Author = {R. Fano}, Publisher = MIT, Title = {Transmission of Information}, Year = 1961} @article{Farago+93, Author = {A. Farag{\'{o}} and G. Lugosi}, Journal = {{IEEE} Transactions on Information Theory}, Pages = {1146--1151}, Title = {Strong universal consistency of neural network classifiers}, Volume = 39, Year = 1993} @article{Farah93, Author = {Farah, M.}, Date-Modified = {2009-02-21 19:45:30 +0100}, Journal = {{Brain and Behavioral Sciences}}, Keywords = {cognitive architecture, face recognition lesions. localization, modularity, neural networks, neuropsychology semantics, vision}, Pages = {?}, Title = {{Neuropsychological Inference with an Interactive Brain: A Critique of the locality assumption}}, Volume = {?}, Year = 1993} @techreport{Fawcett04, Author = {T. Fawcett}, Date-Modified = {2009-02-21 19:45:59 +0100}, Institution = {Hewlett Packard Labs}, Keywords = {ROC, AUC, evaluation, methodology}, Number = {HPL-2003-4}, Title = {{ROC} Graphs: Notes and Practical Considerations for Researchers}, Year = 2004} @incollection{Federici+96, Address = {London}, Author = {S. Federici and V. Pirelli}, Booktitle = {New Methods in Language Processing}, Publisher = {UCL Press}, Title = {Analogy, Computation and Linguistic Theory}, Year = 1996} @techreport{Feldman94, Address = {Ramat-Gan, Israel}, Author = {R. Feldman}, Institution = {Bar-Ilan University}, Title = {Knowledge Discovery in Textual Databases}, Year = 1994} @inproceedings{Feldman95a, Author = {R. Feldman and I. Dagan}, Booktitle = {Proceedings of the First International Conference on Knowledge Discovery (KDD-95)}, Month = {August}, Place = {Montreal}, Title = {{KDT} -- Knowledge Discovery in Texts}, Year = 1995} @inproceedings{Feldman95b, Author = {R. Feldman and I. Dagan}, Booktitle = {Proceedings of the ECML-95 Workshop in Knowledge Discovery}, Month = {May}, Place = {Crete}, Title = {Knowledge Discovery in Textual Databases}, Year = 1995} @misc{Fellbaum+93, Author = {C. Fellbaum and D. Gross and K. Miller}, Title = {Adjectives in WordNet}, Year = 1993} @misc{Fellbaum93, Author = {C. Fellbaum}, Howpublished = {available from: {\tt\small http://www.cogsci.princeton.edu/\ { }wn/}}, Title = {English Verbs as a Semantic Net}, Year = 1993} @book{Fellbaum98, Address = {Cambridge, MA}, Author = {C. Fellbaum}, Date-Modified = {2011-01-29 16:44:26 +0100}, Keywords = {Wordnet}, Publisher = MIT, Title = {{WordNet}: An Electronic Lexical Database}, Year = 1998} @inproceedings{Fernandez+04, Address = {Geneva, Switzerland}, Author = {R. Fern\'andez and J. Ginzburg and S. Lappin}, Booktitle = {Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004}, Month = {August}, Pages = {240--246}, Title = {Classifying Ellipsis in Dialogue: A Machine Learning Approach}, Year = 2004} @article{Ferrer+01, Author = {Ferrer i Cancho, Ramon and Ricard V. Sol\'{e}}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-09-18 14:39:57 +0200}, Journal = {Proceedings of The Royal Society of London. Series B, Biological Sciences}, Keywords = {small-world semantic networks, scaling, lexical networks, human language}, Month = {November}, Number = {1482}, Pages = {2261--2265}, Title = {The small world of human language}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer01theSmall.html}, Volume = {268}, Year = {2001}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer01theSmall.html}} @article{Ferrer+01b, Author = {Ferrer i Cancho, Ramon and Ricard V. Sol\'{e}}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:38:03 +0100}, Journal = {Journal of Quantitative Linguistics}, Keywords = {{Z}ipf's law, origin of language, scaling}, Number = {3}, Pages = {165--173}, Title = {Two regimes in the frequency of words and the origins of complex lexicons: {Z}ipf's law revisited}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer01twoRegimes.html}, Volume = {8}, Year = {2001}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer01twoRegimes.html}} @article{Ferrer+02, Author = {Ferrer i Cancho, Ramon and Ricard V. Sol\'{e}}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:38:15 +0100}, Journal = {Advances in Complex Systems}, Keywords = {Human language; scaling; {Z}ipf's law; monkey languages; random texts}, Number = {1}, Pages = {1--6}, Title = {{Z}ipf's law and random texts}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer02zipf.html}, Volume = {5}, Year = {2002}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer02zipf.html}} @techreport{Ferrer+03, Author = {Ferrer i Cancho, Ramon and R. V. Sol\'{e} and R. Kohler}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-09-18 14:40:09 +0200}, Institution = {Santa Fe Institute}, Keywords = {complex networks, linguistic universals, small-world semantic networks, syntax, scaling}, Note = {Santa Fe Working paper 03-06-042}, Title = {Universality in syntactic dependencies}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrericancho2003SFI.html}, Year = {2003}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrericancho2003SFI.html}} @article{Ferrer+03b, Author = {Ferrer i Cancho, Ramon and Ricard V. Sol\'{e}}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:38:28 +0100}, Journal = {PNAS}, Pages = {788--791}, Title = {Least effort and the origins of scaling in human language}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer03leasteffort.html}, Volume = {100}, Year = {2003}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer03leasteffort.html}} @article{Ferrer+04, Author = {Ferrer i Cancho, Ramon and Ricard V. Sol\'{e}}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:39:18 +0100}, Journal = {Physical Review E}, Number = {051915}, Title = {Patterns in syntactic dependency networks}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer04syntaxPRE.html}, Volume = {69}, Year = {2004}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer04syntaxPRE.html}} @article{Ferrer+05, Author = {Ferrer i Cancho, Ramon and Oliver Riordan and Bela Bollobas}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:39:01 +0100}, Journal = {Proceedings of The Royal Society of London. Series B, Biological Sciences}, Keywords = {{Z}ipf's Law, Syntax, Symbolic Reference, Human Language}, Title = {The consequences of {Z}ipf's law for syntax and symbolic reference}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer05consequencesOfZipfLaw.html}, Year = {2005}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer05consequencesOfZipfLaw.html}} @phdthesis{Ferrer03, Author = {Ferrer i Cancho, Ramon}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:39:35 +0100}, Title = {Language: universals, principles and origins}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrericancho2003phdthesis.html}, Year = {2003}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrericancho2003phdthesis.html}} @article{Ferrer04, Author = {Ferrer i Cancho, Ramon}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:38:40 +0100}, Journal = {Physica A: Statistical Mechanics and its Applications}, Keywords = {{Z}ipf's law; Scaling; Human language; Animal communication}, Month = {January}, Number = {1-2}, Pages = {275--284}, Title = {Decoding least effort and scaling in signal frequency distributions}, Url = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer04physicaA.html}, Volume = {345}, Year = {2005}, Bdsk-Url-1 = {http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer04physicaA.html}} @article{Ferrer05, Author = {Ferrer i Cancho, Ramon}, Date-Added = {2010-01-29 14:37:12 +0100}, Date-Modified = {2010-01-29 14:39:11 +0100}, Journal = {European Physical Journal B}, Pages = {249--257}, Publisher = {EDP Sciences, Societ\`{a} Italiana di Fisica and Springer-Verlag}, Title = {The variation of {Z}ipf's law in human language}, Volume = {44}, Year = {2005}} @article{Fiez+92, Author = {Fiez, J.A. and E.A. Raife and D.A. Balota and J.P. Schwartz and M.E. Raichle and S.E. Stevenson}, Date-Modified = {2008-07-23 16:57:38 +0200}, Journal = {{The Journal of Neuroscience}}, Keywords = {PET, human language processing, working memory, neuroimaging, prefrontal cortex}, Pages = {802--822}, Title = {{A Positron Emission Tomography Study of the Short-Term Maintenance of Verval Information}}, Volume = {16(2)}, Year = 1996} @article{Fillmore+03, Author = {C. Fillmore and C. Johnson and M. Petruck}, Date-Modified = {2009-11-14 19:05:49 +0100}, Journal = {International Journal of Lexicography}, Keywords = {FrameNet, lexicography}, Number = 3, Pages = {235--250}, Title = {Background to FrameNet}, Volume = 16, Year = 2003} @inproceedings{Finch+92, Address = {Bloomington, IN}, Author = {S. Finch and N. Chater}, Booktitle = {Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society}, Pages = {820--825}, Publisher = {Lawrence Erlbaum}, Title = {Bootstrapping Syntactic Categories}, Year = 1992} @phdthesis{Finch93, Author = {S. Finch}, School = {University of Edinburgh}, Title = {Finding structure in language}, Year = 1993} @inproceedings{Finch94, Author = {S. Finch}, Booktitle = {Proceedings of the 4th Conference on Applied Natural Language Processing}, Title = {Exploiting sophisticated representations for document retrieval}, Year = 1994} @inproceedings{Finkel+05, Author = {Finkel, J.R. and Grenager, T. and Manning, C.}, Booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)}, Date-Added = {2009-11-15 00:27:29 +0100}, Date-Modified = {2009-11-15 00:27:44 +0100}, Pages = {363--370}, Title = {Incorporating non-local information into information extraction systems by Gibbs sampling}, Year = {2005}} @inproceedings{Finkel+09, Address = {Boulder, Colorado}, Author = {J. R. Finkel and C. D. Manning}, Booktitle = {Proc. of Human Language Technologies: The 2009 Annual Conference of the NAACL}, Date-Added = {2009-12-26 21:25:40 +0100}, Date-Modified = {2010-09-14 12:53:20 +0200}, Keywords = {joint learning, named-entity recognition, parsing}, Organization = {ACL}, Pages = {326--334}, Title = {Joint parsing and named entity recognition}, Year = {2009}} @inproceedings{Finkel+10, Address = {Uppsala, Sweden}, Author = {Finkel, {J. R.} and Manning, {C. D.}}, Booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics}, Month = {July}, Pages = {720--728}, Publisher = {Association for Computational Linguistics}, Title = {Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data}, Year = {2010}} @article{Fisher87, Author = {D. 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Zamora}, Bibdate = {Wed Jan 15 12:19:41 MST 1997}, Bibsource = {Compendex database}, Classification = {901; 723}, Coden = {PAISDQ}, Conference = {Information Interaction, Proceedings of the 45th ASIS Annual Meeting.}, Date-Added = {2010-01-29 15:04:22 +0100}, Date-Modified = {2010-02-17 19:02:19 +0100}, Isbn = {0-86729-038-2}, Issn = {0044-7870}, Journal = j-PROC-ASIS-AM, Journalabr = {Proc ASIS Annu Meet}, Keywords = {information science, automatic spelling, textual databases, spelling correction, misspellings, correction algorithms}, Lccn = {Z699.A1 .A5 1982}, Meetingaddress = {Columbus, OH, USA}, Pages = {236--238}, Sponsor = {ASIS, Washington, DC, USA}, Title = {Automatic Spelling Error Detection and Correction in Textual Databases}, Volume = {19}, Year = {1982}} @article{Pollock+83, Abstract = {The SPEEDCOP (SPElling Error Detection COrrection Project) project recently completed at Chemical Abstracts Service extracted over 50,000 misspellings from approximately 25,000,000 words of text from seven scientific and scholarly databases. The misspellings were automatically classified and the error types analyzed. The results, which were consistent over the different databases, showed that the expected incidence of misspelling is 0. 2\%, that 90-95\% of spelling errors have only a single mistake, that substitution is homogeneous while transposition is heterogeneous, that omission is the commonest type of misspelling, and that inadvertent doubling of a letter is the most important cause of insertion errors. The more frequently a letter occurs in the text, the more likely it is to be involved in a spelling error. Most misspellings collected by SPEEDCOP are of the type colloquially referred to as `typos' and approximately 90\% are unlikely to be repeated in normal spans of text. 20 rfs.}, Acknowledgement = ack-nhfb, Author = {J.J. Pollock and A. Zamora}, Bibdate = {Wed Jan 15 12:19:41 MST 1997}, Bibsource = {Compendex database}, Classification = {901; 723}, Coden = {AISJB6}, Date-Added = {2010-01-29 15:03:39 +0100}, Date-Modified = {2010-09-18 14:39:33 +0200}, Issn = {0002-8231}, Journal = {Journal of the American Society for Information Science}, Journalabr = {J Am Soc Inf Sci}, Keywords = {languages; engineering writing; spelling errors; spelling correction}, Month = jan, Number = {1}, Pages = {51--58}, Subject = {I.7.1 Computing Methodologies, TEXT PROCESSING, Text Editing, Spelling}, Title = {Collection and Characterization of Spelling Errors in Scientific and Scholarly Text}, Volume = {34}, Year = {1983}} @article{Pollock+84, Abstract = {The SPEEDCOP project recently completed at Chemical Abstracts Service (CAS) extracted over 50,000 misspellings from approximately 25,000,000 words of text from seven scientific and scholarly databases. The misspellings were automatically classified and analyzed and the results used to design and implement a program that proved capable of correcting most such errors. Analysis of the performance of the spelling error detection and correction programs highlighted the features that should be incorporated into a powerful and user-friendly interactive system suitable for nonprogrammers. These include document level thresholds for mispelling detection, automatic reuse of user decisions, and user verification and control of correction. An advantage of the proposed design is that the system automatically customizes itself to its environment. This article is primarily concerned with system design, not implementation details.}, Acknowledgement = ack-nhfb, Author = {J. J. Pollock and A. Zamora}, Bibdate = {Wed Jan 15 12:19:41 MST 1997}, Bibsource = {Compendex database}, Classification = {901; 723}, Coden = {AISJB6}, Date-Added = {2010-01-29 15:03:55 +0100}, Date-Modified = {2010-09-18 14:39:18 +0200}, Issn = {0002-8231}, Journal = j-J-AM-SOC-INF-SCI, Journalabr = {J Am Soc Inf Sci}, Keywords = {spelling correction}, Month = mar, Number = {2}, Pages = {104--109}, Title = {System Design for Detection and Correction of Spelling Errors in Scientific and Scholarly Text}, Volume = {35}, Year = {1984}} @article{Pollock+84b, Address = {New York, NY, USA}, Author = {J. J. Pollock and A. Zamora}, Date-Added = {2010-01-29 15:12:45 +0100}, Date-Modified = {2010-02-12 23:55:07 +0100}, Issn = {0001-0782}, Journal = {Commun. ACM}, Number = {4}, Pages = {358--368}, Publisher = {ACM Press}, Title = {Automatic spelling correction in scientific and scholarly text}, Volume = {27}, Year = {1984}} @incollection{Pols+97, Author = {L. C. W. Pols and U. 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Litman}, Booktitle = {Proceedings of the Seventh Conference on Computational Natural Language Learning}, Title = {Exceptionality and natural language learning}, Year = 2003} @inproceedings{Roth+01, Author = {D. Roth and G. Kao and X. Li and R. Nagarajan and V. Punyakanok and N. Rizzolo and W. Yih and C. O. Alm and L. G. Moran}, Booktitle = {Proceedings of the 9th Text Retrieval Conference, NIST}, Date-Modified = {2009-11-15 14:12:06 +0100}, Title = {Learning Components for a Question Answering System}, Year = 2002} @inproceedings{Roth+01b, Author = {D. Roth and W. Yih}, Booktitle = IJCAI, Date-Modified = {2009-11-15 14:12:13 +0100}, Pages = {1257--1263}, Title = {Relational Learning via Propositional Algorithms: An Information Extraction Case Study}, Year = 2001} @inproceedings{Roth+02, Author = {D. Roth and W. 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Osborne}, Keywords = {CoNLL shared task, named-entity recognition}, Pages = {142--147}, Publisher = {Edmonton, Canada}, Source = {Iris}, Title = {Introduction to the {CoNLL}-2003 Shared Task: Language-Independent Named Entity Recognition}, Year = 2003} @article{Sang+03b, Author = {E. {Tjong Kim Sang} and W. Daelemans and A. H\"othker}, Date-Added = {2009-11-15 14:35:13 +0100}, Date-Modified = {2010-09-14 22:15:59 +0200}, Journal = {Proceedings of CLIN}, Keywords = {clips, subtitling}, Pages = {109--123}, Title = {Reduction of Dutch Sentences for Automatic Subtitling}, Year = {2003}} @inproceedings{Sang+05, Author = {E. {Tjong Kim Sang} and S. Canisius and A. {Van den Bosch} and T. Bogers}, Booktitle = {Proceedings of the Ninth Conference on Natural Language Learning,CoNLL-2005}, Date-Modified = {2010-09-14 12:59:33 +0200}, Keywords = {ilk, semantic role labeling, imix, vi}, Pages = {229--232}, Publisher = {Ann Arbor, MI, USA}, Title = {Applying spelling error techniques for improving semantic role labelling}, Year = 2005} @inproceedings{Sang+05b, Author = {E. {Tjong Kim Sang} and G. Bouma and M. {De Rijke}}, Booktitle = {Proceedings of the AAAI-05 Workshop on Question Answering in Restricted Domains}, Date-Modified = {2010-09-28 10:50:01 +0200}, Pages = {41--45}, Publisher = {Pittsburgh, PA, USA}, Title = {Developing Offline Strategies for Answering Medical Questions}, Year = 2005} @inproceedings{Sang+99, Author = {E. {Tjong Kim Sang} and J. Veenstra}, Booktitle = {Proceedings of EACL'99}, Date-Modified = {2010-02-17 20:30:41 +0100}, Keywords = {ilk, shallow parsing, chunking}, Pages = {173--179}, Publisher = {Bergen, Norway}, Title = {Representing Text Chunks}, Year = 1999} @inproceedings{Sang01, Author = {E. {Tjong Kim Sang}}, Booktitle = {Proceedings of CoNLL-2001}, Date-Modified = {2010-02-17 20:32:14 +0100}, Editors = {W. Daelemans and R. 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Sarle}, Howpublished = {URL: {{\tt ftp://ftp.sas.com/pub/neural/FAQ.html}}}, Title = {Neural Networks Frequently Asked Questions}, Year = 1997} @inproceedings{Sato+90, Author = {Sato, S. and Nagao, M.}, Booktitle = {Proceedings of the 13th conference on Computational linguistics}, Date-Added = {2009-11-15 14:13:54 +0100}, Date-Modified = {2009-11-15 14:14:07 +0100}, Pages = {247--252}, Title = {Toward memory-based translation}, Volume = {3}, Year = {1990}} @inproceedings{Saunders+98, Author = {Saunders, C. and Gammerman, A. and Vovk, V.}, Booktitle = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998)}, Date-Added = {2009-11-15 14:14:12 +0100}, Date-Modified = {2009-11-15 14:14:29 +0100}, Editor = {J.W. Shavlik}, Pages = {515--521}, Title = {Ridge regression learning algorithm in dual variables}, Year = {1998}} @book{Saussure16, Address = {Paris}, Author = {F. {De Saussure}}, Date-Modified = {2010-02-17 20:32:04 +0100}, Keywords = {linguistics}, Note = {Edited posthumously by C. Bally, A. Sechehaye, and A. Riedlinger. Citation page numbers and quotes are from the English translation by Wade Baskin, New York: McGraw-Hill Book Company, 1966}, Publisher = {Payot}, Title = {Cours de linguistique g{\'{e}}n{\'{e}}rale}, Year = 1916} @inproceedings{Sawaf+00, Author = {Sawaf, H. and Schutz, K. and Ney, H.}, Booktitle = {6th International Workshop on Parsing Technologies}, Date-Added = {2009-11-15 14:14:34 +0100}, Date-Modified = {2009-11-15 14:14:44 +0100}, Pages = {231--241}, Title = {On the use of grammar based language models for statistical machine translation}, Year = {2000}} @article{Scha+99, Author = {Remko Scha and Rens Bod and Khalil Sima'an}, Date-Modified = {2010-02-17 21:03:13 +0100}, Journal = {Journal of Experimental and Theoretical Artificial Intelligence}, Keywords = {data-oriented parsing}, Pages = {409--440}, Title = {A memory-based model of syntactic analysis: data-oriented parsing}, Volume = 11, Year = 1999} @article{Scha92, Author = {R. Scha}, Journal = {{Gramma/TTT Tijdschrift voor Taalkunde}}, Pages = {57--77}, Title = {{Virtual Grammars and Creative Algorithms}}, Volume = 1, Year = 1992} @article{Schaal+94, Author = {S. Schaal and C. Atkeson}, Journal = {Control Systems Magazine}, Number = 1, Pages = {57--71}, Title = {Robot juggling: An implementation of memory-based learning}, Volume = 14, Year = 1994} @inproceedings{Schaback+07, Address = {Hyderabad, India}, Author = {J. Schaback and F. Li}, Booktitle = {IJCAI-2007 Workshop on Analytics for Noisy Unstructured Text Data}, Date-Added = {2010-01-29 15:13:14 +0100}, Date-Modified = {2010-02-14 23:14:06 +0100}, Pages = {79--86}, Title = {Multi-Level Feature Extraction for Spelling Correction}, Year = {2007}} @inproceedings{Schabes92, Author = {Yves Schabes}, Booktitle = COLING, Title = {Stochastic lexicalized tree-adjoining grammars}, Year = 1992} @article{Schaffer93, Author = {C. Schaffer}, Journal = {Machine Learning}, Number = 2, Pages = {153--178}, Title = {Overfitting avoidance as bias}, Volume = 10, Year = 1993} @inproceedings{Schaffer94, Author = {C. Schaffer}, Booktitle = {Proceedings of the Eleventh International Machine Learning Conference}, Editor = {W. W. Cohen and H. Hirsch}, Organization = {Rutgers University, New Brunswick, NJ}, Pages = {259--265}, Title = {A conservation law for generalization performance}, Year = 1994} @book{Schank+77, Address = {Hillsdale, NJ}, Author = {R. C. Schank and R. Abelson}, Publisher = {Lawrence Erlbaum Associates}, Title = {Script, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures}, Year = 1977} @inproceedings{Schapire+98a, Author = {R. E. Schapire and Y. Singer and A. Singhal}, Booktitle = {{Proceedings of the 21st Annual International Conference on Research and Development in Information Retrieval, SIGIR '98}}, Title = {{Boosting and Rocchio applied to text filtering}}, Year = 1998} @techreport{Schapire+98b, Author = {R. E. Schapire and Y. Singer}, Institution = {{To appear in Machiene Learning, AT\&T Labs}}, Title = {{BoosTexter: A system for multiclass multi-label text categorization}}, Year = 1998} @article{Schatz97, Author = {Schatz, R. B.}, Journal = {Science}, Month = {Jan}, Pages = {273--444}, Title = {Information Retrieval in Digital Libraries: Bringing Search to the Net}, Volume = 275, Year = 1997} @inproceedings{Shaw+99, Address = {College Park, Maryland, USA}, Author = {J. Shaw and V. Hatzivassiloglou}, Booktitle = {Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics}, Doi = {10.3115/1034678.1034707}, Month = {June}, Pages = {135--143}, Publisher = {Association for Computational Linguistics}, Title = {Ordering Among Premodifiers}, Url = {http://www.aclweb.org/anthology/P99-1018}, Year = {1999}, Bdsk-Url-1 = {http://www.aclweb.org/anthology/P99-1018}, Bdsk-Url-2 = {http://dx.doi.org/10.3115/1034678.1034707}} @incollection{Scheerer86, Address = {Berlin}, Author = {E. Scheerer}, Booktitle = {New trends in graphemics and orthography}, Editor = {G. Augst}, Pages = {262--286}, Publisher = {De Gruyter}, Title = {Orthography and lexical access}, Year = 1986} @incollection{Schmid94, Author = {H. Schmid}, Booktitle = {COLING-94}, Date-Modified = {2009-11-14 17:35:45 +0100}, Keywords = {part-of-speech tagging}, Pages = {172-176}, Title = {Part-of-Speech Tagging with Neural Networks}, Year = 1994} @inproceedings{Schmid94b, Author = {Schmid, H.}, Booktitle = {Proceedings of International Conference on New Methods in Language Processing}, Date-Modified = {2009-11-14 17:33:47 +0100}, Keywords = {part-of-speech tagging, decision trees}, Title = {Probabilistic Part-of-Speech Tagging Using Decision Trees}, Year = 1994} @article{Schneider01, Abstract = {This paper describes the ongoing development of a software spelling normalization system named ZENSPELL. It is intended to assign normalized, present-day English spellings to 18th spelling variants with minimal user intervention while keeping the source text intact and available for comparison. The article examines the possibility of adapting 18th century English newspaper texts in order to make them comply with 20th century spelling rules. The idea is to create a hybrid text: like glossed word-for-word 'translations' of Latin texts, the target text will contain 18th century sentences, but with 20th century orthographic words. Despite somewhat doubtful linguistic qualities, the resulting 'artificial' text will be useful for two purposes: first, lexical searches can be made using one normalized search term instead of having to guess possible spelling variations of the intended term. Second, the target text can be used as input for wordclass taggers such as ENGCG.}, Author = {P. 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Daelemans}, Booktitle = {Proceedings of the Ninth Conference on Natural Language Learning, CoNLL-2005}, Date-Modified = {2010-09-14 13:05:06 +0200}, Keywords = {ilk, information extraction, vi, imix}, Pages = {80--87}, Title = {Improving sequence segmentation learning by predicting trigrams}, Year = 2006} @inproceedings{VandenBosch+06, Address = {Trento, Italy}, Author = {A. {Van den Bosch} and I. Schuurman and V. Vandeghinste}, Booktitle = {Proceedings of the Fifth International Conference on Language Resources and Evaluation, LREC-2006}, Date-Modified = {2010-09-18 14:34:55 +0200}, Keywords = {ilk, part-of-speech tagging, vici, d-coi}, Title = {Transferring {PoS}-tagging and lemmatization tools from spoken to written {D}utch corpus development}, Year = 2006, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/dcoi-taglemma.pdf}} @inproceedings{VandenBosch+06b, Address = {New York, NY}, Author = {A. {Van den Bosch} and S. Canisius}, Booktitle = {Proceedings of the Eighth Meeting of the ACL Special Interest Group in Computational Phonology, SIGPHON '06}, Date-Modified = {2010-09-14 13:00:06 +0200}, Keywords = {ilk, imix, vi, constraint satisfaction inference, letter-phoneme conversion, morphological segmentation}, Pages = {61--69}, Title = {Improved morpho-phonological sequence processing with constraint satisfaction inference}, Year = 2006} @inproceedings{VandenBosch+07, Address = {Leuven, Belgium}, Author = {A. {Van den Bosch} and N. Stroppa and A. Way}, Booktitle = {Proceedings of the METIS-II Workshop on New Approaches to Machine Translation}, Date-Modified = {2010-01-02 21:32:04 +0100}, Editor = {F. Van Eynde and V. Vandeghinste and I. Schuurman}, Keywords = {ilk, memory-based machine translation, example-based machine translation}, Pages = {63--72}, Title = {A memory-based classification approach to marker-based {EBMT}}, Year = 2007, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/metis07.pdf}} @inproceedings{VandenBosch+07b, Address = {Amsterdam, The Netherlands}, Author = {A. {Van den Bosch} and K. {Van der Sloot}}, Booktitle = {Proceedings of the 18th BENELEARN Conference}, Date-Modified = {2010-01-02 21:31:38 +0100}, Editor = {P. Adriaans and M. van Someren and S. Katrenko}, Keywords = {ilk, k-NN}, Title = {Superlinear parallelisation of the {{\em k}}-nearest neighbor classifier}, Year = 2007, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/benelearn07-vandenbosch-sloot.pdf}} @inproceedings{VandenBosch+07c, Address = {Leuven, Belgium}, Author = {A. {Van den Bosch} and G.J. Busser and S. Canisius and W. Daelemans}, Booktitle = {Computational Linguistics in the Netherlands: Selected Papers from the Seventeenth CLIN Meeting}, Date-Modified = {2010-09-14 12:59:57 +0200}, Editor = {P. Dirix and I. Schuurman and V. Vandeghinste and F. {Van Eynde}}, Keywords = {ilk, imix, vi, morphological analysis, dependency parsing, memory-based language processing, part-of-speech tagging, Dutch}, Pages = {99--114}, Title = {An efficient memory-based morpho-syntactic tagger and parser for {Dutch}}, Year = 2007, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/tadpole-final.pdf}} @inproceedings{VandenBosch+07d, Address = {Illinois, US}, Author = {A. {Van den Bosch} and C. Sporleder and M. {Van Erp} and S. Hunt}, Booktitle = {Proceedings of Digital Humanities 2007, the 19th Joint International Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing}, Date-Added = {2010-01-03 01:03:01 +0100}, Date-Modified = {2010-09-14 11:59:15 +0200}, Keywords = {ilk, digital heritage, data cleaning, mitch, data imputation}, Organization = {University of Illinois at Urbana-Champaign}, Pages = {223--224}, Title = {Automatic techniques for generating and correcting cultural heritage collection metadata}, Year = {2007}, Bdsk-Url-1 = {http://www.digitalhumanities.org/dh2007/abstracts/xhtml.xq?id=160}} @inbook{VandenBosch+07e, Address = {Berlin, Germany}, Author = {A. {Van den Bosch} and E. Marsi and A. Soudi}, Chapter = {11}, Date-Added = {2010-01-03 01:17:27 +0100}, Date-Modified = {2010-01-03 01:19:13 +0100}, Editor = {A. Soudi and A. {Van den Bosch} and G. Neumann}, Keywords = {ilk, Arabic, part-of-speech tagging, morphological analysis}, Pages = {203--219}, Publisher = {Springer Verlag}, Title = {Memory-based morphological analysis and part-of-speech tagging of Arabic}, Year = {2007}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/P3-C11-Jan07.pdf}} @inproceedings{VandenBosch+07f, Address = {Utrecht, the Netherlands}, Author = {A. {Van den Bosch} and K. {Van der Sloot}}, Booktitle = {Proceedings of the 19th Belgian-Dutch Artificial Intelligence Conference (BNAIC-2007)}, Date-Added = {2010-01-03 01:22:00 +0100}, Date-Modified = {2010-01-03 01:23:13 +0100}, Editor = {M. Dastani and E. de Jong}, Keywords = {ilk, k-NN, parallelism}, Pages = {65--72}, Title = {Superlinear parallelization of {\em k}-nearest neighbor retrieval}, Year = {2007}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/parallelknn.pdf}} @inproceedings{VandenBosch+08, Address = {Amsterdam, The Netherlands}, Author = {A. {Van den Bosch} and T. Bogers}, Booktitle = {MobileHCI 2008: Proceedings of the 10th International Conference on Human-Computer Interaction with Mobile Devices and Services, IOP-MMI special track,}, Date-Added = {2010-01-03 10:43:51 +0100}, Date-Modified = {2010-09-18 14:34:36 +0200}, Keywords = {ilk, word completion, mobile devices, apropos, vici}, Month = {September}, Pages = {465--470}, Title = {Efficient Context-Sensitive Word Completion for Mobile Devices}, Year = {2008}, Bdsk-Url-1 = {http://ilk.uvt.nl/~toine/publications/vandenbosch.2008.mobilehci-2008-paper.pdf}} @article{VandenBosch+09, Author = {A. {Van den Bosch} and P. Berck}, Date-Added = {2010-01-02 19:13:36 +0100}, Date-Modified = {2010-09-14 13:01:30 +0200}, Journal = {The Prague Bulletin of Mathematical Linguistics}, Keywords = {ilk, vici, memory-based machine translation, memory-based language modeling}, Pages = {17--26}, Title = {Memory-based machine translation and language modeling}, Volume = {91}, Year = {2009}, Bdsk-Url-1 = {http://ufal.mff.cuni.cz/pbml/91/art-bosch.pdf}} @article{VandenBosch+09b, Author = {A. {Van den Bosch} and M. {Van Erp} and C. Sporleder}, Date-Added = {2010-01-02 19:33:15 +0100}, Date-Modified = {2010-09-14 11:59:29 +0200}, Journal = {IEEE Intelligent Systems}, Keywords = {ilk, cultural heritage, data cleaning, mitch}, Number = {2}, Pages = {54--63}, Title = {Making a clean sweep of cultural heritage}, Volume = {34}, Year = {2009}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/MITCH-IS.pdf}} @article{VandenBosch+09c, Author = {A. {Van den Bosch} and H. J. {Van den Herik} and P. Doorenbosch}, Date-Added = {2010-01-02 19:37:57 +0100}, Date-Modified = {2010-01-02 19:39:15 +0100}, Journal = {Interdisciplinary Science Review}, Keywords = {ilk, digital heritage, cultural heritage}, Number = {2--3}, Pages = {129--138}, Title = {Digital discoveries in museums, libraries, and archives: Computer science meets cultural heritage}, Volume = {34}, Year = {2009}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/ISR34.2-01.pdf}} @article{VandenBosch+09d, Author = {A. {Van den Bosch} and P. Lendvai and M. {Van Erp} and S. Hunt and M. {Van der Meij} and R. Dekker}, Date-Added = {2010-01-02 19:39:32 +0100}, Date-Modified = {2010-09-14 11:59:21 +0200}, Journal = {Interdisciplinary Science Review}, Keywords = {ilk, natural history, digital heritage, mitch}, Number = {2--3}, Pages = {206--23}, Title = {Weaving a new fabric of natural history}, Volume = {34}, Year = {2009}, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/ISR34.2-06.pdf}} @inproceedings{VandenBosch+92, Author = {A. {Van den Bosch} and W. Daelemans}, Booktitle = {Proceedings of the Computational Linguistics in the Netherlands meeting 1991}, Date-Modified = {2010-01-02 21:31:17 +0100}, Editor = {J. {Van Eijk} and W. Meyer}, Keywords = {ilk}, Pages = {40--53}, Publisher = {Utrecht: OTS}, Title = {Linguistic pattern matching capabilities of connectionist networks}, Year = 1992} @inproceedings{VandenBosch+93, Author = {A. {Van den Bosch} and W. Daelemans}, Booktitle = {Proceedings of the 6th Conference of the EACL}, Date-Modified = {2010-01-02 21:31:12 +0100}, Keywords = {ilk, grapheme-phoneme conversion}, Pages = {45--53}, Title = {Data-Oriented Methods for Grapheme-to-Phoneme Conversion}, Year = 1993} @inproceedings{VandenBosch+94, Author = {A. {Van den Bosch} and A. Content and W. Daelemans and B. {De Gelder}}, Booktitle = {Proceedings of the 2nd International Conference on Quantitative Linguistics, QUALICO-94}, Date-Modified = {2010-01-02 21:31:06 +0100}, Editor = {A. A. Polikarpov}, Keywords = {ilk, writing systems, orthographic depth}, Pages = {26--30}, Title = {Analysing orthographic depth of different languages using data-oriented algorithms}, Year = 1994} @inproceedings{VandenBosch+95, Author = {A. {Van den Bosch} and A. Weijters and H. J. {Van den Herik} and W. Daelemans}, Booktitle = {Proceedings of the 5th {B}elgian-{D}utch Conference on Machine Learning}, Date-Modified = {2010-09-14 13:05:18 +0200}, Keywords = {ilk, machine learning}, Pages = {118--126}, Title = {The profit of learning exceptions}, Year = 1995} @inproceedings{VandenBosch+95b, Author = {A. {Van den Bosch} and A. Weijters and H. J. {Van den Herik}}, Booktitle = {Proceedings of the Seventh {D}utch Conference on AI, {{\sc naic}}'95}, Date-Modified = {2010-09-14 13:05:29 +0200}, Editor = {J. C. Bioch and Y.-H. Tan}, Keywords = {ilk, lazy learning}, Pages = {211--218}, Title = {Scaling effects with greedy and lazy machine learning algorithms}, Year = 1995} @article{VandenBosch+95c, Author = {A. {Van den Bosch} and A. Content and W. Daelemans and B. {De Gelder}}, Date-Modified = {2010-01-02 21:30:39 +0100}, Journal = {Journal of Quantitative Linguistics}, Keywords = {ilk, writing systems, spelling, grapheme-phoneme conversion}, Number = 3, Title = {Measuring the complexity of writing systems}, Volume = 1, Year = 1995} @inproceedings{VandenBosch+96, Author = {A. {Van den Bosch} and W. Daelemans and A. Weijters}, Booktitle = {Proceedings of the Second International Conference on New Methods in Natural Language Processing, NeMLaP-2, Ankara, Turkey}, Date-Modified = {2010-01-02 21:30:15 +0100}, Editor = {K. Oflazer and H. 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F\"{u}rnkrantz}, Keywords = {ilk, rule learning, feature construction}, Pages = {1--16}, Title = {Feature transformation through rule induction: a case study with the k-NN classifier}, Year = 2004, Bdsk-Url-1 = {http://ilk.uvt.nl/downloads/pub/papers/rbm.pdf}} @inproceedings{VandenBosch04b, Address = {Groningen, The Netherlands}, Author = {A. {Van den Bosch}}, Booktitle = {Proceedings of the Sixteenth Belgian-Dutch Conference on Artificial Intelligence}, Date-Modified = {2010-01-02 21:29:48 +0100}, Editor = {R. Verbrugge and N. Taatgen and L. 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /space/exclam/quotedbl/numbersign/dollar/percent/ampersand/quoteright /parenleft/parenright/asterisk/plus/comma/minus/period/slash /zero/one/two/three/four/five/six/seven/eight/nine/colon/semicolon /less/equal/greater/question/at/A/B/C/D/E/F/G/H/I/J/K/L/M/N /O/P/Q/R/S/T/U/V/W/X/Y/Z/bracketleft/backslash/bracketright /asciicircum/underscore/quoteleft/a/b/c/d/e/f/g/h/i/j/k/l/m /n/o/p/q/r/s/t/u/v/w/x/y/z/braceleft/bar/braceright/asciitilde /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef /.notdef/dotlessi/grave/acute/circumflex/tilde/macron/breve /dotaccent/dieresis/.notdef/ring/cedilla/.notdef/hungarumlaut /ogonek/caron/space/exclamdown/cent/sterling/currency/yen/brokenbar /section/dieresis/copyright/ordfeminine/guillemotleft/logicalnot /hyphen/registered/macron/degree/plusminus/twosuperior/threesuperior /acute/mu/paragraph/periodcentered/cedilla/onesuperior/ordmasculine /guillemotright/onequarter/onehalf/threequarters/questiondown /Agrave/Aacute/Acircumflex/Atilde/Adieresis/Aring/AE/Ccedilla /Egrave/Eacute/Ecircumflex/Edieresis/Igrave/Iacute/Icircumflex /Idieresis/Eth/Ntilde/Ograve/Oacute/Ocircumflex/Otilde/Odieresis /multiply/Oslash/Ugrave/Uacute/Ucircumflex/Udieresis/Yacute /Thorn/germandbls/agrave/aacute/acircumflex/atilde/adieresis /aring/ae/ccedilla/egrave/eacute/ecircumflex/edieresis/igrave /iacute/icircumflex/idieresis/eth/ntilde/ograve/oacute/ocircumflex /otilde/odieresis/divide/oslash/ugrave/uacute/ucircumflex/udieresis /yacute/thorn/ydieresis ] def /Times-Bold reencodeISO def /Times-Italic reencodeISO def /Times-Roman reencodeISO def /arrowHeight 8 def /arrowWidth 4 def /none null def /numGraphicParameters 17 def /stringLimit 65535 def /Begin { save numGraphicParameters dict begin } def /End { end restore } def /SetB { dup type /nulltype eq { pop false /brushRightArrow idef false /brushLeftArrow idef true /brushNone idef } { /brushDashOffset idef /brushDashArray idef 0 ne /brushRightArrow idef 0 ne /brushLeftArrow idef /brushWidth idef false /brushNone idef } ifelse } def /SetCFg { /fgblue idef /fggreen idef /fgred idef } def /SetCBg { /bgblue idef /bggreen idef /bgred idef } def /SetF { /printSize idef /printFont idef } def /SetP { dup type /nulltype eq { pop true /patternNone idef } { dup -1 eq { /patternGrayLevel idef /patternString idef } { /patternGrayLevel idef } ifelse false /patternNone idef } ifelse } def /BSpl { 0 begin storexyn newpath n 1 gt { 0 0 0 0 0 0 1 1 true subspline n 2 gt { 0 0 0 0 1 1 2 2 false subspline 1 1 n 3 sub { /i exch def i 1 sub dup i dup i 1 add dup i 2 add dup false subspline } for n 3 sub dup n 2 sub dup n 1 sub dup 2 copy false subspline } if n 2 sub dup n 1 sub dup 2 copy 2 copy false subspline patternNone not brushLeftArrow not brushRightArrow not and and { ifill } if brushNone not { istroke } if 0 0 1 1 leftarrow n 2 sub dup n 1 sub dup rightarrow } if end } dup 0 4 dict put def /Circ { newpath 0 360 arc patternNone not { ifill } if brushNone not { istroke } if } def /CBSpl { 0 begin dup 2 gt { storexyn newpath n 1 sub dup 0 0 1 1 2 2 true subspline 1 1 n 3 sub { /i exch def i 1 sub dup i dup i 1 add dup i 2 add dup false subspline } for n 3 sub dup n 2 sub dup n 1 sub dup 0 0 false subspline n 2 sub dup n 1 sub dup 0 0 1 1 false subspline patternNone not { ifill } if brushNone not { istroke } if } { Poly } ifelse end } dup 0 4 dict put def /Elli { 0 begin newpath 4 2 roll translate scale 0 0 1 0 360 arc patternNone not { ifill } if brushNone not { istroke } if end } dup 0 1 dict put def /Line { 0 begin 2 storexyn newpath x 0 get y 0 get moveto x 1 get y 1 get lineto brushNone not { istroke } if 0 0 1 1 leftarrow 0 0 1 1 rightarrow end } dup 0 4 dict put def /MLine { 0 begin storexyn newpath n 1 gt { x 0 get y 0 get moveto 1 1 n 1 sub { /i exch def x i get y i get lineto } for patternNone not brushLeftArrow not brushRightArrow not and and { ifill } if brushNone not { istroke } if 0 0 1 1 leftarrow n 2 sub dup n 1 sub dup rightarrow } if end } dup 0 4 dict put def /Poly { 3 1 roll newpath moveto -1 add { lineto } repeat closepath patternNone not { ifill } if brushNone not { istroke } if } def /Rect { 0 begin /t exch def /r exch def /b exch def /l exch def newpath l b moveto l t lineto r t lineto r b lineto closepath patternNone not { ifill } if brushNone not { istroke } if end } dup 0 4 dict put def /Text { ishow } def /idef { dup where { pop pop pop } { exch def } ifelse } def /ifill { 0 begin gsave patternGrayLevel -1 ne { fgred bgred fgred sub patternGrayLevel mul add fggreen bggreen fggreen sub patternGrayLevel mul add fgblue bgblue fgblue sub patternGrayLevel mul add setrgbcolor eofill } { eoclip originalCTM setmatrix pathbbox /t exch def /r exch def /b exch def /l exch def /w r l sub ceiling cvi def /h t b sub ceiling cvi def /imageByteWidth w 8 div ceiling cvi def /imageHeight h def bgred bggreen bgblue setrgbcolor eofill fgred fggreen fgblue setrgbcolor w 0 gt h 0 gt and { l b translate w h scale w h true [w 0 0 h neg 0 h] { patternproc } imagemask } if } ifelse grestore end } dup 0 8 dict put def /istroke { gsave brushDashOffset -1 eq { [] 0 setdash 1 setgray } { brushDashArray brushDashOffset setdash fgred fggreen fgblue setrgbcolor } ifelse brushWidth setlinewidth originalCTM setmatrix stroke grestore } def /ishow { 0 begin gsave fgred fggreen fgblue setrgbcolor printFont printSize scalefont setfont /descender 0 printFont /FontBBox get 1 get printFont /FontMatrix get transform exch pop def /vertoffset 1 printSize sub descender sub def { 0 vertoffset moveto show /vertoffset vertoffset printSize sub def } forall grestore end } dup 0 2 dict put def /patternproc { 0 begin /patternByteLength patternString length def /patternHeight patternByteLength 8 mul sqrt cvi def /patternWidth patternHeight def /patternByteWidth patternWidth 8 idiv def /imageByteMaxLength imageByteWidth imageHeight mul stringLimit patternByteWidth sub min def /imageMaxHeight imageByteMaxLength imageByteWidth idiv patternHeight idiv patternHeight mul patternHeight max def /imageHeight imageHeight imageMaxHeight sub store /imageString imageByteWidth imageMaxHeight mul patternByteWidth add string def 0 1 imageMaxHeight 1 sub { /y exch def /patternRow y patternByteWidth mul patternByteLength mod def /patternRowString patternString patternRow patternByteWidth getinterval def /imageRow y imageByteWidth mul def 0 patternByteWidth imageByteWidth 1 sub { /x exch def imageString imageRow x add patternRowString putinterval } for } for imageString end } dup 0 12 dict put def /min { dup 3 2 roll dup 4 3 roll lt { exch } if pop } def /max { dup 3 2 roll dup 4 3 roll gt { exch } if pop } def /arrowhead { 0 begin transform originalCTM itransform /taily exch def /tailx exch def transform originalCTM itransform /tipy exch def /tipx exch def /dy tipy taily sub def /dx tipx tailx sub def /angle dx 0 ne dy 0 ne or { dy dx atan } { 90 } ifelse def gsave originalCTM setmatrix tipx tipy translate angle rotate newpath 0 0 moveto arrowHeight neg arrowWidth 2 div lineto arrowHeight neg arrowWidth 2 div neg lineto closepath patternNone not { originalCTM setmatrix /padtip arrowHeight 2 exp 0.25 arrowWidth 2 exp mul add sqrt brushWidth mul arrowWidth div def /padtail brushWidth 2 div def tipx tipy translate angle rotate padtip 0 translate arrowHeight padtip add padtail add arrowHeight div dup scale arrowheadpath ifill } if brushNone not { originalCTM setmatrix tipx tipy translate angle rotate arrowheadpath istroke } if grestore end } dup 0 9 dict put def /arrowheadpath { newpath 0 0 moveto arrowHeight neg arrowWidth 2 div lineto arrowHeight neg arrowWidth 2 div neg lineto closepath } def /leftarrow { 0 begin y exch get /taily exch def x exch get /tailx exch def y exch get /tipy exch def x exch get /tipx exch def brushLeftArrow { tipx tipy tailx taily arrowhead } if end } dup 0 4 dict put def /rightarrow { 0 begin y exch get /tipy exch def x exch get /tipx exch def y exch get /taily exch def x exch get /tailx exch def brushRightArrow { tipx tipy tailx taily arrowhead } if end } dup 0 4 dict put def /midpoint { 0 begin /y1 exch def /x1 exch def /y0 exch def /x0 exch def x0 x1 add 2 div y0 y1 add 2 div end } dup 0 4 dict put def /thirdpoint { 0 begin /y1 exch def /x1 exch def /y0 exch def /x0 exch def x0 2 mul x1 add 3 div y0 2 mul y1 add 3 div end } dup 0 4 dict put def /subspline { 0 begin /movetoNeeded exch def y exch get /y3 exch def x exch get /x3 exch def y exch get /y2 exch def x exch get /x2 exch def y exch get /y1 exch def x exch get /x1 exch def y exch get /y0 exch def x exch get /x0 exch def x1 y1 x2 y2 thirdpoint /p1y exch def /p1x exch def x2 y2 x1 y1 thirdpoint /p2y exch def /p2x exch def x1 y1 x0 y0 thirdpoint p1x p1y midpoint /p0y exch def /p0x exch def x2 y2 x3 y3 thirdpoint p2x p2y midpoint /p3y exch def /p3x exch def movetoNeeded { p0x p0y moveto } if p1x p1y p2x p2y p3x p3y curveto end } dup 0 17 dict put def /storexyn { /n exch def /y n array def /x n array def n 1 sub -1 0 { /i exch def y i 3 2 roll put x i 3 2 roll put } for } def %%EndProlog %I Idraw 9 Grid 8 %%Page: 1 1 Begin %I b u %I cfg u %I cbg u %I f u %I p u %I t [ .8 0 0 .8 0 0 ] concat /originalCTM matrix currentmatrix def Begin %I Rect %I b 65535 1 0 0 [] 0 SetB %I cfg Black 0 0 0 SetCFg %I cbg White 1 1 1 SetCBg none SetP %I p n %I t [ 1 0 0 1 148 197 ] concat %I 291 4 291 5 Rect End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-bold-r-*-140-* Times-Bold 14 SetF %I t [ 1 0 0 1 328 734 ] concat %I [ (EXAMPLES) ] Text End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-bold-r-*-140-* Times-Bold 14 SetF %I t [ 1 0 0 1 345 439 ] concat %I [ (CASES) ] Text End Begin %I Line %I b 65535 1 0 1 [] 0 SetB %I cfg Black 0 0 0 SetCFg %I cbg White 1 1 1 SetCBg none SetP %I p n %I t [ 1 0 0 1 148 197 ] concat %I 221 510 221 261 Line End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-bold-r-*-140-* Times-Bold 14 SetF %I t [ 1 0 0 1 179 438 ] concat %I [ (INPUT) ] Text End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-bold-r-*-140-* Times-Bold 14 SetF %I t [ 1 0 0 1 498 438 ] concat %I [ (OUTPUT) ] Text End Begin %I Line %I b 65535 1 0 1 [] 0 SetB %I cfg Black 0 0 0 SetCFg %I cbg White 1 1 1 SetCBg none SetP %I p n %I t [ 1 0 0 1 148 197 ] concat %I 91 231 191 231 Line End Begin %I Line %I b 65535 1 0 1 [] 0 SetB %I cfg Black 0 0 0 SetCFg %I cbg White 1 1 1 SetCBg none SetP %I p n %I t [ 1 0 0 1 148 197 ] concat %I 260 231 320 231 Line End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-medium-i-*-140-* Times-Italic 14 SetF %I t [ 1 0 0 1 286 403 ] concat %I [ (Similarity-Based Reasoning) ] Text End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-medium-i-*-140-* Times-Italic 14 SetF %I t [ 1 0 0 1 384 641 ] concat %I [ (Storage) (Computation of Metrics) ] Text End Begin %I Rect %I b 65535 1 0 1 [] 0 SetB %I cfg Black 0 0 0 SetCFg %I cbg White 1 1 1 SetCBg none SetP %I p n %I t [ 1 0 0 1 148 197 ] concat %I 12 161 430 571 Rect End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-medium-r-*-140-* Times-Roman 14 SetF %I t [ 1 0 0 1 170 757 ] concat %I [ (Memory-Based) (Learning) (Architecture) ] Text End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-medium-r-*-140-* Times-Roman 14 SetF %I t [ 1 0 0 1 495 714 ] concat %I [ (Learning) ] Text End Begin %I Text %I cfg Black 0 0 0 SetCFg %I f *-times-medium-r-*-140-* Times-Roman 14 SetF %I t [ 1 0 0 1 176 386 ] concat %I [ (Performance) ] Text End End %I eop showpage %%Trailer end LanguageMachines-timbl-642727d/docs/texfiles/pos-neg.eps000077500000000000000000005251041451477526200231560ustar00rootroot00000000000000%!PS-Adobe-3.0 EPSF-3.0 %%HiResBoundingBox: 0.000000 0.000000 244.500000 218.001007 %APL_DSC_Encoding: UTF8 %%Title: (Unknown) %%Creator: (Unknown) %%CreationDate: (Unknown) %%For: (Unknown) %%DocumentData: Clean7Bit %%LanguageLevel: 2 %%Pages: 1 %%BoundingBox: 0 0 244 218 %%EndComments %%BeginProlog %%BeginFile: cg-pdf.ps %%Copyright: Copyright 2000-2002 Apple Computer Incorporated. %%Copyright: All Rights Reserved. currentpacking true setpacking /cg_md 140 dict def cg_md begin /L3? languagelevel 3 ge def /bd{bind def}bind def /ld{load def}bd /xs{exch store}bd /xd{exch def}bd /cmmtx matrix def mark /sc/setcolor /scs/setcolorspace /dr/defineresource /fr/findresource /T/true /F/false /d/setdash /w/setlinewidth /J/setlinecap /j/setlinejoin /M/setmiterlimit /i/setflat /rc/rectclip /rf/rectfill /rs/rectstroke /f/fill /f*/eofill /sf/selectfont /s/show /xS/xshow /yS/yshow /xyS/xyshow /S/stroke /m/moveto /l/lineto /c/curveto /h/closepath /n/newpath /q/gsave /Q/grestore counttomark 2 idiv {ld}repeat pop /SC{ /ColorSpace fr scs }bd /cgmtx matrix def /sdmtx{cgmtx currentmatrix pop}bd /CM {cgmtx setmatrix}bd /cm {cmmtx astore CM concat}bd /W{clip newpath}bd /W*{eoclip newpath}bd statusdict begin product end dup (HP) anchorsearch{ pop pop pop true }{ pop (hp) anchorsearch{ pop pop true }{ pop false }ifelse }ifelse { { { pop pop (0)dup 0 4 -1 roll put F charpath }cshow } }{ {F charpath} }ifelse /cply exch bd /cps {cply stroke}bd /pgsave 0 def /bp{/pgsave save store}bd /ep{pgsave restore showpage}def /re{4 2 roll m 1 index 0 rlineto 0 exch rlineto neg 0 rlineto h}bd /scrdict 10 dict def /scrmtx matrix def /patarray 0 def /createpat{patarray 3 1 roll put}bd /makepat{ scrmtx astore pop gsave initgraphics CM patarray exch get scrmtx makepattern grestore setpattern }bd /cg_BeginEPSF{ userdict save/cg_b4_Inc_state exch put userdict/cg_endepsf/cg_EndEPSF load put count userdict/cg_op_count 3 -1 roll put countdictstack dup array dictstack userdict/cg_dict_array 3 -1 roll put 3 sub{end}repeat /showpage {} def 0 setgray 0 setlinecap 1 setlinewidth 0 setlinejoin 10 setmiterlimit [] 0 setdash newpath false setstrokeadjust false setoverprint }bd /cg_EndEPSF{ countdictstack 3 sub { end } repeat cg_dict_array 3 1 index length 3 sub getinterval {begin}forall count userdict/cg_op_count get sub{pop}repeat userdict/cg_b4_Inc_state get restore F setpacking }bd /cg_biproc{currentfile/RunLengthDecode filter}bd /cg_aiproc{currentfile/ASCII85Decode filter/RunLengthDecode filter}bd /ImageDataSource 0 def L3?{ /cg_mibiproc{pop pop/ImageDataSource{cg_biproc}def}bd /cg_miaiproc{pop pop/ImageDataSource{cg_aiproc}def}bd }{ /ImageBandMask 0 def /ImageBandData 0 def /cg_mibiproc{ string/ImageBandMask xs string/ImageBandData xs /ImageDataSource{[currentfile/RunLengthDecode filter dup ImageBandMask/readstring cvx /pop cvx dup ImageBandData/readstring cvx/pop cvx]cvx bind}bd }bd /cg_miaiproc{ string/ImageBandMask xs string/ImageBandData xs /ImageDataSource{[currentfile/ASCII85Decode filter/RunLengthDecode filter dup ImageBandMask/readstring cvx /pop cvx dup ImageBandData/readstring cvx/pop cvx]cvx bind}bd }bd }ifelse /imsave 0 def /BI{save/imsave xd mark}bd /EI{imsave restore}bd /ID{ counttomark 2 idiv dup 2 add dict begin {def} repeat pop /ImageType 1 def /ImageMatrix[Width 0 0 Height neg 0 Height]def currentdict dup/ImageMask known{ImageMask}{F}ifelse exch L3?{ dup/MaskedImage known { pop << /ImageType 3 /InterleaveType 2 /DataDict currentdict /MaskDict << /ImageType 1 /Width Width /Height Height /ImageMatrix ImageMatrix /BitsPerComponent 1 /Decode [0 1] currentdict/Interpolate known {/Interpolate Interpolate}if >> >> }if }if exch {imagemask}{image}ifelse end }bd /cguidfix{statusdict begin mark version end {cvr}stopped{cleartomark 0}{exch pop}ifelse 2012 lt{dup findfont dup length dict begin {1 index/FID ne 2 index/UniqueID ne and {def} {pop pop} ifelse}forall currentdict end definefont pop }{pop}ifelse }bd /t_array 0 def /t_i 0 def /t_c 1 string def /x_proc{ exch t_array t_i get add exch moveto /t_i t_i 1 add store }bd /y_proc{ t_array t_i get add moveto /t_i t_i 1 add store }bd /xy_proc{ t_array t_i 2 copy 1 add get 3 1 roll get 4 -1 roll add 3 1 roll add moveto /t_i t_i 2 add store }bd /sop 0 def /cp_proc/x_proc ld /base_charpath { /t_array xs /t_i 0 def { t_c 0 3 -1 roll put currentpoint t_c cply sop cp_proc }forall /t_array 0 def }bd /sop/stroke ld /nop{}def /xsp/base_charpath ld /ysp{/cp_proc/y_proc ld base_charpath/cp_proc/x_proc ld}bd /xysp{/cp_proc/xy_proc ld base_charpath/cp_proc/x_proc ld}bd /xmp{/sop/nop ld /cp_proc/x_proc ld base_charpath/sop/stroke ld}bd /ymp{/sop/nop ld /cp_proc/y_proc ld base_charpath/sop/stroke ld}bd /xymp{/sop/nop ld /cp_proc/xy_proc ld base_charpath/sop/stroke ld}bd /refnt{ findfont dup length dict copy dup /Encoding 4 -1 roll put definefont pop }bd /renmfont{ findfont dup length dict copy definefont pop }bd L3? dup dup{save exch}if /Range 0 def /Domain 0 def /Encode 0 def /Decode 0 def /Size 0 def /DataSource 0 def /mIndex 0 def /nDomain 0 def /ival 0 def /val 0 def /nDomM1 0 def /sizem1 0 def /srcEncode 0 def /srcDecode 0 def /nRange 0 def /d0 0 def /r0 0 def /di 0 def /ri 0 def /a0 0 def /a1 0 def /r1 0 def /r2 0 def /dx 0 def /Nsteps 0 def /sh3tp 0 def /ymax 0 def /ymin 0 def /xmax 0 def /xmin 0 def /min { 2 copy gt {exch pop}{pop}ifelse }bd /max { 2 copy lt {exch pop}{pop}ifelse }bd /inter { 1 index sub 5 2 roll 1 index sub 3 1 roll sub 3 1 roll div mul add }bd /setupFunEvalN { begin /nDomM1 Domain length 2 idiv 1 sub store /sizem1[ 0 1 nDomM1 { Size exch get 1 sub }for ]store /srcEncode currentdict/Encode known { Encode }{ [ 0 1 nDomM1 { 0 sizem1 3 -1 roll get }for ] }ifelse store /srcDecode currentdict/Decode known {Decode}{Range}ifelse store /nRange Range length 2 idiv store end }bd /FunEvalN { begin nDomM1 -1 0 { 2 mul/mIndex xs Domain mIndex get max Domain mIndex 1 add get min Domain mIndex get Domain mIndex 1 add get srcEncode mIndex get srcEncode mIndex 1 add get inter round cvi 0 max sizem1 mIndex 2 idiv get min nDomM1 1 add 1 roll }for nDomM1 1 add array astore/val xs nDomM1 0 gt { 0 nDomM1 -1 0 { dup 0 gt { /mIndex xs val mIndex get 1 index add Size mIndex 1 sub get mul add }{ val exch get add }ifelse }for }{ val 0 get }ifelse nRange mul /ival xs 0 1 nRange 1 sub { dup 2 mul/mIndex xs ival add DataSource exch get 0 255 srcDecode mIndex 2 copy get 3 1 roll 1 add get inter Range mIndex get max Range mIndex 1 add get min }for end }bd /sh2 { /Coords load aload pop 3 index 3 index translate 3 -1 roll sub 3 1 roll exch sub 2 copy dup mul exch dup mul add sqrt dup scale atan rotate /Function load setupFunEvalN clippath {pathbbox}stopped {0 0 0 0}if newpath /ymax xs /xmax xs /ymin xs /xmin xs currentdict/Extend known { /Extend load 0 get { /Domain load 0 get /Function load FunEvalN sc xmin ymin xmin abs ymax ymin sub rectfill }if }if /dx/Function load/Size get 0 get 1 sub 1 exch div store gsave /di ymax ymin sub store /Function load dup /Domain get dup 0 get exch 1 get 2 copy exch sub dx mul exch { 1 index FunEvalN sc 0 ymin dx di rectfill dx 0 translate }for pop grestore currentdict/Extend known { /Extend load 1 get { /Domain load 1 get /Function load FunEvalN sc 1 ymin xmax 1 sub abs ymax ymin sub rectfill }if }if }bd /shp { 4 copy dup 0 gt{ 0 exch a1 a0 arc }{ pop 0 moveto }ifelse dup 0 gt{ 0 exch a0 a1 arcn }{ pop 0 lineto }ifelse fill dup 0 gt{ 0 exch a0 a1 arc }{ pop 0 moveto }ifelse dup 0 gt{ 0 exch a1 a0 arcn }{ pop 0 lineto }ifelse fill }bd /calcmaxs { xmin dup mul ymin dup mul add sqrt xmax dup mul ymin dup mul add sqrt xmin dup mul ymax dup mul add sqrt xmax dup mul ymax dup mul add sqrt max max max }bd /sh3 { /Coords load aload pop 5 index 5 index translate 3 -1 roll 6 -1 roll sub 3 -1 roll 5 -1 roll sub 2 copy dup mul exch dup mul add sqrt /dx xs 2 copy 0 ne exch 0 ne or { exch atan rotate }{ pop pop }ifelse /r2 xs /r1 xs /Function load dup/Size get 0 get 1 sub /Nsteps xs setupFunEvalN dx r2 add r1 lt{ 0 }{ dx r1 add r2 le { 1 }{ r1 r2 eq { 2 }{ 3 }ifelse }ifelse }ifelse /sh3tp xs clippath {pathbbox}stopped {0 0 0 0}if newpath /ymax xs /xmax xs /ymin xs /xmin xs dx dup mul r2 r1 sub dup mul sub dup 0 gt { sqrt r2 r1 sub atan /a0 exch 180 exch sub store /a1 a0 neg store }{ pop /a0 0 store /a1 360 store }ifelse currentdict/Extend known { /Extend load 0 get r1 0 gt and { /Domain load 0 get/Function load FunEvalN sc { { dx 0 r1 360 0 arcn xmin ymin moveto xmax ymin lineto xmax ymax lineto xmin ymax lineto xmin ymin lineto eofill } { r1 0 gt{0 0 r1 0 360 arc fill}if } { 0 r1 xmin abs r1 add neg r1 shp } { r2 r1 gt{ 0 r1 r1 neg r2 r1 sub div dx mul 0 shp }{ 0 r1 calcmaxs dup r2 add dx mul dx r1 r2 sub sub div neg exch 1 index abs exch sub shp }ifelse } }sh3tp get exec }if }if /d0 0 store /r0 r1 store /di dx Nsteps div store /ri r2 r1 sub Nsteps div store /Function load /Domain load dup 0 get exch 1 get 2 copy exch sub Nsteps div exch { 1 index FunEvalN sc d0 di add r0 ri add d0 r0 shp { d0 0 r0 a1 a0 arc d0 di add 0 r0 ri add a0 a1 arcn fill d0 0 r0 a0 a1 arc d0 di add 0 r0 ri add a1 a0 arcn fill }pop /d0 d0 di add store /r0 r0 ri add store }for pop currentdict/Extend known { /Extend load 1 get r2 0 gt and { /Domain load 1 get/Function load FunEvalN sc { { dx 0 r2 0 360 arc fill } { dx 0 r2 360 0 arcn xmin ymin moveto xmax ymin lineto xmax ymax lineto xmin ymax lineto xmin ymin lineto eofill } { xmax abs r1 add r1 dx r1 shp } { r2 r1 gt{ calcmaxs dup r1 add dx mul dx r2 r1 sub sub div exch 1 index exch sub dx r2 shp }{ r1 neg r2 r1 sub div dx mul 0 dx r2 shp }ifelse } } sh3tp get exec }if }if }bd /sh { begin /ShadingType load dup dup 2 eq exch 3 eq or { gsave newpath /ColorSpace load scs currentdict/BBox known { /BBox load aload pop 2 index sub 3 index 3 -1 roll exch sub exch rectclip }if 2 eq {sh2}{sh3}ifelse grestore }{ pop (DEBUG: shading type unimplemented\n)print flush }ifelse end }bd {restore}if not dup{save exch}if L3?{ /sh/shfill ld /csq/clipsave ld /csQ/cliprestore ld }if {restore}if end setpacking 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0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 cleartomark end %APLT1End /RPMHUL+Helvetica-Oblique /RPMHUL+Helvetica-Oblique findfont dup length dict begin { 1 index /FID ne {def}{pop pop} ifelse } forall FontMatrix [1 0 0.212557 1 0 0] matrix concatmatrix /FontMatrix exch def currentdict end definefont pop %RBIEndFontSubset /RPMHUL+Helvetica-Oblique cguidfix /F2.1/RPMHUL+Helvetica-Oblique renmfont [ /CIEBasedABC 4 dict dup begin /WhitePoint [ 0.9505 1.0000 1.0891 ] def /DecodeABC [ { 1.8008 exp } bind { 1.8008 exp } bind 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6.673828 2.666016 6.000000 3.333984 6.673828 6.673828 ] xS 24.682617 -2.5 m (%)s -13.669922 11.5 m (&'\(\)\))[ 6.000000 2.666016 6.673828 6.000000 6.000000 ] xS 0.60000002 i /Cs1 SC 1 1 1 sc CM 90 173.00101 m 162 173.00101 l 162 101.00101 l 90 101.00101 l h 90 173.00101 m f 1 J 1 j 1 M 0 0 0 sc 1 0 0 -1 -126 317.00101 cm 216 144 m 288 144 l 288 216 l 216 216 l h 216 144 m S /Cs2 SC 0 sc 0 i 1 0 0 -1 126 137.00101 cm -7.6669922 -1 m (-.)[ 7.330078 8.003906 ] xS /F2.1[ 9 0 0 -9 0 0]sf -26.512207 11 m (!"#$%&'\(\)!\)*$\()[ 2.500488 2.997070 5.005371 5.005371 2.500488 5.005371 5.005371 4.500000 1.999512 2.500488 1.999512 4.500000 5.005371 4.500000 ] xS 0.60000002 i /Cs1 SC 1 1 1 sc CM 162 101.00101 m 234 101.00101 l 234 29.001007 l 162 29.001007 l h 162 101.00101 m f 0 0 0 sc 1 0 0 -1 -126 317.00101 cm 288 216 m 360 216 l 360 288 l 288 288 l h 288 216 m S /Cs2 SC 0 sc 0 i 1 0 0 -1 198 65.001007 cm /F1.1[ 12 0 0 -12 0 0]sf -7.9980469 -1 m (-/)[ 7.330078 8.666016 ] xS /F2.1[ 9 0 0 -9 0 0]sf 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8.666016 ] xS /F2.1[ 9 0 0 -9 0 0]sf -30.019043 11 m (.-/\($%+$,-!\)*$\()[ 2.500488 5.005371 1.999512 4.500000 5.005371 2.500488 5.005371 5.005371 5.005371 5.005371 2.500488 1.999512 4.500000 5.005371 4.500000 ] xS 1 0 0 -1 122.5 189.87601 cm /F2.1[ 10 0 0 -10 0 0]sf -16.953125 4.5 m (&'\(\)!\)*$)[ 5.561523 5.561523 5.000000 2.221680 2.778320 2.221680 5.000000 5.561523 ] xS 1 0 0 -1 193.5 189.87601 cm -18.903809 4.5 m (+$,-!\)*$)[ 5.561523 5.561523 5.561523 5.561523 2.778320 2.221680 5.000000 5.561523 ] xS 1 0 0 -1 58 136.87601 cm -15.280762 4.5 m (0'""$0!)[ 5.000000 5.561523 3.330078 3.330078 5.561523 5.000000 2.778320 ] xS 1 0 0 -1 60 64.876007 cm -19.172363 4.5 m (\)+0'""$0!)[ 2.221680 5.561523 5.000000 5.561523 3.330078 3.330078 5.561523 5.000000 2.778320 ] xS 1 0 0 -1 126 8.8760071 cm /F1.1[ 12 0 0 -12 0 0]sf -4.0019531 4.5 m (.)s 1 0 0 -1 198 8.8760071 cm -4.3330078 4.5 m (/)s ep end %%Trailer %%EOF LanguageMachines-timbl-642727d/docs/texfiles/roc-auc.eps000077500000000000000000001055051451477526200231360ustar00rootroot00000000000000%!PS-Adobe-3.0 EPSF-3.0 %%HiResBoundingBox: 0.000000 0.000000 292.989014 267.126007 %APL_DSC_Encoding: UTF8 %%Title: (Unknown) %%Creator: (Unknown) %%CreationDate: (Unknown) %%For: (Unknown) %%DocumentData: Clean7Bit %%LanguageLevel: 2 %%Pages: 1 %%BoundingBox: 0 0 292 267 %%EndComments %%BeginProlog %%BeginFile: cg-pdf.ps %%Copyright: Copyright 2000-2002 Apple Computer Incorporated. %%Copyright: All Rights Reserved. currentpacking true setpacking /cg_md 140 dict def cg_md begin /L3? languagelevel 3 ge def /bd{bind def}bind def /ld{load def}bd /xs{exch store}bd /xd{exch def}bd /cmmtx matrix def mark /sc/setcolor /scs/setcolorspace /dr/defineresource /fr/findresource /T/true /F/false /d/setdash /w/setlinewidth /J/setlinecap /j/setlinejoin /M/setmiterlimit /i/setflat /rc/rectclip /rf/rectfill /rs/rectstroke /f/fill /f*/eofill /sf/selectfont /s/show /xS/xshow /yS/yshow /xyS/xyshow /S/stroke /m/moveto /l/lineto /c/curveto /h/closepath /n/newpath /q/gsave /Q/grestore counttomark 2 idiv {ld}repeat pop /SC{ /ColorSpace fr scs }bd /cgmtx matrix def /sdmtx{cgmtx currentmatrix pop}bd /CM {cgmtx setmatrix}bd /cm {cmmtx astore CM concat}bd /W{clip newpath}bd /W*{eoclip newpath}bd statusdict begin product end dup (HP) anchorsearch{ pop pop pop true }{ pop (hp) anchorsearch{ pop pop true }{ pop false }ifelse }ifelse { { { pop pop (0)dup 0 4 -1 roll put F charpath }cshow } }{ {F charpath} }ifelse /cply exch bd /cps {cply stroke}bd /pgsave 0 def /bp{/pgsave save store}bd /ep{pgsave restore showpage}def /re{4 2 roll m 1 index 0 rlineto 0 exch rlineto neg 0 rlineto h}bd /scrdict 10 dict def /scrmtx matrix def /patarray 0 def /createpat{patarray 3 1 roll put}bd /makepat{ scrmtx astore pop gsave initgraphics CM patarray exch get scrmtx makepattern grestore setpattern }bd /cg_BeginEPSF{ userdict save/cg_b4_Inc_state exch put userdict/cg_endepsf/cg_EndEPSF load put count userdict/cg_op_count 3 -1 roll put countdictstack dup array dictstack userdict/cg_dict_array 3 -1 roll put 3 sub{end}repeat /showpage {} def 0 setgray 0 setlinecap 1 setlinewidth 0 setlinejoin 10 setmiterlimit [] 0 setdash newpath false setstrokeadjust false setoverprint }bd /cg_EndEPSF{ countdictstack 3 sub { end } repeat cg_dict_array 3 1 index length 3 sub getinterval {begin}forall count userdict/cg_op_count get sub{pop}repeat userdict/cg_b4_Inc_state get restore F setpacking }bd /cg_biproc{currentfile/RunLengthDecode filter}bd /cg_aiproc{currentfile/ASCII85Decode filter/RunLengthDecode filter}bd /ImageDataSource 0 def L3?{ /cg_mibiproc{pop pop/ImageDataSource{cg_biproc}def}bd /cg_miaiproc{pop pop/ImageDataSource{cg_aiproc}def}bd }{ /ImageBandMask 0 def /ImageBandData 0 def /cg_mibiproc{ string/ImageBandMask xs string/ImageBandData xs /ImageDataSource{[currentfile/RunLengthDecode filter dup ImageBandMask/readstring cvx /pop cvx dup ImageBandData/readstring cvx/pop cvx]cvx bind}bd }bd /cg_miaiproc{ string/ImageBandMask xs string/ImageBandData xs /ImageDataSource{[currentfile/ASCII85Decode filter/RunLengthDecode filter dup ImageBandMask/readstring cvx /pop cvx dup ImageBandData/readstring cvx/pop cvx]cvx bind}bd }bd }ifelse /imsave 0 def /BI{save/imsave xd mark}bd /EI{imsave restore}bd /ID{ counttomark 2 idiv dup 2 add dict begin {def} repeat pop /ImageType 1 def /ImageMatrix[Width 0 0 Height neg 0 Height]def currentdict dup/ImageMask known{ImageMask}{F}ifelse exch L3?{ dup/MaskedImage known { pop << /ImageType 3 /InterleaveType 2 /DataDict currentdict /MaskDict << /ImageType 1 /Width Width /Height Height /ImageMatrix ImageMatrix /BitsPerComponent 1 /Decode [0 1] currentdict/Interpolate known {/Interpolate Interpolate}if >> >> }if }if exch {imagemask}{image}ifelse end }bd /cguidfix{statusdict begin mark version end {cvr}stopped{cleartomark 0}{exch pop}ifelse 2012 lt{dup findfont dup length dict begin {1 index/FID ne 2 index/UniqueID ne and {def} {pop pop} ifelse}forall currentdict end definefont pop }{pop}ifelse }bd /t_array 0 def /t_i 0 def /t_c 1 string def /x_proc{ exch t_array t_i get add exch moveto /t_i t_i 1 add store }bd /y_proc{ t_array t_i get add moveto /t_i t_i 1 add store }bd /xy_proc{ t_array t_i 2 copy 1 add get 3 1 roll get 4 -1 roll add 3 1 roll add moveto /t_i t_i 2 add store }bd /sop 0 def /cp_proc/x_proc ld /base_charpath { /t_array xs /t_i 0 def { t_c 0 3 -1 roll put currentpoint t_c cply sop cp_proc }forall /t_array 0 def }bd /sop/stroke ld /nop{}def /xsp/base_charpath ld /ysp{/cp_proc/y_proc ld base_charpath/cp_proc/x_proc ld}bd /xysp{/cp_proc/xy_proc ld base_charpath/cp_proc/x_proc ld}bd /xmp{/sop/nop ld /cp_proc/x_proc ld base_charpath/sop/stroke ld}bd /ymp{/sop/nop ld /cp_proc/y_proc ld base_charpath/sop/stroke ld}bd /xymp{/sop/nop ld /cp_proc/xy_proc ld base_charpath/sop/stroke ld}bd /refnt{ findfont dup length dict copy dup /Encoding 4 -1 roll put definefont pop }bd /renmfont{ findfont dup length dict copy definefont pop }bd L3? dup dup{save exch}if /Range 0 def /Domain 0 def /Encode 0 def /Decode 0 def /Size 0 def /DataSource 0 def /mIndex 0 def /nDomain 0 def /ival 0 def /val 0 def /nDomM1 0 def /sizem1 0 def /srcEncode 0 def /srcDecode 0 def /nRange 0 def /d0 0 def /r0 0 def /di 0 def /ri 0 def /a0 0 def /a1 0 def /r1 0 def /r2 0 def /dx 0 def /Nsteps 0 def /sh3tp 0 def /ymax 0 def /ymin 0 def /xmax 0 def /xmin 0 def /min { 2 copy gt {exch pop}{pop}ifelse }bd /max { 2 copy lt {exch pop}{pop}ifelse }bd /inter { 1 index sub 5 2 roll 1 index sub 3 1 roll sub 3 1 roll div mul add }bd /setupFunEvalN { begin /nDomM1 Domain length 2 idiv 1 sub store /sizem1[ 0 1 nDomM1 { Size exch get 1 sub }for ]store /srcEncode currentdict/Encode known { Encode }{ [ 0 1 nDomM1 { 0 sizem1 3 -1 roll get }for ] }ifelse store /srcDecode currentdict/Decode known {Decode}{Range}ifelse store /nRange Range length 2 idiv store end }bd /FunEvalN { begin nDomM1 -1 0 { 2 mul/mIndex xs Domain mIndex get max Domain mIndex 1 add get min Domain mIndex get Domain mIndex 1 add get srcEncode mIndex get srcEncode mIndex 1 add get inter round cvi 0 max sizem1 mIndex 2 idiv get min nDomM1 1 add 1 roll }for nDomM1 1 add array astore/val xs nDomM1 0 gt { 0 nDomM1 -1 0 { dup 0 gt { /mIndex xs val mIndex get 1 index add Size mIndex 1 sub get mul add }{ val exch get add }ifelse }for }{ val 0 get }ifelse nRange mul /ival xs 0 1 nRange 1 sub { dup 2 mul/mIndex xs ival add DataSource exch get 0 255 srcDecode mIndex 2 copy get 3 1 roll 1 add get inter Range mIndex get max Range mIndex 1 add get min }for end }bd /sh2 { /Coords load aload pop 3 index 3 index translate 3 -1 roll sub 3 1 roll exch sub 2 copy dup mul exch dup mul add sqrt dup scale atan rotate /Function load setupFunEvalN clippath {pathbbox}stopped {0 0 0 0}if newpath /ymax xs /xmax xs /ymin xs /xmin xs currentdict/Extend known { /Extend load 0 get { /Domain load 0 get /Function load FunEvalN sc xmin ymin xmin abs ymax ymin sub rectfill }if }if /dx/Function load/Size get 0 get 1 sub 1 exch div store gsave /di ymax ymin sub store /Function load dup /Domain get dup 0 get exch 1 get 2 copy exch sub dx mul exch { 1 index FunEvalN sc 0 ymin dx di rectfill dx 0 translate }for pop grestore currentdict/Extend known { /Extend load 1 get { /Domain load 1 get /Function load FunEvalN sc 1 ymin xmax 1 sub abs ymax ymin sub rectfill }if }if }bd /shp { 4 copy dup 0 gt{ 0 exch a1 a0 arc }{ pop 0 moveto }ifelse dup 0 gt{ 0 exch a0 a1 arcn }{ pop 0 lineto }ifelse fill dup 0 gt{ 0 exch a0 a1 arc }{ pop 0 moveto }ifelse dup 0 gt{ 0 exch a1 a0 arcn }{ pop 0 lineto }ifelse fill }bd /calcmaxs { xmin dup mul ymin dup mul add sqrt xmax dup mul ymin dup mul add sqrt xmin dup mul ymax dup mul add sqrt xmax dup mul ymax dup mul add sqrt max max max }bd /sh3 { /Coords load aload pop 5 index 5 index translate 3 -1 roll 6 -1 roll sub 3 -1 roll 5 -1 roll sub 2 copy dup mul exch dup mul add sqrt /dx xs 2 copy 0 ne exch 0 ne or { exch atan rotate }{ pop pop }ifelse /r2 xs /r1 xs /Function load dup/Size get 0 get 1 sub /Nsteps xs setupFunEvalN dx r2 add r1 lt{ 0 }{ dx r1 add r2 le { 1 }{ r1 r2 eq { 2 }{ 3 }ifelse }ifelse }ifelse /sh3tp xs clippath {pathbbox}stopped {0 0 0 0}if newpath /ymax xs /xmax xs /ymin xs /xmin xs dx dup mul r2 r1 sub dup mul sub dup 0 gt { sqrt r2 r1 sub atan /a0 exch 180 exch sub store /a1 a0 neg store }{ pop /a0 0 store /a1 360 store }ifelse currentdict/Extend known { /Extend load 0 get r1 0 gt and { /Domain load 0 get/Function load FunEvalN sc { { dx 0 r1 360 0 arcn xmin ymin moveto xmax ymin lineto xmax ymax lineto xmin ymax lineto xmin ymin lineto eofill } { r1 0 gt{0 0 r1 0 360 arc fill}if } { 0 r1 xmin abs r1 add neg r1 shp } { r2 r1 gt{ 0 r1 r1 neg r2 r1 sub div dx mul 0 shp }{ 0 r1 calcmaxs dup r2 add dx mul dx r1 r2 sub sub div neg exch 1 index abs exch sub shp }ifelse } }sh3tp get exec }if }if /d0 0 store /r0 r1 store /di dx Nsteps div store /ri r2 r1 sub Nsteps div store /Function load /Domain load dup 0 get exch 1 get 2 copy exch sub Nsteps div exch { 1 index FunEvalN sc d0 di add r0 ri add d0 r0 shp { d0 0 r0 a1 a0 arc d0 di add 0 r0 ri add a0 a1 arcn fill d0 0 r0 a0 a1 arc d0 di add 0 r0 ri add a1 a0 arcn fill }pop /d0 d0 di add store /r0 r0 ri add store }for pop currentdict/Extend known { /Extend load 1 get r2 0 gt and { /Domain load 1 get/Function load FunEvalN sc { { dx 0 r2 0 360 arc fill } { dx 0 r2 360 0 arcn xmin ymin moveto xmax ymin lineto xmax ymax lineto xmin ymax lineto xmin ymin lineto eofill } { xmax abs r1 add r1 dx r1 shp } { r2 r1 gt{ calcmaxs dup r1 add dx mul dx r2 r1 sub sub div exch 1 index exch sub dx r2 shp }{ r1 neg r2 r1 sub div dx mul 0 dx r2 shp }ifelse } } sh3tp get exec }if }if }bd /sh { begin /ShadingType load dup dup 2 eq exch 3 eq or { gsave newpath /ColorSpace load scs currentdict/BBox known { /BBox load aload pop 2 index sub 3 index 3 -1 roll exch sub exch rectclip }if 2 eq {sh2}{sh3}ifelse grestore }{ pop (DEBUG: shading type unimplemented\n)print flush }ifelse end }bd {restore}if not dup{save exch}if L3?{ /sh/shfill ld /csq/clipsave ld /csQ/cliprestore ld }if {restore}if end setpacking %%EndFile %%EndProlog %%BeginSetup %%EndSetup %%Page: 1 1 %%PageBoundingBox: 0 0 292 267 %%BeginPageSetup cg_md begin bp sdmtx %RBIBeginFontSubset: NTZMIF+Helvetica %!PS-TrueTypeFont-1.0000-0.0000-2 14 dict begin/FontName /NTZMIF+Helvetica def /PaintType 0 def /Encoding 256 array 0 1 255{1 index exch/.notdef put}for dup 33 /f put dup 34 /a put dup 35 /l put dup 36 /s put dup 37 /e put dup 38 /space put dup 39 /p put dup 40 /o put dup 41 /i put dup 42 /t put dup 43 /v put dup 44 /r put dup 45 /u put dup 46 /zero put dup 47 /period put dup 48 /five put dup 49 /one put readonly def 42/FontType resourcestatus{pop pop false}{true}ifelse %APLsfntBegin {currentfile 0(%APLsfntEnd\n)/SubFileDecode filter flushfile}if /FontType 42 def /FontMatrix matrix def /FontBBox[2048 -342 1 index div -914 2 index div 2036 3 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algorithms, among which IB1\(hyIG, an implementation of k\(hynearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision\(hytree approximation of IB1\(hyIG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. .SH OPTIONS .B \-a or .B \-a .RS determines the classification algorithm. Possible values are: .B 0 or .B IB the IB1 (k\(hyNN) algorithm (default) .B 1 or .B IGTREE a decision\(hytree\(hybased approximation of IB1 .B 2 or .B TRIBL a hybrid of IB1 and IGTREE .B 3 or .B IB2 an incremental editing version of IB1 .B 4 or .B TRIBL2 a non\(hyparameteric version of TRIBL .RE .B \-b n .RS number of lines used for bootstrapping (IB2 only) .RE .B \-B n .RS number of bins used for discretization of numeric feature values (Default B=20) .RE .BR \-\-Beam = .RS limit +v db output to n highest\(hyvote classes .RE .BR \-\-clones = .RS number f threads to use for parallel testing .RE .B \-c n .RS clipping frequency for prestoring MVDM matrices .RE .B +D .RS store distributions on all nodes (necessary for using +v db with IGTree, but wastes memory otherwise) .RE .B \-\-Diversify .RS rescale weight (see docs) .RE .B \-d val .RS weigh neighbors as function of their distance: Z : equal weights to all (default) ID : Inverse Distance IL : Inverse Linear ED:a : Exponential Decay with factor a (no whitespace!) ED:a:b : Exponential Decay with factor a and b (no whitespace!) .RE .B \-e n .RS estimate time until n patterns tested .RE .B \-f file .RS read from data file 'file' OR use filenames from 'file' for cross validation test .RE .B \-F format .RS assume the specified input format (Compact, C4.5, ARFF, Columns, Binary, Sparse ) .RE .B \-G normalization .RS normalize distributions (+v db option only) Supported normalizations are: .B Probability or .B 0 normalize between 0 and 1 .BR addFactor : or .BR 1 : add f to all possible targets, then normalize between 0 and 1 (default f=1.0). .B logProbability or .B 2 Add 1 to the target Weight, take the 10Log and then normalize between 0 and 1 .RE .B +H or .B \-H .RS write hashed trees (default +H) .RE .B \-i file .RS read the InstanceBase from 'file' (skips phase 1 & 2 ) .RE .B \-I file .RS dump the InstanceBase in 'file' .RE .B \-k n .RS search 'n' nearest neighbors (default n = 1) .RE .B \-L n .RS set value frequency threshold to back off from MVDM to Overlap at level n .RE .B \-l n .RS fixed feature value length (Compact format only) .RE .B \-m string .RS use feature metrics as specified in 'string': The format is : GlobalMetric:MetricRange:MetricRange e.g.: mO:N3:I2,5\-7 C: cosine distance. (Global only. numeric features implied) D: dot product. (Global only. numeric features implied) DC: Dice coefficient O: weighted overlap (default) E: Euclidian distance L: Levenshtein distance M: modified value difference J: Jeffrey divergence S: Jensen\(hyShannon divergence N: numeric values I: Ignore named values .RE .BR \-\-matrixin =file .RS read ValueDifference Matrices from file 'file' .RE .BR \-\-matrixout =file .RS store ValueDifference Matrices in 'file' .RE .B \-n file .RS create a C4.5\-style names file 'file' .RE .B \-M n .RS size of MaxBests Array .RE .B \-N n .RS number of features (default 2500) .RE .B \-o s .RS use s as output filename .RE .BR \-\-occurrences = .RS The input file contains occurrence counts (at the last position) value can be one of: .B train , .B test or .B both .RE .B \-O path .RS save output using 'path' .RE .B \-p n .RS show progress every n lines (default p = 100,000) .RE .B \-P path .RS read data using 'path' .RE .B \-q n .RS set TRIBL threshold at level n .RE .B \-R n .RS solve ties at random with seed n .RE .B \-s .RS use the exemplar weights from the input file .RE .B \-s0 .RS ignore the exemplar weights from the input file .RE .B \-T n .RS use feature n as the class label. (default: the last feature) .RE .B \-t file .RS test using 'file' .RE .B \-t leave_one_out .RS test with the leave\(hyone\(hyout testing regimen (IB1 only). you may add \-\-sloppy to speed up leave\(hyone\(hyout testing (but see docs) .RE .B \-t cross_validate .RS perform cross\(hyvalidation test (IB1 only) .RE .B \-t @file .RS test using files and options described in 'file' Supported options: d e F k m o p q R t u v w x % \- .RE .B \-\-Treeorder =value n .RS ordering of the Tree: DO: none GRO: using GainRatio IGO: using InformationGain 1/V: using 1/# of Values G/V: using GainRatio/# of Valuess I/V: using InfoGain/# of Valuess X2O: using X\(hysquare X/V: using X\(hysquare/# of Values SVO: using Shared Variance S/V: using Shared Variance/# of Values GxE: using GainRatio * SplitInfo IxE: using InformationGain * SplitInfo 1/S: using 1/SplitInfo .RE .B \-u file .RS read value\(hyclass probabilities from 'file' .RE .B \-U file .RS save value\(hyclass probabilities in 'file' .RE .B \-V .RS Show VERSION .RE .B +v level or .B \-v level .RS set or unset verbosity level, where level is: s: work silently o: show all options set b: show node/branch count and branching factor f: show calculated feature weights (default) p: show value difference matrices e: show exact matches as: show advanced statistics (memory consuming) cm: show confusion matrix (implies +vas) cs: show per\(hyclass statistics (implies +vas) cf: add confidence to output file (needs \-G) di: add distance to output file db: add distribution of best matched to output file md: add matching depth to output file. k: add a summary for all k neigbors to output file (sets \-x) n: add nearest neigbors to output file (sets \-x) You may combine levels using '+' e.g. +v p+db or \-v o+di .RE .B \-w n .RS weighting 0 or nw: no weighting 1 or gr: weigh using gain ratio (default) 2 or ig: weigh using information gain 3 or x2: weigh using the chi\(hysquare statistic 4 or sv: weigh using the shared variance statistic 5 or sd: weigh using standard deviation. (all features must be numeric) .RE .B \-w file .RS read weights from 'file' .RE .B \-w file:n .RS read weight n from 'file' .RE .B \-W file .RS calculate and save all weights in 'file' .RE .B +% or .B \-% .RS do or don't save test result (%) to file .RE .B +x or .B \-x .RS do or don't use the exact match shortcut (IB1 and IB2 only, default is \-x) .RE .BR \-X " file" .RS dump the InstanceBase as XML in 'file' .RE .SH BUGS possibly .SH AUTHORS Ko van der Sloot Timbl@uvt.nl Antal van den Bosch Timbl@uvt.nl .SH SEE ALSO .BR timblserver (1) LanguageMachines-timbl-642727d/include/000077500000000000000000000000001451477526200177335ustar00rootroot00000000000000LanguageMachines-timbl-642727d/include/Makefile.am000066400000000000000000000000771451477526200217730ustar00rootroot00000000000000# $Id$ # $URL$ AUTOMAKE_OPTIONS = foreign SUBDIRS = timbl LanguageMachines-timbl-642727d/include/timbl/000077500000000000000000000000001451477526200210425ustar00rootroot00000000000000LanguageMachines-timbl-642727d/include/timbl/.gitignore000066400000000000000000000000031451477526200230230ustar00rootroot00000000000000*~ LanguageMachines-timbl-642727d/include/timbl/BestArray.h000066400000000000000000000047541451477526200231210ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_BESTARRAY_H #define TIMBL_BESTARRAY_H #include #include #include #include "unicode/unistr.h" #include "libxml/parser.h" #include "ticcutils/json.hpp" #include "timbl/Targets.h" namespace Timbl { class neighborSet; class BestRec { friend std::ostream& operator<< ( std::ostream&, const BestRec * ); public: BestRec(); BestRec( const BestRec& ) = delete; // forbid copies BestRec& operator=( const BestRec& ) = delete; // forbid copies ~BestRec(); size_t totalBests() const { return aggregateDist.totalSize(); }; double bestDistance; ClassDistribution aggregateDist; std::vector bestDistributions; std::vector bestInstances; private: }; class BestArray { friend std::ostream& operator<< ( std::ostream&, const BestArray& ); public: BestArray(): _storeInstances(false), _showDi(false), _showDb(false), size(0), maxBests(0) {}; ~BestArray(); void init( unsigned int, unsigned int, bool, bool, bool ); double addResult( double, const ClassDistribution *, const icu::UnicodeString& ); void initNeighborSet( neighborSet& ) const; void addToNeighborSet( neighborSet& , size_t ) const; xmlNode *toXML() const; nlohmann::json to_JSON() const; nlohmann::json record_to_json( const BestRec *, size_t ) const; private: bool _storeInstances; bool _showDi; bool _showDb; unsigned int size; unsigned int maxBests; std::vector bestArray; }; } #endif // TIMBL_BESTARRAY_H LanguageMachines-timbl-642727d/include/timbl/Choppers.h000066400000000000000000000127471451477526200230110ustar00rootroot00000000000000#ifndef TIMBL_CHOPPERS_H #define TIMBL_CHOPPERS_H /* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include // for ostream #include // for vector #include "unicode/unistr.h" #include "unicode/ustream.h" #include "timbl/Types.h" namespace Timbl{ static const icu::UnicodeString DefaultSparseString = "0.0000E-17"; class Chopper { public: Chopper(): vSize(0) {}; virtual ~Chopper() {}; virtual bool chop( const icu::UnicodeString&, size_t ) = 0; const icu::UnicodeString& operator[]( int i ) const { return choppedInput[i]; } const icu::UnicodeString& getField( size_t i ) const { return choppedInput[i]; }; virtual double getExW() const { return -1; }; virtual int getOcc() const { return 1; }; virtual icu::UnicodeString getString() const = 0; void print( std::ostream& os ){ os << getString(); }; void swapTarget( size_t target_pos ){ icu::UnicodeString tmp = choppedInput[target_pos]; for ( size_t i = target_pos+1; i < vSize; ++i ){ choppedInput[i-1] = choppedInput[i]; } choppedInput[vSize-1] = tmp; } static Chopper *create( InputFormatType , bool, int, bool ); static InputFormatType getInputFormat( const icu::UnicodeString&, bool=false ); static size_t countFeatures( const icu::UnicodeString&, InputFormatType, int, bool=false ); protected: virtual void init( const icu::UnicodeString&, size_t, bool ); size_t vSize; icu::UnicodeString strippedInput; std::vector choppedInput; }; class ExChopper: public virtual Chopper { public: ExChopper(): Chopper(), exW(-1.0) {}; double getExW() const override { return exW; }; protected: void init( const icu::UnicodeString&, size_t, bool ) override; double exW; }; class OccChopper: public virtual Chopper { public: OccChopper(): Chopper(), occ(-1) {}; int getOcc() const override { return occ; }; protected: void init( const icu::UnicodeString&, size_t, bool ) override; int occ; }; class C45_Chopper : public virtual Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; }; class C45_ExChopper : public C45_Chopper, public ExChopper { }; class C45_OccChopper : public C45_Chopper, public OccChopper { }; class ARFF_Chopper : public C45_Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; }; class ARFF_ExChopper : public C45_ExChopper { }; class ARFF_OccChopper : public C45_OccChopper { }; class Bin_Chopper : public virtual Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; }; class Bin_ExChopper : public Bin_Chopper, public ExChopper { }; class Bin_OccChopper : public Bin_Chopper, public OccChopper { }; class Compact_Chopper : public virtual Chopper { public: explicit Compact_Chopper( int L ): fLen(L){}; bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; private: int fLen; Compact_Chopper(); }; class Compact_ExChopper : public Compact_Chopper, public ExChopper { public: explicit Compact_ExChopper( int L ): Compact_Chopper( L ){}; private: Compact_ExChopper(); }; class Compact_OccChopper : public Compact_Chopper, public OccChopper { public: explicit Compact_OccChopper( int L ): Compact_Chopper( L ){}; private: Compact_OccChopper(); }; class Columns_Chopper : public virtual Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; }; class Columns_ExChopper : public Columns_Chopper, public ExChopper { }; class Columns_OccChopper : public Columns_Chopper, public OccChopper { }; class Tabbed_Chopper : public virtual Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; }; class Tabbed_ExChopper : public Tabbed_Chopper, public ExChopper { }; class Tabbed_OccChopper : public Tabbed_Chopper, public OccChopper { }; class Sparse_Chopper : public virtual Chopper { public: bool chop( const icu::UnicodeString&, size_t ) override; icu::UnicodeString getString() const override; }; class Sparse_ExChopper : public Sparse_Chopper, public ExChopper { }; class Sparse_OccChopper : public Sparse_Chopper, public OccChopper { }; } #endif // TIMBL_CHOPPERS_H LanguageMachines-timbl-642727d/include/timbl/Common.h000066400000000000000000000035621451477526200224510ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_COMMON_H #define TIMBL_COMMON_H #include #include #include #include #include // for isspace #include // for string namespace Common { const double Epsilon = std::numeric_limits::epsilon(); // smallest x so that 1+x != 1 const int DEFAULT_MAX_FEATS = 2500; // default maximun number of Features std::string Version(); std::string VersionName(); std::string BuildInfo(); std::string VersionInfo( bool ); // obsolete inline int look_ahead( std::istream &is ){ while( is ){ int nc=is.peek(); if ( !isspace(nc) ) return nc; is.get(); } return -1; } inline void skip_spaces( std::istream &is ){ while( is ){ int nc=is.peek(); if ( !isspace(nc) ) return; is.get(); } } inline double Log2(double number){ // LOG base 2. if ( fabs(number) < Epsilon) return(0.0); return log2(number); } } #endif LanguageMachines-timbl-642727d/include/timbl/Features.h000066400000000000000000000164431451477526200230010ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_FEATURES_H #define TIMBL_FEATURES_H #include #include #include #include "timbl/MsgClass.h" #include "timbl/Matrices.h" #include "ticcutils/Unicode.h" namespace Hash { class UnicodeHash; } namespace Timbl { class ValueClass; class TargetValue; class Targets; class metricClass; class SparseValueProbClass { friend std::ostream& operator<< ( std::ostream&, SparseValueProbClass * ); public: typedef std::map< size_t, double > IDmaptype; typedef IDmaptype::const_iterator IDiterator; explicit SparseValueProbClass( size_t d ): dimension(d) {}; void Assign( const size_t i, const double d ) { vc_map[i] = d; }; void Clear() { vc_map.clear(); }; IDiterator begin() const { return vc_map.begin(); }; IDiterator end() const { return vc_map.end(); }; private: IDmaptype vc_map; size_t dimension; }; enum FeatVal_Stat { Unknown, Singleton, SingletonNumeric, NumericValue, NotNumeric }; class FeatureValue: public ValueClass { friend class Feature; friend class Feature_List; friend struct D_D; public: explicit FeatureValue( const icu::UnicodeString& ); FeatureValue( const icu::UnicodeString&, size_t ); ~FeatureValue(); void ReconstructDistribution( const ClassDistribution& vd ) { TargetDist.Merge( vd ); _frequency = TargetDist.totalSize(); }; bool isUnknown() const { return _index == 0; }; SparseValueProbClass *valueClassProb() const { return ValueClassProb; }; private: SparseValueProbClass *ValueClassProb; ClassDistribution TargetDist; }; class Feature: public MsgClass { friend class MBLClass; friend class Feature_List; public: explicit Feature( Hash::UnicodeHash *T ); ~Feature(); bool Ignore() const { return ignore; }; void Ignore( const bool val ){ ignore = val; }; bool setMetricType( const MetricType ); MetricType getMetricType() const; double Weight() const { return weight; }; void SetWeight( const double w ) { weight = w; }; double InfoGain() const { return info_gain; }; void InfoGain( const double w ){ info_gain = w; }; double SplitInfo() const { return split_info; }; void SplitInfo( const double w ){ split_info = w; }; double GainRatio() const { return gain_ratio; }; void GainRatio( const double w ){ gain_ratio = w; }; double ChiSquare() const { return chi_square; }; void ChiSquare( const double w ){ chi_square = w; }; double SharedVariance() const { return shared_variance; }; void SharedVariance( const double w ){ shared_variance = w; }; double StandardDeviation() const { return standard_deviation; }; void StandardDeviation( const double w ){ standard_deviation = w; }; double Min() const { return n_min; }; void Min( const double val ){ n_min = val; }; double Max() const { return n_max; }; void Max( const double val ){ n_max = val; }; double fvDistance( const FeatureValue *, const FeatureValue *, size_t=1 ) const; FeatureValue *add_value( const icu::UnicodeString&, TargetValue *, int=1 ); FeatureValue *add_value( size_t, TargetValue *, int=1 ); FeatureValue *Lookup( const icu::UnicodeString& ) const; bool decrement_value( FeatureValue *, const TargetValue * ); bool increment_value( FeatureValue *, const TargetValue * ); size_t EffectiveValues() const; size_t TotalValues() const; bool isNumerical() const; bool isStorableMetric() const; bool AllocSparseArrays( size_t ); void InitSparseArrays(); bool ArrayRead(){ return vcpb_read; }; bool matrixPresent( bool& ) const; size_t matrix_byte_size() const; bool store_matrix( int = 1 ); void clear_matrix(); bool fill_matrix( std::istream& ); void print_matrix( std::ostream&, bool = false ) const; void print_vc_pb_array( std::ostream& ) const; bool read_vc_pb_array( std::istream & ); FeatVal_Stat prepare_numeric_stats(); void Statistics( double, const Targets&, bool ); void NumStatistics( double, const Targets&, int, bool ); void ClipFreq( size_t f ){ matrix_clip_freq = f; }; size_t ClipFreq() const { return matrix_clip_freq; }; SparseSymetricMatrix *metric_matrix; private: Feature( const Feature& ); Feature& operator=( const Feature& ); Hash::UnicodeHash *TokenTree; metricClass *metric; bool ignore; bool numeric; bool vcpb_read; enum ps_stat{ ps_undef, ps_failed, ps_ok, ps_read }; enum ps_stat PrestoreStatus; MetricType Prestored_metric; void delete_matrix(); double entropy; double info_gain; double split_info; double gain_ratio; double chi_square; double shared_variance; double standard_deviation; size_t matrix_clip_freq; std::vector n_dot_j; std::vector n_i_dot; double n_min; double n_max; double weight; void Statistics( double ); void NumStatistics( std::vector&, double ); void ChiSquareStatistics( std::vector&, const Targets& ); void ChiSquareStatistics( const Targets& ); void SharedVarianceStatistics( const Targets&, int ); void StandardDeviationStatistics(); std::vector values_array; std::unordered_map< size_t, FeatureValue *> reverse_values; bool is_reference; }; class Feature_List: public MsgClass { friend class MBLClass; public: Feature_List(): _eff_feats(0), _num_of_feats(0), _num_of_num_feats(0), _feature_hash(0), _is_reference(false) { } explicit Feature_List( Hash::UnicodeHash *hash ): Feature_List() { _feature_hash = hash; } Feature_List &operator=( const Feature_List& ); ~Feature_List(); void init( size_t, const std::vector& ); Hash::UnicodeHash *hash() const { return _feature_hash; }; size_t effective_feats(){ return _eff_feats; }; Feature *operator[]( size_t i ) const { return feats[i]; }; void write_permutation( std::ostream & ) const; void calculate_permutation( const std::vector& ); size_t _eff_feats; size_t _num_of_feats; size_t _num_of_num_feats; std::vector feats; std::vector perm_feats; std::vector permutation; private: Hash::UnicodeHash *_feature_hash; bool _is_reference; }; } // namespace Timbl #endif // TIMBL_FEATURES_H LanguageMachines-timbl-642727d/include/timbl/GetOptClass.h000066400000000000000000000060361451477526200234100ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_GETOPTCLASS_H #define TIMBL_GETOPTCLASS_H #include #include namespace TiCC { class CL_Options; } namespace Timbl { class TimblExperiment; class GetOptClass: public MsgClass { public: explicit GetOptClass( const TiCC::CL_Options& ); GetOptClass& operator=( const GetOptClass& ) = delete; // forbid copies virtual ~GetOptClass(); GetOptClass *Clone( std::ostream * = 0 ) const; bool parse_options( const TiCC::CL_Options&, const int=0 ); void set_default_options( const int=0 ); bool definitive_options( TimblExperiment * ); AlgorithmType Algo() const { return local_algo; }; int MaxFeatures() const { return MaxFeats; }; VerbosityFlags getVerbosity() { return myVerbosity; }; private: GetOptClass( const GetOptClass& ); AlgorithmType local_algo; MetricType local_metric; OrdeningType local_order; WeightType local_weight; InputFormatType LocalInputFormat; DecayType local_decay; double local_decay_alfa; double local_decay_beta; normType local_normalisation; double local_norm_factor; int MaxFeats; int target_pos; int no_neigh; int mvd_limit; int estimate; int maxbests; int clip_freq; int clones; int BinSize; int BeamSize; int bootstrap_lines; int f_length; int local_progress; int seed; int threshold; int igThreshold; VerbosityFlags myVerbosity; bool opt_init; bool opt_changed; bool do_exact; bool do_hashed; bool min_present; bool N_present; bool keep_distributions; bool do_sample_weights; bool do_ignore_samples; bool do_ignore_samples_test; bool do_query; bool do_all_weights; bool do_sloppy_loo; bool do_silly; bool do_diversify; std::vectormetricsArray; std::ostream *parent_socket_os; std::string inPath; std::string outPath; int occIn; void Error( const std::string& ) const override; inline bool parse_range( std::string&, std::string::iterator&, MetricType ); inline bool parse_metrics( const std::string&, MetricType& ); }; } #endif LanguageMachines-timbl-642727d/include/timbl/IBtree.h000066400000000000000000000237111451477526200223710ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_IBTREE_H #define TIMBL_IBTREE_H #include #include "ticcutils/XMLtools.h" #include "timbl/MsgClass.h" //#define IBSTATS namespace Hash { class UnicodeHash; } namespace Timbl { class IB_InstanceBase; class IG_InstanceBase; class TRIBL_InstanceBase; class TRIBL2_InstanceBase; class Feature; class FeatureValue; class Instance; class Feature_List; class Targets; class TargetValue; class ClassDistribution; class WClassDistribution; class IBtree { friend class InstanceBase_base; friend class IB_InstanceBase; friend class IG_InstanceBase; friend class TRIBL_InstanceBase; friend class TRIBL2_InstanceBase; friend std::ostream &operator<<( std::ostream&, const IBtree& ); friend std::ostream &operator<<( std::ostream&, const IBtree * ); friend xmlNode *to_xml( IBtree *pnt ); friend int count_next( const IBtree * ); public: const TargetValue* targetValue() const { return TValue; }; private: FeatureValue *FValue; const TargetValue *TValue; ClassDistribution *TDistribution; IBtree *link; IBtree *next; IBtree(); explicit IBtree( FeatureValue * ); IBtree( const IBtree& ) = delete; // forbid copies IBtree& operator=( const IBtree& ) = delete; // forbid copies ~IBtree(); IBtree *Reduce( const TargetValue *, unsigned long&, long ); #ifdef IBSTATS static inline IBtree *add_feat_val( FeatureValue *, unsigned int&, IBtree *&, unsigned long& ); #else static inline IBtree *add_feat_val( FeatureValue *, IBtree *&, unsigned long& ); #endif inline ClassDistribution *sum_distributions( bool ); inline IBtree *make_unique( const TargetValue *, unsigned long& ); void cleanDistributions(); void re_assign_defaults( bool, bool ); void assign_defaults( bool, bool, size_t ); void redo_distributions(); void countBranches( unsigned int, std::vector&, std::vector& ); const ClassDistribution *exact_match( const Instance& ) const; protected: const IBtree *search_node( const FeatureValue * ) const; }; typedef std::unordered_map FI_map; class InstanceBase_base: public MsgClass { friend class IG_InstanceBase; friend class TRIBL_InstanceBase; friend class TRIBL2_InstanceBase; InstanceBase_base( const InstanceBase_base& ) = delete; // forbid copies InstanceBase_base& operator=( const InstanceBase_base& ) = delete; // forbid copies friend std::ostream& operator<<( std::ostream &os, const InstanceBase_base& ); friend std::ostream& operator<<( std::ostream &os, const InstanceBase_base * ); public: InstanceBase_base( size_t, unsigned long&, bool, bool ); virtual ~InstanceBase_base( void ); void AssignDefaults( void ); void RedoDistributions(); bool AddInstance( const Instance& ); void RemoveInstance( const Instance& ); void summarizeNodes( std::vector&, std::vector& ); virtual bool MergeSub( InstanceBase_base * ); const ClassDistribution *ExactMatch( const Instance& I ) const { return InstBase->exact_match( I ); }; virtual const ClassDistribution *InitGraphTest( std::vector&, const std::vector *, const size_t, const size_t ); virtual const ClassDistribution *NextGraphTest( std::vector&, size_t& ); unsigned long int GetDistSize( ) const { return NumOfTails; }; virtual const ClassDistribution *IG_test( const Instance& , size_t&, bool&, const TargetValue *& ); virtual IB_InstanceBase *TRIBL_test( const Instance& , size_t, const TargetValue *&, const ClassDistribution *&, size_t& ); virtual IB_InstanceBase *TRIBL2_test( const Instance& , const ClassDistribution *&, size_t& ); bool read_hash( std::istream&, Hash::UnicodeHash&, Hash::UnicodeHash& ) const; virtual InstanceBase_base *Copy() const = 0; virtual InstanceBase_base *clone() const = 0; void Save( std::ostream&, bool=false ); void Save( std::ostream&, const Hash::UnicodeHash&, const Hash::UnicodeHash&, bool=false ); void toXML( std::ostream& ); void printStatsTree( std::ostream&, unsigned int startLevel ); virtual bool ReadIB( std::istream&, Feature_List&, Targets&, int ); virtual bool ReadIB_hashed( std::istream&, Feature_List&, Targets&, int ); virtual void Prune( const TargetValue *, long = 0 ); virtual bool IsPruned() const { return false; }; void CleanPartition( bool ); unsigned long int GetSizeInfo( unsigned long int&, double & ) const; const ClassDistribution *TopDist() const { return TopDistribution; }; bool HasDistributions() const; const TargetValue *TopTarget( bool & ); bool PersistentD() const { return PersistentDistributions; }; unsigned long int nodeCount() const { return ibCount;} ; size_t depth() const { return Depth;} ; const IBtree *instBase() const { return InstBase; }; #ifdef IBSTATS std::vector mismatch; #endif protected: bool DefAss; bool DefaultsValid; bool Random; bool PersistentDistributions; int Version; ClassDistribution *TopDistribution; WClassDistribution *WTop; const TargetValue *TopT; FI_map fast_index; bool tiedTop; IBtree *InstBase; IBtree *LastInstBasePos; std::vector RestartSearch; std::vector SkipSearch; std::vector InstPath; unsigned long int& ibCount; size_t Depth; unsigned long int NumOfTails; IBtree *read_list( std::istream&, Feature_List&, Targets&, int ); IBtree *read_local( std::istream&, Feature_List&, Targets&, int ); IBtree *read_list_hashed( std::istream&, Feature_List&, Targets&, int ); IBtree *read_local_hashed( std::istream&, Feature_List&, Targets&, int ); void write_tree( std::ostream &os, const IBtree * ) const; void write_tree_hashed( std::ostream &os, const IBtree * ) const; bool read_IB( std::istream&, Feature_List& , Targets&, int ); bool read_IB_hashed( std::istream&, Feature_List& , Targets&, int ); void fill_index(); const IBtree *fast_search_node( const FeatureValue * ); }; class IB_InstanceBase: public InstanceBase_base { public: IB_InstanceBase( size_t size, unsigned long& cnt, bool rand ): InstanceBase_base( size, cnt, rand , false ), offSet(0), effFeat(0), testInst(0) {}; IB_InstanceBase *Copy() const override; IB_InstanceBase *clone() const override; const ClassDistribution *InitGraphTest( std::vector&, const std::vector *, const size_t, const size_t ) override; const ClassDistribution *NextGraphTest( std::vector&, size_t& ) override; private: size_t offSet; size_t effFeat; const std::vector *testInst; }; class IG_InstanceBase: public InstanceBase_base { public: IG_InstanceBase( size_t size, unsigned long& cnt, bool rand, bool pruned, bool keep_dists ): InstanceBase_base( size, cnt, rand, keep_dists ), Pruned( pruned ) {}; IG_InstanceBase *clone() const override; IG_InstanceBase *Copy() const override; void Prune( const TargetValue *, long = 0 ) override; void specialPrune( const TargetValue * ); bool IsPruned() const override { return Pruned; }; const ClassDistribution *IG_test( const Instance& , size_t&, bool&, const TargetValue *& ) override; bool ReadIB( std::istream&, Feature_List&, Targets&, int ) override; bool ReadIB_hashed( std::istream&, Feature_List&, Targets&, int ) override; bool MergeSub( InstanceBase_base * ) override; protected: bool Pruned; }; class TRIBL_InstanceBase: public InstanceBase_base { public: TRIBL_InstanceBase( size_t size, unsigned long& cnt, bool rand, bool keep_dists ): InstanceBase_base( size, cnt, rand, keep_dists ), Threshold(0) {}; TRIBL_InstanceBase *clone() const override; TRIBL_InstanceBase *Copy() const override; IB_InstanceBase *TRIBL_test( const Instance&, size_t, const TargetValue *&, const ClassDistribution *&, size_t& ) override; private: IB_InstanceBase *IBPartition( IBtree * ) const; void AssignDefaults( size_t ); size_t Threshold; }; class TRIBL2_InstanceBase: public InstanceBase_base { public: TRIBL2_InstanceBase( size_t size, unsigned long& cnt, bool rand, bool keep_dists ): InstanceBase_base( size, cnt, rand, keep_dists ) { }; TRIBL2_InstanceBase *clone() const override; TRIBL2_InstanceBase *Copy() const override; IB_InstanceBase *TRIBL2_test( const Instance& , const ClassDistribution *&, size_t& ) override; private: IB_InstanceBase *IBPartition( IBtree * ) const; }; } #endif LanguageMachines-timbl-642727d/include/timbl/Instance.h000066400000000000000000000037411451477526200227640ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_INSTANCE_H #define TIMBL_INSTANCE_H #include "ticcutils/Unicode.h" #include "timbl/Targets.h" #include "timbl/Features.h" namespace Hash { class UnicodeHash; } namespace Timbl { class TargetValue; class FeatureValue; class Instance { friend std::ostream& operator<<(std::ostream&, const Instance& ); friend std::ostream& operator<<(std::ostream&, const Instance * ); public: Instance(); explicit Instance( size_t s ): Instance() { Init( s ); }; Instance( const Instance& ) = delete; // inhibit copies Instance& operator=( const Instance& ) = delete; // inhibit copies ~Instance(); void Init( size_t ); void clear(); double ExemplarWeight() const { return sample_weight; }; void ExemplarWeight( const double sw ){ sample_weight = sw; }; int Occurrences() const { return occ; }; void Occurrences( const int o ) { occ = o; }; size_t size() const { return FV.size(); }; std::vector FV; TargetValue *TV; private: double sample_weight; // relative weight int occ; }; } #endif LanguageMachines-timbl-642727d/include/timbl/MBLClass.h000066400000000000000000000223421451477526200226160ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_MBLCLASS_H #define TIMBL_MBLCLASS_H #include "timbl/Instance.h" #include "timbl/BestArray.h" #include "timbl/neighborSet.h" #include "timbl/Options.h" typedef struct _xmlNode xmlNode; namespace Hash { class UnicodeHash; } namespace Timbl { using namespace Common; class InstanceBase_base; class TesterClass; class Chopper; class neighborSet; class MBLClass: public MsgClass { public: bool SetOption( const std::string& ); xmlNode *settingsToXml() const; nlohmann::json settings_to_JSON() const; bool ShowWeights( std::ostream& ) const; bool Verbosity( VerbosityFlags v ) const { return verbosity & v; }; void SetVerbosityFlag( VerbosityFlags v ) { verbosity |= v; }; void ResetVerbosityFlag( VerbosityFlags v ) { verbosity &= ~v; }; bool MBLInit() const { return MBL_init; }; void MBLInit( bool b ) { MBL_init = b; }; bool ExpInvalid( bool b = true ) const { if ( err_cnt > 0 ){ if ( b ){ InvalidMessage(); } return true; } else return false; }; WeightType CurrentWeighting() const { return Weighting; }; InputFormatType InputFormat() const { return input_format; }; bool connectToSocket( std::ostream *, bool = false ); std::ostream *sock_os; bool sock_is_json; mutable nlohmann::json last_error; int getOcc() const { return doOcc; }; protected: explicit MBLClass( const std::string& = "" ); void init_options_table( size_t ); MBLClass& operator=( const MBLClass& ); enum PhaseValue { TrainWords, LearnWords, TestWords, TrainLearnWords }; friend std::ostream& operator<< ( std::ostream&, const PhaseValue& ); enum IB_Stat { Invalid, Normal, Pruned }; bool writeArrays( std::ostream& ); bool readArrays( std::istream& ); bool writeMatrices( std::ostream& ) const; bool readMatrices( std::istream& ); bool writeWeights( std::ostream& ) const; bool readWeights( std::istream&, WeightType ); bool writeNamesFile( std::ostream& ) const; bool ShowOptions( std::ostream& ) const; bool ShowSettings( std::ostream& ) const; void writePermutation( std::ostream& ) const; void LearningInfo( std::ostream& ); virtual ~MBLClass(); void Initialize( size_t ); bool PutInstanceBase( std::ostream& ) const; VerbosityFlags get_verbosity() const { return verbosity; }; void set_verbosity( VerbosityFlags v ) { verbosity = v; }; const Instance *chopped_to_instance( PhaseValue ); bool Chop( const icu::UnicodeString& ); bool HideInstance( const Instance& ); bool UnHideInstance( const Instance& ); icu::UnicodeString formatInstance( const std::vector&, const std::vector&, size_t, size_t ) const; bool setInputFormat( const InputFormatType ); size_t countFeatures( const icu::UnicodeString&, const InputFormatType ) const; InputFormatType getInputFormat( const icu::UnicodeString& ) const; size_t examineData( const std::string& ); void time_stamp( const char *, int =-1 ) const; void TestInstance( const Instance& , InstanceBase_base * = NULL, size_t = 0 ); icu::UnicodeString get_org_input( ) const; const ClassDistribution *ExactMatch( const Instance& ) const; void fillNeighborSet( neighborSet& ) const; void addToNeighborSet( neighborSet& ns, size_t n ) const; double getBestDistance() const; WClassDistribution *getBestDistribution( unsigned int =0 ); IB_Stat IBStatus() const; bool get_ranges( const std::string& ); size_t get_IB_Info( std::istream&, bool&, int&, bool&, std::string& ); size_t NumOfFeatures() const { return features._num_of_feats; }; size_t targetPos() const { return target_pos; }; size_t NumNumFeatures() const { return features._num_of_num_feats; }; size_t EffectiveFeatures() const { return features._eff_feats; }; void IBInfo( std::ostream& os ) const; void MatrixInfo( std::ostream& ) const; int RandomSeed() const { return random_seed; }; void Info( const std::string& ) const override; void Warning( const std::string& ) const override; void Error( const std::string& ) const override; void FatalError( const std::string& ) const override; size_t MaxFeats() const { return MaxFeatures; }; int Progress() const { return progress; }; void Progress( int p ){ progress = p; }; std::string extract_limited_m( size_t ); Targets targets; Feature_List features; InstanceBase_base *InstanceBase; std::ostream *mylog; std::ostream *myerr; size_t TRIBL_offset() const { return tribl_offset; }; unsigned int igOffset() const { return igThreshold; }; unsigned int IB2_offset() const { return ib2_offset; }; void IB2_offset( unsigned int n ) { ib2_offset = n; }; bool Do_Sloppy_LOO() const { return do_sloppy_loo; }; bool doSamples() const { return do_sample_weighting && !do_ignore_samples; }; bool Do_Exact() const { return do_exact_match; }; void Do_Exact( bool b ) { do_exact_match = b; }; void InitWeights(); void diverseWeights(); bool KeepDistributions() const { return keep_distributions; }; void KeepDistributions( bool f ){ keep_distributions = f; }; bool IsClone() const { return is_copy; }; void default_order(); void set_order(void); void calculatePermutation( const std::vector& ); void calculate_fv_entropy( bool ); bool recalculate_stats( Feature_List&, std::vector&, bool ); OptionTableClass Options; PhaseValue runningPhase; WeightType Weighting; metricClass *GlobalMetric; OrdeningType TreeOrder; size_t num_of_neighbors; bool dynamic_neighbors; DecayType decay_flag; std::string exp_name; Instance CurrInst; BestArray bestArray; size_t MaxBests; neighborSet nSet; decayStruct *decay; int beamSize; normType normalisation; double norm_factor; bool is_copy; bool is_synced; unsigned int ib2_offset; int random_seed; double decay_alfa; double decay_beta; bool MBL_init; bool tableFilled; MetricType globalMetricOption; bool do_diversify; bool initProbabilityArrays( bool ); void calculatePrestored(); void initDecay(); void initTesters(); Chopper *ChopInput; int F_length; private: size_t MaxFeatures; std::vector UserOptions; InputFormatType input_format; VerbosityFlags verbosity; size_t target_pos; int clip_factor; int Bin_Size; int progress; size_t tribl_offset; unsigned igThreshold; int mvd_threshold; bool do_sloppy_loo; bool do_exact_match; bool do_silly_testing; bool hashed_trees; bool need_all_weights; bool do_sample_weighting; bool do_ignore_samples; bool no_samples_test; bool keep_distributions; double DBEntropy; TesterClass *tester; int doOcc; bool chopExamples() const { return do_sample_weighting && !( runningPhase == TestWords && no_samples_test ); } bool chopOcc() const { switch( runningPhase ) { case TrainWords: case LearnWords: case TrainLearnWords: return doOcc == 1 || doOcc == 3; case TestWords: return doOcc > 1; default: return false; } }; void InvalidMessage() const ; void do_numeric_statistics( ); void test_instance( const Instance& , InstanceBase_base * = NULL, size_t = 0 ); void test_instance_sim( const Instance& , InstanceBase_base * = NULL, size_t = 0 ); void test_instance_ex( const Instance&, InstanceBase_base * = NULL, size_t = 0 ); bool allocate_arrays(); double RelativeWeight( unsigned int ) const; void writePermSpecial(std::ostream&) const; bool read_the_vals( std::istream& ); MBLClass( const MBLClass& ); }; inline std::ostream& operator<< ( std::ostream& os, const MBLClass::PhaseValue& ph ){ switch( ph ){ case MBLClass::TrainWords: os << "TrainWords"; break; case MBLClass::LearnWords: os << "LearnWords"; break; case MBLClass::TestWords: os << "TestWords"; break; case MBLClass::TrainLearnWords: os << "TrainlearnWords"; break; default: os << "unknown phase"; } return os; } bool empty_line( const icu::UnicodeString& , const InputFormatType ); } #endif // TIMBL_MBLCLASS_H LanguageMachines-timbl-642727d/include/timbl/Makefile.am000066400000000000000000000004301451477526200230730ustar00rootroot00000000000000# $Id$ # $URL$ pkginclude_HEADERS = Common.h GetOptClass.h IBtree.h Matrices.h \ Features.h Targets.h Instance.h \ MBLClass.h MsgClass.h BestArray.h \ StringOps.h TimblAPI.h Options.h \ TimblExperiment.h Types.h neighborSet.h Statistics.h \ Choppers.h Testers.h Metrics.h LanguageMachines-timbl-642727d/include/timbl/Matrices.h000066400000000000000000000067301451477526200227700ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_MATRICES_H #define TIMBL_MATRICES_H template class SparseSymetricMatrix; template std::ostream& operator << (std::ostream&, const SparseSymetricMatrix& ); template class SparseSymetricMatrix { typedef std::map< Class, double > CDmap; typedef std::map< Class, CDmap > CCDmap; friend std::ostream& operator << <> ( std::ostream&, const SparseSymetricMatrix& ); public: void Clear() { my_mat.clear(); }; void Assign( Class i, Class j, double d ){ if ( i == j ) return; if ( i second.find(i); if ( it2 != it1->second.end() ){ return it2->second; } } } else { typename CCDmap::const_iterator it1 = my_mat.find(i); if ( it1 != my_mat.end() ){ typename CDmap::const_iterator it2 = it1->second.find(j); if ( it2 != it1->second.end() ){ return it2->second; } } } return 0.0; }; unsigned int NumBytes(void) const{ unsigned int tot = sizeof(std::map); typename CCDmap::const_iterator it1 = my_mat.begin(); while ( it1 != my_mat.end() ){ tot += sizeof(CDmap); typename CDmap::const_iterator it2 = it1->second.begin(); while ( it2 != it1->second.end() ){ tot += sizeof(double); ++it2; } ++it1; } return tot; }; SparseSymetricMatrix *copy(void) const{ SparseSymetricMatrix *res = new SparseSymetricMatrix(); typename CCDmap::const_iterator it1 = my_mat.begin(); while ( it1 != my_mat.end() ){ typename CDmap::const_iterator it2 = it1->second.begin(); while ( it2 != it1->second.end() ){ res->my_mat[it1->first][it2->first] = it2->second; ++it2; } ++it1; } return res; } private: CCDmap my_mat; }; template inline std::ostream& operator << (std::ostream& os, const SparseSymetricMatrix& m ){ typename SparseSymetricMatrix::CCDmap::const_iterator it1 = m.my_mat.begin(); while ( it1 != m.my_mat.end() ){ typename SparseSymetricMatrix::CDmap::const_iterator it2 = it1->second.begin(); while ( it2 != it1->second.end() ){ os << "[" << it1->first << ",\t" << it2->first << "] " << it2->second << std::endl; ++it2; } ++it1; } return os; } #endif // TIMBL_MATRICES_H LanguageMachines-timbl-642727d/include/timbl/Metrics.h000066400000000000000000000134161451477526200226260ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_METRICS_H #define TIMBL_METRICS_H #include #include namespace Timbl{ class FeatureValue; class metricClass { public: explicit metricClass( MetricType m ): _type(m){}; virtual ~metricClass() {}; MetricType type() const { return _type; }; virtual bool isSimilarityMetric() const = 0; virtual bool isNumerical() const = 0; virtual bool isStorable() const = 0; virtual double distance( const FeatureValue *, const FeatureValue *, size_t=1, double = 1.0 ) const = 0; virtual double get_max_similarity() const { throw std::logic_error( "get_max_similarity not implemented for " + TiCC::toString( _type ) ); } private: MetricType _type; }; metricClass *getMetricClass( MetricType ); class distanceMetricClass: public metricClass { public: explicit distanceMetricClass( MetricType m ): metricClass(m){}; virtual ~distanceMetricClass() {}; bool isSimilarityMetric() const override { return false; }; }; class OverlapMetric: public distanceMetricClass { public: OverlapMetric(): distanceMetricClass( Overlap ){}; bool isNumerical() const override { return false; }; bool isStorable() const override { return false; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class NumericMetricClass: public distanceMetricClass { public: explicit NumericMetricClass( MetricType m ): distanceMetricClass( m ){}; virtual ~NumericMetricClass() {}; bool isNumerical() const override { return true; }; bool isStorable() const override { return false; }; }; class NumericMetric: public NumericMetricClass { public: NumericMetric(): NumericMetricClass( Numeric ){}; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class EuclideanMetric: public NumericMetricClass { public: EuclideanMetric(): NumericMetricClass( Euclidean ){}; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class ValueDiffMetric: public distanceMetricClass { public: ValueDiffMetric(): distanceMetricClass( ValueDiff ){}; bool isNumerical() const override { return false; }; bool isStorable() const override { return true; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class DiceMetric: public distanceMetricClass { public: DiceMetric(): distanceMetricClass( Dice ){}; bool isNumerical() const override { return false; }; bool isStorable() const override { return true; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class JeffreyMetric: public distanceMetricClass { public: JeffreyMetric(): distanceMetricClass( JeffreyDiv ){}; bool isNumerical() const override{ return false; }; bool isStorable() const override { return true; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class JSMetric: public distanceMetricClass { public: JSMetric(): distanceMetricClass( JSDiv ){}; bool isNumerical() const override { return false; }; bool isStorable() const override { return true; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class LevenshteinMetric: public distanceMetricClass { public: LevenshteinMetric(): distanceMetricClass( Levenshtein ){}; bool isNumerical() const override { return false; }; bool isStorable() const override { return true; }; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; }; class similarityMetricClass: public metricClass { public: explicit similarityMetricClass( MetricType m ): metricClass( m ){}; virtual ~similarityMetricClass() {}; bool isSimilarityMetric() const override { return true; }; bool isNumerical() const override { return true; }; bool isStorable() const override { return false; }; }; class CosineMetric: public similarityMetricClass { public: CosineMetric(): similarityMetricClass( Cosine ){}; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; double get_max_similarity() const override { return 1.0; }; }; class DotProductMetric: public similarityMetricClass { public: DotProductMetric(): similarityMetricClass( DotProduct ){}; double distance( const FeatureValue *, const FeatureValue *, size_t, double ) const override; double get_max_similarity() const override { return std::numeric_limits::max(); }; }; } #endif // TIMBL_METRICS_H LanguageMachines-timbl-642727d/include/timbl/MsgClass.h000066400000000000000000000025061451477526200227320ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_MSGCLASS_H #define TIMBL_MSGCLASS_H namespace Timbl { class MsgClass { public: MsgClass(): err_cnt(0) {}; virtual ~MsgClass() {}; virtual void Info( const std::string& ) const; virtual void Warning( const std::string& ) const ; virtual void Error( const std::string& ) const ; virtual void FatalError( const std::string& ) const ; mutable int err_cnt; }; } #endif LanguageMachines-timbl-642727d/include/timbl/Options.h000066400000000000000000000351651451477526200226600ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_OPTIONS_H #define TIMBL_OPTIONS_H #include #include #include #include #include "ticcutils/StringOps.h" namespace Timbl { class OptionClass { friend class OptionTableClass; public: explicit OptionClass( const std::string& n ): Name( n ) {}; virtual ~OptionClass() {}; virtual bool set_option( const std::string& ) = 0; virtual std::ostream& show_opt( std::ostream & ) const = 0; virtual std::ostream& show_full( std::ostream & ) const = 0; protected: const std::string Name; private: OptionClass(const OptionClass&); OptionClass& operator = (const OptionClass&); }; template class OptionClassT: public OptionClass { public: OptionClassT( const std::string& n, Type *tp, Type t ):OptionClass(n), Content(tp) { *Content = t; }; virtual bool set_option( const std::string& line ) override { Type T; bool result = TiCC::stringTo( line, T ); if ( result ) { *Content = T; } return result; }; virtual std::ostream& show_opt( std::ostream &os ) const override { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : " << TiCC::toString(*Content); return os; }; virtual std::ostream& show_full( std::ostream &os ) const override { return show_opt( os ); }; private: Type *Content; OptionClassT(const OptionClassT&); OptionClassT& operator = (const OptionClassT&); }; typedef OptionClassT BoolOption; template <> inline std::ostream& OptionClassT::show_opt( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os.setf( std::ios::boolalpha ); os << Name << " : " << *Content; return os; } template <> inline std::ostream& OptionClassT::show_full( std::ostream &os ) const{ os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os.setf( std::ios::boolalpha ); os << Name << " : false or true [" << *Content << "]"; return os; } typedef OptionClassT VerbosityOption; template <> inline std::ostream& OptionClassT::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : " << TiCC::toString(*Content,true); return os; } typedef OptionClassT InputFormatOption; template <> inline std::ostream& InputFormatOption::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; InputFormatType i = UnknownInputFormat; for ( ++i; i < MaxInputFormat-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT MetricOption; template <> inline std::ostream& OptionClassT::show_full( std::ostream &os )const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; MetricType i = UnknownMetric; for ( ++i; i < MaxMetric-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT AlgorithmOption; template <> inline std::ostream& OptionClassT::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; AlgorithmType i = Unknown_a; for ( ++i; i < Max_a-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT DecayOption; template <> inline std::ostream& DecayOption::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; DecayType i = UnknownDecay; for ( ++i; i < MaxDecay-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT SmoothOption; template <> inline std::ostream& SmoothOption::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; SmoothingType i = UnknownSmoothing; for ( ++i; i < MaxSmoothing-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT WeightOption; template <> inline std::ostream& OptionClassT::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; WeightType i = Unknown_w; for ( ++i; i < Max_w-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT OrdeningOption; template <> inline std::ostream& OptionClassT::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; OrdeningType i = UnknownOrdening; for ( ++i; i < MaxOrdening-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } typedef OptionClassT NormalisationOption; template <> inline std::ostream& NormalisationOption::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : {"; normType i = unknownNorm; for ( ++i; i < maxNorm-1; ++i ){ os << TiCC::toString(i) << ", "; } os << TiCC::toString(i) << "}, [ " << TiCC::toString(*Content) << "]"; return os; } // // Array of options types // template class OptionArrayClass: public OptionClass { public: OptionArrayClass( const std::string& n, std::vector& ta, const size_t size ): OptionClass( n ), TA(ta), Size(size ){}; protected: std::vector& TA; size_t Size; private: OptionArrayClass(const OptionArrayClass&); OptionArrayClass& operator = (const OptionArrayClass&); }; class MetricArrayOption: public OptionArrayClass { public: MetricArrayOption( const std::string& n, std::vector& mp, MetricType& m, size_t s ): OptionArrayClass( n, mp, s ), def(m){ TA.resize(s,m); }; bool set_option( const std::string& line ) override; std::ostream& show_opt( std::ostream &os ) const override; std::ostream& show_full( std::ostream &os ) const override; private: const MetricType& def; }; inline bool MetricArrayOption::set_option( const std::string& line ){ MetricType m = UnknownMetric; size_t i=0; std::vector res; bool result = TiCC::split_at( line, res, "=" ) == 2 && TiCC::stringTo( res[1], m ) && TiCC::stringTo( res[0], i, 0, Size ); if ( result ){ TA[i] = m; } return result; } inline std::ostream& MetricArrayOption::show_opt( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : "; for ( size_t i=0; i < Size; i++ ){ if ( TA[i] != def ){ os << i << ":" << TiCC::toString(TA[i]) << ", "; } } return os; } inline std::ostream& MetricArrayOption::show_full( std::ostream &os ) const { os.width(20); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : comma separated metricvalues, ["; bool first = true; for ( size_t i=0; i < Size; i++ ){ if ( TA[i] != def ){ if ( !first ){ os << ","; } else { first = false; } os << i << ":" << TiCC::toString(TA[i]); } } os << "]"; return os; } // // Limited Type, with min and maxVal // template class OptionClassLT: public OptionClass { public: OptionClassLT( const std::string& n, Type *tp, Type t, Type Min, Type Max ):OptionClass(n), Content( tp), minVal( Min ), maxVal( Max ) { *Content = t; }; virtual bool set_option( const std::string& line ) override { Type T; bool result = TiCC::stringTo( line, T, minVal, maxVal ); if ( result ) { *Content = T; } return result; }; virtual std::ostream& show_opt( std::ostream &os ) const override { os.width(20); os.setf( std::ios::showpoint ); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : " << *Content; return os; }; virtual std::ostream& show_full( std::ostream &os ) const override { os.width(20); os.setf( std::ios::showpoint ); os.setf( std::ios::left, std::ios::adjustfield ); os << Name << " : { " << minVal << " - " << maxVal << "}, [" << *Content << "]"; return os; }; private: Type *Content; Type minVal; Type maxVal; OptionClassLT(const OptionClassLT&); OptionClassLT& operator = (const OptionClassLT&); }; typedef OptionClassLT IntegerOption; typedef OptionClassLT UnsignedOption; typedef OptionClassLT SizeOption; typedef OptionClassLT RealOption; enum SetOptRes { Opt_OK, Opt_Frozen, Opt_Unknown, Opt_Ill_Val}; struct ci_less { // case-independent (ci) compare_less binary function struct nocase_compare { bool operator() (const unsigned char& c1, const unsigned char& c2) const { return tolower (c1) < tolower (c2); } }; bool operator() (const std::string & s1, const std::string & s2) const { return std::lexicographical_compare (s1.begin(), s1.end(), // source range s2.begin(), s2.end(), // dest range nocase_compare()); // comparison } }; class OptionTableClass { public: OptionTableClass(): table_frozen(false){}; OptionTableClass( const OptionTableClass& ) = delete; // forbid copies OptionTableClass& operator=( const OptionTableClass& ) = delete; // forbid copies ~OptionTableClass(){ for ( const auto& it : global_table ){ delete it.second; } for ( const auto& it : runtime_table ){ delete it.second; } }; bool Add( OptionClass *opt ){ // std::cerr << "Table add: " << opt->Name << std::endl; runtime_table[opt->Name] = opt; return true; }; void FreezeTable(void); bool TableFrozen(void){ return table_frozen; }; SetOptRes SetOption( const std::string& ); void Show_Settings( std::ostream& ) const; void Show_Options( std::ostream& ) const; private: bool table_frozen; std::map runtime_table; std::map global_table; inline OptionClass *look_up( const std::string&, bool & ); }; inline void OptionTableClass::FreezeTable(void){ global_table = runtime_table; runtime_table.clear(); table_frozen = true; } inline void OptionTableClass::Show_Settings( std::ostream& os ) const{ for ( const auto& it: global_table ){ it.second->show_opt( os ) << std::endl; } for ( const auto& it: runtime_table ){ it.second->show_opt( os ) << std::endl; } } inline void OptionTableClass::Show_Options( std::ostream& os ) const { for ( const auto& it: global_table ){ it.second->show_full( os ) << std::endl; } for ( const auto& it: runtime_table ){ it.second->show_full( os ) << std::endl; } } inline void split_line( const std::string& line, std::string& name, std::string& value ){ std::vector results; size_t i = TiCC::split_at( line, results, ":" ); switch (i){ case 2: name = TiCC::trim(results[0]); // fallthrough case 1: value = TiCC::trim(results[1]); default: break; } } inline OptionClass *OptionTableClass::look_up( const std::string& option_name, bool &runtime ){ // std::cerr << "lookup: " << option_name << std::endl; const auto itr = runtime_table.find( option_name ); if ( itr != runtime_table.end() ){ runtime = true; // std::cerr << "FOUND: runtime= " << option_name << std::endl; return itr->second; } else { const auto itg = global_table.find( option_name ); if ( itg != global_table.end() ){ runtime = table_frozen; // std::cerr << "FOUND global= " << option_name << std::endl; return itg->second; } } return NULL; } inline SetOptRes OptionTableClass::SetOption( const std::string& line ){ SetOptRes result = Opt_OK; bool runtime = false; std::string option_name; std::string value; split_line( line, option_name, value ); OptionClass *option = look_up( option_name, runtime ); if ( option ){ if ( !runtime ){ result = Opt_Frozen; // may not be changed at this stage } else if ( !option->set_option( value ) ){ result = Opt_Ill_Val; // illegal value } } else { result = Opt_Unknown; // What the hell ??? } return result; } } #endif LanguageMachines-timbl-642727d/include/timbl/Statistics.h000066400000000000000000000050311451477526200233440ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_STATISTICS_H #define TIMBL_STATISTICS_H #include "timbl/MsgClass.h" namespace Timbl { class Targets; class TargetValue; class ConfusionMatrix: public MsgClass { size_t size; std::vector > mat; public: explicit ConfusionMatrix( size_t ); virtual ~ConfusionMatrix(); void Increment( const TargetValue*, const TargetValue* ); void Print( std::ostream&, const Targets& ) const; void FScore( std::ostream&, const Targets&, bool ) const; void merge( const ConfusionMatrix * ); }; class StatisticsClass { public: StatisticsClass(): _data(0), _skipped(0), _correct(0), _tieOk(0), _tieFalse(0), _exact(0) {}; void clear() { _data =0; _skipped = 0; _correct = 0; _tieOk = 0; _tieFalse = 0; _exact = 0; }; void addLine() { ++_data; } void addSkipped() { ++_skipped; } void addCorrect() { ++_correct; } void addTieCorrect() { ++_tieOk; } void addTieFailure() { ++_tieFalse; } void addExact() { ++_exact; } unsigned int dataLines() const { return _data; }; unsigned int skippedLines() const { return _skipped; }; unsigned int totalLines() const { return _data + _skipped; }; unsigned int testedCorrect() const { return _correct; }; unsigned int tiedCorrect() const { return _tieOk; }; unsigned int tiedFailure() const { return _tieFalse; }; unsigned int exactMatches() const { return _exact; }; void merge( const StatisticsClass& ); private: unsigned int _data; unsigned int _skipped; unsigned int _correct; unsigned int _tieOk; unsigned int _tieFalse; unsigned int _exact; }; } #endif LanguageMachines-timbl-642727d/include/timbl/StringOps.h000066400000000000000000000027341451477526200231510ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_STRING_OPS_H #define TIMBL_STRING_OPS_H #include #include #include #include #include #include "unicode/unistr.h" namespace Timbl { bool compare_nocase( const std::string&, const std::string& ); bool compare_nocase_n( const std::string&, const std::string& ); icu::UnicodeString StrToCode( const icu::UnicodeString&, bool=true ); icu::UnicodeString CodeToStr( const icu::UnicodeString& ); std::string correct_path( const std::string&, const std::string&, bool = true ); } #endif LanguageMachines-timbl-642727d/include/timbl/Targets.h000066400000000000000000000166501451477526200226340ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_TARGETS_H #define TIMBL_TARGETS_H #include #include #include #include "unicode/unistr.h" #include "timbl/MsgClass.h" #include "ticcutils/Unicode.h" namespace Hash { class UnicodeHash; } namespace Timbl { class ValueClass { public: ValueClass( const icu::UnicodeString& n, size_t i ): _name( n ), _index( i ), _frequency( 1 ) {}; ValueClass( const ValueClass& ) = delete; // forbid copies ValueClass& operator=( const ValueClass& ) = delete; // forbid copies virtual ~ValueClass() {}; void ValFreq( size_t f ){ _frequency = f; }; void IncValFreq( int f ){ _frequency += f; }; size_t ValFreq( ) const { return _frequency; }; void incr_val_freq(){ ++_frequency; }; void decr_val_freq(){ --_frequency; }; size_t Index() const { return _index; }; const icu::UnicodeString& name() const { return _name; }; const std::string name_string() const { return TiCC::UnicodeToUTF8(_name);}; // temporary for backward compatability const icu::UnicodeString& name_u() const { return _name; }; // HACK const std::string Name() const { return TiCC::UnicodeToUTF8(_name); }; // HACK // REMOVE ^^^^ friend std::ostream& operator<<( std::ostream& os, ValueClass const *vc ); protected: const icu::UnicodeString& _name; size_t _index; size_t _frequency; }; class TargetValue: public ValueClass { public: TargetValue( const icu::UnicodeString&, size_t ); }; class Targets: public MsgClass { friend class MBLClass; friend class WClassDistribution; friend class ConfusionMatrix; public: explicit Targets( Hash::UnicodeHash *T ): target_hash( T ), is_reference(false) {}; ~Targets(); Targets& operator=( const Targets& ); void init(); TargetValue *add_value( const icu::UnicodeString&, int freq = 1 ); TargetValue *add_value( size_t, int freq = 1 ); TargetValue *Lookup( const icu::UnicodeString& ) const; TargetValue *ReverseLookup( size_t ) const; bool decrement_value( TargetValue * ); bool increment_value( TargetValue * ); TargetValue *MajorityClass() const; size_t EffectiveValues() const; size_t TotalValues() const; size_t num_of_values() const { return values_array.size(); }; Hash::UnicodeHash *hash() const { return target_hash; }; private: Hash::UnicodeHash *target_hash; std::vector values_array; std::unordered_map< size_t, TargetValue *> reverse_values; bool is_reference; }; class Vfield{ friend class ClassDistribution; friend class WClassDistribution; friend std::ostream& operator<<( std::ostream&, const Vfield& ); friend std::ostream& operator<<( std::ostream&, const Vfield * ); public: Vfield( const TargetValue *val, int freq, double w ): value(val), frequency(freq), weight(w) {}; Vfield( const Vfield& in ): value(in.value), frequency(in.frequency), weight(in.weight) {}; Vfield& operator=( const Vfield& ) = delete; // forbid copies ~Vfield(){}; std::ostream& put( std::ostream& ) const; const TargetValue *Value() const { return value; }; void Value( const TargetValue *t ){ value = t; }; size_t Freq() const { return frequency; }; void IncFreq( int inc=1 ) { frequency += inc; }; void AddFreq( int f ) { frequency += f; weight += f; }; void DecFreq() { frequency -= 1; }; double Weight() const { return weight; }; void SetWeight( double w ){ weight = w; }; size_t Index(); protected: const TargetValue *value; size_t frequency; double weight; private: }; class WClassDistribution; class ClassDistribution{ friend std::ostream& operator<<( std::ostream&, const ClassDistribution& ); friend std::ostream& operator<<( std::ostream&, const ClassDistribution * ); friend class WClassDistribution; public: typedef std::map VDlist; typedef VDlist::const_iterator dist_iterator; ClassDistribution( ): total_items(0) {}; ClassDistribution( const ClassDistribution& ); virtual ~ClassDistribution(){ clear(); }; size_t totalSize() const{ return total_items; }; size_t size() const{ return distribution.size(); }; bool empty() const{ return distribution.empty(); }; void clear(); dist_iterator begin() const { return distribution.begin(); }; dist_iterator end() const { return distribution.end(); }; virtual const TargetValue* BestTarget( bool&, bool = false ) const; void Merge( const ClassDistribution& ); virtual void SetFreq( const TargetValue *, int, double=1.0 ); virtual bool IncFreq( const TargetValue *, size_t, double=1.0 ); void DecFreq( const TargetValue * ); static ClassDistribution *read_distribution( std::istream&, Targets&, bool ); static ClassDistribution *read_distribution_hashed( std::istream&, Targets&, bool ); const std::string DistToString() const; const std::string DistToStringW( int ) const; double Confidence( const TargetValue * ) const; virtual const std::string SaveHashed() const; virtual const std::string Save() const; bool ZeroDist() const { return total_items == 0; }; double Entropy() const; ClassDistribution *to_VD_Copy( ) const; virtual WClassDistribution *to_WVD_Copy() const; protected: virtual void DistToString( std::string&, double=0 ) const; virtual void DistToStringWW( std::string&, int ) const; const TargetValue* BestTargetN( bool &, bool = false ) const; const TargetValue* BestTargetW( bool &, bool = false ) const; virtual ClassDistribution *clone( ) const { return new ClassDistribution(); }; size_t total_items; VDlist distribution; }; class WClassDistribution: public ClassDistribution { public: WClassDistribution(): ClassDistribution() {}; const TargetValue* BestTarget( bool &, bool = false ) const override; void SetFreq( const TargetValue *, int, double ) override; bool IncFreq( const TargetValue *, size_t, double ) override; WClassDistribution *to_WVD_Copy( ) const override; const std::string SaveHashed() const override; const std::string Save() const override; void Normalize(); void Normalize_1( double, const Targets& ); void Normalize_2(); void MergeW( const ClassDistribution&, double ); private: void DistToString( std::string&, double=0 ) const override; void DistToStringWW( std::string&, int ) const override; WClassDistribution *clone() const override { return new WClassDistribution; }; }; } #endif // TINBL_TARGETS_H LanguageMachines-timbl-642727d/include/timbl/Testers.h000066400000000000000000000072331451477526200226510ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_TESTERS_H #define TIMBL_TESTERS_H namespace Timbl{ class metricTestFunction { public: virtual ~metricTestFunction(){}; virtual double test( const FeatureValue *, const FeatureValue *, const Feature * ) const = 0; }; class overlapTestFunction: public metricTestFunction { public: double test( const FeatureValue *FV, const FeatureValue *G, const Feature *Feat ) const override; }; class valueDiffTestFunction: public metricTestFunction { public: explicit valueDiffTestFunction( int t ): metricTestFunction(), threshold( t ) {}; double test( const FeatureValue *, const FeatureValue *, const Feature * ) const override; protected: int threshold; }; class TesterClass { public: TesterClass( const Feature_List& ); TesterClass( const TesterClass& ) = delete; // inhibit copies TesterClass& operator=( const TesterClass& ) = delete; // inhibit copies virtual ~TesterClass(){}; void init( const Instance&, size_t, size_t ); virtual size_t test( const std::vector&, size_t, double ) = 0; virtual double getDistance( size_t ) const = 0; protected: size_t _size; size_t effSize; size_t offSet; const std::vector *FV; const std::vector &features; const std::vector &permutation; std::vector permFeatures; std::vector distances; private: }; class DistanceTester: public TesterClass { public: DistanceTester( const Feature_List&, int ); ~DistanceTester(); double getDistance( size_t ) const override; size_t test( const std::vector&, size_t, double ) override; private: std::vector metricTest; }; class SimilarityTester: public TesterClass { public: explicit SimilarityTester( const Feature_List& pf ): TesterClass( pf ){}; ~SimilarityTester() {}; virtual size_t test( const std::vector&, size_t, double ) override = 0; protected: private: }; class CosineTester: public SimilarityTester { public: explicit CosineTester( const Feature_List& pf ): SimilarityTester( pf ){}; double getDistance( size_t ) const override; size_t test( const std::vector&, size_t, double ) override; private: }; class DotProductTester: public SimilarityTester { public: explicit DotProductTester( const Feature_List& pf ): SimilarityTester( pf ){}; double getDistance( size_t ) const override; size_t test( const std::vector&, size_t, double ) override; private: }; TesterClass* getTester( MetricType, const Feature_List&, int ); } #endif // TIMBL_TESTERS_H LanguageMachines-timbl-642727d/include/timbl/TimblAPI.h000066400000000000000000000135661451477526200226270ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_API_H #define TIMBL_API_H #include #include #include "ticcutils/CommandLine.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/neighborSet.h" #include "timbl/TimblExperiment.h" namespace Timbl{ inline std::string Version() { return Common::Version(); } inline std::string VersionName() { return Common::VersionName(); } inline std::string BuildInfo() { return Common::BuildInfo(); } enum Algorithm { UNKNOWN_ALG, IB1, IB2, IGTREE, TRIBL, TRIBL2, LOO, CV }; enum Weighting { UNKNOWN_W, UD, NW, GR, IG, X2, SV, SD }; class TimblAPI { friend class TimblExperiment; public: TimblAPI( const TiCC::CL_Options&, const std::string& = "" ); TimblAPI( const std::string&, const std::string& = "" ); TimblAPI( const TimblAPI& ); ~TimblAPI(); bool isValid() const; bool Valid() const; TimblExperiment *grabAndDisconnectExp(){ TimblExperiment *res = 0; if ( Valid() ){ res = pimpl; pimpl = 0; } return res; } bool Prepare( const std::string& = "" ); bool CVprepare( const std::string& = "", Weighting = GR, const std::string& = "" ); bool Learn( const std::string& = "" ); bool Increment_u( const icu::UnicodeString& ); bool Increment( const std::string& ); bool Decrement_u( const icu::UnicodeString& ); bool Decrement( const std::string& ); bool Expand( const std::string& ); bool Remove( const std::string& ); bool Test( const std::string& = "", const std::string& = "", const std::string& = "" ); bool NS_Test( const std::string& = "", const std::string& = "" ); const TargetValue *Classify( const std::string& ); const TargetValue *Classify( const std::string&, const ClassDistribution *& ); const TargetValue *Classify( const std::string&, double& ); const TargetValue *Classify( const std::string&, const ClassDistribution *&, double& ); const TargetValue *Classify( const icu::UnicodeString& ); const TargetValue *Classify( const icu::UnicodeString&, const ClassDistribution *& ); const TargetValue *Classify( const icu::UnicodeString&, double& ); const TargetValue *Classify( const icu::UnicodeString&, const ClassDistribution *&, double& ); const neighborSet *classifyNS( const icu::UnicodeString& ); bool classifyNS( const icu::UnicodeString&, neighborSet& ); bool classifyNS( const std::string& in, neighborSet& st ){ return classifyNS( TiCC::UnicodeFromUTF8(in), st ); } const Instance *lastHandledInstance() const; const Targets& myTargets() const; bool Classify( const std::string&, std::string& ); bool Classify( const std::string&, std::string&, double& ); bool Classify( const std::string&, std::string&, std::string&, double& ); bool Classify( const icu::UnicodeString&, icu::UnicodeString& ); bool ShowBestNeighbors( std::ostream& ) const; size_t matchDepth() const; double confidence() const; bool matchedAtLeaf() const; std::string ExpName() const; static std::string VersionInfo( bool = false ); bool SaveWeights( const std::string& = "" ); bool GetWeights( const std::string& = "", Weighting = UNKNOWN_W ); double GetAccuracy(); Weighting CurrentWeighting() const; Weighting GetCurrentWeights( std::vector& ) const; bool WriteInstanceBase( const std::string& = "" ); bool WriteInstanceBaseXml( const std::string& = "" ); bool WriteInstanceBaseLevels( const std::string& = "", unsigned int=0 ); bool GetInstanceBase( const std::string& = "" ); bool WriteArrays( const std::string& = "" ); bool WriteMatrices( const std::string& = "" ); bool GetArrays( const std::string& = "" ); bool GetMatrices( const std::string& = "" ); bool WriteNamesFile( const std::string& = "" ); bool ShowWeights( std::ostream& ) const; bool ShowOptions( std::ostream& ) const; bool ShowSettings( std::ostream& ) const; bool ShowIBInfo( std::ostream& ) const; bool ShowStatistics( std::ostream& ) const; bool SetOptions( const std::string& ); bool SetIndirectOptions( const TiCC::CL_Options& ); bool SetThreads( int c ); std::string extract_limited_m( int ) const; Algorithm Algo() const; InputFormatType getInputFormat() const; size_t NumOfFeatures() const; static size_t Default_Max_Feats(); bool initExperiment(); private: TimblAPI(); TimblAPI& operator=( const TimblAPI& ); // forbid copies TimblExperiment *pimpl; bool i_am_fine; }; const std::string to_string( const Algorithm ); const std::string to_string( const Weighting ); bool string_to( const std::string&, Algorithm& ); bool string_to( const std::string&, Weighting& ); typedef ClassDistribution ValueDistribution; // for backward compatability typedef WClassDistribution WValueDistribution; // for backward compatability } #endif // TIMBL_API_H LanguageMachines-timbl-642727d/include/timbl/TimblExperiment.h000066400000000000000000000375561451477526200243430ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_EXPERIMENT_H #define TIMBL_EXPERIMENT_H #include #include #include #include #include "ticcutils/XMLtools.h" #include "timbl/Statistics.h" #include "timbl/MsgClass.h" #include "timbl/MBLClass.h" namespace TiCC { class CL_Options; } namespace Timbl { extern const std::string timbl_short_opts; extern const std::string timbl_long_opts; extern const std::string timbl_serv_short_opts; extern const std::string timbl_indirect_opts; class TimblAPI; class ConfusionMatrix; class GetOptClass; class TargetValue; class Instance; class resultStore: public MsgClass { public: resultStore(): rawDist(0), dist(0), disposable(false), isTop(false), beam(0), norm(unknownNorm), factor(0.0), best_target(0), targets(0) {}; resultStore( const resultStore& ) = delete; // inhibit copies resultStore& operator=( const resultStore& ) = delete; // inhibit copies ~resultStore(); bool reset( int, normType, double, const Targets& ); void clear(); void addConstant( const ClassDistribution *, const TargetValue * ); void addTop( const ClassDistribution *, const TargetValue * ); void addDisposable( ClassDistribution *, const TargetValue * ); const WClassDistribution *getResultDist(); std::string getResult(); void prepare(); void normalize(); double confidence() const { if ( dist ){ return dist->Confidence( best_target ); } else { return 0.0; } }; double confidence( const TargetValue* tv ) const { if ( dist ){ return dist->Confidence( tv ); } else { return 0.0; } }; private: const ClassDistribution *rawDist; WClassDistribution *dist; bool disposable; bool isTop; int beam; normType norm; double factor; const TargetValue *best_target; const Targets *targets; std::string topCache; std::string resultCache; }; class fCmp { public: bool operator()( const FeatureValue* F, const FeatureValue* G ) const{ return F->Index() > G->Index(); } }; typedef std::map, fCmp> fileIndex; typedef std::map fileDoubleIndex; std::ostream& operator<< ( std::ostream&, const fileIndex& ); std::ostream& operator<< ( std::ostream&, const fileDoubleIndex& ); class threadData; class TimblExperiment: public MBLClass { friend class TimblAPI; friend class threadData; friend class threadBlock; public: virtual ~TimblExperiment(); virtual TimblExperiment *clone() const = 0; TimblExperiment& operator=( const TimblExperiment& ); virtual bool Prepare( const std::string& = "", bool = true, bool = false ); virtual bool CVprepare( const std::string& = "", WeightType = GR_w, const std::string& = "" ); virtual bool Increment( const icu::UnicodeString& ){ FatalError( "Increment" ); return false; }; virtual bool Decrement( const icu::UnicodeString& ){ FatalError( "Decrement" ); return false; }; virtual bool Expand( const std::string& ); virtual bool Remove( const std::string& ){ FatalError( "Remove" ); return false;}; virtual bool Test( const std::string&, const std::string& ); virtual bool NS_Test( const std::string&, const std::string& ); virtual void InitInstanceBase() = 0; virtual bool ReadInstanceBase( const std::string& ); virtual bool WriteInstanceBase( const std::string& ); bool chopLine( const icu::UnicodeString& ); bool WriteInstanceBaseXml( const std::string& ); bool WriteInstanceBaseLevels( const std::string&, unsigned int ); bool WriteNamesFile( const std::string& ) const; virtual bool Learn( const std::string& = "", bool = true ); int Estimate() const { return estimate; }; void Estimate( int e ){ estimate = e; }; int Clones() const { return numOfThreads; }; void Clones( int cl ) { numOfThreads = cl; }; void setOutPath( const std::string& s ){ outPath = s; }; TimblExperiment *CreateClient( int ) const; TimblExperiment *splitChild() const; bool SetOptions( int, const char *[] ); bool SetOptions( const std::string& ); bool SetOptions( const TiCC::CL_Options& ); bool IndirectOptions( const TiCC::CL_Options& ); bool ConfirmOptions(); GetOptClass *getOptParams() const { return OptParams; }; void setOptParams( GetOptClass *op ) { OptParams = op; }; bool WriteArrays( const std::string& ); bool GetArrays( const std::string& ); bool WriteMatrices( const std::string& ); bool GetMatrices( const std::string& ); bool SaveWeights( const std::string& ); bool GetWeights( const std::string&, WeightType ); bool GetCurrentWeights( std::vector& ); xmlNode *weightsToXML(); nlohmann::json weights_to_JSON(); bool ShowOptions( std::ostream& ); bool ShowSettings( std::ostream& ); xmlNode *settingsToXML(); nlohmann::json settings_to_JSON(); bool showBestNeighbors( std::ostream& ) const; xmlNode *bestNeighborsToXML() const; nlohmann::json best_neighbors_to_JSON() const; bool showStatistics( std::ostream& ) const; void showInputFormat( std::ostream& ) const; const std::string& ExpName() const { return exp_name; }; void setExpName( const std::string& s ) { exp_name = s; }; bool Classify( const std::string& , std::string&, std::string&, double& ); bool Classify( const icu::UnicodeString& , icu::UnicodeString& ); bool Classify( const icu::UnicodeString&, icu::UnicodeString&, icu::UnicodeString&, double& ); size_t matchDepth() const { return match_depth; }; double confidence() const { return bestResult.confidence(); }; bool matchedAtLeaf() const { return last_leaf; }; nlohmann::json classify_to_JSON( const std::string& ); nlohmann::json classify_to_JSON( const std::vector& ); virtual AlgorithmType Algorithm() const = 0; const TargetValue *Classify( const icu::UnicodeString& Line, const ClassDistribution *& db, double& di ){ const TargetValue *res = classifyString( Line, di ); if ( res ){ normalizeResult(); db = bestResult.getResultDist(); } return res; } const TargetValue *Classify( const icu::UnicodeString& Line ){ double dum_d; return classifyString( Line, dum_d ); } const TargetValue *Classify( const icu::UnicodeString& Line, const ClassDistribution *& db ){ double dum_d; const TargetValue *res = classifyString( Line, dum_d ); if ( res ){ normalizeResult(); db = bestResult.getResultDist(); } return res; } const TargetValue *Classify( const icu::UnicodeString& Line, double& di ){ return classifyString( Line, di ); } const neighborSet *NB_Classify( const icu::UnicodeString& ); virtual void initExperiment( bool = false ); protected: TimblExperiment( const AlgorithmType, const std::string& = "" ); virtual bool checkLine( const icu::UnicodeString& ); virtual bool ClassicLearn( const std::string& = "", bool = true ); virtual const TargetValue *LocalClassify( const Instance&, double&, bool& ); virtual bool GetInstanceBase( std::istream& ) = 0; virtual void showTestingInfo( std::ostream& ); virtual bool checkTestFile(); bool learnFromFileIndex( const fileIndex&, std::istream& ); bool initTestFiles( const std::string&, const std::string& ); void show_results( std::ostream&, const double, const std::string&, const TargetValue *, const double ) ; void testInstance( const Instance&, InstanceBase_base *, size_t = 0 ); void normalizeResult(); const neighborSet *LocalClassify( const Instance& ); bool nextLine( std::istream &, icu::UnicodeString&, int& ); bool nextLine( std::istream &, icu::UnicodeString& ); bool skipARFFHeader( std::istream & ); void show_progress( std::ostream& os, time_t, unsigned int ); bool createPercFile( const std::string& = "" ) const; void show_speed_summary( std::ostream& os, const timeval& ) const; void show_ignore_info( std::ostream& os ) const; void show_weight_info( std::ostream& os ) const; void show_metric_info( std::ostream& os ) const; double sum_remaining_weights( size_t ) const; bool build_file_index( const std::string&, fileIndex& ); bool build_file_multi_index( const std::string&, fileDoubleIndex& ); bool Initialized; GetOptClass *OptParams; AlgorithmType algorithm; std::string CurrentDataFile; std::string WFileName; std::string outPath; std::string testStreamName; std::string outStreamName; std::ifstream testStream; std::ofstream outStream; unsigned long ibCount; ConfusionMatrix *confusionInfo; std::vector instances; StatisticsClass stats; resultStore bestResult; size_t match_depth; bool last_leaf; private: TimblExperiment( const TimblExperiment& ); int estimate; int numOfThreads; const TargetValue *classifyString( const icu::UnicodeString&, double& ); }; class IB1_Experiment: public TimblExperiment { public: IB1_Experiment( const size_t N = DEFAULT_MAX_FEATS, const std::string& s= "", const bool init = true ); bool Increment( const icu::UnicodeString& ) override; bool Decrement( const icu::UnicodeString& ) override; bool Remove( const std::string& ) override; AlgorithmType Algorithm() const override { return IB1_a; }; void InitInstanceBase() override; bool NS_Test( const std::string&, const std::string& ) override; protected: TimblExperiment *clone() const override { return new IB1_Experiment( MaxFeats(), "", false ); }; bool checkTestFile() override; bool checkLine( const icu::UnicodeString& ) override; bool Increment( const Instance& I ) { return UnHideInstance( I ); }; bool Decrement( const Instance& I ) { return HideInstance( I ); }; private: bool GetInstanceBase( std::istream& ) override; }; class IB2_Experiment: public IB1_Experiment { public: IB2_Experiment( size_t N, const std::string& s="" ): IB1_Experiment( N, s ) { IB2_offset( 0 ); }; bool Prepare( const std::string& = "", bool=false, bool=false ) override; bool Expand( const std::string& ) override; bool Remove( const std::string& ) override; bool Learn( const std::string& = "", bool = false ) override; AlgorithmType Algorithm() const override { return IB2_a; }; protected: bool checkTestFile() override; TimblExperiment *clone() const override { return new IB2_Experiment( MaxFeats() ); }; bool Expand_N( const std::string& ); bool show_learn_progress( std::ostream& os, time_t, size_t ); }; class LOO_Experiment: public IB1_Experiment { public: LOO_Experiment( int N, const std::string& s = "" ): IB1_Experiment( N, s ) { }; bool Test( const std::string&, const std::string& ) override; AlgorithmType Algorithm() const override { return LOO_a; }; bool ReadInstanceBase( const std::string& ) override; void initExperiment( bool = false ) override; protected: bool checkTestFile() override; void showTestingInfo( std::ostream& ) override; }; class CV_Experiment: public IB1_Experiment { public: CV_Experiment( int N = DEFAULT_MAX_FEATS, const std::string& s = "" ): IB1_Experiment( N, s ), CV_fileW(Unknown_w) { }; CV_Experiment( const CV_Experiment& ) = delete; // forbid copies CV_Experiment& operator=( const CV_Experiment& ) = delete; // forbid copies bool Learn( const std::string& = "", bool = true ) override; bool Prepare( const std::string& = "", bool=true, bool=false ) override; bool Test( const std::string&, const std::string& ) override; bool CVprepare( const std::string& = "", WeightType = GR_w, const std::string& = "" ) override; AlgorithmType Algorithm() const override { return CV_a; }; protected: bool checkTestFile() override; bool get_file_names( const std::string& ); private: std::vector FileNames; std::string CV_WfileName; std::string CV_PfileName; WeightType CV_fileW; }; class TRIBL_Experiment: public TimblExperiment { public: TRIBL_Experiment( const size_t N = DEFAULT_MAX_FEATS, const std::string& s = "", const bool init = true ): TimblExperiment( TRIBL_a, s ) { if ( init ) init_options_table( N ); }; void InitInstanceBase() override; protected: TimblExperiment *clone() const override { return new TRIBL_Experiment( MaxFeats(), "", false ); }; void showTestingInfo( std::ostream& ) override; bool checkTestFile() override; AlgorithmType Algorithm() const override { return TRIBL_a; }; bool checkLine( const icu::UnicodeString& ) override; const TargetValue *LocalClassify( const Instance&, double&, bool& ) override; private: bool GetInstanceBase( std::istream& ) override; }; class TRIBL2_Experiment: public TimblExperiment { public: TRIBL2_Experiment( const size_t N = DEFAULT_MAX_FEATS, const std::string& s = "", const bool init = true ): TimblExperiment( TRIBL2_a, s ) { if ( init ) init_options_table( N ); }; void InitInstanceBase() override; protected: TimblExperiment *clone() const override { return new TRIBL2_Experiment( MaxFeats(), "", false ); }; bool checkTestFile() override; AlgorithmType Algorithm() const override { return TRIBL2_a; }; bool checkLine( const icu::UnicodeString& ) override; const TargetValue *LocalClassify( const Instance& , double&, bool& ) override; private: bool GetInstanceBase( std::istream& ) override; }; class IG_Experiment: public TimblExperiment { public: IG_Experiment( const size_t N = DEFAULT_MAX_FEATS, const std::string& s = "", const bool init = true ): TimblExperiment( IGTREE_a, s ) { if ( init ) init_options_table( N ); }; AlgorithmType Algorithm() const override { return IGTREE_a; }; void InitInstanceBase() override; bool WriteInstanceBase( const std::string& ) override; bool ReadInstanceBase( const std::string& ) override; void initExperiment( bool = false ) override; bool Expand( const std::string& ) override { FatalError( "Expand not supported for IGTree" ); return false; }; protected: TimblExperiment *clone() const override{ return new IG_Experiment( MaxFeats(), "", false ); }; bool ClassicLearn( const std::string& = "", bool = true ) override; bool checkTestFile() override; void showTestingInfo( std::ostream& ) override; bool checkLine( const icu::UnicodeString& ) override; bool sanityCheck() const; const TargetValue *LocalClassify( const Instance&, double&, bool& ) override; private: bool GetInstanceBase( std::istream& ) override; }; } #endif // TIMBL_EXPERIMENT_H LanguageMachines-timbl-642727d/include/timbl/Types.h000066400000000000000000000305171451477526200223250ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_TYPES_H #define TIMBL_TYPES_H #include #include #include #include #include #include #include #include #include "ticcutils/StringOps.h" #include "timbl/StringOps.h" namespace Timbl { enum InputFormatType { UnknownInputFormat, Compact, C4_5, Columns, Tabbed, ARFF, SparseBin, Sparse, MaxInputFormat }; inline InputFormatType& operator++( InputFormatType &I ){ return I = ( MaxInputFormat == I ) ? UnknownInputFormat : InputFormatType(I+1); } enum WeightType { Unknown_w, No_w, GR_w, IG_w, X2_w, SV_w, SD_w, UserDefined_w, Max_w }; inline WeightType& operator++( WeightType &W ){ return W = ( Max_w == W ) ? Unknown_w : WeightType(W+1); } enum AlgorithmType { Unknown_a, IB1_a, IB2_a, IGTREE_a, TRIBL_a, TRIBL2_a, LOO_a, CV_a, Max_a }; inline AlgorithmType& operator++( AlgorithmType &W ){ return W = ( Max_a == W ) ? Unknown_a : AlgorithmType(W+1); } enum MetricType { UnknownMetric, Ignore, Numeric, DotProduct, Cosine, Overlap, Levenshtein, Dice, ValueDiff, JeffreyDiv, JSDiv, Euclidean, MaxMetric }; inline MetricType& operator++( MetricType &W ){ return W = ( MaxMetric == W ) ? UnknownMetric : MetricType(W+1); } enum OrdeningType { UnknownOrdening, DataFile, NoOrder, GROrder, IGOrder, OneoverFeature, OneoverSplitInfo, GRoverFeature, IGoverFeature, GREntropyOrder, IGEntropyOrder, X2Order, SVOrder, SDOrder, X2overFeature, SVoverFeature, SDoverFeature, MaxOrdening }; inline OrdeningType& operator++( OrdeningType &W ){ return W = ( MaxOrdening == W ) ? UnknownOrdening : OrdeningType(W+1); } enum VerbosityFlags { NO_VERB=0, SILENT=1, OPTIONS=2, FEAT_W=4, VD_MATRIX=8, EXACT=16, DISTANCE=32, DISTRIB=64, NEAR_N=128, ADVANCED_STATS=256, CONF_MATRIX=512, CLASS_STATS=1024, CLIENTDEBUG=2048, ALL_K=4096, MATCH_DEPTH=8192, BRANCHING=16384, CONFIDENCE=32768, MAX_VERB }; inline VerbosityFlags operator~( VerbosityFlags V ){ return (VerbosityFlags)( ~(int)V ); } inline VerbosityFlags operator|( VerbosityFlags V1, VerbosityFlags V2 ){ return (VerbosityFlags)( (int)V1|(int)V2 ); } inline VerbosityFlags& operator|= ( VerbosityFlags& f, VerbosityFlags g ){ f = (f | g); return f; } inline VerbosityFlags operator& ( VerbosityFlags f, VerbosityFlags g ){ return (VerbosityFlags)((int)f & (int)g ); } inline VerbosityFlags& operator&= ( VerbosityFlags& f, VerbosityFlags g ){ f = (f & g); return f; } enum OptType { UnknownOpt, StringT, IntegerT, BooleanT, VerbosityT, IFormatT, AlgoT, MetricT, WeightT, OrdeningT, MaxOpt }; inline OptType& operator++( OptType &W ){ return W = ( MaxOpt == W ) ? UnknownOpt : OptType(W+1); } enum DecayType { UnknownDecay, Zero, InvDist, InvLinear, ExpDecay, MaxDecay }; inline DecayType& operator++( DecayType &W ){ return W = ( MaxDecay == W ) ? UnknownDecay : DecayType(W+1); } enum SmoothingType { UnknownSmoothing, Default, Lidstone, MaxSmoothing }; inline SmoothingType& operator++( SmoothingType &W ){ return W = ( MaxSmoothing == W ) ? UnknownSmoothing : SmoothingType(W+1); } enum normType { unknownNorm, noNorm, probabilityNorm, addFactorNorm, logProbNorm, maxNorm }; inline normType& operator++( normType &W ){ return W = ( maxNorm == W ) ? noNorm : normType(W+1); } extern const std::string DecayName[][2]; extern const std::string OrdeningName[][2]; extern const std::string WeightName[][2]; extern const std::string MetricName[][2]; extern const std::string InputFormatName[][2]; extern const std::string AlgorithmName[][2]; extern const std::string SmoothingName[][2]; extern const std::string VerbosityName[][2]; extern const std::string NormalisationName[][2]; WeightType charToWeig( char ); AlgorithmType charToAlg( char ); normType charToNorm( char ); } namespace TiCC { // // We create specializations of TiCC templates // the must be placed in the TiCC namespace (isn't it?) // using namespace Timbl; template <> inline DecayType stringTo( const std::string& str ) { DecayType d = UnknownDecay; for ( ++d; d < MaxDecay; ++d ){ if ( compare_nocase( str, DecayName[d][0] ) || compare_nocase( str, DecayName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to decayType failed" ) ); } template <> inline std::string toString( const DecayType& W, bool b ){ if ( b ){ return DecayName[W][1]; } else { return DecayName[W][0]; } } template <> inline OrdeningType stringTo( const std::string& str ) { OrdeningType d = UnknownOrdening; for ( ++d; d < MaxOrdening; ++d ){ if ( compare_nocase( str, OrdeningName[d][0] ) || compare_nocase( str, OrdeningName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to ordeningType failed" ) ); } template <> inline std::string toString( const OrdeningType& W, bool b ){ return OrdeningName[W][(b?1:0)]; } template <> inline MetricType stringTo( const std::string& str ) { MetricType d = UnknownMetric; for ( ++d; d < MaxMetric; ++d ){ if ( compare_nocase( str, MetricName[d][0] ) || compare_nocase( str, MetricName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to metricType failed" ) ); } template <> inline std::string toString( const MetricType& W, bool b ){ if ( b ) return MetricName[W][1]; else return MetricName[W][0]; } template <> inline WeightType stringTo( const std::string& str ) { WeightType w = Unknown_w; if ( str.length() == 1 && isdigit(str[0]) ){ w = charToWeig( str[0] ); } if ( w != Unknown_w ) return w; for ( ++w; w < Max_w; ++w ){ if ( compare_nocase( str, WeightName[w][0] ) || compare_nocase( str, WeightName[w][1] ) ){ return w; } } throw( std::runtime_error( "conversion from string '" + str + "' to weightType failed" ) ); } template <> inline std::string toString( const WeightType& W, bool b ){ if ( b ){ return WeightName[W][1]; } else { return WeightName[W][0]; } } template <> inline AlgorithmType stringTo( const std::string& str ) { AlgorithmType a = Unknown_a; if ( str.length() == 1 && isdigit(str[0]) ){ a = charToAlg( str[0] ); } if ( a != Unknown_a ) return a; for ( ++a; a < Max_a; ++a ){ if ( compare_nocase( str, AlgorithmName[a][0] ) || compare_nocase( str, AlgorithmName[a][1] ) ){ return a; } } throw( std::runtime_error( "conversion from string '" + str + "' to algorithmType failed" ) ); } template <> inline std::string toString( const AlgorithmType& a, bool b ){ if ( b ) return AlgorithmName[a][1]; else return AlgorithmName[a][0]; } template <> inline InputFormatType stringTo( const std::string& str ){ InputFormatType d = UnknownInputFormat; for ( ++d; d < MaxInputFormat; ++d ){ if ( compare_nocase( str, InputFormatName[d][0] ) || compare_nocase( str, InputFormatName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to weightType failed" ) ); } template <> inline std::string toString( const InputFormatType& i, bool b ){ if ( b ){ return InputFormatName[i][1]; } else { return InputFormatName[i][0]; } } template <> inline SmoothingType stringTo( const std::string& str ) { SmoothingType d = UnknownSmoothing; for ( ++d; d < MaxSmoothing; ++d ){ if ( compare_nocase( str, SmoothingName[d][0] ) || compare_nocase( str, SmoothingName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to smoothingType failed" ) ); } template <> inline std::string toString( const SmoothingType& s, bool b ){ if ( b ){ return SmoothingName[s][1]; } else { return SmoothingName[s][0]; } } template <> inline normType stringTo( const std::string& str ) { normType d = unknownNorm; if ( str.length() == 1 && isdigit(str[0]) ){ d = charToNorm( str[0] ); } if ( d != unknownNorm ) return d; for ( ++d; d < maxNorm; ++d ){ if ( compare_nocase( str, NormalisationName[d][0] ) || compare_nocase( str, NormalisationName[d][1] ) ){ return d; } } throw( std::runtime_error( "conversion from string '" + str + "' to normalisationType failed" ) ); } template <> inline std::string toString( const normType& s, bool b ){ if ( b ){ return NormalisationName[s][1]; } else { return NormalisationName[s][0]; } } inline bool string_to_verbflag( const std::string& line, VerbosityFlags &a ){ unsigned int i; for ( i=0; VerbosityName[i][0][0] != '\0'; i++ ) if ( compare_nocase( line, VerbosityName[i][0] ) || compare_nocase( line, VerbosityName[i][1] ) ){ if ( i==0 ){ a = NO_VERB; } else{ a = (VerbosityFlags)(1<<(i-1)); } return true; } return false; } template <> inline VerbosityFlags stringTo( const std::string& str ) { std::vector tmp; size_t cnt = TiCC::split_at( str, tmp, "+" ); VerbosityFlags V = NO_VERB; for ( size_t i=0; i < cnt; ++i ){ VerbosityFlags Flag; if ( string_to_verbflag( tmp[i], Flag ) ){ V |= Flag; } else { throw( std::runtime_error( "conversion from string '" + str + "' to verbosityFlag failed" ) ); } } return V; } inline std::string verbosity_to_string( int v, bool full ){ if ( v == 0 ) return VerbosityName[0][(full?1:0)]; else { std::string OutLine; bool first = true; for ( unsigned int i=1; VerbosityName[i][0][0] != '\0'; ++i ) if ( v & (1<<(i-1)) ){ if (first) first = false; else OutLine += '+'; OutLine += VerbosityName[i][(full?1:0)]; } return OutLine; } } template <> inline std::string toString( const VerbosityFlags& v, bool full ){ return verbosity_to_string( (int)v, full ); } inline std::string toString( const AlgorithmType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const MetricType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const WeightType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const InputFormatType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const DecayType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const OrdeningType& a, bool b=false ){ return toString( a, b ); } inline std::string toString( const VerbosityFlags& a, bool b=false ){ return toString( a, b ); } } #endif LanguageMachines-timbl-642727d/include/timbl/neighborSet.h000066400000000000000000000072071451477526200234720ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #ifndef TIMBL_NEIGHBORSET_H #define TIMBL_NEIGHBORSET_H #include "timbl/Types.h" namespace Timbl { class ClassDistribution; class WClassDistribution; class decayStruct { friend std::ostream& operator<<( std::ostream&, const decayStruct& ); friend std::ostream& operator<<( std::ostream&, const decayStruct * ); public: decayStruct():alpha(0),beta(0){}; decayStruct(double a, double b ):alpha(a),beta(b){}; virtual ~decayStruct(){}; virtual std::ostream& put( std::ostream& ) const = 0; virtual DecayType type() const = 0; double alpha; double beta; }; class zeroDecay: public decayStruct { public: zeroDecay():decayStruct(){}; std::ostream& put( std::ostream& ) const override; DecayType type() const override { return Zero;}; }; class invLinDecay: public decayStruct { public: invLinDecay():decayStruct(){}; std::ostream& put( std::ostream& ) const override; DecayType type() const override { return InvLinear;}; }; class invDistDecay: public decayStruct { public: invDistDecay():decayStruct(){}; std::ostream& put( std::ostream& ) const override; DecayType type() const override { return InvDist;}; }; class expDecay: public decayStruct { public: explicit expDecay( double alp ): decayStruct(alp,1.0){}; expDecay( double alp, double bet ): decayStruct(alp,bet){}; std::ostream& put( std::ostream& ) const override; DecayType type() const override { return ExpDecay;}; }; class neighborSet { friend std::ostream& operator<<( std::ostream&, const neighborSet& ); friend std::ostream& operator<<( std::ostream&, const neighborSet * ); friend class BestArray; public: neighborSet(); ~neighborSet(); neighborSet( const neighborSet& in ); neighborSet& operator=( const neighborSet& ); size_t size() const; void reserve( size_t ); void clear(); void truncate( size_t ); void merge( const neighborSet& ); double getDistance( size_t ) const; double bestDistance() const { return getDistance(0); }; const ClassDistribution *getDistribution( size_t ) const; WClassDistribution *bestDistribution( const decayStruct * =0, size_t =0 ) const ; double relativeWeight( const decayStruct *, size_t ) const; bool setShowDistance( bool b ) const { bool ret = showDistance; showDistance = b; return ret; } bool setShowDistribution( bool b ) const { bool ret = showDistribution; showDistribution = b; return ret; } private: mutable bool showDistance; mutable bool showDistribution; void push_back( double, const ClassDistribution & ); std::vector distances; std::vector distributions; }; } #endif LanguageMachines-timbl-642727d/m4/000077500000000000000000000000001451477526200166305ustar00rootroot00000000000000LanguageMachines-timbl-642727d/m4/.gitignore000066400000000000000000000001231451477526200206140ustar00rootroot00000000000000*~ libtool.m libtool.m4 ltoptions.m4 ltsugar.m4 ltversion.m4 lt~obsolete.m4 pkg.m4 LanguageMachines-timbl-642727d/m4/Makefile.am000066400000000000000000000000641451477526200206640ustar00rootroot00000000000000# $Id: $ # $URL: $ extra_DIST = ax_openmp.m4 pkg.m4LanguageMachines-timbl-642727d/m4/ac_osx_pkg.m4000066400000000000000000000040621451477526200212110ustar00rootroot00000000000000# osx_pkg.m4 - Macros to add OSX brew locations to pkg-config. -*- Autoconf -*- # serial 1 (pkg-config-0.24) # # Copyright © 2018 Ko van der Sloot # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # AC_OSX_PKG_ALL() # add all /opt/{package} directories to the PKG_CONFIG search path # ---------------------------------- AC_DEFUN([AC_OSX_PKG_ALL], [ case ${host_os} in linux*) # linux is wellbehaved ;; darwin*) # darwin isn't for i in `ls /usr/local/opt/` do if test -d "/usr/local/opt/$i/lib/pkgconfig" then export PKG_CONFIG_PATH="$PKG_CONFIG_PATH:/usr/local/opt/$i/lib/pkgconfig" fi done ;; esac ]) # AC_OSX_PKG_ALL([LIST_OF_PACKAGES]) # fore every packake in LIST_OF_PACKAGES, add the /opt/{package} directory # to the PKG_CONFIG search path # ---------------------------------- AC_DEFUN([AC_OSX_PKG], [ case ${host_os} in linux*) # linux is wellbehaved ;; darwin*) # darwin isn't for i in $* do if test -d "/usr/local/opt/$i/lib/pkgconfig" then export PKG_CONFIG_PATH="$PKG_CONFIG_PATH:/usr/local/opt/$i/lib/pkgconfig" fi done ;; esac ]) LanguageMachines-timbl-642727d/src/000077500000000000000000000000001451477526200170775ustar00rootroot00000000000000LanguageMachines-timbl-642727d/src/.gitignore000066400000000000000000000000731451477526200210670ustar00rootroot00000000000000*~ *.o *.lo *.la .deps .libs timbl simpletest* *.out *.log LanguageMachines-timbl-642727d/src/BestArray.cxx000066400000000000000000000243401451477526200215220ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include "timbl/BestArray.h" #include #include #include // for DBL_DIG, DBL_MAX #include // for max #include // for fabs #include // for UnicodeString #include // for operator<< #include // for icu #include "timbl/Common.h" #include "timbl/neighborSet.h" #include "ticcutils/XMLtools.h" #include "ticcutils/StringOps.h" #include "ticcutils/Unicode.h" #include "ticcutils/json.hpp" namespace Timbl { using namespace std; using namespace icu; using namespace Common; using namespace nlohmann; BestRec::BestRec(): bestDistance( 0.0 ) {} BestRec::~BestRec(){ for ( auto const& b : bestDistributions ){ delete b; } } BestArray::~BestArray(){ for ( auto const& b : bestArray ){ delete b; } } ostream& operator<< ( ostream& os, const BestRec *b ){ if ( b ){ os << b->aggregateDist.DistToString(); int OldPrec = os.precision(DBL_DIG-1); os.setf(ios::showpoint); os << "\t" << b->bestDistance; os.precision(OldPrec); os << endl; } else { os << "bestrec is null!" << endl; } return os; } void BestArray::init( unsigned int numN, unsigned int maxB, bool storeI, bool showDi, bool showDb ){ _storeInstances = storeI; _showDi = showDi; _showDb = showDb; maxBests = maxB; // When necessary, take a larger array. (initialy it has 0 length) // Also check if verbosity has changed and a BestInstances array // is required. // size_t S = size; size = numN; if ( S < size ){ bestArray.reserve( size ); for ( size_t k=S; k < size; ++k ) { bestArray.push_back( new BestRec() ); } } size_t penalty = 0; for ( const auto& best : bestArray ){ best->bestDistance = (DBL_MAX - numN) + penalty++; if ( best->bestInstances.empty() ){ if ( _storeInstances ){ best->bestInstances.reserve( maxBests ); best->bestDistributions.reserve( maxBests ); } } else { for ( auto const& bd : best->bestDistributions ){ delete bd; } best->bestInstances.clear(); best->bestDistributions.clear(); } best->aggregateDist.clear(); } } double BestArray::addResult( double Distance, const ClassDistribution *Distr, const UnicodeString& neighbor ){ // We have the similarity in Distance, and a num_of_neighbors // dimensional array with best similarities. // Check, and add/replace/move/whatever. // for ( unsigned int k = 0; k < size; ++k ) { BestRec *best = bestArray[k]; if (fabs(Distance - best->bestDistance) < Epsilon) { // Equal...just add to the end. // best->aggregateDist.Merge( *Distr ); if ( _storeInstances && best->bestInstances.size() < maxBests ){ best->bestInstances.push_back( neighbor ); best->bestDistributions.push_back( Distr->to_VD_Copy() ); } break; } // Check if better than bests[k], insert (or replace if // it's the lowest of the k bests). // /* Example (no_n = 3): k distance number 0 2 3 1 4 2 2 6 1 sim = 1 (dus beste) */ else if (Distance < best->bestDistance) { if (k == size - 1) { // // Replace. // best->bestDistance = Distance; if ( _storeInstances ){ for ( const auto& it : best->bestDistributions ){ delete it; } best->bestInstances.clear(); best->bestDistributions.clear(); best->bestInstances.push_back( neighbor ); best->bestDistributions.push_back( Distr->to_VD_Copy() ); } best->aggregateDist.clear(); best->aggregateDist.Merge( *Distr ); } else { // // Insert. First shift the rest up. // BestRec *keep = bestArray[size-1]; for ( size_t i = size - 1; i > k; i--) { bestArray[i] = bestArray[i-1]; } // i // // And now insert. // keep->bestDistance = Distance; if ( _storeInstances ){ for ( const auto& it :keep->bestDistributions ){ delete it; } keep->bestInstances.clear(); keep->bestDistributions.clear(); keep->bestInstances.push_back( neighbor ); keep->bestDistributions.push_back( Distr->to_VD_Copy() ); } keep->aggregateDist.clear(); keep->aggregateDist.Merge( *Distr ); bestArray[k] = keep; } break; } // Distance < fBest } // k return bestArray[size-1]->bestDistance; } void BestArray::initNeighborSet( neighborSet& ns ) const { ns.clear(); for ( auto const& best : bestArray ){ ns.push_back( best->bestDistance, best->aggregateDist ); } } void BestArray::addToNeighborSet( neighborSet& ns, size_t n ) const { ns.push_back( bestArray[n-1]->bestDistance, bestArray[n-1]->aggregateDist ); } xmlNode *BestArray::toXML() const { xmlNode *top = TiCC::XmlNewNode( "neighborset" ); size_t k = 0; for ( auto const& best : bestArray ){ ++k; if ( _storeInstances ){ size_t totalBests = best->totalBests(); if ( totalBests == 0 ){ break; // TRIBL algorithms do this! } xmlNode *nbs = TiCC::XmlNewChild( top, "neighbors" ); TiCC::XmlSetAttribute( nbs, "k", TiCC::toString(k) ); TiCC::XmlSetAttribute( nbs, "total", TiCC::toString(totalBests) ); TiCC::XmlSetAttribute( nbs, "distance", TiCC::toString( best->bestDistance ) ); if ( maxBests < totalBests ){ TiCC::XmlSetAttribute( nbs, "limited", TiCC::toString( maxBests ) ); } for ( unsigned int m=0; m < best->bestInstances.size(); ++m ){ xmlNode *nb = TiCC::XmlNewChild( nbs, "neighbor" ); TiCC::XmlNewTextChild( nb, "instance", TiCC::UnicodeToUTF8(best->bestInstances[m]) ); if ( _showDb ){ TiCC::XmlNewTextChild( nb, "distribution", best->bestDistributions[m]->DistToString() ); } } } else { if ( best->aggregateDist.ZeroDist() ){ break; } xmlNode *nbs = TiCC::XmlNewChild( top, "neighbors" ); TiCC::XmlSetAttribute( nbs, "k", TiCC::toString(k) ); if ( _showDb ){ TiCC::XmlNewTextChild( nbs, "distribution", best->aggregateDist.DistToString() ); } if ( _showDi ){ TiCC::XmlNewTextChild( nbs, "distance", TiCC::toString(best->bestDistance) ); } } } return top; } json neighbor_to_json( const UnicodeString& nb, const string& db ){ json result; result["instance"] = TiCC::UnicodeToUTF8(nb); if ( !db.empty() ){ result["distribution"] = db; } return result; } json BestArray::record_to_json( const BestRec *best, size_t k ) const { json result; if ( _storeInstances ){ size_t totalBests = best->totalBests(); if ( totalBests > 0 ){ // TRIBL algorithms returns 0 this! result["k"] = k; result["total"] = totalBests; result["distance"] = best->bestDistance; if ( maxBests < totalBests ){ result["limited"] = maxBests; } if ( best->bestInstances.size() == 0 ){ } else if ( best->bestInstances.size() == 1 ){ string db; if ( _showDb ){ db = best->bestDistributions[0]->DistToString(); } result["neighbor"] = neighbor_to_json( best->bestInstances[0], db ); } else { json arr = json::array(); for ( unsigned int m=0; m < best->bestInstances.size(); ++m ){ string db; if ( _showDb ){ db = best->bestDistributions[m]->DistToString(); } arr.push_back( neighbor_to_json( best->bestInstances[m], db ) ); } result["neighbor"] = arr; } } } else { if ( !best->aggregateDist.ZeroDist() ){ result["k"] = k; if ( _showDb ){ result["distribution"] = best->aggregateDist.DistToString(); } if ( _showDi ){ result["distance"] = best->bestDistance; } } } return result; } json BestArray::to_JSON() const { json result; if ( bestArray.size() == 0 ){ return result; // empty } else if ( bestArray.size() == 1 ){ result = record_to_json( bestArray[0], 1 ); return result; } else { result = json::array(); size_t k = 0; for ( auto const& best : bestArray ){ result.push_back( record_to_json( best, ++k) ); } } return result; } ostream& operator<< ( ostream& os, const BestArray& bA ){ size_t k = 0; for ( auto const& best : bA.bestArray ){ ++k; if ( bA._storeInstances ){ size_t totalBests = best->totalBests(); if ( totalBests == 0 ){ break; // TRIBL algorithms do this! } os << "# k=" << k << ", " << totalBests << " Neighbor(s) at distance: "; int OldPrec = os.precision(DBL_DIG-1); os.setf(ios::showpoint); os << "\t" << best->bestDistance; os.precision(OldPrec); if ( bA.maxBests <= best->bestInstances.size() ){ os << " (only " << bA.maxBests << " shown)"; } os << endl; for ( unsigned int m=0; m < best->bestInstances.size(); ++m ){ os << "#\t" << best->bestInstances[m]; if ( bA._showDb ){ os << best->bestDistributions[m]->DistToString() << endl; } else { os << " -*-" << endl; } } } else { if ( best->aggregateDist.ZeroDist() ){ break; } os << "# k=" << k << "\t"; if ( bA._showDb ){ os << best->aggregateDist.DistToString(); } if ( bA._showDi ){ int OldPrec = os.precision(DBL_DIG-1); os.setf(ios::showpoint); os << best->bestDistance; os.precision(OldPrec); } os << endl; } } return os; } } LanguageMachines-timbl-642727d/src/CVExperiment.cxx000066400000000000000000000115541451477526200222020ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/StringOps.h" #include "timbl/TimblExperiment.h" namespace Timbl { using namespace std; bool CV_Experiment::Prepare( const string& f, bool, bool ){ cerr << "CV prepare " << f << endl; return true; } bool CV_Experiment::CVprepare( const string& wgtFile, WeightType w, const string& probFile ){ CV_WfileName = wgtFile; CV_fileW = w; CV_PfileName = probFile; return true; } bool CV_Experiment::Learn( const string& f, bool ){ cerr << "CV Learn " << f << endl; return true; } bool CV_Experiment::checkTestFile(){ if ( !IB1_Experiment::checkTestFile() ){ return false; } else if ( doSamples() ){ FatalError( "Cannot Cross validate on a file with Examplar Weighting" ); return false; } else if ( Verbosity(FEAT_W) ){ LearningInfo( *mylog ); } return true; } bool CV_Experiment::get_file_names( const string& FileName ){ if ( !ExpInvalid() ){ size_t size = 0; ifstream file_names( FileName, ios::in ); if ( !file_names ){ Error( "Unable to read CV filenames from " + FileName ); return false; } string name; while ( getline( file_names, name ) ){ size_t tmp = examineData( name ); if ( tmp != 0 ){ if ( !Verbosity(SILENT) ){ *mylog << "Examine datafile '" << name << "' gave the following results:" << endl << "Number of Features: " << tmp << endl; showInputFormat( *mylog ); } FileNames.push_back(name); if ( size == 0 ){ size = tmp; } else { if ( tmp != size ) { Error( "mismatching number of features in file " + name + "of CV filelist " + FileName ); return false; } } } else { Error( "unable to determine number of features in file " + name + "of CV filelist " + FileName ); return false; } } if ( FileNames.size() < 3 ){ Error( "Not enough filenames found in CV filelist " + FileName + " at least 3 required" ); return false; } return true; } return false; } bool CV_Experiment::Test( const string& FileName, const string& OutFile ){ if ( !ConfirmOptions() ){ return false; } (void)OutFile; bool result = false; VerbosityFlags keep = get_verbosity(); set_verbosity( SILENT ); if ( get_file_names( FileName ) ){ *mylog << "Starting Cross validation test on files:" << endl; for ( const auto& name : FileNames ){ *mylog << name << endl; } size_t NumOfFiles = FileNames.size(); TimblExperiment::Prepare( FileNames[1], false ); TimblExperiment::Learn( FileNames[1], false ); for ( size_t filenum = 2; filenum < NumOfFiles; ++filenum ){ Expand( FileNames[filenum] ); } string outName; string percName; for ( size_t SkipFile = 0; SkipFile < NumOfFiles-1; ++SkipFile ) { outName = correct_path( FileNames[SkipFile], outPath, false ); outName += ".cv"; percName = outName; percName += ".%"; set_verbosity( keep ); if ( CV_WfileName != "" ){ GetWeights( CV_WfileName, CV_fileW ); } if ( !CV_PfileName.empty() ){ GetArrays( CV_PfileName ); } result = TimblExperiment::Test( FileNames[SkipFile], outName ); if ( result ){ result = createPercFile( percName ); } if ( !result ){ return false; } set_verbosity( SILENT ); Expand( FileNames[SkipFile] ); Remove( FileNames[SkipFile+1] ); } outName = correct_path( FileNames[NumOfFiles-1], outPath, false ); outName += ".cv"; percName = outName; percName += ".%"; set_verbosity( keep ); if ( CV_WfileName != "" ){ GetWeights( CV_WfileName, CV_fileW ); } if ( !CV_PfileName.empty() ){ GetArrays( CV_PfileName ); } result = TimblExperiment::Test( FileNames[NumOfFiles-1], outName ); if ( result ){ result = createPercFile( percName ); } } return result; } } LanguageMachines-timbl-642727d/src/Choppers.cxx000066400000000000000000000325121451477526200214110ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include "timbl/Choppers.h" #include // for isspace #include #include #include #include #include "ticcutils/StringOps.h" #include "ticcutils/Unicode.h" #include "ticcutils/PrettyPrint.h" #include "timbl/Types.h" using namespace std; using namespace icu; namespace Timbl{ Chopper *Chopper::create( InputFormatType IF, bool doEx, int fLen, bool doOcc ){ Chopper *result = 0; switch ( IF ){ case C4_5: if ( doOcc ){ result = new C45_OccChopper(); } else if ( doEx ){ result = new C45_ExChopper(); } else { result = new C45_Chopper(); } break; case ARFF: if ( doOcc ){ result = new ARFF_OccChopper(); } else if ( doEx ){ result = new ARFF_ExChopper(); } else { result = new ARFF_Chopper(); } break; case SparseBin: if ( doOcc ){ result = new Bin_OccChopper(); } else if ( doEx ){ result = new Bin_ExChopper(); } else { result = new Bin_Chopper(); } break; case Sparse: if ( doOcc ){ result = new Sparse_OccChopper(); } else if ( doEx ){ result = new Sparse_ExChopper(); } else { result = new Sparse_Chopper(); } break; case Columns: if ( doOcc ){ result = new Columns_OccChopper(); } else if ( doEx ){ result = new Columns_ExChopper(); } else { result = new Columns_Chopper(); } break; case Tabbed: if ( doOcc ){ result = new Tabbed_OccChopper(); } else if ( doEx ){ result = new Tabbed_ExChopper(); } else { result = new Tabbed_Chopper(); } break; case Compact: if ( doOcc ){ result = new Compact_OccChopper( fLen ); } else if ( doEx ) { result = new Compact_ExChopper( fLen ); } else { result = new Compact_Chopper( fLen ); } break; default: break; } return result; } void Chopper::init( const UnicodeString& s, size_t len, bool stripDot ) { vSize = len+1; choppedInput.resize(vSize); UnicodeString split = s; // cerr << " strip input:" << split << endl; // trim spaces at end split = TiCC::rtrim( split ); if ( stripDot ){ // now trim at most 1 trailing dot if ( split[split.length()-1] == '.' ){ split.remove( split.length()-1 ); } } // trim more spaces at end strippedInput = TiCC::rtrim( split ); // cerr << "stripped input:" << strippedInput << endl; } static UnicodeString extractWeight( const UnicodeString& buffer, UnicodeString& wght ) { // cerr << "extract weight from '" << buffer << "'" << endl; UnicodeString tmp = buffer; // first remove trailing whitespace and dots tmp = TiCC::rtrim( tmp, " ." ); // cerr << "step 1: '" << tmp << "'" << endl; int e_pos = tmp.length()-1; for ( ; e_pos >= 0; --e_pos ){ if ( tmp[e_pos] == ' ' || tmp[e_pos] == '\t' ){ break; } } if ( e_pos == 0 ){ wght = ""; } else { wght = UnicodeString( tmp, e_pos+1 ); tmp.remove( e_pos ); } tmp = TiCC::rtrim( tmp, "\t ." ); // cerr << "result='" << tmp << "' with weight: '" << wght << "'" << endl; return tmp; } static UnicodeString extractOcc( const UnicodeString& Buffer, UnicodeString& occ ) { return extractWeight( Buffer, occ ); } size_t Chopper::countFeatures( const UnicodeString& inBuffer, InputFormatType IF, int F_length, bool chopTail ) { size_t result = 0; UnicodeString buffer = inBuffer; if ( chopTail ){ UnicodeString dummy; buffer = extractWeight( buffer, dummy ); } size_t len = buffer.length(); switch ( IF ){ case ARFF: case C4_5: for ( int i=0; i < buffer.length(); ++i ){ if ( buffer[i] == ','){ ++result; } }; break; case Compact: if ( F_length == 0 ){ throw runtime_error( "-F Compact specified, but Feature Length not set." " (-l option)" ); } else { result = (len / F_length) - 1; } break; case Columns: { vector parts = TiCC::split( buffer ); result = parts.size() - 1; }; break; case Tabbed: { vector parts = TiCC::split_at( buffer, "\t" ); result = parts.size() - 1; }; break; default: throw logic_error( "CountFeatures: Illegal value in switch:" + TiCC::toString(IF) ); }; return result; } InputFormatType Chopper::getInputFormat( const UnicodeString& inBuffer, bool stripTail ) { InputFormatType IF = UnknownInputFormat; UnicodeString buffer = inBuffer; if ( stripTail ){ UnicodeString dummy; buffer = extractWeight( buffer, dummy ); } size_t len = buffer.length(); int c45Cnt = 0; int columnCnt = 0; for ( unsigned int i = 0; i < len; ++i ) { if ( buffer[i] == ',' ) { ++c45Cnt; } else if ( isspace( buffer[i] ) ){ ++columnCnt; while ( i < len && isspace( buffer[i+1] ) ) ++i; if ( i >= len-1 ){ // just trailing spaces! --columnCnt; } } } if ( columnCnt == 0 && c45Cnt == 0 ){ IF = Compact; } else if ( c45Cnt >= columnCnt ){ IF = C4_5; } else { IF = Columns; } return IF; } void ExChopper::init( const UnicodeString& s, size_t len, bool stripDot ) { UnicodeString split = s; vSize = len+1; choppedInput.resize(vSize); // trim trailing spaces split = TiCC::rtrim( split ); UnicodeString wght; split = extractWeight( split, wght ); if ( wght.isEmpty() ){ throw logic_error( "Missing sample weight" ); } else { double tmp; if ( !TiCC::stringTo( wght, tmp ) ){ throw runtime_error( "Wrong sample weight: '" + TiCC::UnicodeToUTF8(wght) + "'" ); } else { exW = tmp; } } if ( stripDot ){ // now trim at most 1 trailing dot if ( split[split.length()-1] == '.' ){ split.remove( split.length()-1 ); } } // trim more trailing spaces strippedInput = TiCC::rtrim( split ); } void OccChopper::init( const UnicodeString& s, size_t len, bool stripDot ) { UnicodeString split = s; occ = 1; vSize = len+1; choppedInput.resize(vSize); // first trim trailing spaces split = TiCC::rtrim( split ); UnicodeString occS; // get occ split = extractOcc( split, occS ); if ( occS.isEmpty() ){ throw logic_error( "Missing occurrence" ); } else { int tmp; if ( !TiCC::stringTo( occS, tmp ) ){ throw runtime_error( "Wrong (non-integer) occurrence value: '" + TiCC::UnicodeToUTF8(occS) + "'" ); } else { occ = tmp; } } if ( stripDot ){ // now trim at most 1 trailing dot if ( split[split.length()-1] == '.' ){ split.remove( split.length()-1 ); } } // strip remaining trailing spaces strippedInput = TiCC::rtrim( split ); } using TiCC::operator<<; bool C45_Chopper::chop( const UnicodeString& InBuf, size_t len ){ // Function that takes a line, and chops it up into substrings, // which represent the feature-values and the target-value. init( InBuf, len, true ); vector splits = TiCC::split_at( strippedInput, "," ); size_t res = splits.size(); if ( res != vSize ){ return false; } for ( size_t i=0; i < res ; ++i ){ choppedInput[i] = StrToCode( splits[i] ); } // cerr << "Chopped input=" << choppedInput << endl; return true; } UnicodeString C45_Chopper::getString() const{ UnicodeString res; for ( const auto& part : choppedInput ) { res += CodeToStr( part ) + ","; } return res; } bool ARFF_Chopper::chop( const UnicodeString& InBuf, size_t len ){ // Lines look like this: // one, two, three , bla. // the termination dot is optional // WhiteSpace is skipped! return C45_Chopper::chop( InBuf, len ); } bool Bin_Chopper::chop( const UnicodeString& InBuf, size_t len ) { // Lines look like this: // 12, 25, 333, bla. // the termination dot is optional init( InBuf, len, true ); for ( size_t m = 0; m < vSize-1; ++m ){ choppedInput[m] = "0"; } vector parts = TiCC::split_exact_at( strippedInput, "," ); for ( auto const& p : parts ){ if ( &p == &parts.back() ){ choppedInput[vSize-1] = p; break; } size_t k; if ( !TiCC::stringTo( p, k ) ){ return false; } if ( k < 1 || k > vSize ){ return false; } else { choppedInput[k-1] = "1"; } } return true; } UnicodeString Bin_Chopper::getString() const { UnicodeString res; int i = 1; for ( const auto& part : choppedInput ){ if ( &part == &choppedInput.back() ){ break; } if ( part[0] == '1' ){ res += TiCC::toUnicodeString(i) + ","; } ++i; } res += choppedInput.back() + ","; return res; } bool Compact_Chopper::chop( const UnicodeString& InBuf, size_t leng ){ init( InBuf, leng, false ); // Lines look like this: // ====AKBVAK // v1v2v3v4tt // Get & add the target. // size_t len = strippedInput.length(); if ( len != vSize * fLen ){ return false; } size_t i = 0; for ( auto& part : choppedInput ){ size_t index = i * fLen; // Scan the value. // part.remove(); for ( int j = 0; j < fLen; ++j ) { part += strippedInput[index++]; } ++i; } return ( i == vSize ); // Enough? } UnicodeString Compact_Chopper::getString() const { UnicodeString res; for ( const auto& part : choppedInput ){ res += CodeToStr( part ); } return res; } bool Columns_Chopper::chop( const UnicodeString& InBuf, size_t len ){ // Lines look like this: // one two three bla init( InBuf, len, false ); vector splits = TiCC::split( strippedInput ); size_t res = splits.size(); if ( res != vSize ){ return false; } for ( size_t i=0; i < res ; ++i ){ choppedInput[i] = StrToCode( splits[i] ); } return true; } UnicodeString Columns_Chopper::getString() const { UnicodeString res = TiCC::join( choppedInput ); return res; } bool Tabbed_Chopper::chop( const UnicodeString& InBuf, size_t len ){ // Lines look like this: // oneTABtwoTAB TABthreeTABbla init( InBuf, len, false ); vector splits = TiCC::split_at( strippedInput, "\t" ); size_t res = splits.size(); if ( res != vSize ){ return false; } for ( size_t i=0; i < res ; ++i ){ choppedInput[i] = StrToCode( splits[i], false ); } return true; } UnicodeString Tabbed_Chopper::getString() const { UnicodeString res; for ( const auto& part : choppedInput ){ res += CodeToStr( part ) + "\t"; } return res; } bool Sparse_Chopper::chop( const UnicodeString& InBuf, size_t len ){ // Lines look like this: // (12,value1) (25,value2) (333,value3) bla. // the termination dot is optional init( InBuf, len, true ); for ( size_t m = 0; m < vSize-1; ++m ){ choppedInput[m] = DefaultSparseString; } choppedInput[vSize-1] = ""; vector entries = TiCC::split_at_first_of( strippedInput, "()" ); size_t num_ent = entries.size(); if ( num_ent < 1 ){ return false; } for ( const auto& ent : entries ){ --num_ent; vector parts = TiCC::split_at( ent, "," ); size_t num = parts.size(); if ( num != 2 ){ if ( num == 1 && num_ent == 0 ){ // the target has no ',' parts[0].trim(); choppedInput[vSize-1] = parts[0]; return !choppedInput[vSize-1].isEmpty(); } return false; } if ( num_ent == 0 ){ // missing a target! return false; } size_t index; if ( !TiCC::stringTo( parts[0], index ) ){ return false; } if ( index < 1 || index >= vSize ){ return false; } choppedInput[index-1] = StrToCode( parts[1] ); } return true; } UnicodeString Sparse_Chopper::getString() const { UnicodeString res; int i = 1; for ( const auto& part : choppedInput ){ if ( &part == &choppedInput.back() ){ break; } if ( part != DefaultSparseString ){ res += "(" + TiCC::toUnicodeString( i ) + ","; res += CodeToStr(part); res += ")"; } ++i; } res += choppedInput.back() + ","; return res; } } LanguageMachines-timbl-642727d/src/Common.cxx000066400000000000000000000025061451477526200210560ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include "timbl/Common.h" #include "config.h" using namespace std; namespace Common { string VersionInfo( bool full ){ // obsolete if ( full ){ return BuildInfo(); } else { return Version(); } } string Version() { return VERSION; } string VersionName() { return PACKAGE_STRING; } string BuildInfo() { return Version() + ", compiled on " + __DATE__ + ", " + __TIME__; } } LanguageMachines-timbl-642727d/src/Features.cxx000066400000000000000000000616771451477526200214220ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include // for sort() #include // for accumulate() #include // for fabs() #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Metrics.h" #include "timbl/Matrices.h" #include "timbl/Instance.h" #include "ticcutils/Unicode.h" #include "ticcutils/UniHash.h" namespace Timbl { using namespace std; using namespace Common; using icu::UnicodeString; FeatureValue::FeatureValue( const UnicodeString& value, size_t hash_val ): ValueClass( value, hash_val ), ValueClassProb( 0 ) { } FeatureValue::FeatureValue( const UnicodeString& s ): ValueClass( s, 0 ), ValueClassProb(0){ _frequency = 0; } FeatureValue::~FeatureValue( ){ delete ValueClassProb; } Feature::Feature( Hash::UnicodeHash *T ): metric_matrix( 0 ), TokenTree(T), metric( 0 ), ignore( false ), numeric( false ), vcpb_read( false ), PrestoreStatus(ps_undef), Prestored_metric( UnknownMetric ), entropy( 0.0 ), info_gain (0.0), split_info(0.0), gain_ratio(0.0), chi_square(0.0), shared_variance(0.0), standard_deviation(0.0), matrix_clip_freq(10), n_min (0.0), n_max (0.0), weight(0.0), is_reference(false) {} Feature::Feature( const Feature& in ): MsgClass( in ){ *this = in; is_reference = true; } Feature& Feature::operator=( const Feature& in ){ if ( this != &in ){ metric_matrix = in.metric_matrix; metric = in.metric; PrestoreStatus = in.PrestoreStatus; Prestored_metric = in.Prestored_metric; ignore = in.ignore; numeric = in.numeric; vcpb_read = in.vcpb_read; entropy = in.entropy; info_gain = in.info_gain; split_info = in.split_info; gain_ratio = in.gain_ratio; chi_square = in.chi_square; shared_variance = in.shared_variance; standard_deviation = in.standard_deviation; matrix_clip_freq = in.matrix_clip_freq; n_dot_j = in.n_dot_j; n_i_dot = in.n_i_dot; n_min = in.n_min; n_max = in.n_max; weight = in.weight; values_array = in.values_array; reverse_values = in.reverse_values; TokenTree = in.TokenTree; } return *this; } void Feature::InitSparseArrays(){ if ( !is_reference ){ // Loop over all values. // for ( const auto& FV : values_array ){ size_t freq = FV->ValFreq(); FV->ValueClassProb->Clear(); if ( freq > 0 ){ // Loop over all present classes. // for ( const auto& tit : FV->TargetDist ){ FV->ValueClassProb->Assign( tit.second->Index(), tit.second->Freq()/(double)freq ); } } } } } size_t Feature::EffectiveValues() const { return count_if( values_array.begin(), values_array.end(), [&]( const FeatureValue* v ){ return (v->ValFreq() > 0); } ); } size_t Feature::TotalValues() const { return accumulate( values_array.begin(), values_array.end(), 0, [&]( size_t r, const FeatureValue *v ){ return r + v->ValFreq(); } ); } FeatureValue *Feature::Lookup( const UnicodeString& str ) const { FeatureValue *result = NULL; unsigned int hash_val = TokenTree->lookup( str ); if ( hash_val > 0 ) { auto const& it = reverse_values.find( hash_val ); if ( it != reverse_values.end() ){ result = it->second; } } return result; } FeatureValue *Feature::add_value( const UnicodeString& valstr, TargetValue *tv, int freq ){ unsigned int hash_val = TokenTree->hash( valstr ); // cerr << "hash(" << valstr << ") geeft: " << hash_val << endl; return add_value( hash_val, tv, freq ); } FeatureValue *Feature::add_value( size_t hash_val, TargetValue *tv, int freq ){ auto const& it = reverse_values.find( hash_val ); if ( it == reverse_values.end() ){ const UnicodeString& value = TokenTree->reverse_lookup( hash_val ); // cerr << "lookup(" << index << ") geeft: " << value << endl; // we want to store the singleton value for this index // so we MUST reverse lookup the index FeatureValue *fv = new FeatureValue( value, hash_val ); fv->ValFreq( freq ); reverse_values[hash_val] = fv; values_array.push_back( fv ); } else { it->second->IncValFreq( freq ); } FeatureValue *result = reverse_values[hash_val]; if ( tv ){ result->TargetDist.IncFreq(tv, freq ); } return result; } bool Feature::increment_value( FeatureValue *FV, const TargetValue *tv ){ bool result = false; if ( FV ){ FV->incr_val_freq(); if ( tv ){ FV->TargetDist.IncFreq(tv,1); } result = true; } return result; } bool Feature::decrement_value( FeatureValue *FV, const TargetValue *tv ){ bool result = false; if ( FV ){ FV->decr_val_freq(); if ( tv ){ FV->TargetDist.DecFreq(tv); } result = true; } return result; } bool Feature::AllocSparseArrays( size_t Dim ){ // Loop over all values. // for ( const auto& FV : values_array ){ // Loop over all classes. if ( FV->ValueClassProb == NULL ){ if ( !(FV->ValueClassProb = new SparseValueProbClass( Dim )) ){ return false; } } } return true; } bool Feature::isNumerical() const { if ( metric && metric->isNumerical() ){ return true; } else { return false; } } bool Feature::isStorableMetric() const { if ( metric && metric->isStorable() ){ return true; } else { return false; } } struct D_D { D_D(): dist(0), value(0.0) {}; explicit D_D( FeatureValue *fv ): value(0.0) { if ( !TiCC::stringTo( fv->name(), value ) ){ throw( logic_error("called DD with an non-numeric value" ) ); } dist = &fv->TargetDist; } ClassDistribution *dist; double value; }; bool dd_less( const D_D* dd1, const D_D* dd2 ){ return dd1->value < dd2->value; } void Feature::NumStatistics( vector& FVBin, double DBentropy ){ size_t BinSize = FVBin.size(); double Prob, FVEntropy; size_t TotalVals = TotalValues(); entropy = 0.0; vector ddv; size_t dd_len = values_array.size(); ddv.reserve( dd_len ); for ( const auto& FV : values_array ){ if ( FV->ValFreq() > 0 ){ ddv.push_back( new D_D( FV ) ); } } sort( ddv.begin(), ddv.end(), dd_less ); int num_per_bin = (int)floor( (double)dd_len / BinSize); size_t rest = dd_len - num_per_bin * BinSize; if ( rest ){ num_per_bin++; } int jj = 0; int cnt = 0; for ( const auto& it: ddv ){ FVBin[jj]->TargetDist.Merge( *it->dist ); if ( ++cnt >= num_per_bin ){ ++jj; if ( --rest == 0 ){ --num_per_bin; } cnt = 0; } } for ( auto const& it: ddv ){ delete it; } for ( size_t k=0; k < BinSize; k++ ){ FeatureValue *pnt = FVBin[k]; size_t Freq = pnt->TargetDist.totalSize(); pnt->ValFreq( Freq ); if ( Freq > 0 ){ // Entropy for this FV pair. // FVEntropy = 0.0; for ( const auto& it : pnt->TargetDist ){ Prob = it.second->Freq()/(double)Freq; FVEntropy += Prob * Log2(Prob); } entropy += -FVEntropy * Freq / (double)TotalVals; } } entropy = fabs( entropy ); // Info gain. // info_gain = DBentropy - entropy; // And the split info. // split_info = 0.0; for ( size_t l=0; l < BinSize; ++l ){ size_t Freq = FVBin[l]->ValFreq(); if ( Freq > 0 ){ Prob = Freq / (double)TotalVals; split_info += Prob * Log2(Prob); } } split_info = -split_info; // Gain ratio. // if ( fabs(split_info) FVBin(BinSize); for ( int i=0; i < BinSize; ++i ){ UnicodeString dumname = "dum" + TiCC::toUnicodeString( i ); FVBin[i] = new FeatureValue( dumname ); } NumStatistics( FVBin, DBentropy ); if ( full ){ ChiSquareStatistics( FVBin, Targs ); int cnt = 0; // count effective values in Bin for ( int i=0; i < BinSize; ++i ){ if ( FVBin[i]->ValFreq() > 0 ){ ++cnt; } } SharedVarianceStatistics( Targs, cnt ); } for ( const auto& it : FVBin ){ delete it; } } void Feature::Statistics( double DBentropy ){ size_t TotalVals = TotalValues(); entropy = 0.0; // Loop over the values. for ( const auto& fv : values_array ){ // Entropy for this FV pair. size_t Freq = fv->ValFreq(); if ( Freq > 0 ){ double FVEntropy = 0.0; for ( const auto& tit : fv->TargetDist ){ double Prob = tit.second->Freq() / (double)Freq; FVEntropy += Prob * Log2(Prob); } entropy += -FVEntropy * Freq / (double)TotalVals; } } entropy = fabs( entropy ); // Info. gain. // info_gain = DBentropy - entropy; if ( info_gain < 0.0 ){ info_gain = 0.0; } // And the split. info. // split_info = 0.0; for ( const auto& fv : values_array ){ double Prob = fv->ValFreq() / (double)TotalVals; if ( Prob > 0 ) { split_info += Prob * Log2(Prob); } } split_info = -split_info; // Gain ratio. // if ( fabs(split_info) < Epsilon ){ gain_ratio = 0.0; } else { gain_ratio = info_gain / split_info; } } void Feature::ChiSquareStatistics( vector& FVA, const Targets& Targs ){ size_t Num_Vals = FVA.size(); chi_square = 0.0; long int n_dot_dot = 0; size_t Size = Targs.num_of_values(); n_dot_j.resize(Size,0); n_i_dot.resize(Num_Vals,0); for ( size_t j = 0; j < Size; ++j ){ // ALL values should be zeroed n_dot_j[j] = 0; } for ( size_t i = 0; i < Num_Vals; ++i ){ n_i_dot[i] = 0; // ALL values should be zeroed FeatureValue *fv = FVA[i]; for ( const auto& tit : fv->TargetDist ){ n_dot_j[tit.second->Index()-1] += tit.second->Freq(); n_i_dot[i] += tit.second->Freq(); } n_dot_dot += n_i_dot[i]; } if ( n_dot_dot != 0 ){ for ( size_t m = 0; m < Num_Vals; ++m ){ FeatureValue *fv = FVA[m]; size_t n = 0; for ( const auto& it : fv->TargetDist ){ if ( n >= Size ){ break; } while ( n < it.second->Index()-1 ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; chi_square += tmp; } if ( n == it.second->Index()-1 ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; if ( fabs(tmp) > Epsilon){ chi_square += ( (tmp - it.second->Freq()) * (tmp - it.second->Freq()) ) / tmp; } } else { break; } } while ( n < Size ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; chi_square += tmp; } } } } void Feature::ChiSquareStatistics( const Targets& Targs ){ chi_square = 0.0; long int n_dot_dot = 0; size_t Size = Targs.num_of_values(); size_t Num_Vals = values_array.size(); n_dot_j.resize(Size,0); n_i_dot.resize(Num_Vals,0); for ( size_t j = 0; j < Size; ++j ){ // ALL values should be zeroed n_dot_j[j] = 0; } int i = 0; for ( const auto& fv : values_array ){ n_i_dot[i] = 0; // ALL values should be zeroed for ( const auto& t_it : fv->TargetDist ){ long int fr = t_it.second->Freq(); n_dot_j[t_it.second->Index()-1] += fr; n_i_dot[i] += fr; } n_dot_dot += n_i_dot[i]; ++i; } if ( n_dot_dot != 0 ){ int m = 0; for ( const auto& fv : values_array ){ size_t n = 0; for ( const auto& t_it : fv->TargetDist ){ if ( n >= Size ){ break; } size_t id = t_it.second->Index()-1; long int fr = t_it.second->Freq(); while ( n < id ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; chi_square += tmp; } if ( n == id ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; if ( fabs(tmp) > Epsilon ){ chi_square += ( (tmp - fr ) * (tmp - fr ) ) / tmp; } } else { break; } } while ( n < Size ){ double tmp = ((double)n_dot_j[n++] * (double)n_i_dot[m]) / (double)n_dot_dot; chi_square += tmp; } ++m; } } } double Feature::fvDistance( const FeatureValue *F, const FeatureValue *G, size_t limit ) const { double result = 0.0; if ( F != G ){ bool dummy; if ( metric->isStorable() && matrixPresent( dummy ) && F->ValFreq() >= matrix_clip_freq && G->ValFreq() >= matrix_clip_freq ){ result = metric_matrix->Extract( F, G ); } else if ( metric->isNumerical() ) { result = metric->distance( F, G, limit, Max() - Min() ); } else { result = metric->distance( F, G, limit ); } } return result; } Feature_List &Feature_List::operator=( const Feature_List& l ){ if ( this != &l ){ _num_of_feats = l._num_of_feats; feats.resize(_num_of_feats); perm_feats.resize(_num_of_feats); permutation = l.permutation; _feature_hash = l._feature_hash; // shared ?? for ( unsigned int i=0; i < _num_of_feats; ++i ){ feats[i] = new Feature( *l.feats[i] ); } for ( unsigned int i=0; i < _num_of_feats; ++i ){ if ( l.perm_feats[i] ) { perm_feats[i] = feats[permutation[i]]; } else { perm_feats[i] = 0; } } _is_reference = true; _eff_feats = l._eff_feats; _num_of_num_feats = l._num_of_num_feats; } return *this; } Feature_List::~Feature_List(){ if ( !_is_reference ){ delete _feature_hash; } for ( const auto& it : feats ){ delete it; } feats.clear(); } void Feature_List::init( size_t size, const vector& UserOptions ) { _num_of_feats = size; _feature_hash = new Hash::UnicodeHash(); // all features share the same hash feats.resize(_num_of_feats,NULL); perm_feats.resize(_num_of_feats,NULL); for ( size_t i=0; i< _num_of_feats; ++i ){ feats[i] = new Feature( _feature_hash ); } _eff_feats = _num_of_feats; _num_of_num_feats = 0; // the user thinks about features running from 1 to _num_of_feats+1 // we know better, so shift the UserOptions one down. for ( size_t j = 0; j < _num_of_feats; ++j ){ MetricType m = UserOptions[j+1]; if ( m == Ignore ){ feats[j]->Ignore( true ); --_eff_feats; } else { feats[j]->setMetricType( m ); if ( feats[j]->isNumerical() ){ ++_num_of_num_feats; } } } } void Feature_List::write_permutation( ostream &os ) const { os << "< "; for ( const auto& it : permutation ){ os << it + 1; if ( &it != &permutation.back()) os << ", "; } os << " >"; } void Feature_List::calculate_permutation( const vector& W ){ vector WR = W; size_t IgnoredFeatures = 0; permutation.resize(_num_of_feats); for ( size_t j=0; j < _num_of_feats; ++j ){ permutation[j] = j; if ( feats[j]->Ignore() ){ WR[j] = -0.1; // To be shure that they are placed AFTER // those which are realy Zero IgnoredFeatures++; } } if ( IgnoredFeatures == _num_of_feats ){ Error( "All features seem to be ignored! Nothing to do" ); exit(1); } else { for ( size_t k=0; k < _num_of_feats; ++k ){ size_t Max = 0; for ( size_t m=1; m < _num_of_feats; ++m ){ if ( WR[m] > WR[Max] ){ Max = m; } } WR[Max] = -1; permutation[k] = Max; } } for ( size_t j=0; j < _num_of_feats; ++j ){ if ( j < _eff_feats ){ perm_feats[j] = feats[permutation[j]]; } else { perm_feats[j] = NULL; } } } Feature::~Feature(){ if ( !is_reference ){ delete_matrix(); delete metric; for ( const auto& it : values_array ){ delete it; } } reverse_values.clear(); } bool Feature::matrixPresent( bool& isRead ) const { isRead = false; if ( metric_matrix != 0 ){ if ( PrestoreStatus == ps_ok ){ return true; } else if ( PrestoreStatus == ps_read ){ isRead = true; return true; } } return false; } size_t Feature::matrix_byte_size() const { if ( metric_matrix ){ return metric_matrix->NumBytes(); } else { return 0; } } FeatVal_Stat Feature::prepare_numeric_stats(){ bool first = true; for ( const auto& fv : values_array ){ size_t freq = fv->ValFreq(); if ( freq > 0 ){ double tmp = -1; if ( !TiCC::stringTo( fv->name(), tmp ) ){ Warning( "a Non Numeric value '" + fv->name_string() + "' in Numeric Feature!" ); return NotNumeric; } if ( first ){ first = false; n_min = tmp; n_max = tmp; } else if ( tmp < n_min ){ n_min = tmp; } else if ( tmp > n_max ){ n_max = tmp; } } } if ( fabs(n_max - n_min) < Epsilon ){ return SingletonNumeric; } else { return NumericValue; } } inline int min( int i1, int i2 ) { return (i1>i2?i2:i1); } inline size_t min( size_t i1, size_t i2 ) { return (i1>i2?i2:i1); } void Feature::SharedVarianceStatistics( const Targets& Targ, int eff_cnt ){ size_t NumInst = Targ.TotalValues(); int NumCats = Targ.EffectiveValues(); int k = min( NumCats, eff_cnt ) - 1; if ( k == 0 || NumInst == 0 ){ shared_variance = 0; } else { shared_variance = chi_square / (double)( NumInst * k ); } } void Feature::StandardDeviationStatistics( ){ double sum = 0.0; vector store( values_array.size() ); for ( unsigned int i=0; i < values_array.size(); ++i ){ const FeatureValue *FV = values_array[i]; double val = TiCC::stringTo( FV->name() ); store[i] = val; sum += val; } double total = 0.0; for ( unsigned int i=0; i < values_array.size(); ++i ){ double diff = sum - store[i]; total += diff*diff; } standard_deviation = sqrt( total / values_array.size() ); } void Feature::clear_matrix(){ if ( PrestoreStatus == ps_read ){ return; } else { delete_matrix(); } } void Feature::delete_matrix(){ if ( metric_matrix ){ metric_matrix->Clear(); delete metric_matrix; } metric_matrix = 0; PrestoreStatus = ps_undef; } bool Feature::setMetricType( const MetricType M ){ if ( !metric || M != metric->type() ){ delete metric; metric = getMetricClass(M); return true; } else { return false; } } MetricType Feature::getMetricType() const { return metric->type(); } bool Feature::store_matrix( int limit){ // // Store a complete distance matrix. // if ( PrestoreStatus == ps_read ){ return true; } if ( !metric_matrix ){ metric_matrix = new SparseSymetricMatrix(); } if ( PrestoreStatus != ps_failed && metric->isStorable( ) ) { try { for ( const auto& FV_i : values_array ){ for ( const auto& FV_j : values_array ){ if ( FV_i->ValFreq() >= matrix_clip_freq && FV_j->ValFreq() >= matrix_clip_freq && ( Prestored_metric != metric->type() || fabs(metric_matrix->Extract(FV_i,FV_j)) < Epsilon ) ){ double dist = metric->distance( FV_i, FV_j, limit ); metric_matrix->Assign( FV_i, FV_j, dist ); } } } } catch( ... ){ cout << "hit the ground!" << endl; PrestoreStatus = ps_failed; return false; }; PrestoreStatus = ps_ok; } if ( PrestoreStatus == ps_ok ){ Prestored_metric = metric->type(); } return true; } ostream& operator<< (std::ostream& os, SparseValueProbClass *VPC ){ if ( VPC ) { int old_prec = os.precision(); os.precision(3); os.setf( std::ios::fixed ); auto it = VPC->vc_map.begin(); for ( size_t k = 1; k <= VPC->dimension; ++k ){ os.setf(std::ios::right, std::ios::adjustfield); if ( it != VPC->vc_map.end() && it->first == k ){ os << "\t" << it->second; ++it; } else { os << "\t" << 0.0; } } os << setprecision( old_prec ); } else { os << "(Null SA)"; } return os; } void Feature::print_vc_pb_array( ostream &os ) const { for ( const auto& FV : values_array ){ if ( FV->ValueClassProb ){ os << FV << FV->ValueClassProb << endl; } } } bool Feature::read_vc_pb_array( istream &is ){ unsigned int Num = 0; bool first = true; // clear all existing arrays for ( const auto& FV : values_array ){ if ( FV->ValueClassProb ){ delete FV->ValueClassProb; FV->ValueClassProb = NULL; } } UnicodeString buf; while ( TiCC::getline( is, buf ) ){ if ( buf.length() < 8 ){ // "empty" line separates matrices break; } vector parts = TiCC::split( buf ); if ( first ){ Num = parts.size() - 1; first = false; } UnicodeString name = parts[0]; FeatureValue *FV = Lookup( name ); if ( !FV ){ Warning( "Unknown FeatureValue '" + TiCC::UnicodeToUTF8(name) + "' in file, (skipped) " ); continue; } else { FV->ValueClassProb = new SparseValueProbClass( Num ); for ( size_t i=0; i < Num; ++i ){ UnicodeString tname = parts[i+1]; double value; if ( !TiCC::stringTo( tname, value ) ){ Error( "Found illegal value '" + TiCC::UnicodeToUTF8(tname) + "'" ); return false; } else if ( value > Epsilon ) { FV->ValueClassProb->Assign( i, value ); } } } } // check if we've got all the values, assign a default if not so for ( const auto& FV : values_array ){ if ( FV->ValueClassProb == NULL ){ FV->ValueClassProb = new SparseValueProbClass( Num ); } } vcpb_read = true; return true; } bool Feature::fill_matrix( istream &is ) { if ( !metric_matrix ){ metric_matrix = new SparseSymetricMatrix(); } else { metric_matrix->Clear(); } UnicodeString line; while ( TiCC::getline(is,line) ){ if ( line.isEmpty() ){ break; } vector arr = TiCC::split_at( line, " " ); size_t num = arr.size(); double d; if ( num != 2 ){ Error( "wrong line in inputfile" ); return false; } else if ( arr[0].length() < 2 ){ Error( "wrong line in inputfile" ); return false; } else if ( !TiCC::stringTo( arr[1], d ) ) { Error( "wrong line in inputfile" ); return false; } else { UnicodeString stripped = UnicodeString( arr[0], 1,arr[0].length()-2) ; vector parts = TiCC::split_at( stripped, ",\t" ); if ( parts.size() != 2 ){ Error( "wrong line in inputfile" ); return false; } else { FeatureValue *F1 = Lookup(parts[0]); FeatureValue *F2 = Lookup(parts[1]); metric_matrix->Assign( F1, F2, d ); } } } PrestoreStatus = ps_read; return true; } void Feature::print_matrix( ostream &os, bool full ) const { // // Print the matrix. // int old_prec = os.precision(); ios::fmtflags old_flags = os.flags(); os.unsetf(std::ios_base::floatfield); if ( full ){ for ( const auto& FV_i : values_array ){ os.width(6); os.setf(ios::left, ios::adjustfield); os << FV_i << ":"; os.width(12); os.precision(3); os.setf(ios::right, ios::adjustfield); for ( const auto& FV_j : values_array ){ os.width(12); os.precision(3); os.setf(ios::right,ios::adjustfield ); if ( FV_i->ValFreq() < matrix_clip_freq || FV_j->ValFreq() < matrix_clip_freq ){ os << "*"; } else { os << metric_matrix->Extract(FV_i,FV_j); } } os << endl; } } else { os << *metric_matrix << endl; } os << setprecision( old_prec ); os.flags( old_flags ); } } // namespace Timbl LanguageMachines-timbl-642727d/src/GetOptClass.cxx000066400000000000000000000662771451477526200220350ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include "ticcutils/CommandLine.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Options.h" #include "timbl/MsgClass.h" #include "timbl/Metrics.h" #include "timbl/Instance.h" #include "timbl/GetOptClass.h" #include "timbl/TimblExperiment.h" using namespace std; namespace Timbl { void GetOptClass::set_default_options( int Max ){ local_algo = IB1_a; local_metric = UnknownMetric; local_order = UnknownOrdening; local_weight = Unknown_w; local_decay = Zero; local_decay_alfa = 1.0; local_decay_beta = 1.0; local_normalisation = unknownNorm; local_norm_factor = 1; no_neigh = 1; mvd_limit = 1; estimate = 0; maxbests = 500; BinSize = 0; BeamSize = 0; clip_freq = 10; clones = 1; bootstrap_lines = -1; local_progress = 100000; seed = -1; do_exact = false; do_hashed = true; min_present = false; keep_distributions = false; do_sample_weights = false; do_ignore_samples = false; do_ignore_samples_test = false; do_query = false; do_all_weights = false; do_sloppy_loo = false; do_silly = false; do_diversify = false; if ( MaxFeats == -1 ){ MaxFeats = Max; LocalInputFormat = UnknownInputFormat; // InputFormat and verbosity myVerbosity = NO_VERB; // are not reset! } target_pos = -1; metricsArray.resize(MaxFeats+1, UnknownMetric ); outPath = ""; occIn = 0; } GetOptClass::GetOptClass( const TiCC::CL_Options& opts ): LocalInputFormat( UnknownInputFormat ), MaxFeats(-1), target_pos(-1), f_length( 0 ), threshold( -1 ), igThreshold( -1 ), myVerbosity( NO_VERB ), opt_init( false ), opt_changed( false ), N_present( false ), parent_socket_os( 0 ) { int MaxF = DEFAULT_MAX_FEATS; string optie; if ( opts.is_present( 'N', optie ) ){ N_present = true; MaxF = TiCC::stringTo( optie ); } set_default_options( MaxF ); } GetOptClass::~GetOptClass( ){ } GetOptClass::GetOptClass( const GetOptClass& in ): MsgClass(in), local_algo( in.local_algo ), local_metric( in.local_metric ), local_order( in.local_order ), local_weight( in.local_weight ), LocalInputFormat( in.LocalInputFormat ), local_decay( in.local_decay ), local_decay_alfa( in.local_decay_alfa ), local_decay_beta( in.local_decay_beta ), local_normalisation( in.local_normalisation ), local_norm_factor( in.local_norm_factor ), MaxFeats( in.MaxFeats ), target_pos( in.target_pos ), no_neigh( in.no_neigh ), mvd_limit( in.mvd_limit ), estimate( in.estimate ), maxbests( in.maxbests ), clip_freq( in.clip_freq ), clones( in.clones ), BinSize( in.BinSize ), BeamSize( in.BeamSize ), bootstrap_lines( in.bootstrap_lines ), f_length( in.f_length ), local_progress( in.local_progress ), seed( in.seed ), threshold( in.threshold ), igThreshold( in.igThreshold ), myVerbosity( in.myVerbosity ), opt_init( in.opt_init ), opt_changed( in.opt_changed ), do_exact( in.do_exact ), do_hashed( in.do_hashed ), min_present( in.min_present ), N_present(false), keep_distributions( in.keep_distributions ), do_sample_weights( in.do_sample_weights ), do_ignore_samples( in.do_ignore_samples ), do_ignore_samples_test( in.do_ignore_samples_test ), do_query( in.do_query ), do_all_weights( false ), do_sloppy_loo( false ), do_silly( in.do_silly ), do_diversify( in.do_diversify ), metricsArray( in.metricsArray ), parent_socket_os( in.parent_socket_os ), outPath( in.outPath ), occIn( in.occIn ) { } GetOptClass *GetOptClass::Clone( ostream *sock_os ) const{ GetOptClass *result = new GetOptClass(*this); result->parent_socket_os = sock_os; return result; } void GetOptClass::Error( const string& out_line ) const { if ( parent_socket_os ){ *parent_socket_os << "ERROR { " << out_line << " }" << endl; } else { cerr << "Error:" << out_line << endl; } } bool GetOptClass::definitive_options( TimblExperiment *Exp ){ if ( opt_changed || !opt_init ){ opt_changed = false; bool first = !opt_init; opt_init = true; string optline; if ( first ){ // the following options can only be set once! // If you try it anyway, you should get a MblClass warning... if ( LocalInputFormat == SparseBin ){ if ( !N_present ){ Error( "Missing -N option, mandatory for -F Binary" ); return false; } } if ( LocalInputFormat == Sparse ){ if ( !N_present ){ Error( "Missing -N option, mandatory for -F Sparse" ); return false; } } if ( LocalInputFormat != UnknownInputFormat ){ optline = "INPUTFORMAT: " + TiCC::toString(LocalInputFormat); if ( !Exp->SetOption( optline ) ){ return false; } } if ( target_pos != -1 ){ optline = "TARGET_POS: " + TiCC::toString(target_pos-1); if ( !Exp->SetOption( optline ) ){ return false; } } if ( keep_distributions ){ optline = "KEEP_DISTRIBUTIONS: true"; if ( !Exp->SetOption( optline ) ){ return false; } } if ( do_sloppy_loo ){ if ( local_algo != LOO_a ){ Error( "sloppy only valid for LOO algorithm" ); return false; } else { optline = "DO_SLOPPY_LOO: true"; if ( !Exp->SetOption( optline ) ){ return false; } } } if ( do_silly ){ optline = "DO_SILLY: true"; if ( !Exp->SetOption( optline ) ){ return false; } } if ( do_diversify ){ optline = "DO_DIVERSIFY: true"; if ( !Exp->SetOption( optline ) ){ return false; } } if ( f_length > 0 ){ optline = "FLENGTH: " + TiCC::toString(f_length); if ( !Exp->SetOption( optline ) ){ return false; } } if ( local_weight != Unknown_w ){ optline = "WEIGHTING: " + TiCC::toString(local_weight); Exp->SetOption( optline ); } if ( do_all_weights ){ optline = "ALL_WEIGHTS: true"; Exp->SetOption( optline ); } optline = "MAXBESTS: " + TiCC::toString(maxbests); Exp->SetOption( optline ); if ( BinSize > 0 ){ optline = "BIN_SIZE: " + TiCC::toString(BinSize); Exp->SetOption( optline ); } if ( BeamSize > 0 ){ optline = "BEAM_SIZE: " + TiCC::toString(BeamSize); Exp->SetOption( optline ); } if ( local_algo == TRIBL_a && threshold < 0 ){ Error( "-q is missing for TRIBL algorithm" ); return false; } if ( threshold >= 0 ){ if ( local_algo != TRIBL_a ){ Error( "-q option only valid for TRIBL algorithm" ); return false; } if ( threshold == 0 ){ Error( "invalid -q option. Must be > 0 " ); return false; } optline = "TRIBL_OFFSET: " + TiCC::toString(threshold); Exp->SetOption( optline ); } if ( igThreshold > 0 ){ optline = "IG_THRESHOLD: " + TiCC::toString(igThreshold); Exp->SetOption( optline ); } if ( local_order != UnknownOrdening ){ optline = "TREE_ORDER: " + TiCC::toString(local_order); Exp->SetOption( optline ); } if ( !outPath.empty() ){ Exp->setOutPath( outPath ); } } //first if ( clones > 0 ){ Exp->Clones( clones ); } if ( estimate < 10 ){ Exp->Estimate( 0 ); } else { Exp->Estimate( estimate ); } if ( myVerbosity & CONFIDENCE ){ if ( local_normalisation == unknownNorm ){ Error( "Invalid option +vcf, while -G is missing!" ); return false; } } if ( myVerbosity & DISTRIB ){ if ( !keep_distributions && local_algo == IGTREE_a ){ myVerbosity &= ~DISTRIB; Error( "Invalid option +vdb, while +D is missing!" ); return false; } } if ( myVerbosity & ALL_K ){ if ( local_algo == IGTREE_a ){ Error( "Invalid option +vk, impossible with IGtree algorithm" ); return false; } else if ( !(myVerbosity & DISTRIB) ){ // silently add +vdb when +vk is set myVerbosity |= DISTRIB; } } if ( myVerbosity & NEAR_N ){ if ( local_algo == IGTREE_a ){ Error( "Invalid option +vn, impossible with IGtree algorithm" ); return false; } } if ( myVerbosity & CONF_MATRIX || myVerbosity & CLASS_STATS ) myVerbosity |= ADVANCED_STATS; if ( do_exact ){ Exp->SetOption( "EXACT_MATCH: true" ); } else { Exp->SetOption( "EXACT_MATCH: false" ); } if ( do_hashed ) { Exp->SetOption( "HASHED_TREE: true" ); } else { Exp->SetOption( "HASHED_TREE: false" ); } if ( occIn > 0 && do_sample_weights ){ Error( "--occurrences and -s cannot be combined!" ); return false; } if ( occIn > 0 ){ Exp->SetOption( "HANDLE_OCCURRENCES: " + TiCC::toString(occIn) ); } else if ( do_sample_weights ){ Exp->SetOption( "EXEMPLAR_WEIGHTS: true" ); if ( do_ignore_samples ){ Exp->SetOption( "IGNORE_EXEMPLAR_WEIGHTS: true" ); } else { Exp->SetOption( "IGNORE_EXEMPLAR_WEIGHTS: false" ); } if ( do_ignore_samples_test ){ Exp->SetOption( "NO_EXEMPLAR_WEIGHTS_TEST: true" ); } else { Exp->SetOption( "NO_EXEMPLAR_WEIGHTS_TEST: false" ); } } else { Exp->SetOption( "EXEMPLAR_WEIGHTS: false" ); } if ( local_metric == UnknownMetric ){ // Ok, so NO defaults at all (API usage for instance) local_metric = Overlap; fill( metricsArray.begin(), metricsArray.end(), Overlap ); } optline = "GLOBAL_METRIC: " + TiCC::toString(local_metric); Exp->SetOption( optline ); if ( bootstrap_lines > 0 ){ optline = "IB2_OFFSET: " + TiCC::toString(bootstrap_lines); Exp->SetOption( optline ); } if ( local_normalisation != unknownNorm ){ optline = "NORMALISATION: " + TiCC::toString( local_normalisation ); Exp->SetOption( optline ); if ( local_normalisation == addFactorNorm ){ optline = "NORM_FACTOR: " + TiCC::toString( local_norm_factor ); Exp->SetOption( optline ); } } optline = "MVD_LIMIT: " + TiCC::toString(mvd_limit); Exp->SetOption( optline ); optline = "NEIGHBORS: " + TiCC::toString(no_neigh); if ( Exp->SetOption( optline ) ){ optline = "DECAY: " + TiCC::toString(local_decay); if ( Exp->SetOption( optline ) ){ optline = "DECAYPARAM_A: " + TiCC::toString(local_decay_alfa); if ( Exp->SetOption( optline ) ){ optline = "DECAYPARAM_B: " + TiCC::toString(local_decay_beta); if ( Exp->SetOption( optline ) ){ optline = "CLIP_FACTOR: " + TiCC::toString(clip_freq); if ( Exp->SetOption( optline ) ){ optline = "SEED: " + TiCC::toString(seed); if ( Exp->SetOption( optline ) ){ optline = "PROGRESS: " + TiCC::toString(local_progress); if ( Exp->SetOption( optline ) ){ optline = "VERBOSITY: " + TiCC::toString(myVerbosity); if ( Exp->SetOption( optline ) ){ for ( size_t i=0; i < metricsArray.size(); ++i ){ optline = "METRICS: " + TiCC::toString( i ) + "=" + TiCC::toString(metricsArray[i]); if (!Exp->SetOption( optline ) ){ Error( "changing metric is not possible at this stage" ); return false; } } if ( do_query ){ Exp->ShowSettings( cerr ); do_query = false; } return true; } } } } } } } } return false; } return true; } inline bool GetOptClass::parse_range( string& line, string::iterator& it, MetricType Value ){ size_t m; while( it != line.end() && *it != ':' ){ auto eit = it; while( eit != line.end() && isdigit( *eit ) ) ++eit; string tmp = string( it, eit ); size_t k; if ( TiCC::stringTo( tmp, k, 1, metricsArray.size() ) ){ if ( metricsArray[k] != UnknownMetric && metricsArray[k] != Value ){ Error( "metric of feature " + tmp + " is multiply changed!" ); return false; } metricsArray[k] = Value; } else { Error( "illegal value in metric description: -m " + line ); return false; } it = eit; if ( it == line.end() ){ return true; } else if ( *it == ',' ){ ++it; } else if ( *it == '-' ){ ++it; eit = it; while( eit != line.end() && isdigit( *eit ) ) ++eit; tmp = string( it, eit ); m = TiCC::stringTo(tmp); if ( m == 0 || m > metricsArray.size() ){ Error( "illegal value in metric description: -m " + line ); return false; } it = eit; if ( it != line.end() && (*it != ',' && *it != ':' ) ){ Error( "illegal value in metric description: -m " + line ); return false; } if ( m < k ){ Error( "illegal value in metric description: -m " + line ); return false; } else { for ( size_t j=k+1; j <= m && j < metricsArray.size(); ++j ){ if ( metricsArray[j] != UnknownMetric && metricsArray[j] != Value ){ Error( "metric of feature " + TiCC::toString(j) + " is multiply changed!" ); return false; } metricsArray[j] = Value; } } if ( it != line.end() && *it == ',' ) { ++it; } } } return true; } inline bool GetOptClass::parse_metrics( const string& Mline, MetricType& Def ){ string line = TiCC::trim( Mline ); TiCC::to_upper( line ); auto p = line.begin(); if ( p != line.end() ){ switch ( *p++ ){ case 'O' : Def = Overlap; break; case 'J' : Def = JeffreyDiv; break; case 'S' : Def = JSDiv; break; case 'M' : Def = ValueDiff; break; case 'N' : Def = Numeric; break; case 'E' : Def = Euclidean; break; case 'D' : if ( p == line.end() || *p == ':' ){ Def = DotProduct; } else { if ( *p == 'C' ){ Def = Dice; ++p; } } break; case 'C' : Def = Cosine; break; case 'L' : Def = Levenshtein; break; case 'I' : Def = Ignore; break; default: Error( "illegal default value for metric: -m " + Mline ); return false; } if ( p == line.end() ){ // // only -m options, no further specifications // if ( Def == Ignore ){ Error( "Ignore without further specification for metric: -m " + Mline ); return false; } else { // set the defaults fill( metricsArray.begin(), metricsArray.end(), Def ); return true; } } else if ( *p != ':' ){ Error( "missing ':' after default value in -m option" ); return false; } else { // deviating options expected. reset the array fill( metricsArray.begin(), metricsArray.end(), UnknownMetric ); ++p; MetricType TmpMT; while( p != line.end() ){ switch ( *p ){ case 'O' : TmpMT = Overlap; break; case 'S' : TmpMT = JSDiv; break; case 'J' : TmpMT = JeffreyDiv; break; case 'D' : if ( *(p+1) && *(p+1) == 'C' ){ ++p; TmpMT = Dice; } else { Error( "illegal value in metric description: -m " + Mline ); return false; } break; case 'M' : TmpMT = ValueDiff; break; case 'E' : TmpMT = Euclidean; break; case 'N' : TmpMT = Numeric; break; case 'I' : TmpMT = Ignore; break; default: Error( "illegal value in metric description: -m " + Mline ); return false; } metricClass *tmpMC = getMetricClass(Def); if ( TmpMT != Ignore && tmpMC->isSimilarityMetric() ){ Error( "Similarity metric " + TiCC::toString( Def ) + " only accepts -I specifications: -m " + Mline ); delete tmpMC; return false; } delete tmpMC; ++p; if ( !parse_range( line, p, TmpMT ) ){ return false; } if ( p == line.end() ){ break; } if ( *p != ':' ){ Error( "missing ':' in metric description" ); return false; } else { ++p; } } if ( p != line.end() ){ Error( "illegal value in metric description: -m " + Mline ); return false; } else { // // set defaults for those still unset // replace( metricsArray.begin(), metricsArray.end(), UnknownMetric, Def ); } } return true; } else { return false; } } inline bool isBoolOrEmpty( const string& in, bool& val ){ if ( in.empty() ){ val = true; return true; } else { string s = TiCC::uppercase( in ); if ( s == "TRUE" || s == "YES" || s == "FALSE" || s == "NO" ){ val = ( s == "TRUE" || s == "YES" ); return true; } } return false; } bool GetOptClass::parse_options( const TiCC::CL_Options& opts, const int mode ){ opt_changed = true; // cerr << "options: " << opts << endl; // cerr << "mode: " << mode << endl; for ( auto const& curr_opt: opts ){ // cerr << "process " << curr_opt << endl; bool mood = curr_opt.get_mood(); bool longOpt = curr_opt.is_long(); string value = curr_opt.value(); char opt_char = curr_opt.opt_char(); string option = curr_opt.option(); try { // cerr << "try " << opt_char << endl; switch (opt_char) { case 'a': { AlgorithmType tmp_a = IB1_a; if ( !TiCC::stringTo( value, tmp_a ) ){ Error( "illegal -a value: " + value ); return false; } else if ( tmp_a != IB1_a ){ if ( local_algo == LOO_a || local_algo == CV_a ){ Error( "only IB1 algorithm is allowed for: " + TiCC::toString(local_algo) ); return false; } else { local_algo = tmp_a; } } } break; case 'b': bootstrap_lines = TiCC::stringTo( value ); if ( bootstrap_lines < 1 ){ Error( "illegal value for -b option: " + value ); return false; } break; case 'B': if ( longOpt ){ if ( option == "Beam" ){ if ( !TiCC::stringTo( value, BeamSize ) || BeamSize <= 0 ){ Error( "illegal value for --Beam option: " + value ); return false; } } } else if ( value.find("eam") != string::npos ){ Error( "invalid option: Did you mean '--B" + value + "'?" ); return false; } else { BinSize = TiCC::stringTo( value ); if ( BinSize <= 1 ){ Error( "illegal value for -B option: " + value ); return false; } } break; case 'c': if ( longOpt ){ if ( option == "clones" ){ if ( !TiCC::stringTo( value, clones ) || clones <= 0 ){ Error( "invalid value for --clones option: '" + value + "'" ); return false; } } } else { if ( !TiCC::stringTo( value, clip_freq ) || clip_freq < 0 ){ Error( "illegal value for -c option: " + value ); return false; } } break; case 'd': { string::size_type pos1 = value.find( ":" ); if ( pos1 == string::npos ){ pos1 = value.find_first_of( "0123456789" ); if ( pos1 != string::npos ){ if ( ! ( TiCC::stringTo( string( value, 0, pos1 ), local_decay ) && TiCC::stringTo( string( value, pos1 ), local_decay_alfa ) ) ){ Error( "illegal value for -d option: " + value ); return false; } } else if ( !TiCC::stringTo( value, local_decay ) ){ Error( "illegal value for -d option: " + value ); return false; } } else { string::size_type pos2 = value.find( ':', pos1+1 ); if ( pos2 == string::npos ){ pos2 = value.find_first_of( "0123456789", pos1+1 ); if ( pos2 != string::npos ){ if ( ! ( TiCC::stringTo( string( value, 0, pos1 ), local_decay ) && TiCC::stringTo( string( value, pos2 ), local_decay_alfa ) ) ){ Error( "illegal value for -d option: " + value ); return false; } } else { Error( "illegal value for -d option: " + value ); return false; } } else { if ( ! ( TiCC::stringTo( string( value, 0, pos1 ), local_decay ) && TiCC::stringTo( string( value, pos1+1, pos2-pos1-1 ), local_decay_alfa ) && TiCC::stringTo( string( value, pos2+1 ), local_decay_beta ) ) ){ Error( "illegal value for -d option: " + value ); return false; } } } break; } case 'D': if ( longOpt ){ if ( option == "Diversify" ){ do_diversify = true; } else { Error( "invalid option: Did you mean '--Diversify' ?" ); return false; } } else { keep_distributions = mood; } break; case 'e': if ( !TiCC::stringTo( value, estimate ) || estimate < 0 ){ Error( "illegal value for -e option: " + value ); return false; } break; case 'F': if ( !TiCC::stringTo( value, LocalInputFormat ) ){ Error( "illegal value for -F option: " + value ); return false; } break; case 'G': if ( value.empty() ){ local_normalisation = probabilityNorm; } else { string::size_type pos1 = value.find( ":" ); if ( pos1 == string::npos ){ local_normalisation = TiCC::stringTo( value ); local_norm_factor = 1; } else { local_normalisation = TiCC::stringTo( string( value, 0, pos1 ) ); if ( !TiCC::stringTo( string( value, pos1+1 ), local_norm_factor ) || local_norm_factor < Epsilon ){ Error( "illegal value for -G option: " + value ); return false; } } if ( local_normalisation == unknownNorm ){ Error( "illegal value for -G option: " + value ); return false; } } break; case 'H': do_hashed = mood; break; case 'k': if ( !TiCC::stringTo( value, no_neigh ) || no_neigh <= 0 ){ Error( "illegal value for -k option: " + value ); return false; } break; case 'l': if ( !TiCC::stringTo( value, f_length ) || f_length <= 0 ){ Error( "illegal value for -l option: " + value ); return false; } break; case 'L': { string::size_type pos1 = value.find( ":" ); if ( pos1 == string::npos ){ pos1 = value.find_first_of( "0123456789" ); if ( pos1 != string::npos ){ if ( !TiCC::stringTo( value, mvd_limit ) || mvd_limit <= 0 ){ Error( "illegal value for -L option: " + value ); return false; } } } else { if ( !TiCC::stringTo( string( value, pos1+1 ), mvd_limit ) || mvd_limit <= 0 ){ Error( "illegal value for -L option: " + value ); return false; } } break; } case 'm': if ( !parse_metrics( value, local_metric ) ){ return false; } break; case 'M': if ( !TiCC::stringTo( value, maxbests ) || maxbests <= 0 ){ Error( "illegal value for -M option: " + value ); return false; } break; case 'N': // skip previously parsed NumOfFeatures info. break; case 'O': outPath = value; break; case 'o': if ( longOpt ){ if ( option == "occurrences" ){ if ( value == "train" ){ occIn = 1; } else if ( value == "test" ){ occIn = 2; } else if ( value == "both" ){ occIn = 3; } else { Error( "invalid --ocurrences value. (expected train,test or both)" ); return false; } } } break; case 'p': local_progress = TiCC::stringTo( value ); break; case 'q': threshold = TiCC::stringTo( value ); break; case 'Q': do_query = true; break; case 'R': if ( !TiCC::stringTo( value, seed ) ){ Error( "Integer argument for Random Seed expected (-R option)" ); return false; } break; case 's': if ( longOpt ){ if ( option == "sloppy" ){ bool val; if ( !isBoolOrEmpty(value,val) ){ Error( "invalid value for sloppy: '" + value + "'" ); return false; } do_sloppy_loo = val; } else if ( option == "silly" ){ bool val; if ( !isBoolOrEmpty(value,val) ){ Error( "invalid value for silly: '" + value + "'" ); return false; } do_silly = val; } } else { //short opt, so -s if ( value.empty() ){ do_sample_weights = true; } else { int val; if ( TiCC::stringTo( value, val ) ){ if ( val == 0 ){ do_ignore_samples = true; do_ignore_samples_test = false; do_sample_weights = true; } else if ( val == 1 ){ do_ignore_samples_test = true; do_sample_weights = true; } } if ( !do_sample_weights) { Error( "invalid value for -s: '" + value + "' (maybe you meant --s" + value + " ?)" ); return false; } } } break; case 't': { AlgorithmType tmp_a = IB1_a; if ( compare_nocase( value, "leave_one_out" ) ){ tmp_a = LOO_a; } else if ( compare_nocase( value, "cross_validate" ) ){ tmp_a = CV_a; } if ( local_algo != IB1_a && tmp_a != IB1_a ){ Error( "only IB1 algorithm is allowed for: " + TiCC::toString(tmp_a) ); return false; } local_algo = tmp_a; } break; case 'T': { if ( longOpt ){ if ( option == "Threshold" ){ if ( !TiCC::stringTo(value, igThreshold ) || igThreshold < 0 ){ Error( "invalid value for Threshold: " + value ); return false; } } else if ( option == "Treeorder" ){ if ( !TiCC::stringTo( value, local_order ) ){ Error( "invalid value for Treeorder: " + value ); return false; } } } else if ( value.find("hreshold") != string::npos || value.find("reeorder") != string::npos ){ Error( "invalid option: Did you mean '--T" + value + "' ?" ); return false; } else if ( !TiCC::stringTo( value, target_pos ) || target_pos <= 0 ){ Error( "illegal value for -T option: " + value ); return false; } } break; case 'v':{ VerbosityFlags Flag = NO_VERB; if ( !TiCC::stringTo( value, Flag ) ){ Error( "illegal value for +/- v option: " + value ); return false; } else { if ( mode == 2 && ( !(Flag & (SILENT|DISTANCE|DISTRIB|NEAR_N|CONF_MATRIX) ) ) ){ Error( "-v option: " + TiCC::toString(Flag) + " is not allowed at this stage." ); return false; } else if ( Flag > 0 ){ if ( mood ){ myVerbosity |= Flag; } else { myVerbosity &= ~Flag; } } else { myVerbosity = NO_VERB; } } } break; case 'w': { if ( !TiCC::stringTo( value, local_weight ) ) return false; }; break; case 'W': { do_all_weights = true; }; break; case 'x': do_exact = mood; break; default: Warning( string("unhandled option: ") + opt_char + " " + value ); } } catch( std::runtime_error& err ) { Error( string("invalid value for option '-") + opt_char + "' (" + value + ")" ); return false; } } return true; } } LanguageMachines-timbl-642727d/src/IBprocs.cxx000066400000000000000000000307741451477526200211770ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include "ticcutils/StringOps.h" #include "timbl/IBtree.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/MBLClass.h" using namespace std; namespace Timbl { bool MBLClass::HideInstance( const Instance& Inst ){ bool result = true; InstanceBase->RemoveInstance( Inst ); MBL_init = do_sloppy_loo; // must be only true if you are REALY sure for ( size_t i=0; i < EffectiveFeatures() && result; ++i ){ features.perm_feats[i]->clear_matrix(); if ( !features.perm_feats[i]->decrement_value( Inst.FV[i], Inst.TV ) ){ FatalError( "Unable to Hide an Instance!" ); result = false; } } if ( result ){ targets.decrement_value( Inst.TV ); } return result; } bool MBLClass::UnHideInstance( const Instance& Inst ){ bool result = true; InstanceBase->AddInstance( Inst ); MBL_init = do_sloppy_loo; // must be only true if you are REALY sure for ( size_t i=0; i < EffectiveFeatures() && result; ++i ){ features.perm_feats[i]->clear_matrix(); if ( !features.perm_feats[i]->increment_value( Inst.FV[i], Inst.TV ) ){ FatalError( "Unable to UnHide this Instance!" ); result = false; } } if ( result ){ targets.increment_value( Inst.TV ); } return result; } MBLClass::IB_Stat MBLClass::IBStatus() const { if ( !InstanceBase ){ return Invalid; } else if (InstanceBase->IsPruned() ){ return Pruned; } else { return Normal; } } void MBLClass::IBInfo( ostream& os ) const { double Compres; unsigned long int CurSize; unsigned long int CurBytes; CurBytes = InstanceBase->GetSizeInfo( CurSize, Compres ); ios::fmtflags OldFlg = os.setf( ios::fixed, ios::floatfield ); int OldPrec = os.precision(2); os << "\nSize of InstanceBase = " << CurSize << " Nodes, (" << CurBytes << " bytes), " << Compres << " % compression" << endl; if ( Verbosity(BRANCHING) ) { vector terminals; vector nonTerminals; unsigned int summedNodes = 0; unsigned int endNodes = 0; os << "branching info:" << endl; os << " level | feature | nodes | nonterms | terminals | b-factor | b-factor-n" << endl; InstanceBase->summarizeNodes( terminals, nonTerminals ); unsigned int i = 0; auto nIt = nonTerminals.begin(); auto tIt = terminals.begin(); while ( nIt != nonTerminals.end() ){ endNodes += *tIt; int nodes; if ( i == 0 ){ nodes = 1; os << setw(8) << 0 << " |" << setw(8) << "top" << " |" << setw(10) << 1 << " |" << setw(10) << 1 << " |" << setw(10) << 0 << " |" << setw(10) << double(*nIt + *tIt) << " |" << setw(10) << double(*nIt) << endl; } else { nodes = *(nIt-1) + *(tIt-1); if ( nodes == 0 ){ break; } os << setw(8) << i << " |"<< setw(8) << features.permutation[i-1] + 1 << " |" << setw(10) << nodes << " |" << setw(10) << *(nIt-1) << " |" << setw(10) << *(tIt-1) << " |" << setw(10) << (*nIt + *tIt)/double(nodes) << " |" << setw(10) << (*nIt?(*nIt)/double(*(nIt-1)):0) << endl; } summedNodes += nodes; ++i; ++nIt; ++tIt; } os << "total: nodes = " << summedNodes << " endnodes = " << endNodes << " factor = " << summedNodes/double(endNodes) << endl; } os.precision( OldPrec ); os.setf( OldFlg ); } string string_tok( const string& s, string::size_type& pos, const string& seps ){ string::size_type b_pos = s.find_first_not_of( seps, pos ); if ( b_pos != string::npos ){ pos = s.find_first_of( seps, b_pos ); if ( pos == string::npos ){ return string( s, b_pos ); } else { return string( s, b_pos, pos - b_pos ); } } else { pos = string::npos; } return ""; } size_t MBLClass::get_IB_Info( istream& is, bool& Pruned, int& Version, bool& Hashed, string& range_buf ){ size_t result = 0; if ( ExpInvalid() ){ Error( "Can't retrieve Instance-Base\n" ); return result; } if ( Options.TableFrozen() || NumOfFeatures() != 0 ){ Warning( "unable to read an Instance Base while another" " experiment is already loaded" ); return result; } bool info_ok = true; size_t depth = 0; int version = -1; Hashed = false; range_buf = ""; string buffer; vector splits; bool more = ( look_ahead(is) == '#' && getline( is, buffer ) ); while ( info_ok && more ){ splits = TiCC::split( buffer ); size_t num = splits.size(); if ( num > 2 ){ if ( compare_nocase_n( "Status:", splits[1] ) ){ version = 2; if ( splits[2] == "pruned" ){ Pruned = true; } else if ( splits[2] == "complete" ){ Pruned = false; } else { Error( "Unknown Status Information in Instance-Base file." ); info_ok = false; } } else if ( compare_nocase_n( "Algorithm:", splits[1] ) ) { version = 1; if ( compare_nocase( splits[2], "IG-tree" ) ){ Pruned = true; } else if ( compare_nocase( splits[2], "MBL" ) ){ Pruned = false; } else { Error( "Unknown Algorithm Information in Instance-Base file." ); info_ok = false; } } else if ( compare_nocase_n( "Permutation:", splits[1] ) ){ if ( splits[2][0] != '<' ){ Error( "missing `<` while reading permutation" ); info_ok = false; } else if ( splits[num-1][0] != '>' ){ Error( "missing `>` while reading permutation" ); info_ok = false; } else { string perms; for ( size_t i=3; i < num-1; ++i ){ perms = perms + splits[i]; // Maybe we could use splits directly? } bool excl = false; features._eff_feats = 0; size_t i = 0; string::size_type pos = 0; // skip < while ( info_ok && pos != string::npos && i < MaxFeatures ){ i++; if ( !excl ){ ++features._eff_feats; } string tmp = string_tok( perms, pos, ", !" ); size_t index = TiCC::stringTo( tmp ); features.permutation.push_back( --index ); if ( index >= MaxFeatures ){ Error ( "illegal value " + TiCC::toString(index) + " in permutation, not between 1 and " + TiCC::toString( MaxFeatures ) ); info_ok = false; break; } if ( excl ){ UserOptions[index+1] = Ignore; } if ( pos == string::npos ){ break; } while ( isspace(perms[pos]) ) ++pos; switch ( perms[pos] ){ case ',': ++pos; break; case '!': ++pos; excl = true; break; default: Error ( "missing `,` while reading permutation" ); info_ok = false; } } if ( info_ok ){ depth = i; } } } else if ( compare_nocase_n( "Numeric:", splits[1] ) ){ if ( splits[2][0] != '.' ){ string::size_type pos = 0; while ( pos != string::npos ){ string tmp = string_tok( splits[2], pos, ",. " ); if ( tmp != "" ){ int k = TiCC::stringTo( tmp ); UserOptions[k] = Numeric; } } getline( is, range_buf ); } } else if ( compare_nocase_n( "Bin_Size:", splits[1] ) ){ int siz = TiCC::stringTo( splits[2] ); if ( siz < 2 || siz > 1000000 ){ Error( "invalid Bin_Size found: " + splits[2] ); info_ok = false; } else { Bin_Size = siz; } } else if ( compare_nocase_n( "Version", splits[1] ) ){ version = TiCC::stringTo( splits[2] ); if ( version >= 3 && num > 3 ){ if ( compare_nocase_n( "(Hashed)", splits[3] ) ){ Hashed = true; } } } } more = ( look_ahead(is) == '#' && getline( is, buffer ) ); } if ( version < 0 ) { Error( "missing Version information in Instance-Base file" ); info_ok = false; } else if ( version < 4 ) { Error( "A Version " + TiCC::toString(version) + " type InstanceBase file is found:\n" " You should recreate it as it is no longer supported" "\n in this version of the timbl package" ); } Version = version; if ( info_ok ){ result = depth; return result; } else { Error( "Can't retrieve Instance-Base\n" ); return 0; } } bool MBLClass::get_ranges( const string& rangeline ){ if ( NumNumFeatures() == 0 ){ return true; } istringstream is( rangeline ); string buf; char kar; bool result = false; is >> kar; // skip # is >> ws >> buf; if ( !compare_nocase_n( "Ranges:", buf ) ){ Error( "missing Ranges line in Instance-Base file" ); } else { is >> ws; if ( look_ahead(is) == '.' ){ result = true; } else { do { int k; is >> k; if ( UserOptions[k] != Numeric ){ Error( "Found range info for feature " + TiCC::toString(k) + ", which is Not defined as Numeric!" ); result = false; } else { is >> ws >> buf; double min, max; // this is rather awfull // sscanf is not realy what we want. But notice: // the buffer might contain: [-4--0] !!!! ARGL // simply using split('-') is wrong. // we should have used a different separator! // But we didn't int scancount = sscanf( buf.c_str(), "[%lf-%lf]", &min, &max ); if ( scancount == 2 ){ features[k-1]->Min( min ); features[k-1]->Max( max ); if ( is ){ is >> ws >> buf; if ( !buf.empty() && (buf[0] == '.' || buf[0] == ',' ) ){ result = true; } else { result = false; } } else { buf = "."; result = true; } } else { result = false; } } } while ( result && buf[0] != '.' ); } } return result; } inline void MBLClass::writePermSpecial( ostream &os ) const{ // write out the permutation and mark the last feature which is // NOT to be ignored with an exclamation mark, for instance: // < 5, 2, 3! 1, 4 > bool excl = false; os << "< "; for ( size_t j=0; j < NumOfFeatures()-1; ++j ){ if ( !excl && features[features.permutation[j+1]]->Ignore() ){ excl = true; os << features.permutation[j]+1 << "! "; } else { os << features.permutation[j]+1 << ", "; } } os << features.permutation[NumOfFeatures()-1]+1 << " >" << endl; } bool MBLClass::PutInstanceBase( ostream& os ) const { bool result = true; if ( ExpInvalid() ){ result = false; } else if ( InstanceBase == 0 ){ Warning( "unable to write an Instance Base, nothing learned yet" ); } else { os << "# Status: " << (InstanceBase->IsPruned()?"pruned":"complete") << endl; os << "# Permutation: "; writePermSpecial( os ); os << "# Numeric: "; bool first = true; for ( size_t i=0; i < NumOfFeatures(); ++i ){ if ( !features[i]->Ignore() && features[i]->isNumerical() ){ if ( !first ){ os << ", "; } else { first = false; } os << i+1; } } os << '.' << endl; if ( NumNumFeatures() > 0 ){ os << "# Ranges: "; first = true; for ( size_t j=0; j < NumOfFeatures(); ++j ){ if ( !features[j]->Ignore() && features[j]->isNumerical() ){ if ( !first ){ os << " , "; } else { first = false; } os << j+1 << " [" << features[j]->Min() << "-" << features[j]->Max() << "]"; } } os << " ." << endl; } os << "# Bin_Size: " << Bin_Size << endl; if ( hashed_trees ){ InstanceBase->Save( os, *targets.hash(), *features.hash(), keep_distributions ); } else { InstanceBase->Save( os, keep_distributions ); } } return result; } } LanguageMachines-timbl-642727d/src/IBtree.cxx000066400000000000000000001350571451477526200210100ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include "ticcutils/StringOps.h" #include "ticcutils/UniHash.h" #include "ticcutils/XMLtools.h" #include "timbl/Common.h" #include "timbl/MsgClass.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/IBtree.h" using namespace std; using namespace icu; namespace Timbl { using namespace Common; IBtree::IBtree(): FValue(0), TValue(0), TDistribution(0), link(0), next(0) { } IBtree::IBtree( FeatureValue *_fv ): FValue(_fv), TValue( 0 ), TDistribution( 0 ), link(0), next(0) { } IBtree::~IBtree(){ delete TDistribution; delete link; delete next; } #ifdef IBSTATS inline IBtree *IBtree::add_feat_val( FeatureValue *FV, unsigned int& mm, IBtree *& tree, unsigned long& cnt ){ #else inline IBtree *IBtree::add_feat_val( FeatureValue *FV, IBtree *& tree, unsigned long& cnt ){ #endif // Add a Featurevalue to the IB. IBtree **pnt = &tree; while ( *pnt ){ if ( (*pnt)->FValue == FV ){ // already there, so bail out. return *pnt; } else if ( (*pnt)->FValue->Index() < FV->Index() ){ #ifdef IBSTATS ++mm; #endif pnt = &((*pnt)->next); } else { // need to add a new node before the current one IBtree *tmp = *pnt; *pnt = new IBtree( FV ); ++cnt; (*pnt)->next = tmp; return *pnt; } } // add at the end. *pnt = new IBtree( FV ); ++cnt; return *pnt; } static int IBtree_Indent = 0; ostream &operator<<( ostream &os, const IBtree& grap ){ // output an IBtree somewhat orderly. const IBtree *pnt = &grap; while ( pnt ) { if ( pnt->link || pnt->FValue ){ os << pnt->FValue; if ( pnt->TValue ){ os << "(" << pnt->TValue << ")" ; } if ( pnt->TDistribution ){ os << pnt->TDistribution ; } if ( pnt->link ){ os << "\t"; IBtree_Indent++; os << pnt->link; IBtree_Indent--; } else { os << endl; } } else { if ( pnt->TValue ){ os << "(" << pnt->TValue << ")" ; if ( pnt->link ){ os << "\t"; IBtree_Indent++; os << pnt->link; IBtree_Indent--; } } if ( pnt->TDistribution ){ os << pnt->TDistribution ; } os << endl; } if (pnt->next){ for ( int j=0; jnext; } return os; } ostream &operator<<( ostream &os, const IBtree *grap ){ if ( grap ){ os << *grap; } else { os << "null"; } return os; } ostream& operator<<( ostream &os, const InstanceBase_base& ib ){ os << "INSTANCE BASE, tree:" << endl; os << ib.InstBase << endl; return os; } ostream& operator<<( ostream &os, const InstanceBase_base *ib ){ if ( ib ){ os << *ib; } else { os << "empty INSTANCE BASE"; } return os; } unsigned long int InstanceBase_base::GetSizeInfo( unsigned long int& CurSize, double &Compression ) const { unsigned long int MaxSize = (Depth+1) * NumOfTails; CurSize = ibCount; Compression = 100*(1-(double)CurSize/(double)MaxSize); return CurSize * sizeof(IBtree); } void InstanceBase_base::write_tree( ostream &os, const IBtree *pnt ) const { // part of saving a tree in a recoverable manner os << " (" << pnt->TValue << " "; if ( pnt->link ){ if ( PersistentDistributions && pnt->TDistribution ){ os << pnt->TDistribution->Save(); } pnt = pnt->link; if ( pnt->FValue ){ os << "["; while ( pnt ){ os << pnt->FValue << " "; write_tree( os, pnt ); pnt = pnt->next; if ( pnt ){ os << ","; } } os << "]\n"; } else if ( !PersistentDistributions && pnt->TDistribution ){ os << pnt->TDistribution->Save(); } } else if ( pnt->TDistribution ){ os << pnt->TDistribution->Save(); } os << ")\n"; } void InstanceBase_base::write_tree_hashed( ostream &os, const IBtree *pnt ) const { // part of saving a tree in a recoverable manner os << "(" << pnt->TValue->Index(); if ( pnt->link ){ if ( PersistentDistributions && pnt->TDistribution ){ os << pnt->TDistribution->SaveHashed(); } pnt = pnt->link; if ( pnt->FValue ){ os << "["; while ( pnt ){ os << pnt->FValue->Index(); write_tree_hashed( os, pnt ); pnt = pnt->next; if ( pnt ){ os << ","; } } os << "]\n"; } else if ( pnt->TDistribution && !PersistentDistributions ){ os << pnt->TDistribution->SaveHashed(); } } else if ( pnt->TDistribution ){ os << pnt->TDistribution->SaveHashed(); } os << ")\n"; } const TargetValue *InstanceBase_base::TopTarget( bool &tie ) { if ( !DefaultsValid || !DefAss ){ TopT = 0; } if ( TopT == 0 ){ if ( !TopDistribution ){ // added to satisfy scan-build throw logic_error( "TopDistribution=0, might not happen!" ); } TopT = TopDistribution->BestTarget( tiedTop, Random); } tie = tiedTop; return TopT; } void InstanceBase_base::Save( ostream &os, bool persist ) { // save an IBtree for later use. bool temp_persist = PersistentDistributions; PersistentDistributions = persist; AssignDefaults(); bool dummy; os << "# Version " << Version << "\n#\n(" << TopTarget( dummy ) << " " << TopDistribution->Save(); IBtree *pnt = InstBase; if ( pnt ){ os << "["; while ( pnt ){ os << pnt->FValue; write_tree( os, pnt ); pnt = pnt->next; if ( pnt ){ os << ","; } } os << "]\n"; } os << ")\n"; PersistentDistributions = temp_persist; } xmlNode *to_node( const FeatureValue *fv ){ xmlNode *result = TiCC::XmlNewNode( "feature" ); TiCC::XmlAddContent( result, fv->name_string() ); return result; } xmlNode *to_node( const TargetValue *tv ){ xmlNode *result = TiCC::XmlNewNode( "target" ); TiCC::XmlAddContent( result, tv->name_string() ); return result; } xmlNode *to_node( const ClassDistribution *d ){ xmlNode *result = TiCC::XmlNewNode( "distribution" ); TiCC::XmlAddContent( result, d->DistToString() ); return result; } int count_next( const IBtree *pnt ){ int cnt = 0; const IBtree *tmp = pnt; while ( tmp ){ cnt++; tmp = tmp->next; } return cnt; } xmlNode *to_xml( IBtree *pnt ) { xmlNode *nodes = TiCC::XmlNewNode( "nodes" ); int cnt = count_next( pnt ); TiCC::XmlSetAttribute( nodes, "nodecount", TiCC::toString( cnt ) ); while ( pnt ){ xmlNode *node = TiCC::XmlNewChild( nodes, "node" ); if ( pnt->FValue ){ xmlAddChild( node, to_node( pnt->FValue ) ); } if ( pnt->TValue ){ xmlAddChild( node, to_node( pnt->TValue ) ); } if ( pnt->link ){ if ( pnt->link->FValue ){ xmlAddChild( node, to_xml(pnt->link) ); } else if ( pnt->link->TDistribution ){ xmlAddChild( node, to_node( pnt->link->TDistribution ) ); } } else if ( pnt->TDistribution ){ xmlAddChild( node, to_node( pnt->TDistribution ) ); } pnt = pnt->next; } return nodes; } void InstanceBase_base::toXML( ostream &os ) { // save an IBtree for later use. TiCC::XmlDoc doc( "root" ); xmlNode *root = doc.getRoot(); xmlAddChild( root, TiCC::XmlNewComment( "Version " + TiCC::toString(Version) ) ); bool dummy; xmlAddChild( root, to_node( TopTarget( dummy ) ) ); if ( PersistentDistributions ){ xmlAddChild( root, to_node( TopDistribution ) ); } IBtree *pnt = InstBase; xmlNode *tree = to_xml( pnt ); xmlAddChild( root, tree ); os << doc << endl; } UnicodeString VectoString( const vector& vec ){ UnicodeString result; for ( auto const& fv : vec ){ result += " " + fv->name(); } return result; } void InstanceBase_base::printStatsTree( ostream &os, unsigned int startLevel ) { if ( !PersistentDistributions ){ os << "no statsTree written, use IG tree and +D while training" << endl; } else { os << "statistics from level " << startLevel << " upwards" << endl; unsigned int level = startLevel; while ( level < Depth ){ IBtree *branch = InstBase; while ( branch ){ unsigned int l = level; IBtree *pnt = branch; vector pad; while ( pnt && l-- > 0 ){ pad.push_back( pnt->FValue ); pnt = pnt->link; } if ( pnt ){ os << level << " [" << VectoString(pad) << " " << pnt->FValue << " ] " << pnt->TDistribution << " < "; pnt = pnt->link; while ( pnt ){ os << pnt->FValue; pnt = pnt->next; if ( pnt ){ os << " "; } } os << " >" << endl; } branch = branch->next; } ++level; } } } void save_hash( ostream& os, const Hash::UnicodeHash& cats, const Hash::UnicodeHash& feats ){ int Size = cats.num_of_entries(); os << "Classes" << endl; for ( int i=1; i <= Size; ++i ){ os << i << "\t" << cats.reverse_lookup( i ) << endl; } Size = feats.num_of_entries(); os << "Features" << endl; for ( int i=1; i <= Size; ++i ){ os << i << "\t" << feats.reverse_lookup( i ) << endl; } os << endl; } void InstanceBase_base::Save( ostream& os, const Hash::UnicodeHash& cats, const Hash::UnicodeHash& feats, bool persist ) { // save an IBtree for later use. bool temp_persist = PersistentDistributions; PersistentDistributions = persist; AssignDefaults(); os << "# Version " << Version << " (Hashed)\n#" << endl; save_hash( os , cats, feats ); bool dummy; os << "(" << TopTarget( dummy )->Index() << TopDistribution->SaveHashed(); IBtree *pnt = InstBase; if ( pnt ){ os << "["; while ( pnt ){ os << pnt->FValue->Index(); write_tree_hashed( os, pnt ); pnt = pnt->next; if ( pnt ){ os << ","; } } os << "]\n"; } os << ")\n"; PersistentDistributions = temp_persist; } IBtree* InstanceBase_base::read_list( istream &is, Feature_List& feats, Targets& Targ, int level ){ IBtree *result = NULL; IBtree **pnt = &result; bool goon = true; char delim; while ( is && goon ) { is >> delim; // skip the opening `[` or separating ',' *pnt = read_local( is, feats, Targ, level ); if ( !(*pnt) ){ delete result; return NULL; } pnt = &((*pnt)->next); goon = ( look_ahead(is) == ',' ); } is >> delim; // skip closing `]` return result; } IBtree* InstanceBase_base::read_list_hashed( istream &is, Feature_List& feats, Targets& Targ, int level ){ IBtree *result = NULL; IBtree **pnt = &result; bool goon = true; char delim; while ( is && goon ) { is >> delim; // skip the opening `[` or separating ',' *pnt = read_local_hashed( is, feats, Targ, level ); if ( !(*pnt) ){ delete result; return NULL; } pnt = &((*pnt)->next); goon = ( (look_ahead(is) == ',') ); } is >> delim; // skip closing `]` return result; } IBtree *InstanceBase_base::read_local( istream &is, Feature_List& feats, Targets& Targ, int level ){ if ( !is ){ return NULL; } IBtree *result = new IBtree(); ++ibCount; UnicodeString buf; char delim; is >> ws >> buf; result->FValue = feats.perm_feats[level]->add_value( buf, NULL, 1 ); is >> delim; if ( !is || delim != '(' ){ Error( "missing `(` in Instance Base file" ); delete result; return NULL; } is >> ws >> buf; result->TValue = Targ.Lookup( buf ); int nxt = look_ahead(is); if ( nxt == '{' ){ try { result->TDistribution = ClassDistribution::read_distribution( is, Targ, false ); } catch ( const exception& e ){ Warning( e.what() ); Error( "problems reading a distribution from InstanceBase file" ); delete result; return 0; } // also we have to update the targetinformation of the featurevalue // so we can recalculate the statistics later on. if ( result->FValue->ValFreq() > 0 ){ result->FValue->ReconstructDistribution( *(result->TDistribution) ); } } if ( look_ahead(is) == '[' ){ result->link = read_list( is, feats, Targ, level+1 ); if ( !(result->link) ){ delete result; return 0; } } else if ( look_ahead(is) == ')' && result->TDistribution ){ result->link = new IBtree(); ++ibCount; result->link->TValue = result->TValue; if ( PersistentDistributions ){ result->link->TDistribution = result->TDistribution->to_VD_Copy(); } else { result->link->TDistribution = result->TDistribution; result->TDistribution = NULL; } NumOfTails++; } is >> delim; if ( delim != ')' ){ Error( "missing `)` in Instance Base file" ); delete result; return NULL; } return result; } IBtree *InstanceBase_base::read_local_hashed( istream &is, Feature_List& feats, Targets& Targ, int level ){ if ( !is ){ return NULL; } IBtree *result = new IBtree(); ++ibCount; char delim; int index; is >> index; result->FValue = feats.perm_feats[level]->add_value( index, NULL, 1 ); is >> delim; if ( !is || delim != '(' ){ Error( "missing `(` in Instance Base file" ); delete result; return NULL; } is >> index; result->TValue = Targ.ReverseLookup( index ); int nxt = look_ahead(is); if ( nxt == '{' ){ // // A distribution is found, must be the last featurevalue // (the dummy node is not stored) // OR we have Persistent Distributions try { result->TDistribution = ClassDistribution::read_distribution_hashed( is, Targ, false ); } catch ( const exception& e ){ Warning( e.what() ); Error( "problems reading a hashed distribution from InstanceBase file" ); delete result; return 0; } } if ( look_ahead(is) == '[' ){ result->link = read_list_hashed( is, feats, Targ, level+1 ); if ( !(result->link) ){ delete result; return NULL; } } else if ( look_ahead(is) == ')' && result->TDistribution ){ // // make a dummy node for the targetdistributions just read // result->link = new IBtree(); ++ibCount; result->link->TValue = result->TValue; if ( PersistentDistributions ){ result->link->TDistribution = result->TDistribution->to_VD_Copy(); } else { result->link->TDistribution = result->TDistribution; result->TDistribution = NULL; } NumOfTails++; } is >> delim; if ( delim != ')' ){ Error( "missing `)` in Instance Base file" ); delete result; return NULL; } return result; } bool InstanceBase_base::ReadIB( istream &is, Feature_List& feats, Targets& Targ, int expected_version ){ if ( read_IB( is, feats, Targ, expected_version ) ){ InstBase->redo_distributions(); ClassDistribution *Top = InstBase->sum_distributions( PersistentDistributions ); delete Top; // still a bit silly but the Top Distribution is known // but we need to cleanup behind us also return true; } else { return false; } } void InstanceBase_base::fill_index(){ IBtree *pnt = InstBase; while ( pnt ){ fast_index[pnt->FValue->Index()] = pnt; pnt = pnt->next; } } bool IG_InstanceBase::ReadIB( istream &is, Feature_List& feats, Targets& Targ, int expected_version ){ if ( read_IB( is, feats, Targ, expected_version ) ){ if ( PersistentDistributions ){ ClassDistribution *Top = InstBase->sum_distributions( PersistentDistributions ); delete Top; // still a bit silly but the Top Distribution is known // but we need to cleanup behind us also } return true; } else { return false; } } bool InstanceBase_base::read_IB( istream &is, Feature_List& feats, Targets& Targs, int expected_version ){ NumOfTails = 0; DefAss = true; // always for a restored tree DefaultsValid = true; // always for a restored tree Version = expected_version; char delim; is >> delim; if ( !is || delim != '(' ){ Error( "missing first `(` in Instance Base file" ); } else { // first we get the value of the TopTarget. It's in the file // for backward compability string buf; is >> ws >> buf; delete TopDistribution; TopDistribution = 0; if ( look_ahead(is) == '{' ){ // Now read the TopDistribution, to get the Targets // in the right order in Targ try { TopDistribution = ClassDistribution::read_distribution( is, Targs, true ); } catch ( const exception& e ){ Warning( e.what() ); } } if ( !TopDistribution ){ Error( "problems reading Top Distribution from Instance Base file" ); } else { if ( look_ahead( is ) == '[' ){ InstBase = read_list( is, feats, Targs, 0 ); } if ( InstBase ){ is >> ws >> buf; if ( buf.empty() || buf[0] != ')' ){ Error( "missing last `)` in Instance base file, found " + buf ); } } } } return (InstBase != NULL); } bool InstanceBase_base::read_hash( istream& is, Hash::UnicodeHash& cats, Hash::UnicodeHash& feats ) const { UnicodeString line; is >> ws; is >> line; if ( line.caseCompare( "Classes", 0 ) ){ Error( "missing 'Classes' keyword in Hashinfo" ); return false; } is >> ws; while ( TiCC::getline( is, line ) ){ vector vals = TiCC::split( line ); if ( vals.size() == 2 ){ // just ignore index! cats.hash( vals[1] ); } else { break; } is >> ws; } if ( line.caseCompare( "Features", 0 ) ){ Error( "missing 'Features' keyword in Hashinfo" ); return false; } while ( TiCC::getline( is, line ) ){ vector vals = TiCC::split( line ); if ( vals.size() == 2 ){ // just ignore index! feats.hash( vals[1] ); } else { break; } } return true; } bool InstanceBase_base::ReadIB_hashed( istream& is, Feature_List& feats, Targets& Targs, int expected_version ){ if ( read_IB_hashed( is, feats, Targs, expected_version ) ){ InstBase->redo_distributions(); ClassDistribution *Top = InstBase->sum_distributions( PersistentDistributions ); delete Top; // still a bit silly but the Top Distribution is known // but we need to cleanup behind us also return true; } else { return false; } } bool IG_InstanceBase::ReadIB_hashed( istream& is, Feature_List& feats, Targets& Targs, int expected_version ){ if ( read_IB_hashed( is, feats, Targs, expected_version ) ){ if ( PersistentDistributions ){ ClassDistribution *Top = InstBase->sum_distributions( PersistentDistributions ); delete Top; // still a bit silly but the Top Distribution is known // but we need to cleanup behind us also } return true; } else { return false; } } bool InstanceBase_base::read_IB_hashed( istream& is, Feature_List& feats, Targets& Targs, int expected_version ){ char delim; NumOfTails = 0; DefAss = true; // always for a restored tree DefaultsValid = true; // always for a restored tree Version = expected_version; read_hash( is, *Targs.hash(), *feats.hash() ); is >> delim; if ( !is || delim != '(' ){ Error( "missing first `(` in Instance Base file" ); } else { // first we get the value of the TopTarget. It's in the file // for backward compability int dum; is >> dum; delete TopDistribution; TopDistribution = 0; if ( look_ahead(is) == '{' ){ // Now read the TopDistribution, to get the Targets // in the right order in Targ try { TopDistribution = ClassDistribution::read_distribution_hashed( is, Targs, true ); } catch ( const string& what ){ Warning( what ); } if ( !TopDistribution ){ Error( "problems reading Top Distribution from Instance Base file" ); } } else { Error( "problems reading Top Distribution from Instance Base file" ); } if ( look_ahead( is ) == '[' ){ InstBase = read_list_hashed( is, feats, Targs, 0 ); } if ( InstBase ){ is >> delim; if ( delim != ')' ){ Error( "missing last `)` in Instance base file, found: " + string(1,delim) ); } } } return (InstBase != NULL); } bool InstanceBase_base::HasDistributions() const { if ( InstBase && InstBase->link ){ return InstBase->link->TDistribution != NULL; } else { return false; } } inline ClassDistribution *IBtree::sum_distributions( bool keep ){ // create a new distribution at this level by summing up the // distibutions of all branches. ClassDistribution *result; if ( !keep ){ if ( TDistribution ){ if ( FValue ){ result = TDistribution; TDistribution = NULL; } else { result = TDistribution->to_VD_Copy(); } } else { result = new ClassDistribution(); } IBtree *pnt = this->next; while ( pnt ){ if ( pnt->TDistribution ){ result->Merge( *(pnt->TDistribution) ); } if ( FValue ){ delete pnt->TDistribution; pnt->TDistribution = NULL; } pnt = pnt->next; } } else { if ( TDistribution ){ result = TDistribution->to_VD_Copy(); } else { result = new ClassDistribution(); } IBtree *pnt = this->next; while ( pnt ){ if ( pnt->TDistribution ){ result->Merge( *(pnt->TDistribution) ); } pnt = pnt->next; } } return result; } void IBtree::assign_defaults( bool Random, bool persist, size_t level ){ // recursively gather Distribution information up to the top. // at each Node we use that info to calculate the Default target. // when level > 1 the info might be persistent for IGTREE use IBtree *pnt = this; bool dummy; while ( pnt ){ if ( pnt->link ){ if ( !pnt->TDistribution ){ pnt->link->assign_defaults( Random, persist, level-1 ); pnt->TDistribution = pnt->link->sum_distributions( level > 1 && persist ); } } pnt->TValue = pnt->TDistribution->BestTarget( dummy, Random ); pnt = pnt->next; } } void IBtree::re_assign_defaults( bool Random, bool persist ){ // recursively gather Distribution information up to the top. // at each Node we use that info to calculate the Default target. IBtree *pnt = this; bool dummy; while ( pnt ){ if ( pnt->link ){ delete pnt->TDistribution; pnt->link->re_assign_defaults( Random, persist ); pnt->TDistribution = pnt->link->sum_distributions( persist ); } pnt->TValue = pnt->TDistribution->BestTarget( dummy, Random ); pnt = pnt->next; } } void IBtree::redo_distributions(){ // recursively gather Distribution information up to the top. // removing old info... // at each node we also Reconstruct Feature distributions // we keep the Target value that was given! IBtree *pnt = this; while ( pnt ){ if ( pnt->link ){ pnt->link->redo_distributions(); delete pnt->TDistribution; pnt->TDistribution = pnt->link->sum_distributions( false ); if ( pnt->FValue->ValFreq() > 0 ){ pnt->FValue->ReconstructDistribution( *(pnt->TDistribution) ); } } pnt = pnt->next; } } inline IBtree *IBtree::make_unique( const TargetValue *Top, unsigned long& cnt ){ // remove branches with the same target as the Top, except when they // still have a subbranch, which means that they are an exception. IBtree **tmp, *dead, *result; result = this; tmp = &result; while ( *tmp ){ if ( (*tmp)->TValue == Top && (*tmp)->link == NULL ){ dead = *tmp; *tmp = (*tmp)->next; dead->next=NULL; --cnt; delete dead; } else { tmp = &((*tmp)->next); } } return result; } inline IBtree *IBtree::Reduce( const TargetValue *Top, unsigned long& cnt, long depth ){ // recursively cut default nodes, (with make unique,) starting at the // leaves of the Tree and moving back to the top. IBtree *pnt = this; while ( pnt ){ if ( pnt->link != NULL ){ pnt->link = pnt->link->Reduce( pnt->TValue, cnt, depth-1 ); } pnt = pnt->next; } if ( depth <= 0 ){ return make_unique( Top, cnt ); } else { return this; } } const ClassDistribution *IBtree::exact_match( const Instance& Inst ) const { // Is there an exact match between the Instance and the IB // If so, return the best Distribution. const IBtree *pnt = this; int pos = 0; while ( pnt ){ if ( pnt->link == NULL ){ if ( pnt->TDistribution->ZeroDist() ){ return NULL; } else { return pnt->TDistribution; } } else if ( Inst.FV[pos]->isUnknown() ){ return NULL; } else if ( pnt->FValue == Inst.FV[pos] ){ if ( pnt->FValue->ValFreq() == 0 ){ return NULL; } else { pnt = pnt->link; pos++; } } else { pnt = pnt->next; } } return NULL; } InstanceBase_base::InstanceBase_base( size_t depth, unsigned long int&cnt, bool Rand, bool persist ): DefAss( false ), DefaultsValid( false ), Random( Rand ), PersistentDistributions( persist ), Version( 4 ), TopDistribution( new ClassDistribution ), WTop( 0 ), TopT( 0 ), tiedTop(false), InstBase( 0 ), LastInstBasePos( 0 ), ibCount( cnt ), Depth( depth ), NumOfTails( 0 ) { InstPath.resize(depth,0); RestartSearch.resize(depth,0); SkipSearch.resize(depth,0); } InstanceBase_base::~InstanceBase_base(){ // the Instance can become very large, with even millions of 'next' pointers // so recursive deletion will use a lot of stack // therefore we choose to iterate the first level(s). IBtree *pnt1 = InstBase; while ( pnt1 ){ IBtree *toDel1 = pnt1; pnt1 = pnt1->next; toDel1->next = 0; IBtree *pnt2 = toDel1->link; toDel1->link = 0; while ( pnt2 ){ IBtree *toDel2 = pnt2; pnt2 = pnt2->next; toDel2->next = 0; IBtree *pnt3 = toDel2->link; toDel2->link = 0; while ( pnt3 ){ IBtree *toDel3 = pnt3; pnt3 = pnt3->next; toDel3->next = 0; delete toDel3; } delete toDel2; } delete toDel1; } delete TopDistribution; delete WTop; } IB_InstanceBase *IB_InstanceBase::clone() const { return new IB_InstanceBase( Depth, ibCount, Random ); } IB_InstanceBase *IB_InstanceBase::Copy() const { IB_InstanceBase *result = clone(); result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = InstBase; result->LastInstBasePos = LastInstBasePos; delete result->TopDistribution; result->TopDistribution = TopDistribution; return result; } IG_InstanceBase *IG_InstanceBase::clone() const { return new IG_InstanceBase( Depth, ibCount, Random, Pruned, PersistentDistributions ); } IG_InstanceBase *IG_InstanceBase::Copy() const { IG_InstanceBase *result = clone(); result->Pruned = Pruned; result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = InstBase; result->LastInstBasePos = LastInstBasePos; delete result->TopDistribution; result->TopDistribution = TopDistribution; return result; } void IBtree::countBranches( unsigned int l, std::vector& terminals, std::vector& nonTerminals ){ if ( link && link->FValue != 0 ){ ++nonTerminals[l]; link->countBranches( l+1, terminals, nonTerminals ); } else { ++terminals[l]; } if ( next ){ next->countBranches( l, terminals, nonTerminals ); } } void InstanceBase_base::summarizeNodes( std::vector& terminals, std::vector& nonTerminals ){ terminals.clear(); nonTerminals.clear(); terminals.resize( Depth+1, 0 ); nonTerminals.resize( Depth+1, 0 ); if ( InstBase ){ InstBase->countBranches( 0, terminals, nonTerminals ); } } TRIBL_InstanceBase *TRIBL_InstanceBase::clone() const { return new TRIBL_InstanceBase( Depth, ibCount, Random, PersistentDistributions ); } TRIBL_InstanceBase *TRIBL_InstanceBase::Copy() const { TRIBL_InstanceBase *result = clone(); result->Threshold = Threshold; result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = InstBase; result->LastInstBasePos = LastInstBasePos; delete result->TopDistribution; result->TopDistribution = TopDistribution; return result; } TRIBL2_InstanceBase *TRIBL2_InstanceBase::clone() const { return new TRIBL2_InstanceBase( Depth, ibCount, Random, PersistentDistributions ); } TRIBL2_InstanceBase *TRIBL2_InstanceBase::Copy() const { TRIBL2_InstanceBase *result = clone(); result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = InstBase; result->LastInstBasePos = LastInstBasePos; delete result->TopDistribution; result->TopDistribution = TopDistribution; return result; } IB_InstanceBase* TRIBL_InstanceBase::IBPartition( IBtree *sub ) const { int i=0; IBtree *tmp = sub; while ( tmp && tmp->link ){ i++; tmp = tmp->link; } IB_InstanceBase *result = new IB_InstanceBase( i, ibCount, Random ); result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = sub; if ( sub ){ delete result->TopDistribution; result->TopDistribution = sub->sum_distributions( false ); } return result; } IB_InstanceBase* TRIBL2_InstanceBase::IBPartition( IBtree *sub ) const { int i=0; IBtree *tmp = sub; while ( tmp && tmp->link ){ i++; tmp = tmp->link; } IB_InstanceBase *result = new IB_InstanceBase( i, ibCount, Random ); result->DefAss = DefAss; result->DefaultsValid = DefaultsValid; result->NumOfTails = NumOfTails; // only usefull for Server??? result->InstBase = sub; if ( sub ){ delete result->TopDistribution; result->TopDistribution = sub->sum_distributions( false ); } return result; } void InstanceBase_base::CleanPartition( bool distToo ){ InstBase = 0; // prevent deletion of InstBase in next step! if ( !distToo ){ TopDistribution = 0; // save TopDistribution for deletion } delete this; } void InstanceBase_base::AssignDefaults(){ if ( !DefaultsValid ){ if ( !DefAss ){ InstBase->assign_defaults( Random, PersistentDistributions, Depth ); } else { InstBase->re_assign_defaults( Random, PersistentDistributions ); } ClassDistribution *Top = InstBase->sum_distributions( PersistentDistributions ); delete Top; // still a bit silly but the Top Distribution is known } DefAss = true; DefaultsValid = true; } void TRIBL_InstanceBase::AssignDefaults( size_t threshold ){ if ( Threshold != threshold ){ Threshold = threshold; DefaultsValid = false; } if ( !DefaultsValid ){ InstBase->assign_defaults( Random, PersistentDistributions, Threshold ); } DefAss = true; DefaultsValid = true; } void InstanceBase_base::Prune( const TargetValue *, long ){ FatalError( "You Cannot Prune this kind of tree! " ); } void IG_InstanceBase::Prune( const TargetValue *top, long depth ){ AssignDefaults( ); if ( !Pruned ) { InstBase = InstBase->Reduce( top, ibCount, depth ); Pruned = true; } } void IG_InstanceBase::specialPrune( const TargetValue *top ){ IBtree *pnt = InstBase->link; // we have to fix the toptarget here, because the node // is build incremental ClassDistribution dist; while ( pnt ){ if ( pnt->TDistribution ){ dist.Merge( *pnt->TDistribution ); } pnt = pnt->next; } bool dummy; InstBase->TValue = dist.BestTarget( dummy, Random ); InstBase = InstBase->Reduce( top, ibCount, 0 ); Pruned = true; } bool InstanceBase_base::AddInstance( const Instance& Inst ){ bool sw_conflict = false; // add one instance to the IB IBtree *hlp; IBtree **pnt = &InstBase; #ifdef IBSTATS if ( mismatch.size() == 0 ){ mismatch.resize(Depth+1, 0); } #endif if ( !InstBase ){ for ( unsigned int i = 0; i < Depth; ++i ){ *pnt = new IBtree( Inst.FV[i] ); ++ibCount; pnt = &((*pnt)->link); } LastInstBasePos = InstBase; } else { for ( unsigned int i = 0; i < Depth; ++i ){ #ifdef IBSTATS hlp = IBtree::add_feat_val( Inst.FV[i], mismatch[i], *pnt, ibCount ); #else hlp = IBtree::add_feat_val( Inst.FV[i], *pnt, ibCount ); #endif if ( i==0 && hlp->next == 0 ){ LastInstBasePos = hlp; } pnt = &(hlp->link); } } if ( *pnt == NULL ){ *pnt = new IBtree(); ++ibCount; if ( abs( Inst.ExemplarWeight() ) > Epsilon ){ (*pnt)->TDistribution = new WClassDistribution(); } else { (*pnt)->TDistribution = new ClassDistribution; } NumOfTails++; } int occ = Inst.Occurrences(); if ( abs( Inst.ExemplarWeight() ) > Epsilon ){ sw_conflict = (*pnt)->TDistribution->IncFreq( Inst.TV, occ, Inst.ExemplarWeight() ); } else { (*pnt)->TDistribution->IncFreq(Inst.TV, occ ); } TopDistribution->IncFreq(Inst.TV, occ ); DefaultsValid = false; return !sw_conflict; } bool InstanceBase_base::MergeSub( InstanceBase_base *ib ){ if ( ib->InstBase ){ // we place the InstanceBase of ib in front of the current InstanceBase // the assumption is that both are sorted on ascending index, and that // the indices in ib are all smaller then those in the current IB if ( !InstBase ){ InstBase = ib->InstBase; } else { IBtree *ibPnt = ib->InstBase; if ( ib->LastInstBasePos->FValue->Index() >= InstBase->FValue->Index() ){ Error( "MergeSub assumes sorted ans unique additions!" ); return false; } else { ib->LastInstBasePos->next = InstBase; InstBase = ibPnt; } } } else { Warning( "adding empty instancebase?" ); } NumOfTails += ib->NumOfTails; TopDistribution->Merge( *ib->TopDistribution ); #ifdef IBSTATS if ( ib->mismatch.size() > 0 ){ if ( mismatch.size() == 0 ){ mismatch.resize( ib->mismatch.size(), 0 ); } for ( unsigned int i = 0; i < mismatch.size(); ++i ){ mismatch[i] += ib->mismatch[i]; } } #endif DefaultsValid = false; DefAss = false; ib->InstBase = 0; return true; } void IBtree::cleanDistributions() { IBtree *pnt = this; while ( pnt ){ delete pnt->TDistribution; pnt->TDistribution = 0; if ( pnt->link ){ pnt->link->cleanDistributions(); } pnt = pnt->next; } } bool IG_InstanceBase::MergeSub( InstanceBase_base *ib ){ if ( ib->InstBase ){ if ( !PersistentDistributions ){ ib->InstBase->cleanDistributions(); } if ( !InstBase ){ InstBase = ib->InstBase; } else { IBtree *ibPnt = ib->InstBase; while( ibPnt ){ IBtree *ibPntNext = ibPnt->next; ibPnt->next = 0; const FeatureValue *fv = ibPnt->FValue; IBtree **pnt = &InstBase; if ( (*pnt)->FValue->Index() < fv->Index() ){ Error( "MergeSub assumes sorted additions!" ); return false; } if ( (*pnt)->FValue->Index() == fv->Index() ){ // this may happen // snip the link and insert at our link IBtree *snip = ibPnt->link; ibPnt->link = 0; delete ibPnt->TDistribution; ibPnt->TDistribution = 0; --ib->ibCount; delete ibPnt; while ( snip ){ if ( PersistentDistributions ){ (*pnt)->TDistribution->Merge( *snip->TDistribution ); } else { delete snip->TDistribution; } IBtree **tmp = &(*pnt)->link; while ( *tmp && (*tmp)->FValue->Index() < snip->FValue->Index() ){ tmp = &(*tmp)->next; } IBtree *nxt = snip->next; snip->next = 0; if ( *tmp ){ if ( (*tmp)->FValue->Index() == snip->FValue->Index() ){ return false; } snip->next = *tmp; } *tmp = snip; snip = nxt; } } else { ibPnt->next = *pnt; *pnt = ibPnt; } ibPnt = ibPntNext; } } } NumOfTails += ib->NumOfTails; TopDistribution->Merge( *ib->TopDistribution ); #ifdef IBSTATS if ( ib->mismatch.size() > 0 ){ if ( mismatch.size() == 0 ){ mismatch.resize( ib->mismatch.size(), 0 ); } for ( unsigned int i = 0; i < mismatch.size(); ++i ){ mismatch[i] += ib->mismatch[i]; } } #endif Pruned = true; DefaultsValid = true; DefAss = true; ib->InstBase = 0; return true; } void InstanceBase_base::RemoveInstance( const Instance& Inst ){ for ( int occ=0; occ < Inst.Occurrences(); ++occ ){ // remove an instance from the IB int pos = 0; IBtree *pnt = InstBase; while ( pnt ){ if ( pnt->link == NULL ){ pnt->TDistribution->DecFreq(Inst.TV); TopDistribution->DecFreq(Inst.TV); break; } else { if ( pnt->FValue == Inst.FV[pos] ){ pnt = pnt->link; pos++; } else { pnt = pnt->next; } } } } DefaultsValid = false; } const ClassDistribution *InstanceBase_base::InitGraphTest( vector&, const vector *, const size_t, const size_t ){ FatalError( "InitGraphTest" ); return 0; } const IBtree *IBtree::search_node( const FeatureValue *fv ) const { const IBtree *pnt = 0; if ( fv ){ if ( fv->isUnknown() ){ return 0; } pnt = this; while ( pnt ){ if ( pnt->FValue == fv ){ break; } pnt = pnt->next; } } return pnt; } const IBtree *InstanceBase_base::fast_search_node( const FeatureValue *fv ) { const IBtree *result = 0; if ( fast_index.empty() ){ fill_index(); } if ( fv ){ if ( fv->isUnknown() ){ return 0; } auto const& It = fast_index.find( fv->Index() ); if ( It != fast_index.end() ){ result = It->second; } } return result; } //#define DEBUGTESTS const ClassDistribution *IB_InstanceBase::InitGraphTest( vector& Path, const vector *inst, const size_t off, const size_t eff ){ const IBtree *pnt; const ClassDistribution *result = NULL; testInst = inst; offSet = off; effFeat = eff; #ifdef DEBUGTESTS cerr << "initTest for " << *inst << endl; #endif pnt = InstBase; for ( unsigned int i = 0; i < Depth; ++i ){ if ( !pnt ){ // added to satisfy scan-build throw logic_error( "pnt may never be 0!" ); } InstPath[i] = pnt; RestartSearch[i] = pnt; if ( i == 0 ){ pnt = fast_search_node( (*testInst)[offSet+i] ); } else { pnt = pnt->search_node( (*testInst)[offSet+i] ); } if ( pnt ){ // found an exact match, so mark restart position if ( RestartSearch[i] == pnt ){ RestartSearch[i] = pnt->next; } SkipSearch[i] = pnt; InstPath[i] = pnt; } else { // no exact match at this level. Just start with the first.... RestartSearch[i] = NULL; SkipSearch[i] = NULL; pnt = InstPath[i]; } Path[i] = pnt->FValue; #ifdef DEBUGTESTS cerr << "set Path[" << i << "] to " << Path[i] << endl; #endif pnt = pnt->link; if ( pnt && pnt->link == NULL ){ result = pnt->TDistribution; break; } } while ( result && result->ZeroDist() ){ // This might happen when doing LOO or CV tests size_t TmpPos = effFeat-1; result = NextGraphTest( Path, TmpPos ); } #ifdef DEBUGTESTS cerr << "Start test" << Path << endl; #endif return result; } const ClassDistribution *InstanceBase_base::NextGraphTest( vector&, size_t& ){ FatalError( "NextGraphTest" ); return 0; } const ClassDistribution *IB_InstanceBase::NextGraphTest( vector& Path, size_t& pos ){ const IBtree *pnt = NULL; const ClassDistribution *result = NULL; bool goon = true; while ( !pnt && goon ){ if ( RestartSearch[pos] == NULL ) { // No exact match here, so no real problems pnt = InstPath[pos]->next; // cerr << "NO MATCH increment "; // if ( pnt ) // cerr << pnt->FValue; // cerr << endl; } else { pnt = RestartSearch[pos]; // cerr << "restart met " << pnt->FValue << endl; RestartSearch[pos] = NULL; } if ( pnt && pnt == SkipSearch[pos] ){ pnt = pnt->next; } if ( !pnt ) { if ( pos == 0 ){ goon = false; } else { pos--; // cerr << "decremented pos to " << pos << endl; } } } if ( pnt && goon ) { InstPath[pos] = pnt; Path[pos] = pnt->FValue; #ifdef DEBUGTESTS cerr << "set Path[" << pos<< "] to " << Path[pos] << endl; #endif pnt = pnt->link; for ( size_t j=pos+1; j < Depth; ++j ){ const IBtree *tmp = pnt->search_node( (*testInst)[offSet+j] ); if ( tmp ){ // we found an exact match, so mark Restart position if ( pnt == tmp ){ RestartSearch[j] = pnt->next; } else { RestartSearch[j] = pnt; } SkipSearch[j] = tmp; InstPath[j] = tmp; Path[j] = tmp->FValue; pnt = tmp->link; } else { // no exact match at this level. Just start with the first.... RestartSearch[j] = NULL; SkipSearch[j] = NULL; InstPath[j] = pnt; Path[j] = pnt->FValue; pnt = pnt->link; } #ifdef DEBUGTESTS cerr << "set Path[" << j<< "] to " << Path[j] << endl; #endif } if ( pnt ){ result = pnt->TDistribution; } } if ( result && result->ZeroDist() ){ // This might happen when doing LOO or CV tests size_t TmpPos = effFeat-1; result = NextGraphTest( Path, TmpPos ); if ( TmpPos < pos ){ pos = TmpPos; } } #ifdef DEBUGTESTS cerr << "try next " << Path << " pos = " << pos << endl; #endif return result; } const ClassDistribution *InstanceBase_base::IG_test( const Instance& , size_t &, bool &, const TargetValue *& ){ FatalError( "IG_test " ); return NULL; } const ClassDistribution *IG_InstanceBase::IG_test( const Instance& Inst, size_t &end_level, bool &leaf, const TargetValue *&result ) { // The Test function for the IG algorithm, returns a pointer to the // distribution of the last matching position in the Tree, it's position // in the Instance Base and the default TargetValue result = NULL; ClassDistribution *Dist = NULL; int pos = 0; leaf = false; const IBtree *pnt = fast_search_node( Inst.FV[pos] ); while ( pnt ){ result = pnt->TValue; if ( PersistentDistributions ){ Dist = pnt->TDistribution; } pnt = pnt->link; if ( pnt && !pnt->FValue ){ pnt = NULL; } leaf = (pnt == NULL); ++pos; if ( pnt ){ pnt = pnt->search_node( Inst.FV[pos] ); } } end_level = pos; if ( end_level == 0 ){ if ( !WTop && TopDistribution ){ WTop = TopDistribution->to_WVD_Copy(); } Dist = WTop; } return Dist; } IB_InstanceBase *InstanceBase_base::TRIBL_test( const Instance& , size_t, const TargetValue *&, const ClassDistribution *&, size_t & ){ FatalError( "TRIBL_test " ); return NULL; } IB_InstanceBase *InstanceBase_base::TRIBL2_test( const Instance& , const ClassDistribution *&, size_t & ){ FatalError( "TRIBL2_test " ); return NULL; } IB_InstanceBase *TRIBL_InstanceBase::TRIBL_test( const Instance& Inst, size_t threshold, const TargetValue *&TV, const ClassDistribution *&dist, size_t &level ) { // The Test function for the TRIBL algorithm, returns a pointer to the // Target at the last matching position in the Tree, // or the subtree Instance Base necessary for IB1 IBtree *pnt = InstBase; #pragma omp critical AssignDefaults( threshold ); TV = NULL; dist = NULL; IB_InstanceBase *subt = NULL; size_t pos = 0; while ( pnt && pos < threshold ){ if ( pnt->FValue == Inst.FV[pos] ){ dist = pnt->TDistribution; TV = pnt->TValue; pnt = pnt->link; if ( pnt && !pnt->FValue ){ dist = pnt->TDistribution; pnt = NULL; } pos++; } else { pnt = pnt->next; } } if ( pos == threshold ){ if ( pnt ){ subt = IBPartition( pnt ); dist = NULL; } else { level = pos; } } else { if ( pos == 0 && dist == NULL ){ if ( !WTop && TopDistribution ){ WTop = TopDistribution->to_WVD_Copy(); } dist = WTop; bool dummy; TV = TopTarget( dummy ); } else { level = pos; } } return subt; } IB_InstanceBase *TRIBL2_InstanceBase::TRIBL2_test( const Instance& Inst, const ClassDistribution *& dist, size_t &level ){ // The Test function for the TRIBL2 algorithm, returns a pointer to the // the subtree Instance Base necessary for IB1 IBtree *pnt = InstBase; dist = NULL; #pragma omp critical AssignDefaults(); int pos = 0; IB_InstanceBase *subtree = NULL; IBtree *last_match = pnt; while ( pnt ){ if ( pnt->FValue == Inst.FV[pos] ){ // a match, go deeper pnt = pnt->link; last_match = pnt; pos++; if ( pnt && !pnt->FValue ){ // at the end, an exact match dist = pnt->TDistribution; last_match = NULL; break; } } else { pnt = pnt->next; } } if ( last_match ){ subtree = IBPartition( last_match ); level = pos; } return subtree; } } // namespace Timbl LanguageMachines-timbl-642727d/src/IGExperiment.cxx000066400000000000000000000361121451477526200221660ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/IBtree.h" #include "timbl/Instance.h" #include "timbl/TimblExperiment.h" #include "ticcutils/Timer.h" #include "ticcutils/PrettyPrint.h" namespace Timbl { using namespace std; using namespace icu; using TiCC::operator<<; void IG_Experiment::InitInstanceBase(){ srand( RandomSeed() ); default_order(); set_order(); runningPhase = TrainWords; InstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), false, KeepDistributions() ); } void IG_Experiment::initExperiment( bool ){ if ( !ExpInvalid() ) { if ( !MBL_init ){ // do this only when necessary stats.clear(); delete confusionInfo; confusionInfo = 0; if ( Verbosity(ADVANCED_STATS) ){ confusionInfo = new ConfusionMatrix( targets.num_of_values() ); } if ( !is_copy ){ InitWeights(); if ( do_diversify ){ diverseWeights(); } srand( random_seed ); } MBL_init = true; } } } bool IG_Experiment::checkTestFile(){ if ( TimblExperiment::checkTestFile() ){ return sanityCheck(); } else { return false; } } ostream& operator<< ( ostream& os, const fileDoubleIndex& fmi ){ os << "["; for ( const auto& it : fmi ){ os << it.first << " " << it.second << endl; } os << "]"; return os; } bool IG_Experiment::ClassicLearn( const string& FileName, bool warnOnSingleTarget ){ bool result = true; if ( is_synced ) { CurrentDataFile = FileName; } if ( CurrentDataFile == "" ){ if ( FileName == "" ){ Warning( "unable to build an InstanceBase: No datafile defined yet" ); result = false; } else { if ( !Prepare( FileName, warnOnSingleTarget ) || ExpInvalid() ){ result = false; } } } else if ( FileName != "" && CurrentDataFile != FileName ){ Error( "Unable to Learn from file '" + FileName + "'\n" "while previously instantiated from file '" + CurrentDataFile + "'" ); result = false; } if ( result ) { TiCC::Timer learnT; learnT.start(); InitInstanceBase(); if ( ExpInvalid() ){ return false; } if ( EffectiveFeatures() < 2 ){ fileIndex fmIndex; result = build_file_index( CurrentDataFile, fmIndex ); if ( result ){ stats.clear(); if ( !Verbosity(SILENT) ) { Info( "\nPhase 3: Learning from Datafile: " + CurrentDataFile ); time_stamp( "Start: ", 0 ); } UnicodeString Buffer; IG_InstanceBase *outInstanceBase = 0; TargetValue *TopTarget = targets.MajorityClass(); // cerr << "MAJORITY CLASS = " << TopTarget << endl; // Open the file. // ifstream datafile( CurrentDataFile, ios::in); // for ( const auto& fit : fmIndex ){ for ( const auto& sit : fit.second ){ datafile.clear(); datafile.seekg( sit ); nextLine( datafile, Buffer ); chopLine( Buffer ); // Progress update. // if ( ( stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } chopped_to_instance( TrainWords ); if ( !outInstanceBase ){ outInstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), false, true ); } outInstanceBase->AddInstance( CurrInst ); } } if ( outInstanceBase ){ // cerr << "Out Instance Base" << endl; // time_stamp( "Start Pruning: " ); // cerr << outInstanceBase << endl; outInstanceBase->Prune( TopTarget ); // time_stamp( "Finished Pruning: " ); // cerr << outInstanceBase << endl; // time_stamp( "Before Merge: " ); // cerr << InstanceBase << endl; if ( !InstanceBase->MergeSub( outInstanceBase ) ){ FatalError( "Merging InstanceBases failed. PANIC" ); return false; } delete outInstanceBase; } } } else { fileDoubleIndex fmIndex; result = build_file_multi_index( CurrentDataFile, fmIndex ); // cerr << "indexing took " << t << endl; if ( result ){ stats.clear(); if ( !Verbosity(SILENT) ) { Info( "\nPhase 3: Learning from Datafile: " + CurrentDataFile ); time_stamp( "Start: ", 0 ); } UnicodeString Buffer; IG_InstanceBase *PartInstanceBase = 0; IG_InstanceBase *outInstanceBase = 0; TargetValue *TopTarget = targets.MajorityClass(); // cerr << "MAJORITY CLASS = " << TopTarget << endl; // Open the file. // ifstream datafile( CurrentDataFile, ios::in); // for ( const auto& dit : fmIndex ){ // FeatureValue *the_fv = dit.first; // cerr << "handle feature '" << the_fv << "' met index " // << the_fv->Index() << endl; if ( dit.second.size() < 1 ){ FatalError( "panic" ); } if ( igOffset() > 0 && dit.second.size() > igOffset() ){ // cerr << "within offset!" << endl; IG_InstanceBase *TmpInstanceBase = 0; TmpInstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), false, true ); for ( const auto& fit : dit.second ) { for ( const auto& sit : fit.second ){ datafile.clear(); datafile.seekg( sit ); nextLine( datafile, Buffer ); chopLine( Buffer ); // Progress update. // if ( ( stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } chopped_to_instance( TrainWords ); if ( !PartInstanceBase ){ PartInstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), false, true ); } // cerr << "add instance " << &CurrInst << endl; PartInstanceBase->AddInstance( CurrInst ); } if ( PartInstanceBase ){ // time_stamp( "Start Pruning: " ); // cerr << PartInstanceBase << endl; PartInstanceBase->Prune( TopTarget, 2 ); // time_stamp( "Finished Pruning: " ); // cerr << PartInstanceBase << endl; if ( !TmpInstanceBase->MergeSub( PartInstanceBase ) ){ FatalError( "Merging InstanceBases failed. PANIC" ); return false; } // cerr << "after Merge: intermediate result" << endl; // cerr << TmpInstanceBase << endl; delete PartInstanceBase; PartInstanceBase = 0; } else { // cerr << "Partial IB is empty" << endl; } } // time_stamp( "Start Final Pruning: " ); // cerr << TmpInstanceBase << endl; TmpInstanceBase->specialPrune( TopTarget ); // time_stamp( "Finished Final Pruning: " ); // cerr << TmpInstanceBase << endl; if ( !InstanceBase->MergeSub( TmpInstanceBase ) ){ FatalError( "Merging InstanceBases failed. PANIC" ); return false; } // cerr << "finale Merge gave" << endl; // cerr << InstanceBase << endl; delete TmpInstanceBase; } else { // cerr << "other case!" << endl; for ( const auto& fit : dit.second ){ for ( const auto& sit : fit.second ){ datafile.clear(); datafile.seekg( sit ); nextLine( datafile, Buffer ); chopLine( Buffer ); // Progress update. // if ( ( stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } chopped_to_instance( TrainWords ); if ( !outInstanceBase ){ outInstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), false, true ); } // cerr << "add instance " << &CurrInst << endl; outInstanceBase->AddInstance( CurrInst ); } } if ( outInstanceBase ){ // cerr << "Out Instance Base" << endl; // time_stamp( "Start Pruning: " ); // cerr << outInstanceBase << endl; outInstanceBase->Prune( TopTarget ); // time_stamp( "Finished Pruning: " ); // cerr << outInstanceBase << endl; // time_stamp( "Before Merge: " ); // cerr << InstanceBase << endl; if ( !InstanceBase->MergeSub( outInstanceBase ) ){ FatalError( "Merging InstanceBases failed. PANIC" ); return false; } delete outInstanceBase; outInstanceBase = 0; } } } } } if ( !Verbosity(SILENT) ){ time_stamp( "Finished: ", stats.dataLines() ); } learnT.stop(); if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); Info( "Learning took " + learnT.toString() ); } #ifdef IBSTATS cerr << "final mismatches: " << InstanceBase->mismatch << endl; #endif } return result; } bool IG_Experiment::checkLine( const UnicodeString& line ){ if ( TimblExperiment::checkLine( line ) ){ return sanityCheck(); } else { return false; } } bool IG_Experiment::sanityCheck() const { bool status = true; if ( IBStatus() != Pruned ){ Warning( "you tried to apply the IGTree algorithm on a complete," "(non-pruned) Instance Base" ); status = false; } if ( num_of_neighbors != 1 ){ Warning( "number of neighbors must be 1 for IGTree test!" ); status = false; } if ( decay_flag != Zero ){ Warning( "Decay impossible for IGTree test, (while k=1)" ); status = false; } if ( globalMetricOption != Overlap ){ Warning( "Metric must be Overlap for IGTree test." ); status = false; } return status; } const TargetValue *IG_Experiment::LocalClassify( const Instance& Inst, double& Distance, bool& exact ){ match_depth = -1; last_leaf = false; exact = false; bool Tie = false; initExperiment(); if ( !bestResult.reset( beamSize, normalisation, norm_factor, targets ) ){ Warning( "no normalisation possible because a BeamSize is specified\n" "output is NOT normalized!" ); } const TargetValue *TV = NULL; const ClassDistribution *ResultDist; ResultDist = InstanceBase->IG_test( Inst, match_depth, last_leaf, TV ); if ( match_depth == 0 ){ // when level 0, ResultDist == TopDistribution TV = InstanceBase->TopTarget( Tie ); } Distance = sum_remaining_weights( match_depth ); if ( ResultDist && InstanceBase && InstanceBase->PersistentD() ){ if ( match_depth == 0 ) { bestResult.addTop( ResultDist, TV ); } else { bestResult.addConstant( ResultDist, TV ); } } if ( confusionInfo ){ confusionInfo->Increment( Inst.TV, TV ); } bool correct = Inst.TV && ( TV == Inst.TV ); if ( correct ){ stats.addCorrect(); if ( Tie ){ stats.addTieCorrect(); } } else if ( Tie ){ stats.addTieFailure(); } return TV; } void IG_Experiment::showTestingInfo( ostream& os ){ if ( !Verbosity(SILENT) ) { if ( Verbosity(OPTIONS) ){ ShowSettings( os ); } os << endl << "Starting to test, Testfile: " << testStreamName << endl << "Writing output in: " << outStreamName << endl << "Algorithm : IGTree" << endl; show_ignore_info( os ); show_weight_info( os ); os << endl; } } bool IG_Experiment::WriteInstanceBase( const string& FileName ){ bool result = false; if ( ConfirmOptions() ){ ofstream outfile( FileName, ios::out | ios::trunc ); if (!outfile) { Warning( "can't open outputfile: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Writing Instance-Base in: " + FileName ); } if ( PutInstanceBase( outfile ) ){ string tmp = FileName; tmp += ".wgt"; ofstream wf( tmp ); if ( !wf ){ Error( "can't write default weightfile " + tmp ); } else if ( writeWeights( wf ) ){ if ( !Verbosity(SILENT) ){ Info( "Saving Weights in " + tmp ); } result = true; } } } } return result; } bool IG_Experiment::GetInstanceBase( istream& is ){ bool result = false; bool Pruned; bool Hashed; int Version; string range_buf; size_t numF = get_IB_Info( is, Pruned, Version, Hashed, range_buf ); if ( numF == 0 ){ return false; } else if ( !Pruned ){ Error( "Instance-base is NOT Pruned!, invalid for " + TiCC::toString(algorithm) + " Algorithm" ); } else { TreeOrder = DataFile; Initialize( numF ); if ( !get_ranges( range_buf ) ){ Warning( "couldn't retrieve ranges..." ); } else { srand( RandomSeed() ); InstanceBase = new IG_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), Pruned, KeepDistributions() ); int pos=0; for ( size_t i=0; i < NumOfFeatures(); ++i ){ features[i]->SetWeight( 1.0 ); if ( features[features.permutation[i]]->Ignore() ){ features.perm_feats[i] = NULL; } else { features.perm_feats[pos++] = features[features.permutation[i]]; } } if ( Hashed ){ result = InstanceBase->ReadIB_hashed( is, features, targets, Version ); } else { result = InstanceBase->ReadIB( is, features, targets, Version ); } if ( result ){ if ( !InstanceBase->HasDistributions() ){ if ( KeepDistributions() ){ Error( "Instance base doesn't contain Distributions, " "+D option impossible" ); } else if ( Verbosity(DISTRIB) ){ Info( "Instance base doesn't contain Distributions, " "+vDB option disabled ...." ); ResetVerbosityFlag(DISTRIB); } } } } } return result; } bool IG_Experiment::ReadInstanceBase( const string& FileName ){ bool result = false; if ( ConfirmOptions() ){ ifstream infile( FileName, ios::in ); if ( !infile ) { Error( "can't open: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Reading Instance-Base from: " + FileName ); } if ( GetInstanceBase( infile ) ){ if ( !Verbosity(SILENT) ){ writePermutation( cout ); } string tmp = FileName; tmp += ".wgt"; ifstream wf( tmp ); if ( !wf ){ Error( "cant't find default weightsfile " + tmp ); } else if ( readWeights( wf, CurrentWeighting() ) ){ WFileName = tmp; if ( !Verbosity(SILENT) ){ Info( "Reading weights from " + tmp ); } } result = true; } } } return result; } } LanguageMachines-timbl-642727d/src/Instance.cxx000066400000000000000000000033571451477526200213770ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include "timbl/Types.h" #include "timbl/Instance.h" using namespace std; namespace Timbl { Instance::Instance(): TV(NULL), sample_weight(0.0), occ(1) { } Instance::~Instance(){ clear(); } void Instance::clear(){ for ( auto& it : FV ){ if ( it ){ if ( it->isUnknown() ){ delete it; } } it = 0; } TV = 0; sample_weight = 0.0; occ = 1; } void Instance::Init( size_t len ){ FV.resize( len, 0 ); } ostream& operator<<( ostream& os, const Instance *I ){ if ( I ){ os << *I; } else { os << " Empty Instance"; } return os; } ostream& operator<<( ostream& os, const Instance& I ){ for ( const auto& it : I.FV ){ os << it << ", "; } os << I.TV << " " << I.sample_weight; return os; } } LanguageMachines-timbl-642727d/src/LOOExperiment.cxx000066400000000000000000000116671451477526200223300ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include "timbl/MsgClass.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/IBtree.h" #include "timbl/Instance.h" #include "timbl/MBLClass.h" #include "timbl/TimblExperiment.h" namespace Timbl { using namespace std; using namespace icu; void LOO_Experiment::initExperiment( bool all_vd ){ if ( !ExpInvalid() ){ if ( !MBL_init ){ // do this only when necessary initDecay(); if ( !is_copy ){ calculate_fv_entropy( true ); if ( initProbabilityArrays( all_vd ) ){ calculatePrestored(); } else { Error( "not enough memory for Probability Arrays in (" + string(__FILE__) + "," + TiCC::toString(__LINE__) + ")\n" + "ABORTING now" ); throw std::bad_alloc(); } InitWeights(); if ( do_diversify ){ diverseWeights(); } srand( random_seed ); } initTesters(); MBL_init = true; } } } bool LOO_Experiment::checkTestFile(){ // no need to test the Testfile // it is the same as the trainfile, so already checked if ( doSamples() ){ FatalError( "Cannot Leave One Out on a file with Examplar Weighting" ); return false; } return true; } void LOO_Experiment::showTestingInfo( ostream& os ){ if ( !Verbosity(SILENT) ){ if ( Verbosity(OPTIONS ) ){ ShowSettings( os ); } os << endl << "Starting to test using Leave One Out"; if ( Do_Sloppy_LOO() ) { os << " using SLOPPY metric calculations" << endl; } else { os << endl; } os << "Writing output in: " << outStreamName << endl << "Algorithm : LOO" << endl; show_metric_info( os ); show_weight_info( os ); os << decay << endl; } } bool LOO_Experiment::Test( const string& FileName, const string& OutFile ){ bool result = false; if ( initTestFiles( FileName, OutFile ) ){ if ( InstanceBase->nodeCount() == InstanceBase->depth() + 1 ){ // protect ourselves against 1-line trainfiles FatalError( "the file '" + FileName + "' contains only 1 usable line. LOO impossible!" ); } initExperiment(); stats.clear(); delete confusionInfo; confusionInfo = 0; if ( Verbosity(ADVANCED_STATS) ){ confusionInfo = new ConfusionMatrix( targets.num_of_values() ); } showTestingInfo( *mylog ); // Start time. // time_t lStartTime; time(&lStartTime); timeval startTime; gettimeofday( &startTime, 0 ); if ( InputFormat() == ARFF ){ skipARFFHeader( testStream ); } UnicodeString Buffer; while ( nextLine( testStream, Buffer ) ){ if ( !chopLine( Buffer ) ){ Warning( "testfile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } else { chopped_to_instance( TestWords ); Decrement( CurrInst ); double final_distance = 0.0; bool exact = false; const TargetValue *ResultTarget = LocalClassify( CurrInst, final_distance, exact ); normalizeResult(); string dString = bestResult.getResult(); double confi = 0; if ( Verbosity(CONFIDENCE) ){ confi = confidence(); } // Write it to the output file for later analysis. show_results( outStream, confi, dString, ResultTarget, final_distance ); if ( exact ){ // remember that a perfect match may be incorrect! if ( Verbosity(EXACT) ){ *mylog << "Exacte match:\n" << get_org_input() << endl; } } if ( !Verbosity(SILENT) ){ // Display progress counter. show_progress( *mylog, lStartTime, stats.dataLines() ); } Increment( CurrInst ); } }// end while. if ( !Verbosity(SILENT) ){ time_stamp( "Ready: ", stats.dataLines() ); show_speed_summary( *mylog, startTime ); showStatistics( *mylog ); } result = true; } return result; } bool LOO_Experiment::ReadInstanceBase( const string& ){ Error( "cannot combine Leave One Out with retrieving an Instancebase " ); return false; } } LanguageMachines-timbl-642727d/src/MBLClass.cxx000066400000000000000000001622601451477526200212320ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include #include #include #include #include "ticcutils/StringOps.h" #include "ticcutils/PrettyPrint.h" #include "ticcutils/Timer.h" #include "ticcutils/UniHash.h" #include "timbl/MsgClass.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Options.h" #include "timbl/Instance.h" #include "timbl/IBtree.h" #include "timbl/BestArray.h" #include "timbl/Testers.h" #include "timbl/Metrics.h" #include "timbl/Choppers.h" #include "timbl/MBLClass.h" using namespace std; using namespace icu; using namespace nlohmann; namespace Timbl { using TiCC::operator<<; void MBLClass::init_options_table( size_t Size ){ if ( tableFilled ){ return; } else { tableFilled = true; } MaxFeatures = Size; UserOptions.resize(MaxFeatures+1); //cerr << "fill table() for " << (void*)this << endl; bool stat = Options.Add( new IntegerOption( "FLENGTH", &F_length, 0, 1, 32 ) ) && Options.Add( new SizeOption( "MAXBESTS", &MaxBests, 500, 10, 100000 ) ) && Options.Add( new SizeOption( "TRIBL_OFFSET", &tribl_offset, 0, 0, MaxFeatures ) ) && Options.Add( new UnsignedOption( "IG_THRESHOLD", &igThreshold, 1000, 0, std::numeric_limits::max() ) ) && Options.Add( new InputFormatOption( "INPUTFORMAT", &input_format, UnknownInputFormat ) ) && Options.Add( new OrdeningOption( "TREE_ORDER", &TreeOrder, UnknownOrdening ) ) && Options.Add( new BoolOption( "ALL_WEIGHTS", &need_all_weights, false ) ) && Options.Add( new WeightOption( "WEIGHTING", &Weighting, GR_w ) ) && Options.Add( new IntegerOption( "BIN_SIZE", &Bin_Size, 20, 2, 10000 ) ) && Options.Add( new UnsignedOption( "IB2_OFFSET", &ib2_offset, 0, 1, 10000000 ) ) && Options.Add( new BoolOption( "KEEP_DISTRIBUTIONS", &keep_distributions, false ) ) && Options.Add( new BoolOption( "DO_SLOPPY_LOO", &do_sloppy_loo, false ) ) && Options.Add( new SizeOption( "TARGET_POS", &target_pos, std::numeric_limits::max(), 0, MaxFeatures ) ); // cerr << "STAT 1=" << (stat?"true":"false") << endl; if ( stat ){ stat = Options.Add( new BoolOption( "DO_SILLY", &do_silly_testing, false ) ) && Options.Add( new BoolOption( "DO_DIVERSIFY", &do_diversify, false ) ) && Options.Add( new DecayOption( "DECAY", &decay_flag, Zero ) ) && Options.Add( new IntegerOption( "SEED", &random_seed, -1, -1, RAND_MAX ) ) && Options.Add( new IntegerOption( "BEAM_SIZE", &beamSize, 0, 1, INT_MAX ) ) && Options.Add( new RealOption( "DECAYPARAM_A", &decay_alfa, 1.0, 0.0, DBL_MAX ) ) && Options.Add( new RealOption( "DECAYPARAM_B", &decay_beta, 1.0, 0.0, DBL_MAX ) ) && Options.Add( new NormalisationOption( "NORMALISATION", &normalisation, noNorm ) ) && Options.Add( new RealOption( "NORM_FACTOR", &norm_factor, 1.0, Epsilon, DBL_MAX ) ) && Options.Add( new BoolOption( "EXEMPLAR_WEIGHTS", &do_sample_weighting, false ) ) && Options.Add( new BoolOption( "IGNORE_EXEMPLAR_WEIGHTS", &do_ignore_samples, true ) ) && Options.Add( new BoolOption( "NO_EXEMPLAR_WEIGHTS_TEST", &no_samples_test, true ) ) && Options.Add( new VerbosityOption( "VERBOSITY", &verbosity, NO_VERB ) ) && Options.Add( new BoolOption( "EXACT_MATCH", &do_exact_match, false ) ) && Options.Add( new BoolOption( "HASHED_TREE", &hashed_trees, true ) ) && Options.Add( new MetricOption( "GLOBAL_METRIC", &globalMetricOption, Overlap ) ) && Options.Add( new MetricArrayOption( "METRICS", UserOptions, globalMetricOption, MaxFeatures+1 ) ) && Options.Add( new IntegerOption( "MVD_LIMIT", &mvd_threshold, 1, 1, 100000 ) ) && Options.Add( new SizeOption( "NEIGHBORS", &num_of_neighbors, 1, 1, 100000 ) ) && Options.Add( new IntegerOption( "PROGRESS", &progress, 10000, 1, INT_MAX ) ) && Options.Add( new IntegerOption( "HANDLE_OCCURRENCES", &doOcc, 0, 0, 3 ) ) && Options.Add( new IntegerOption( "CLIP_FACTOR", &clip_factor, 10, 0, 1000000 ) ); } // cerr << "STAT 2=" << (stat?"true":"false") << endl; if ( !stat ){ FatalError( "Too many options for OptionTable" ); } } void MBLClass::InvalidMessage(void) const{ if ( err_cnt++ == 1 ){ Warning( "A preceding error prevents any operation on this " "Timbl Object\n" "other experiments might not be influenced" ); } else { Warning( "This Experiment is invalid due to errors" ); } } bool MBLClass::SetOption( const string& line ){ bool result = false; if ( !ExpInvalid(true) ){ // Info( "set Option:" + line ); enum SetOptRes opt_res = Options.SetOption( line ); switch ( opt_res ){ case Opt_OK: // OK MBL_init = false; // To assure redoing initializing stuff result = true; break; case Opt_Frozen: Warning( "SetOption '" + line + "' ignored.\nThis option may not " "be changed after an InstanceBase is already created" ); break; case Opt_Unknown: Warning( "SetOption '" + line + "' failed.\nOption unknown" ); break; case Opt_Ill_Val: Error( "SetOption '" + line + "' failed.\nIllegal value for this option" ); break; } } return result; } MBLClass::MBLClass( const string& name ): MsgClass(), sock_os(0), sock_is_json(false), targets(NULL), InstanceBase(NULL), mylog(&cout), myerr(&cerr), runningPhase(LearnWords), Weighting(GR_w), GlobalMetric(0), TreeOrder(UnknownOrdening), num_of_neighbors(1), dynamic_neighbors(false), decay_flag(Zero), exp_name( name ), MaxBests(500), decay(0), beamSize(0), normalisation(noNorm), norm_factor(1.0), is_copy(false), is_synced(false), ib2_offset(0), random_seed(-1), decay_alfa(1.0), decay_beta(1.0), MBL_init(false), tableFilled(false), globalMetricOption(Overlap), do_diversify(false), ChopInput(0), F_length(0), MaxFeatures(0), input_format(UnknownInputFormat), verbosity(NO_VERB), target_pos(std::numeric_limits::max()), clip_factor(10), Bin_Size(20), progress(10000), tribl_offset(0), igThreshold(1000), mvd_threshold(1), do_sloppy_loo(false), do_exact_match(false), do_silly_testing(false), hashed_trees(true), need_all_weights(false), do_sample_weighting(false), do_ignore_samples(true), no_samples_test(true), keep_distributions(false), DBEntropy(-1.0), tester(0), doOcc(0) { } MBLClass &MBLClass::operator=( const MBLClass& m ){ if ( this != &m ){ is_copy = true; is_synced = false; init_options_table( m.MaxFeatures ); F_length = m.F_length; MaxBests = m.MaxBests; TreeOrder = m.TreeOrder; decay_flag = m.decay_flag; input_format = m.input_format; random_seed = m.random_seed; beamSize = m.beamSize; decay_alfa = m.decay_alfa; decay_beta = m.decay_beta; normalisation = m.normalisation; norm_factor = m.norm_factor; do_sample_weighting = m.do_sample_weighting; do_ignore_samples = m.do_ignore_samples; no_samples_test = m.no_samples_test; keep_distributions = m.keep_distributions; verbosity = m.verbosity; do_exact_match = m.do_exact_match; sock_os = 0; sock_is_json = false; globalMetricOption = m.globalMetricOption; if ( m.GlobalMetric ){ GlobalMetric = getMetricClass( m.GlobalMetric->type() ); } UserOptions = m.UserOptions; mvd_threshold = m.mvd_threshold; num_of_neighbors = m.num_of_neighbors; dynamic_neighbors = m.dynamic_neighbors; target_pos = m.target_pos; progress = m.progress; Bin_Size = m.Bin_Size; tribl_offset = m.tribl_offset; ib2_offset = m.ib2_offset; clip_factor = m.clip_factor; runningPhase = m.runningPhase; Weighting = m.Weighting; do_sloppy_loo = m.do_sloppy_loo; do_silly_testing = m.do_silly_testing; do_diversify = m.do_diversify; tester = 0; decay = 0; targets = m.targets; features = m.features; MBL_init = false; need_all_weights = false; InstanceBase = m.InstanceBase->Copy(); DBEntropy = -1.0; ChopInput = 0; setInputFormat( m.input_format ); CurrInst.Init( NumOfFeatures() ); myerr = m.myerr; mylog = m.mylog; } return *this; } MBLClass::~MBLClass(){ // cerr << "MBLClass delete " << endl; CurrInst.clear(); if ( !is_copy ){ // cerr << "NO copy: also delete instancebase" << endl; delete InstanceBase; } else { if ( is_synced ){ // cerr << "is synced: also delete instancebase" << endl; delete InstanceBase; } else { // cerr << "only clean partition" << endl; InstanceBase->CleanPartition( false ); } } delete GlobalMetric; delete tester; delete decay; delete ChopInput; } void MBLClass::Info( const string& out_line ) const { #pragma omp critical { // Info NEVER to socket ! if ( exp_name != "" ){ *mylog << "-" << exp_name << "-" << out_line << endl; } else { *mylog << out_line << endl; } } } void MBLClass::Warning( const string& out_line ) const { #pragma omp critical { if ( sock_os ){ if ( sock_is_json ){ json out_json; out_json["status"] = "error"; out_json["message"] = out_line; last_error = out_json; } else { *sock_os << "ERROR { " << out_line << " }" << endl; } } else { if ( exp_name != "" ){ *myerr << "Warning:-" << exp_name << "-" << out_line << endl; } else { *myerr << "Warning: " << out_line << endl; } } } } void MBLClass::Error( const string& out_line ) const { if ( sock_os ){ if ( sock_is_json ){ json out_json; out_json["status"] = "error"; out_json["message"] = out_line; last_error = out_json; } else { *sock_os << "ERROR { " << out_line << " }" << endl; } } else { if ( exp_name != "" ){ *myerr << "Error:-" << exp_name << "-" << out_line << endl; } else { *myerr << "Error: " << out_line << endl; } } ++err_cnt; } void MBLClass::FatalError( const string& out_line ) const { if ( sock_os ){ if ( sock_is_json ){ json out_json; out_json["status"] = "error"; out_json["message"] = out_line; last_error = out_json; } else { *sock_os << "ERROR { " << out_line << " }" << endl; } } else { if ( exp_name != "" ){ *myerr << "FatalError:-" << exp_name << "-" << out_line << endl; } else { *myerr << "FatalError: " << out_line << endl; } throw( runtime_error("Stopped") ); } } bool MBLClass::ShowOptions( ostream& os ) const { os << "Possible Experiment Settings (current value between []):" << endl; Options.Show_Options( os ); os << endl; return true; } bool MBLClass::ShowSettings( ostream& os ) const{ os << "Current Experiment Settings :" << endl; Options.Show_Settings( os ); os << endl; return true; } bool MBLClass::connectToSocket( ostream *ss, bool is_json ){ if ( sock_os ){ throw( logic_error( "connectToSocket:: already connected!" ) ); } else { sock_os = ss; if ( sock_os && sock_os->good() ){ sock_is_json = is_json; return true; } else { FatalError( "connecting streams to socket failed" ); } } return false; } xmlNode *MBLClass::settingsToXml() const{ ostringstream tmp; Options.Show_Settings( tmp ); vector lines = TiCC::split_at( tmp.str(), "\n" ); xmlNode *result = TiCC::XmlNewNode("settings"); for ( const auto& line : lines ){ vector parts = TiCC::split_at( line, ":" ); if ( parts.size() ==2 ){ string tag = TiCC::trim( parts[0] ); string val = TiCC::trim( parts[1] ); TiCC::XmlNewTextChild( result, tag, val ); } } return result; } json MBLClass::settings_to_JSON() const{ ostringstream tmp; Options.Show_Settings( tmp ); vector lines = TiCC::split_at( tmp.str(), "\n" ); json result; json arr = json::array(); for ( const auto& line : lines ){ vector parts = TiCC::split_at( line, ":" ); if ( parts.size() ==2 ){ string tag = TiCC::trim( parts[0] ); string val = TiCC::trim( parts[1] ); // a lot of values are integers, some float's // in fact they should be added as such, not as strings.... json element; element[tag] = val; arr.push_back( element ); } } result["settings"] = arr; return result; } bool MBLClass::ShowWeights( ostream &os ) const { if ( ExpInvalid() ){ return false; } else { int OldPrec = os.precision(DBL_DIG); size_t pos = 0; for ( auto const& feat : features.feats ){ os.precision(DBL_DIG); os << "Feature " << ++pos << "\t : " << feat->Weight() << endl; } os.precision(OldPrec); } return true; } string MBLClass::extract_limited_m( size_t lim ){ default_order(); set_order(); string result; MetricType gm = globalMetricOption; result += TiCC::toString( gm ); set ignore; map> metrics; for ( size_t k=0; k < NumOfFeatures(); ++k ){ if ( features[features.permutation[k]]->Ignore() ){ // cerr << "Add " << k+1 << " to ignore" << endl; ignore.insert(k+1); } else { MetricType m = features[features.permutation[k]]->getMetricType(); if ( m != gm ){ metrics[TiCC::toString( m )].insert(k+1); } } } for ( size_t i=lim+ignore.size(); i < NumOfFeatures(); ++i ){ ignore.insert( features.permutation[i]+1 ); } if ( !ignore.empty() ){ result += ":I"; for ( auto it = ignore.begin(); it != ignore.end(); ++it ){ size_t value = *it; size_t steps = 0; for ( ; value <= *ignore.rbegin(); ++value ){ if ( ignore.find(value) == ignore.end() ){ break; } ++steps; } if ( value == *it+1 ){ // so only one value, output it if ( *it != *ignore.begin() ){ result += ","; } result += TiCC::toString(*it) + ","; } else if ( value == *it+2 ){ // so only two values, output them , separated result += TiCC::toString(*it); ++it; result += "," + TiCC::toString(*it); } else { // a range. output with a hyphen result += TiCC::toString(*it) + "-" + TiCC::toString( value-1) + ","; for ( size_t j=0; j < steps-1;++j){ ++it; if ( it == ignore.end() ){ --it; break; } } } } result.pop_back(); } result += ":"; for ( const auto& it : metrics ){ bool first = true; for ( const auto& ig : it.second ){ if ( ignore.find( ig ) == ignore.end() ){ if ( first ){ result += it.first; first = false; } result += TiCC::toString( ig ) + ","; } } if ( result.back() == ',' ){ result.pop_back(); result.push_back(':'); } } while ( result.back() == ':' || result.back() == ',' ){ result.pop_back(); } return result; } void MBLClass::writePermutation( ostream& os ) const { os << "Feature Permutation based on " << ( Weighting==UserDefined_w?"weightfile":TiCC::toString(TreeOrder, true)) << " :" << endl; features.write_permutation( os ); os << endl; } void MBLClass::time_stamp( const char *line, int number ) const { if ( !Verbosity(SILENT) ){ ostringstream ostr; ostr << line; if ( number > -1 ){ ostr.width(6); ostr.setf(ios::right, ios::adjustfield); ostr << number << " @ "; } else { ostr << " "; } ostr << TiCC::Timer::now(); Info( ostr.str() ); } } void MBLClass::InitWeights(void){ for ( auto const& feat : features.feats ){ if ( feat->Ignore() ){ feat->SetWeight( 0.0 ); } else { switch ( Weighting ){ case IG_w: feat->SetWeight( feat->InfoGain() ); break; case GR_w: feat->SetWeight( feat->GainRatio() ); break; case X2_w: feat->SetWeight( feat->ChiSquare() ); break; case SV_w: feat->SetWeight( feat->SharedVariance() ); break; case SD_w: feat->SetWeight( feat->StandardDeviation() ); break; case UserDefined_w: break; case No_w: feat->SetWeight( 1.0 ); break; case Unknown_w: case Max_w: FatalError( "InitWeights: Invalid Weight in switch: " + TiCC::toString( Weighting ) ); break; } } } } void MBLClass::diverseWeights(void){ double minW = DBL_MAX; for ( auto const& feat : features.feats ){ if ( feat->Ignore() ){ continue; } if ( feat->Weight() < minW ){ minW = feat->Weight(); } } for ( auto const& feat : features.feats ){ if ( feat->Ignore() ){ continue; } feat->SetWeight( (feat->Weight() - minW ) + Epsilon ); } } void MBLClass::default_order(){ if ( TreeOrder == UnknownOrdening ){ switch ( Weighting ){ case GR_w: TreeOrder = GROrder; break; case IG_w: TreeOrder = IGOrder; break; case X2_w: TreeOrder = X2Order; break; case SV_w: TreeOrder = SVOrder; break; case SD_w: TreeOrder = SDOrder; break; case No_w: TreeOrder = NoOrder; break; case UserDefined_w: TreeOrder = GROrder; break; default: FatalError( "Illegal Weighting Value in Switch: " + TiCC::toString( Weighting ) ); break; } } } void MBLClass::set_order(){ calculate_fv_entropy(false); vector Order(NumOfFeatures()); size_t i = 0; for ( auto const& feat : features.feats ){ switch( TreeOrder ){ case DataFile: Order[i] = feat->Weight(); break; case NoOrder: Order[i] = (double)(NumOfFeatures()-i); break; case IGOrder: Order[i] = feat->InfoGain(); break; case GROrder: Order[i] = feat->GainRatio(); break; case IGEntropyOrder: Order[i] = feat->InfoGain() * feat->SplitInfo(); break; case GREntropyOrder: Order[i] = feat->GainRatio() * feat->SplitInfo(); break; case X2Order: Order[i] = feat->ChiSquare(); break; case SVOrder: Order[i] = feat->SharedVariance(); break; case SDOrder: Order[i] = feat->StandardDeviation(); break; case OneoverFeature: Order[i] = 1.0 / feat->values_array.size(); break; case GRoverFeature: Order[i] = feat->GainRatio() / feat->values_array.size(); break; case IGoverFeature: Order[i] = feat->InfoGain() / feat->values_array.size(); break; case X2overFeature: Order[i] = feat->ChiSquare() / feat->values_array.size(); break; case SVoverFeature: Order[i] = feat->SharedVariance() / feat->values_array.size(); break; case SDoverFeature: Order[i] = feat->StandardDeviation() / feat->values_array.size(); break; case OneoverSplitInfo: Order[i] = 1.0 / feat->SplitInfo(); break; case UnknownOrdening: case MaxOrdening: FatalError( "Setorder: Illegal Order Value in Switch: " + TiCC::toString( TreeOrder ) ); break; } ++i; } features.calculate_permutation( Order ); if ( !Verbosity(SILENT) ){ writePermutation( *mylog ); } } void MBLClass::MatrixInfo( ostream& os ) const { unsigned int TotalCount = 0; bool dummy; size_t m = 1; for ( const auto& feat : features.feats ){ if ( !feat->Ignore() && feat->isStorableMetric() && feat->matrixPresent( dummy ) ){ unsigned int Count = feat->matrix_byte_size(); os << "Size of value-matrix[" << m << "] = " << Count << " Bytes " << endl; TotalCount += Count; } ++m; } if ( TotalCount ){ os << "Total Size of value-matrices " << TotalCount << " Bytes " << endl << endl; } } bool MBLClass::readMatrices( istream& is ){ string line; bool skip = false; bool anything = false; while ( getline( is, line ) ){ line = TiCC::trim( line ); if ( line.empty() ){ continue; } if ( line.compare( 0, 7, "Feature" ) != 0 ){ if ( skip ){ continue; } else { return false; } } else { skip = false; line = line.substr( 8 ); string::size_type pos = line.find_first_not_of("0123456789"); string nums = line.substr( 0, pos ); size_t num; if ( !TiCC::stringTo( nums, num ) ){ FatalError( "no feature index found in the inputfile" ); } else { if ( pos == string::npos ){ line = ""; } else { line = TiCC::trim( line.substr( pos ) ); } if ( line.empty() ){ if ( !features[num-1]->isStorableMetric() ){ Warning( "Ignoring entry for feature " + nums + " which is NOT set to a storable metric type." + " use -m commandline option to set metrics" ); skip = true; } else if ( !features[num-1]->fill_matrix( is ) ){ return false; } else { Info( "read ValueMatrix for feature " + nums ); anything = true; } } } } } if ( !anything ){ Error( "NO metric values found" ); return false; } return true; } bool MBLClass::writeMatrices( ostream& os ) const { size_t pos = 0; for ( const auto& feat : features.feats ){ os << "Feature " << ++pos; bool dummy; if ( !feat->matrixPresent( dummy ) ){ os << " not available.\n" << endl; } else { os << endl; feat->print_matrix( os ); } } return os.good(); } bool MBLClass::readArrays( istream& is ){ bool result = true; size_t num; size_t index = 1; string buf; char kar; do { is >> ws >> buf; if ( compare_nocase_n( "feature", buf ) ){ is >> ws >> kar; // skip # if ( kar != '#' ){ Error( "Input out-of-sync, a '#' was expected" ); result = false; } else { is >> num; if ( num != index ){ Error( "Wrong feature number " + TiCC::toString(num) + " in file, " + TiCC::toString(index) + " expected" ); result = false; } else if ( index > NumOfFeatures() ){ Error( "Too many features matrices in this file " ); result = false; } else { is >> ws >> buf; if ( compare_nocase_n( "Ignored", buf ) ){ if ( features[index-1]->Ignore() ){ ++index; continue; } else { Error( "Feature #" + TiCC::toString(index) + " may not be ignored..."); result = false; } } else if ( compare_nocase_n( "Numeric", buf ) ){ if ( features[index-1]->isNumerical() ){ ++index; continue; } else { Error( "Feature #" + TiCC::toString(index) + " is not Numeric..." ); result = false; } } else if ( !compare_nocase_n( "Matrix", buf ) ){ Error( "Problem in Probability file, missing matrix info" ); result = false; } else if ( features[index-1]->Ignore() || features[index-1]->isNumerical() ){ Warning( "Matrix info found for feature #" + TiCC::toString(index) + " (skipped)" ); ++index; } else { is.ignore( std::numeric_limits::max(), '\n' ); result = features[index-1]->read_vc_pb_array( is ); ++index; } } } } } while ( result && !is.eof() && !is.bad() ); if ( index < NumOfFeatures()+1 ){ Error( "Not enough features matrices in this file " ); result = false; } return result; } bool MBLClass::writeArrays( ostream& os ) { if ( ExpInvalid() ){ return false; } else if ( !initProbabilityArrays( false ) ){ Warning( "couldn't Calculate probability Arrays's" ); return false; } else { // Print the possible classes. // os << "Targets : "; for ( const auto& it : targets.values_array ){ os << it; if ( &it != &targets.values_array.back() ){ os << ","; } } os << "." << endl << endl; size_t pos = 0; for ( const auto& feat : features.feats ){ os << "feature # " << ++pos ; if ( feat->Ignore() ){ os << " Ignored, (-s option)" << endl; } else if ( feat->isNumerical() ){ os << " Numeric, (-N option)" << endl; } else { os << " Matrix: " << endl; feat->print_vc_pb_array( os ); os << endl; } } return true; } } bool MBLClass::allocate_arrays(){ size_t Dim = targets.values_array.size(); for ( const auto& feat : features.feats ){ if ( !feat->Ignore() && !feat->isNumerical() ) { if ( !feat->AllocSparseArrays( Dim ) ){ return false; } } } return true; } bool MBLClass::initProbabilityArrays( bool force ){ bool result = true; if ( !is_copy ){ result = allocate_arrays(); if ( result ){ for ( const auto& feat : features.feats ){ if ( !feat->Ignore() && !feat->isNumerical() ){ feat->ClipFreq( (int)rint(clip_factor * log((double)feat->EffectiveValues()))); if ( !feat->ArrayRead() && ( force || feat->isStorableMetric() ) ){ feat->InitSparseArrays(); } } } // j } } return result; } /* For mvd metric. */ void MBLClass::calculatePrestored(){ if ( !is_copy ){ for ( size_t j = tribl_offset; j < EffectiveFeatures(); ++j ) { if ( !features.perm_feats[j]->Ignore() && features.perm_feats[j]->isStorableMetric() ){ features.perm_feats[j]->store_matrix( mvd_threshold ); } } if ( Verbosity(VD_MATRIX) ){ size_t pos = 0; for ( auto const& feat : features.feats ){ ++pos; if ( !feat->Ignore() ){ bool dummy; *mylog << "Value Difference matrix of feature # " << pos << endl; if ( feat->matrixPresent( dummy ) ){ feat->print_matrix( *mylog, true ); *mylog << endl; } else { *mylog << "Not available." << endl; } } } } } } const Instance *MBLClass::chopped_to_instance( PhaseValue phase ){ CurrInst.clear(); if ( NumOfFeatures() != target_pos ) { ChopInput->swapTarget( target_pos ); } int occ = ChopInput->getOcc(); if ( occ > 1 ){ CurrInst.Occurrences( occ ); } switch ( phase ){ case LearnWords: // Add the target. CurrInst.TV = targets.add_value( ChopInput->getField( NumOfFeatures() ), occ ); // Now add the Feature values. for ( size_t i = 0; i < NumOfFeatures(); ++i ){ // when learning, no need to bother about Permutation if ( features[i]->Ignore() ) { // but this might happen, take care! CurrInst.FV[i] = NULL; } else { // Add it to the Instance. // cerr << "Feature add: " << ChopInput->getField(i) << endl; CurrInst.FV[i] = features[i]->add_value( ChopInput->getField(i), CurrInst.TV, occ ); } } // i // cerr << "new instance: " << CurrInst << endl; break; case TrainWords: // Lookup for TreeBuilding // First the Features for ( size_t k = 0; k < EffectiveFeatures(); ++k ){ size_t j = features.permutation[k]; CurrInst.FV[k] = features[j]->Lookup( ChopInput->getField(j) ); } // k // and the Target CurrInst.TV = targets.Lookup( ChopInput->getField( NumOfFeatures() ) ); break; case TrainLearnWords: // Lookup for Incremental TreeBuilding // Assumes that somehow Permutation and effective_feats are known // First the Target CurrInst.TV = targets.add_value( (*ChopInput)[NumOfFeatures()], occ ); // Then the Features for ( size_t l = 0; l < EffectiveFeatures(); ++l ){ size_t j = features.permutation[l]; CurrInst.FV[l] = features[j]->add_value((*ChopInput)[j], CurrInst.TV, occ ); } // for l break; case TestWords: // Lookup for Testing // This might fail for unknown values, then we create a dummy value for ( size_t m = 0; m < EffectiveFeatures(); ++m ){ size_t j = features.permutation[m]; const UnicodeString& fld = ChopInput->getField(j); CurrInst.FV[m] = features[j]->Lookup( fld ); if ( !CurrInst.FV[m] ){ // for "unknown" values have to add a dummy value CurrInst.FV[m] = new FeatureValue( fld ); } } // i // the last string is the target CurrInst.TV = targets.Lookup( ChopInput->getField(NumOfFeatures()) ); break; default: FatalError( "Wrong value in Switch: " + TiCC::toString(phase) ); } if ( ( phase != TestWords ) && doSamples() ){ double exW = ChopInput->getExW(); if ( exW < 0 ){ exW = 1.0; } CurrInst.ExemplarWeight( exW ); } return &CurrInst; } bool empty_line( const UnicodeString& Line, const InputFormatType IF ){ // determine wether Line is empty or a commentline bool result = ( Line.isEmpty() || ( IF == ARFF && // ARFF "comment" ( Line[0] == '%' || Line[0] == '@' ) ) ); if ( result ){ for ( int i=0; i < Line.length();++i ){ if ( !u_isspace( Line[i] ) ){ return false; } } } return result; } UnicodeString MBLClass::get_org_input( ) const { return ChopInput->getString(); } void MBLClass::LearningInfo( ostream& os ) { if ( !ExpInvalid() && !Verbosity(SILENT) ){ calculate_fv_entropy( !MBL_init ); os.setf(ios::showpoint ); int OldPrec = os.precision(8); os << "DB Entropy : " << DBEntropy << endl; os << "Number of Classes : " << targets.EffectiveValues() << endl; os << endl; if ( Verbosity(FEAT_W) ){ if ( CurrentWeighting() == SD_w ){ os << "Feats\tVals\tStandard Deviation" << endl; size_t pos = 0; for ( const auto& feat : features.feats ){ os << setw(5) << ++pos; os.setf(ios::right, ios::adjustfield); if ( feat->Ignore() ){ os << " (ignored) " << endl; } else { os.setf(ios::right, ios::adjustfield); os << setw(7) << feat->EffectiveValues() << "\t" << feat->StandardDeviation(); if ( feat->isNumerical() ){ os << " NUMERIC"; } os << endl; } } os << endl; os.precision(OldPrec); } else if ( need_all_weights ){ os << "Feats\tVals\tX-square\tVariance\tInfoGain\tGainRatio" << endl; size_t pos = 0; for ( const auto& feat : features.feats ) { os << setw(5) << ++pos; os.setf(ios::right, ios::adjustfield); if ( feat->Ignore() ){ os << " (ignored) " << endl; } else { os.setf(ios::right, ios::adjustfield); os << setw(7) << feat->EffectiveValues() << "\t" << feat->ChiSquare() << "\t" << feat->SharedVariance() << "\t" << feat->InfoGain() << "\t" << feat->GainRatio(); if ( feat->isNumerical() ){ os << " NUMERIC"; } os << endl; } } os << endl; os.precision(OldPrec); } else { os << "Feats\tVals\tInfoGain\tGainRatio" << endl; size_t pos = 0; for ( const auto& feat : features.feats ) { os << setw(5) << ++pos; os.setf(ios::right, ios::adjustfield); if ( feat->Ignore() ){ os << " (ignored) " << endl; } else { os.setf(ios::right, ios::adjustfield); os << setw(7) << feat->EffectiveValues() << "\t" << feat->InfoGain() << "\t" << feat->GainRatio(); if ( feat->isNumerical() ){ os << " NUMERIC"; } os << endl; } } os << endl; os.precision(OldPrec); } } } } bool MBLClass::writeWeights( ostream& os ) const { bool result = false; if ( !ExpInvalid() ){ if ( features[0] == NULL ){ Warning( "unable to save Weights, nothing learned yet" ); } else { os << "# DB Entropy: " << DBEntropy << endl; os << "# Classes: " << targets.values_array.size() << endl; os << "# Lines of data: " << targets.TotalValues() << endl; int OldPrec = os.precision(DBL_DIG); if ( CurrentWeighting() == SD_w ){ os << "#" << endl; os << "# " << TiCC::toString( SD_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; size_t pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << feat->StandardDeviation() << endl; } } os << "#" << endl; } else { os << "# " << TiCC::toString( No_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; size_t pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << 1.0 << endl; } } os << "#" << endl; os << "# " << TiCC::toString( GR_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << feat->GainRatio() << endl; } } os << "#" << endl; os << "# " << TiCC::toString( IG_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << feat->InfoGain() << endl; } } if ( need_all_weights ){ os << "#" << endl; os << "# " << TiCC::toString( SV_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << feat->SharedVariance() << endl; } } os << "#" << endl; os << "# " << TiCC::toString( X2_w ) << endl; os << "# Fea." << "\t" << "Weight" << endl; pos = 0; for ( const auto& feat : features.feats ){ os.precision(DBL_DIG); os << ++pos << "\t"; if ( feat->Ignore() ){ os << "Ignore" << endl; } else { os << feat->ChiSquare() << endl; } } os << "#" << endl; } } os.precision(OldPrec); result = true; } } return result; } bool MBLClass::read_the_vals( istream& is ){ vector done( NumOfFeatures(), false );; string Buffer; while ( getline( is, Buffer) ){ if ( !Buffer.empty() ){ if ( Buffer[0] == '#'){ break; } // Line looks like: // 28 0.445481 // or: // 13 Ignore // vector vals = TiCC::split( Buffer ); if ( vals.size() == 2 ){ size_t i_f = TiCC::stringTo( vals[0] ); if ( i_f > NumOfFeatures() ){ Error( "in weightsfile, Feature index > Maximum, (" + TiCC::toString(NumOfFeatures()) + ")" ); } else if ( done[i_f-1] ){ Error( "in weightsfile, Feature index " + vals[0] + " is mentioned twice" ); } else { done[i_f-1] = true; if ( !compare_nocase( vals[1], "Ignore" ) ){ double w; if ( !TiCC::stringTo( vals[1], w ) ){ Error( "in weightsfile, Feature " + vals[0] + " has illegal value: " + vals[1] ); } else { features[i_f-1]->SetWeight( w ); if ( features[i_f-1]->Ignore() ){ Warning( "in weightsfile, " "Feature " + vals[0] + " has value: " + TiCC::toString( w ) + " assigned, but will be ignored" ); } } } else { features[i_f-1]->SetWeight( 0.0 ); if ( !features[i_f-1]->Ignore() ){ Warning( "in weightsfile, Feature " + vals[0] + " has value: 'Ignore', we will use: 0.0 " ); } } } } } } bool result = true; for ( size_t j=0; j < NumOfFeatures(); ++j ){ if ( !done[j] ) { Error( "in weightsfile, Feature index " + TiCC::toString(j+1) + " is not mentioned" ); result = false; } } return result; } bool MBLClass::readWeights( istream& is, WeightType wanted ){ if ( !ExpInvalid() ){ bool old_style = true; bool result = false; string Buffer; while( getline( is, Buffer ) ) { // A comment starts with '#' // if ( Buffer.empty() ){ continue; } else { if ( Buffer[0] == '#'){ vector vals = TiCC::split_at( Buffer, " " ); if ( vals.size() == 2 ){ WeightType tmp_w = Unknown_w; if ( !TiCC::stringTo( vals[1], tmp_w ) ){ continue; } else { old_style = false; if ( tmp_w == wanted ){ getline( is, Buffer ); result = read_the_vals( is ); break; } } } } } } if ( is.eof() ){ if ( old_style ){ // wanted weighting not found // Old style weightsfile? // Warning( "Old Style weightsfile. Please update" ); is.clear(); is.seekg(0); size_t pos = 0; while( getline( is, Buffer ) ) { // A comment starts with '#' // if ( Buffer.empty() ){ pos = is.tellg(); continue; } else { if ( Buffer[0] == '#'){ pos = is.tellg(); continue; } is.seekg(pos); result = read_the_vals( is ); break; } } } } if ( !result ){ Warning( "Unable to retrieve " + TiCC::toString( wanted ) + " Weights" ); Warning( "unable to continue" ); return false; } // make shure all weights are correct // Paranoid? for ( const auto& feat : features.feats ){ feat->InfoGain( feat->Weight() ); feat->GainRatio( feat->Weight() ); feat->ChiSquare( feat->Weight() ); feat->SharedVariance( feat->Weight() ); feat->StandardDeviation( 0.0 ); } Weighting = UserDefined_w; } return true; } bool MBLClass::recalculate_stats( Feature_List& feats, vector& feat_status, bool check_change ){ bool changed = false; for ( size_t g = 0; g < NumOfFeatures(); ++g ) { feat_status[g] = Unknown; if ( feats.feats[g]->Ignore() ){ continue; } bool metricChanged = false; MetricType TmpMetricType = UserOptions[g+1]; metricClass *tmpMetric = getMetricClass( TmpMetricType ); if ( tmpMetric->isNumerical() ){ feat_status[g] = feats[g]->prepare_numeric_stats(); if ( feat_status[g] == SingletonNumeric && input_format == SparseBin && GlobalMetric->isSimilarityMetric( ) ){ // ok } else if ( feat_status[g] != NumericValue ){ if ( GlobalMetric->isNumerical() ){ TmpMetricType = Overlap; } else { TmpMetricType = globalMetricOption; } } } else if ( feats[g]->values_array.size() == 1 ){ feat_status[g] = Singleton; } delete tmpMetric; if ( check_change ){ bool isRead; if ( feats.feats[g]->metric && feats.feats[g]->getMetricType() != TmpMetricType && feats.feats[g]->isStorableMetric() && feats.feats[g]->matrixPresent( isRead ) && isRead ){ Error( "The metric " + TiCC::toString(feats.feats[g]->getMetricType()) + " for feature " + TiCC::toString( g+1 ) + " is set from a file. It cannot be changed!" ); abort(); } metricChanged = !feats.feats[g]->setMetricType(TmpMetricType); } if ( metricChanged ){ changed = true; } } // end g return changed; } void MBLClass::calculate_fv_entropy( bool always ){ bool realy_first = DBEntropy < 0.0; bool redo = always || realy_first; if ( redo ){ // if it's the first time (DBEntropy == 0 ) or // if always, we have to (re)calculate everything double Entropy = 0.0; // first get the Database Entropy size_t totval = targets.TotalValues(); for ( const auto& it : targets.values_array ){ double Ratio = it->ValFreq() / (double)totval; if ( Ratio > 0 ){ Entropy += Ratio * Log2(Ratio); } } DBEntropy = fabs(-Entropy); allocate_arrays(); // create ValueClassProb arrays.. } // Loop over the Features, see if the numerics are non-singular // and do the statistics for those features where the metric is changed. vector feat_status(NumOfFeatures()); bool changed = recalculate_stats( features, feat_status, redo ); if ( ( CurrentWeighting() == SD_w || GlobalMetric->isSimilarityMetric() ) && changed ){ // check to see if ALL features are still Numeric. // otherwise we can't do Standard Deviation weighting, // or Similarity Metrics! bool first = true; string str1; for ( size_t ff = 0; ff < NumOfFeatures(); ++ff ){ if ( feat_status[ff] == NotNumeric ){ if ( first ){ str1 += "The following feature(s) have non numeric value: "; first = false; } else { str1 += ", "; } size_t n = ff; while ( ff < NumOfFeatures()-1 && feat_status[ff+1] == NotNumeric ){ ++ff; } if ( n != ff ){ str1 += to_string(n+1) + "-" + to_string(ff+1); } else { str1 + to_string(ff+1); } } } if ( !first ){ Error( str1 ); if ( GlobalMetric->isSimilarityMetric() ){ Error( "Therefore InnerProduct/Cosine operations are impossible" ); } else { Error( "Therefore " + TiCC::toString(CurrentWeighting()) + " weighting is impossible" ); } return; } } // Give a warning for singular features, except when it's // a result of a forced recalculation if ( realy_first ){ bool first = true; string str1; for ( size_t ff = 0; ff < NumOfFeatures(); ++ff ) { if ( feat_status[ff] == Singleton || feat_status[ff] == SingletonNumeric ){ if ( first ){ str1 += "The following feature(s) have only 1 value: "; first = false; } else { str1 += ", "; } size_t n = ff; while ( ff < NumOfFeatures()-1 && ( feat_status[ff+1] == Singleton || feat_status[ff+1] == SingletonNumeric ) ){ ++ff; } if ( n != ff ){ str1 += to_string(n+1) + "-" + to_string(ff+1); } else { str1 += to_string(ff+1); } } } if ( !first && !is_copy ){ Warning( str1 ); } string str2; first = true; for ( size_t ff = 0; ff < NumOfFeatures(); ++ff ){ if ( feat_status[ff] == NotNumeric ){ if ( first ){ str2 += "The following feature(s) contained non-numeric values and" "\nwill be treated as NON-Numeric: "; first = false; } else { str2 += ", "; } size_t n = ff; while ( ff < NumOfFeatures()-1 && feat_status[ff+1] == NotNumeric ) ff++; if ( n != ff ){ str2 += to_string(n+1) + "-" + to_string(ff+1); } else { str2 += to_string(ff+1); } } } if ( !first ){ Warning( str2 ); } } if ( redo ){ for ( const auto& feat : features.feats ){ if ( Weighting != UserDefined_w ){ if ( CurrentWeighting() == SD_w ){ feat->StandardDeviationStatistics( ); } else if ( feat->isNumerical() ){ feat->NumStatistics( DBEntropy, targets, Bin_Size, need_all_weights ); } else { feat->Statistics( DBEntropy, targets, need_all_weights ); } } } } } bool MBLClass::writeNamesFile( ostream& os ) const { bool result = true; if ( ExpInvalid() ){ result = false; } else { // Print the possible classes. // for ( const auto& it : targets.values_array ){ os << it; if ( &it != &targets.values_array.back() ){ os << ","; } } os << "." << endl << endl; size_t pos = 0; for ( auto const& feat : features.feats ){ os << "a" << ++pos << ": "; if ( feat->Ignore() ){ os << "Ignore" << endl; } else if ( feat->isNumerical() ){ os << "Numeric" << endl; } else { // Loop over the values. // for ( const auto& val : feat->values_array ){ os << val; if ( &val != &feat->values_array.back() ){ os << ","; } } os << "." << endl; } } } return result; } bool MBLClass::Chop( const UnicodeString& line ) { try { return ChopInput->chop( line, NumOfFeatures() ); } catch ( const exception& e ){ Warning( e.what() ); return false; } } bool MBLClass::setInputFormat( const InputFormatType IF ){ if ( ChopInput ){ delete ChopInput; ChopInput = 0; } ChopInput = Chopper::create( IF, chopExamples(), F_length, chopOcc() ); if ( ChopInput ){ input_format = IF; return true; } return false; } const ClassDistribution *MBLClass::ExactMatch( const Instance& inst ) const { const ClassDistribution *result = NULL; if ( !GlobalMetric->isSimilarityMetric() && ( do_exact_match || ( num_of_neighbors == 1 && !( Verbosity( NEAR_N | ALL_K) ) ) ) ){ result = InstanceBase->ExactMatch( inst ); } return result; } double MBLClass::getBestDistance() const { return nSet.bestDistance(); } WClassDistribution *MBLClass::getBestDistribution( unsigned int k ){ return nSet.bestDistribution( decay, k ); } UnicodeString MBLClass::formatInstance( const vector& OrgFV, const vector& RedFV, size_t OffSet, size_t Size ) const { UnicodeString result; Instance inst( Size ); for ( size_t i=0; i< OffSet; ++i ){ inst.FV[i] = OrgFV[i]; } for ( size_t j=OffSet; j< Size; ++j ){ inst.FV[j] = RedFV[j-OffSet]; } vector InvPerm(NumOfFeatures(),0); for ( size_t i=0; i< NumOfFeatures(); ++i ){ InvPerm[features.permutation[i]] = i; } for ( size_t j=0; j< NumOfFeatures(); ++j ){ switch ( input_format ) { case C4_5: // fall through case ARFF: if ( features[j]->Ignore() ){ result += "-*-,"; } else { result += inst.FV[InvPerm[j]]->name() + ","; } break; case Sparse: if ( inst.FV[InvPerm[j]]->name() != DefaultSparseString ){ result += "(" + TiCC::toUnicodeString(j+1) + "," + CodeToStr( inst.FV[InvPerm[j]]->name() ) + ")"; } break; case SparseBin: if ( inst.FV[InvPerm[j]]->name()[0] == '1' ){ result += TiCC::toUnicodeString( j+1 ) + ","; } break; case Columns: if ( features[j]->Ignore() ){ result += "-*- "; } else { result += inst.FV[InvPerm[j]]->name() + " "; } break; case Tabbed: if ( features[j]->Ignore() ){ result += "-*- "; } else { result += inst.FV[InvPerm[j]]->name() + "\t"; } break; default: if ( features[j]->Ignore() ){ result += UnicodeString( F_length, '*', F_length ); } else { result += inst.FV[InvPerm[j]]->name(); } break; } } return result; } inline double WeightFun( double D, double W ){ return D / (W + Common::Epsilon); } void MBLClass::test_instance_ex( const Instance& Inst, InstanceBase_base *IB, size_t ib_offset ){ vector CurrentFV(NumOfFeatures()); const ClassDistribution *best_distrib = IB->InitGraphTest( CurrentFV, &Inst.FV, ib_offset, EffectiveFeatures() ); if ( !best_distrib ){ // no use to do more work then return; } tester->init( Inst, EffectiveFeatures(), ib_offset ); auto lastpos = best_distrib->begin(); Vfield *Bpnt = lastpos->second; size_t EffFeat = EffectiveFeatures() - ib_offset; size_t CurPos = 0; while ( Bpnt ) { // call test() with a maximum threshold, to prevent stepping out early size_t EndPos = tester->test( CurrentFV, CurPos, DBL_MAX ); if ( EndPos != EffFeat ){ throw( logic_error( "Exemplar testing: test should not stop before last feature" ) ); } ClassDistribution ResultDist; ResultDist.SetFreq( Bpnt->Value(), Bpnt->Freq() ); UnicodeString origI; if ( Verbosity(NEAR_N) ){ origI = formatInstance( Inst.FV, CurrentFV, ib_offset, NumOfFeatures() ); } double Distance = WeightFun( tester->getDistance(EndPos), Bpnt->Weight() ); bestArray.addResult( Distance, &ResultDist, origI ); CurPos = EndPos-1; ++lastpos; if ( lastpos != best_distrib->end() ){ Bpnt = lastpos->second; } else { best_distrib = IB->NextGraphTest( CurrentFV, CurPos ); Bpnt = NULL; if ( best_distrib ){ lastpos = best_distrib->begin(); if ( lastpos != best_distrib->end() ){ Bpnt = lastpos->second; } } } } } void MBLClass::initDecay(){ if ( decay ){ delete decay; decay = 0; } switch ( decay_flag ){ case InvDist: decay = new invDistDecay(); break; case InvLinear: decay = new invLinDecay(); break; case ExpDecay: decay = new expDecay( decay_alfa, decay_beta ); break; case Zero: // fall through default: break; } } void MBLClass::initTesters() { delete GlobalMetric; GlobalMetric = getMetricClass( globalMetricOption ); delete tester; tester = getTester( globalMetricOption, features, mvd_threshold ); } void MBLClass::test_instance( const Instance& Inst, InstanceBase_base *IB, size_t ib_offset ){ vector CurrentFV(NumOfFeatures()); double Threshold = DBL_MAX; size_t EffFeat = EffectiveFeatures() - ib_offset; const ClassDistribution *best_distrib = IB->InitGraphTest( CurrentFV, &Inst.FV, ib_offset, EffectiveFeatures() ); tester->init( Inst, EffectiveFeatures(), ib_offset ); size_t CurPos = 0; while ( best_distrib ){ size_t EndPos = tester->test( CurrentFV, CurPos, Threshold + Epsilon ); if ( EndPos == EffFeat ){ // we finished with a certain amount of succes double Distance = tester->getDistance(EndPos); if ( Distance >= 0.0 ){ UnicodeString origI; if ( Verbosity(NEAR_N) ){ origI = formatInstance( Inst.FV, CurrentFV, ib_offset, NumOfFeatures() ); } Threshold = bestArray.addResult( Distance, best_distrib, origI ); if ( do_silly_testing ){ Threshold = DBL_MAX; } } else { Error( "DISTANCE == " + TiCC::toString(Distance) ); FatalError( "we are dead" ); } } else { ++EndPos; // out of luck, compensate for roll-back } size_t pos=EndPos-1; while ( true ){ // rollback if ( tester->getDistance(pos) <= Threshold ){ CurPos = pos; best_distrib = IB->NextGraphTest( CurrentFV, CurPos ); break; } if ( pos == 0 ){ break; } --pos; } } } void MBLClass::test_instance_sim( const Instance& Inst, InstanceBase_base *IB, size_t ib_offset ){ vector CurrentFV(NumOfFeatures()); size_t EffFeat = EffectiveFeatures() - ib_offset; const ClassDistribution *best_distrib = IB->InitGraphTest( CurrentFV, &Inst.FV, ib_offset, EffectiveFeatures() ); tester->init( Inst, EffectiveFeatures(), ib_offset ); while ( best_distrib ){ double dummy_t = -1.0; size_t dummy_p = 0; // similarity::test() doesn't need CurPos, nor a Threshold // it recalculates the whole vector size_t EndPos = tester->test( CurrentFV, dummy_p, dummy_t ); if ( EndPos == EffFeat ){ // this should always be true! double Distance = tester->getDistance(EndPos); if ( Distance >= 0.0 ){ UnicodeString origI; if ( Verbosity(NEAR_N) ){ origI = formatInstance( Inst.FV, CurrentFV, ib_offset, NumOfFeatures() ); } bestArray.addResult( Distance, best_distrib, origI ); } else if ( GlobalMetric->type() == DotProduct ){ Error( "The Dot Product metric fails on your data: intermediate result too big to handle," ); Info( "you might consider using the Cosine metric '-mC' " ); FatalError( "timbl terminated" ); } else { Error( "negative similarity DISTANCE: " + TiCC::toString(Distance) ); FatalError( "we are dead" ); } } else { throw( logic_error( "Similarity testing: test should consider all features" ) ); } --EndPos; best_distrib = IB->NextGraphTest( CurrentFV, EndPos ); } } void MBLClass::TestInstance( const Instance& Inst, InstanceBase_base *SubTree, size_t level ){ // must be cleared for EVERY test if ( doSamples() ){ test_instance_ex( Inst, SubTree, level ); } else { if ( GlobalMetric->isSimilarityMetric( ) ){ test_instance_sim( Inst, SubTree, level ); } else { test_instance( Inst, SubTree, level ); } } } size_t MBLClass::countFeatures( const UnicodeString& inBuffer, const InputFormatType IF ) const { size_t result = 0; if ( IF == Sparse || IF == SparseBin ){ return NumOfFeatures(); } else { try { result = Chopper::countFeatures( inBuffer, IF, F_length, chopExamples() || chopOcc() ); } catch( const runtime_error& e ){ Error( e.what() ); } catch( const exception& e ){ FatalError( e.what() ); } } return result; } InputFormatType MBLClass::getInputFormat( const UnicodeString& inBuffer ) const { return Chopper::getInputFormat( inBuffer, chopExamples() || chopOcc() ); } size_t MBLClass::examineData( const string& FileName ){ // Looks at the data files, counts number of features. // and sets input_format variables. // size_t NumF = 0; InputFormatType IF = UnknownInputFormat; // Open the file. // if ( FileName == "" ) { Warning( "couldn't initialize: No FileName specified " ); return 0; } else { UnicodeString Buffer; ifstream datafile( FileName, ios::in); if (!datafile) { Warning( "can't open DataFile: " + FileName ); return 0; } else if ( input_format != UnknownInputFormat ){ // The format is somehow already known, so use that if ( input_format == SparseBin || input_format == Sparse ){ NumF = MaxFeatures; } else { if ( !TiCC::getline( datafile, Buffer ) ) { Warning( "empty data file" ); } else { bool more = true; if ( input_format == ARFF ){ while ( Buffer.caseCompare( "@DATA", 5 ) ){ if ( !TiCC::getline( datafile, Buffer ) ){ Warning( "empty data file" ); more = false; break; }; } if ( more && !TiCC::getline( datafile, Buffer ) ){ Warning( "empty data file" ); more = false; }; } while ( more && empty_line( Buffer, input_format ) ){ if ( !TiCC::getline( datafile, Buffer ) ){ Warning( "empty data file" ); more = false; }; } // now we have a usable line, //analyze it using the User defined input_format NumF = countFeatures( Buffer, input_format ); } } IF = input_format; } else if ( !TiCC::getline( datafile, Buffer ) ){ Warning( "empty data file: " + FileName ); } // We start by reading the first line so we can figure out the number // of Features, and see if the file is comma seperated or not, etc. // else{ if ( IF == ARFF ){ // Remember, we DON't want to auto-detect ARFF while ( Buffer.caseCompare( "@DATA", 5 ) ){ if ( !TiCC::getline( datafile, Buffer ) ) { Warning( "no ARRF data after comments: " + FileName ); return 0; } } do { if ( !TiCC::getline( datafile, Buffer ) ) { Warning( "no ARRF data after comments: " + FileName ); return 0; } } while ( empty_line( Buffer, input_format ) ); } else { while ( empty_line( Buffer, input_format ) ) { if ( !TiCC::getline( datafile, Buffer ) ) { Warning( "no data after comments: " + FileName ); return 0; } } // We found a useful line! // Now determine the input_format (if not already known, // and Count Features as well. } IF = getInputFormat( Buffer ); NumF = countFeatures( Buffer, IF ); } } if ( NumF > 0 ){ if ( input_format != UnknownInputFormat && input_format != IF ){ Warning( "assumed inputformat differs from specified!" ); return 0; } else { if ( NumF > MaxFeatures ){ Error( "Number of Features exceeds the maximum number. " "(currently " + TiCC::toString(MaxFeatures) + ")\nPlease increase.\n" ); return 0; } setInputFormat( IF ); } } return NumF; } void MBLClass::Initialize( size_t numF ){ // Allocate memory. Will be reused again and again .... // if ( target_pos == std::numeric_limits::max() ){ target_pos = numF; // the default } else if ( target_pos > numF ){ FatalError( "Initialize: TARGET_POS cannot exceed NUM_OF_FEATURES+1 " + TiCC::toString( numF+1 ) ); } targets.init(); features.init( numF, UserOptions ); CurrInst.Init( numF ); delete GlobalMetric; GlobalMetric = getMetricClass( globalMetricOption ); Options.FreezeTable(); if ( Weighting > IG_w || TreeOrder >= X2Order ){ need_all_weights = true; } } } // namespace LanguageMachines-timbl-642727d/src/Makefile.am000066400000000000000000000013641451477526200211370ustar00rootroot00000000000000AM_CPPFLAGS = -I@top_srcdir@/include AM_CXXFLAGS = -std=c++14 -W -Wall -O3 -g -pedantic bin_PROGRAMS = timbl check_PROGRAMS = simpletest TESTS = $(check_PROGRAMS) TESTS_ENVIRONMENT = topsrcdir=$(top_srcdir) simpletest_SOURCES = simpletest.cxx CLEANFILES = dimin.out LDADD = libtimbl.la timbl_SOURCES = Timbl.cxx lib_LTLIBRARIES = libtimbl.la libtimbl_la_LDFLAGS= -version-info 7:0:0 libtimbl_la_SOURCES = Common.cxx \ GetOptClass.cxx IBtree.cxx IBprocs.cxx \ Targets.cxx Features.cxx Instance.cxx \ MBLClass.cxx MsgClass.cxx \ StringOps.cxx TimblAPI.cxx Choppers.cxx\ TimblExperiment.cxx IGExperiment.cxx Metrics.cxx Testers.cxx \ TRIBLExperiments.cxx LOOExperiment.cxx CVExperiment.cxx \ Types.cxx neighborSet.cxx Statistics.cxx BestArray.cxx LanguageMachines-timbl-642727d/src/Metrics.cxx000066400000000000000000000274031451477526200212370ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/Metrics.h" #include "unicode/schriter.h" using namespace std; using Common::Epsilon; using Common::Log2; //#define METRIC_DEBUG namespace Timbl{ double lv_distance( const icu::UnicodeString& source, const icu::UnicodeString& target ){ // code taken from: http://www.merriampark.com/ldcpp.htm // Levenshtein Distance Algorithm: C++ Implementation // by Anders Sewerin Johansen // Step 1 const size_t n = source.length(); const size_t m = target.length(); if (n == 0) { return (double)m; } if (m == 0) { return (double)n; } // Good form to declare a TYPEDEF typedef std::vector< std::vector > Tmatrix; Tmatrix matrix(n+1); // Size the vectors in the 2.nd dimension. Unfortunately C++ doesn't // allow for allocation on declaration of 2.nd dimension of vec of vec for ( size_t i = 0; i <= n; ++i ) { matrix[i].resize(m+1); } // Step 2 for ( size_t i = 0; i <= n; ++i ) { matrix[i][0]=i; } for ( size_t i = 0; i <= m; ++i ) { matrix[0][i]=i; } // Step 3 for ( size_t i = 1; i <= n; ++i ) { const char s_i = source[i-1]; // Step 4 for ( size_t j = 1; j <= m; ++j ) { const char t_j = target[j-1]; // Step 5 int cost; if (s_i == t_j) { cost = 0; } else { cost = 1; } // Step 6 const size_t above = matrix[i-1][j]; const size_t left = matrix[i][j-1]; const size_t diag = matrix[i-1][j-1]; size_t cell = min( above + 1, min(left + 1, diag + cost)); // Step 6A: Cover transposition, in addition to deletion, // insertion and substitution. This step is taken from: // Berghel, Hal ; Roach, David : "An Extension of Ukkonen's // Enhanced Dynamic Programming ASM Algorithm" // (http://www.acm.org/~hlb/publications/asm/asm.html) if (i>2 && j>2) { size_t trans=matrix[i-2][j-2]+1; if (source[i-2]!=t_j) { trans++; }; if (s_i!=target[j-2]) { trans++; }; if (cell>trans) { cell=trans; }; } matrix[i][j]=cell; } } return (double)matrix[n][m]; } double dc_distance( const icu::UnicodeString& string1, const icu::UnicodeString& string2 ){ // code taken from: // http://en.wikibooks.org/wiki/Algorithm_implementation/Strings/Dice's_coefficient unsigned int ls1 = string1.length(); unsigned int ls2 = string2.length(); double dice; int overlap = 0; int total = 0; if ( ls1 <= 1 || ls2 <= 1 ){ // back-off naar unigrammen set string1_unigrams; set string2_unigrams; icu::StringCharacterIterator it1(string1); while ( it1.hasNext() ){ string1_unigrams.insert(it1.current32()); it1.next32(); } icu::StringCharacterIterator it2(string2); while ( it2.hasNext() ){ string2_unigrams.insert(it2.current32()); it2.next32(); } for ( const auto& ug : string2_unigrams ){ if ( string1_unigrams.find( ug ) != string1_unigrams.end() ){ ++overlap; } } total = string1_unigrams.size() + string2_unigrams.size(); } else { set string1_bigrams; set string2_bigrams; for ( unsigned int i = 0; i < (ls1 - 1); ++i ) { // extract character bigrams from string1 string1_bigrams.insert( icu::UnicodeString( string1, i, 2 ) ); } for ( unsigned int i = 0; i < (ls2 - 1); ++i ) { // extract character bigrams from string2 string2_bigrams.insert( icu::UnicodeString( string2, i, 2 ) ); } for ( const auto& bg : string2_bigrams ){ if ( string1_bigrams.find( bg ) != string1_bigrams.end() ){ ++overlap; } } total = string1_bigrams.size() + string2_bigrams.size(); } dice = (double)(overlap * 2) / (double)total; // we will return 1 - dice coefficient as distance return 1.0 - dice; } double vd_distance( const SparseValueProbClass *r, const SparseValueProbClass *s ){ double result = 0.0; if ( ! ( r && s ) ){ return 1.0; } auto p1 = r->begin(); auto p2 = s->begin(); while( p1 != r->end() && p2 != s->end() ){ if ( p2->first < p1->first ){ result += p2->second; ++p2; } else if ( p2->first == p1->first ){ result += fabs( p1->second - p2->second ); ++p1; ++p2; } else { result += p1->second; ++p1; } } while ( p1 != r->end() ){ result += p1->second; ++p1; } while ( p2 != s->end() ){ result += p2->second; ++p2; } result = result / 2.0; return result; } double p_log_p_div_q( double p, double q ) { if ( abs(q) < Epsilon ){ return 0; } return p * Log2( p/q ); } double jd_distance( const SparseValueProbClass *r, const SparseValueProbClass *s ){ double part1 = 0.0; double part2 = 0.0; auto p1 = r->begin(); auto p2 = s->begin(); while( p1 != r->end() && p2 != s->end() ){ if ( p2->first < p1->first ){ part2 += p2->second; ++p2; } else if ( p2->first == p1->first ){ part1 += p_log_p_div_q( p1->second, p2->second ); part2 += p_log_p_div_q( p2->second, p1->second ); ++p1; ++p2; } else { part1 += p1->second; ++p1; } } while ( p1 != r->end() ){ part1 += p1->second; ++p1; } while ( p2 != s->end() ){ part2 += p2->second; ++p2; } double result = part1 + part2; result = result / 2.0; return result; } double k_log_k_div_m( double k, double l ) { if ( abs(k+l) < Epsilon ){ return 0; } return k * Log2( (2.0 * k)/( k + l ) ); } double js_distance( const SparseValueProbClass *r, const SparseValueProbClass *s ){ double part1 = 0.0; double part2 = 0.0; auto p1 = r->begin(); auto p2 = s->begin(); while( p1 != r->end() && p2 != s->end() ){ if ( p2->first < p1->first ){ part2 += p2->second; ++p2; } else if ( p2->first == p1->first ){ part1 += k_log_k_div_m( p1->second, p2->second ); part2 += k_log_k_div_m( p2->second, p1->second ); ++p1; ++p2; } else { part1 += p1->second; ++p1; } } while ( p1 != r->end() ){ part1 += p1->second; ++p1; } while ( p2 != s->end() ){ part2 += p2->second; ++p2; } double result = part1 + part2; result = result / 2.0; return result; } metricClass *getMetricClass( MetricType mt ){ switch ( mt ){ case Overlap: return new OverlapMetric(); break; case Numeric: return new NumericMetric(); break; case Euclidean: return new EuclideanMetric(); break; case Cosine: return new CosineMetric(); break; case DotProduct: return new DotProductMetric(); break; case ValueDiff: return new ValueDiffMetric(); break; case JeffreyDiv: return new JeffreyMetric(); break; case JSDiv: return new JSMetric(); break; case Levenshtein: return new LevenshteinMetric(); break; case Dice: return new DiceMetric(); break; case Ignore: return 0; break; default: throw logic_error("getMetricClass: unknown MetricType " + TiCC::toString(mt) ); } } double OverlapMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t, double ) const { if ( F == G ){ return 0.0; } else { return 1.0; } } inline bool FV_to_real( const FeatureValue *FV, double &result ){ if ( FV ){ if ( TiCC::stringTo( FV->name(), result ) ){ return true; } } return false; } double JeffreyMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t limit, double ) const { double result = 0.0; if ( G != F ){ if ( F->ValFreq() < limit || G->ValFreq() < limit ){ #ifdef METRIC_DEBUG cerr << "result = 1.0 vanwege F.valFreq=" << F->ValFreq() << " en G.valFreq()=" << G ->ValFreq() << " met limiet= " << limit << endl; #endif result = 1.0; } else { result = jd_distance( F->valueClassProb(), G->valueClassProb() ); } } return result; } double JSMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t limit, double ) const { double result = 0.0; if ( G != F ){ if ( F->ValFreq() < limit || G->ValFreq() < limit ){ #ifdef METRIC_DEBUG cerr << "result = 1.0 vanwege F.valFreq=" << F->ValFreq() << " en G.valFreq()=" << G ->ValFreq() << " met limiet= " << limit << endl; #endif result = 1.0; } else { result = js_distance( F->valueClassProb(), G->valueClassProb() ); } } return result; } double LevenshteinMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t, double) const { double result = 0.0; if ( G != F ){ result = lv_distance( F->name(), G->name() ); } return result; } double DiceMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t, double ) const { double result = 0.0; if ( G != F ){ result = dc_distance( F->name(), G->name() ); } return result; } double ValueDiffMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t limit, double ) const { double result = 0.0; if ( G != F ){ if ( F->ValFreq() < limit || G->ValFreq() < limit ){ #ifdef METRIC_DEBUG cerr << "result = 1.0 vanwege F.valFreq=" << F->ValFreq() << " en G.valFreq()=" << G ->ValFreq() << " met limiet= " << limit << endl; #endif result = 1.0; } else { result = vd_distance( F->valueClassProb(), G->valueClassProb() ); } } return result; } double NumericMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t, double scale ) const { double r1=0, r2=0, result; if ( FV_to_real( F, r1 ) && FV_to_real( G, r2 ) ){ result = fabs( (r1-r2)/ ( scale ) ); } else { result = 1.0; } return result; } double EuclideanMetric::distance( const FeatureValue *F, const FeatureValue *G, size_t, double scale ) const { double r1=0, r2=0, result; if ( FV_to_real( F, r1 ) && FV_to_real( G, r2 ) ){ result = sqrt(fabs(r1*r1-r2*r2))/ ( scale ); } else { result = 1.0; } return result; } double DotProductMetric::distance( const FeatureValue *, const FeatureValue *, size_t, double ) const { throw( logic_error( "unimplemented distance() for Dotproduct metric!" ) ); } double CosineMetric::distance( const FeatureValue *, const FeatureValue *, size_t, double ) const { throw( logic_error( "unimplemented distance() for Cosine metric!" ) ); } } LanguageMachines-timbl-642727d/src/MsgClass.cxx000066400000000000000000000035601451477526200213430ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include "timbl/MsgClass.h" using std::cerr; using std::endl; using std::string; namespace Timbl { void MsgClass::Info( const string& out_line ) const { cerr << out_line << endl; } void MsgClass::Warning( const string& out_line ) const { cerr << "Warning:" << out_line << endl; } void MsgClass::Error( const string& out_line ) const { ++err_cnt; cerr << "Error:" << out_line << endl; } void MsgClass::FatalError( const string& out_line ) const { cerr << "Fatal timbl Error:" << out_line << endl << "Please send a bugreport to timbl@uvt.nl" << endl << "include enough information, like:" << endl << "- Type of computer, type and version of OS, " << "and type and version of the compiler" << endl << "- Which Commands and switches were used" << endl << "- Which input was used, and which output was produced" << endl; throw std::runtime_error( "aborted" ); } } LanguageMachines-timbl-642727d/src/Statistics.cxx000066400000000000000000000143561451477526200217660ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include "timbl/Common.h" #include "timbl/MsgClass.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/Statistics.h" namespace Timbl { using std::bad_alloc; using std::ostream; using std::ios; using std::ios_base; using std::endl; using Common::Epsilon; ConfusionMatrix::ConfusionMatrix( size_t s ): size(s){ try { mat.resize(size+1); for ( size_t i=0; i <= size; ++i ){ mat[i].resize(size,0); } } catch( const bad_alloc& ){ Error ( "Not enough memory for ConfusionMatrix" ); throw; } } ConfusionMatrix::~ConfusionMatrix(){ for ( unsigned int i=0; i <= size; ++i ){ mat[i].clear(); } mat.clear(); } void ConfusionMatrix::Increment( const TargetValue *t1, const TargetValue *t2 ){ if ( t2 ){ if ( t1 ){ ++mat[t1->Index()-1][t2->Index()-1]; } else { ++mat[size][t2->Index()-1]; } } else { throw std::out_of_range( "ConfusionMatrix, index out of range" ); } } void ConfusionMatrix::Print( ostream& os, const Targets& targets ) const { os << "Confusion Matrix:" << endl; os << " "; for ( const auto& val : targets.values_array ){ // Print the class names. os.width(6); os.setf(ios::right, ios::adjustfield); os << val << " "; } os << endl; os << " "; for ( unsigned int i=0; i < size; ++i ){ os << "-------"; } os << endl; for ( unsigned int i=0; i < targets.values_array.size(); ++i ){ os.width(6); os.setf(ios::right, ios::adjustfield); os << targets.values_array[i] << " | "; for ( const auto& mv : mat[i] ){ os.width(6); os.setf(ios::right, ios::adjustfield); os << mv << " "; } os << endl; if ( i == targets.values_array.size() - 1 ){ os << " -*- | "; for ( const auto& mv : mat[size] ){ os.width(6); os.setf(ios::right, ios::adjustfield); os << mv << " "; } os << endl; } } os << endl; } void pf( ostream& os, size_t d ){ os.width(4); os << " \t" << d; } void pf( ostream& os, double d ){ if ( d < 0 ){ os << " \t (nan)\t"; } else { os.setf(ios::showpoint); os << " \t" << d; } } void ConfusionMatrix::FScore( ostream& os, const Targets& targets, bool cs_too ) const { double maf = 0.0; double mif = 0.0; double maa = 0.0; double mia = 0.0; ios_base::fmtflags flags = os.flags(ios::fixed); int oldPrec = os.precision(5); size_t effF = 0; size_t testF = 0; size_t effA = 0; if ( cs_too ){ os << "Scores per Value Class:" << endl; os << "class |\tTP\tFP\tTN\tFN\tprecision\trecall(TPR)\tFPR\t\tF-score\t\tAUC" << endl; } for ( unsigned int i=0; i < targets.values_array.size(); ++i ){ // so we loop over all known (trained) target values size_t TP = 0; size_t FP = 0; size_t FN = 0; size_t TN = 0; ValueClass *tv = targets.values_array[i]; size_t testCount = 0; for ( unsigned int j=0; j < size; ++j ){ testCount += mat[i][j]; if ( i == j ){ TP = mat[i][j]; } else { FN += mat[i][j]; } } testF += testCount; for ( unsigned int j=0; j <= size; ++j ){ if ( j != i ){ FP += mat[j][i]; } } for ( unsigned int j=0; j <= size; ++j ){ if ( j != i ){ for ( unsigned int k=0; k < size; ++k ){ if ( k != i ){ TN += mat[j][k]; } } } } double precision; if ( TP + FP == 0 ){ precision = -1; } else { precision = TP / double(TP + FP); } double TPR; if ( TP + FN == 0 ){ TPR = -1; } else { TPR = TP / double(TP + FN); } double FPR; if ( FP + TN == 0 ){ FPR = -1; } else { FPR = FP / double(FP + TN); } double f_score; if ( precision < 0 || TPR < 0 || fabs(precision + TPR) < Epsilon ){ f_score = -1; } else { f_score = ( 2 * precision * TPR ) / (precision + TPR ); ++effF; maf += f_score; mif += (f_score * testCount); } double AUC; if ( TPR < 0 || FPR < 0 ){ AUC = -1; } else { AUC = ( 0.5 * TPR * FPR ) + ( TPR * ( 1.0 - FPR ) ) + ( 0.5 * ( ( 1.0 - TPR ) * ( 1.0 - FPR ) ) ); ++effA; maa += AUC; mia += (AUC * testCount); } if ( cs_too ){ os.width( 6 ); os << tv << " | "; os.width(0); pf(os,TP); pf(os,FP); pf(os,TN); pf(os,FN); pf(os,precision); pf(os,TPR); pf(os,FPR); pf(os,f_score); pf(os,AUC); os << endl; } } maf = maf / effF; mif = mif / testF; maa = maa / effA; mia = mia / testF; os.precision( oldPrec ); os.flags( flags ); os << "F-Score beta=1, microav: " << mif << endl; os << "F-Score beta=1, macroav: " << maf << endl; os << "AUC, microav: " << mia << endl; os << "AUC, macroav: " << maa << endl; } void ConfusionMatrix::merge( const ConfusionMatrix *cm ){ if ( cm ){ for ( size_t i=0; i <= size; ++i ){ for ( size_t j=0; j < size; ++j ){ mat[i][j] += cm->mat[i][j]; } } } } void StatisticsClass::merge( const StatisticsClass& in ){ _data += in._data; _skipped += in._skipped; _correct += in._correct; _tieOk += in._tieOk; _tieFalse += in._tieFalse; _exact += in._exact; } } LanguageMachines-timbl-642727d/src/StringOps.cxx000066400000000000000000000071451451477526200215620ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include "ticcutils/StringOps.h" #include "timbl/StringOps.h" #include "unicode/ustream.h" using namespace std; using namespace icu; namespace Timbl { UnicodeString StrToCode( const UnicodeString &par, bool trim ){ UnicodeString In = par; // cerr << "string to code IN: '" << In << "'" << endl; if ( trim ){ In.trim(); } else { In = par; } UnicodeString Out; for ( int i=0; i < In.length(); ++i ){ switch ( In[i] ){ case ' ': Out += '\\'; Out += '_'; break; case '\t': Out += '\\'; Out += 't'; break; case '\\': Out += '\\'; Out += '\\'; break; default: Out += In[i]; } } // cerr << "string to code Out: '" << Out << "'" << endl; return Out; } UnicodeString CodeToStr( const UnicodeString& in ){ UnicodeString out; for( int i=0; i < in.length(); ++i ){ if ( in[i] == '\\' ){ ++i; if ( i == in.length() ){ out += '\\'; break; } else { switch ( in[i] ){ case '_': out += ' '; break; case '\\': out += '\\'; break; case 't': out += '\t'; break; default: out += '\\'; out += in[i]; } } } else { out += in[i]; } } return out; } bool nocase_cmp( char c1, char c2 ){ return toupper(c1) == toupper(c2); } bool compare_nocase( const string& s1, const string& s2 ){ if ( s1.size() == s2.size() && equal( s1.begin(), s1.end(), s2.begin(), nocase_cmp ) ){ return true; } else { return false; } } bool compare_nocase_n( const string& s1, const string& s2 ){ if ( s1.size() <= s2.size() && equal( s1.begin(), s1.end(), s2.begin(), nocase_cmp ) ){ return true; } else { return false; } } string correct_path( const string& filename, const string& path, bool keep_origpath ){ // if filename contains pathinformation, it is replaced with path, except // when keep_origpath is true. // if filename contains NO pathinformation, path is always appended. // of course we don't append if the filename is empty or just '-' ! if ( path != "" && filename != "" && filename[0] != '-' ){ bool add_slash = path.back() != '/'; string result = path; if ( add_slash ){ result += "/"; } string::size_type pos = filename.rfind( '/' ); if ( pos == string::npos ){ result += filename; } else if ( keep_origpath ){ result += filename; } else { result += filename.substr( pos+1 ); } return result; } else { return filename; } } } // namespace Timbl LanguageMachines-timbl-642727d/src/TRIBLExperiments.cxx000066400000000000000000000260671451477526200227360ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include // for srand() #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Options.h" #include "timbl/Instance.h" #include "timbl/IBtree.h" #include "timbl/MBLClass.h" #include "timbl/TimblExperiment.h" namespace Timbl { using namespace std; void TRIBL_Experiment::InitInstanceBase(){ srand( RandomSeed() ); default_order(); set_order(); runningPhase = TrainWords; InstanceBase = new TRIBL_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), KeepDistributions() ); } void TRIBL2_Experiment::InitInstanceBase(){ srand( RandomSeed() ); default_order(); set_order(); runningPhase = TrainWords; InstanceBase = new TRIBL2_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), KeepDistributions() ); } bool TRIBL_Experiment::checkTestFile(){ if ( !TimblExperiment::checkTestFile() ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the " + TiCC::toString( algorithm) + " algorithm on a pruned Instance Base" ); return false; } else if ( TRIBL_offset() == 0 ){ Error( "TRIBL algorithm impossible while threshold not set\n" ); return false; } return true; } bool TRIBL2_Experiment::checkTestFile(){ if ( !TimblExperiment::checkTestFile() ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the " + TiCC::toString( algorithm) + " algorithm on a pruned Instance Base" ); return false; } return true; } const TargetValue *TRIBL_Experiment::LocalClassify( const Instance& Inst, double& Distance, bool& exact ){ const TargetValue *Res = NULL; bool Tie = false; exact = false; if ( !bestResult.reset( beamSize, normalisation, norm_factor, targets ) ){ Warning( "no normalisation possible because a BeamSize is specified\n" "output is NOT normalized!" ); } const ClassDistribution *ExResultDist = ExactMatch( Inst ); if ( ExResultDist ){ Distance = 0.0; Res = ExResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); bestResult.addConstant( ExResultDist, Res ); exact = Do_Exact(); } else { size_t level = 0; const ClassDistribution *TrResultDist = 0; initExperiment(); IB_InstanceBase *SubTree = InstanceBase->TRIBL_test( Inst, TRIBL_offset(), Res, TrResultDist, level ); if ( !SubTree ){ match_depth = level; last_leaf = false; Distance = sum_remaining_weights(level); if ( TrResultDist ){ if ( level == 0 ){ bestResult.addTop( TrResultDist, Res ); } else { bestResult.addConstant( TrResultDist, Res ); } } } else { testInstance( Inst, SubTree, TRIBL_offset() ); bestArray.initNeighborSet( nSet ); WClassDistribution *ResultDist = getBestDistribution(); Res = ResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); if ( Tie ){ ++num_of_neighbors; testInstance( Inst, SubTree, TRIBL_offset() ); bestArray.addToNeighborSet( nSet, num_of_neighbors ); WClassDistribution *ResultDist2 = getBestDistribution(); bool Tie2 = false; const TargetValue *Res2 = ResultDist2->BestTarget( Tie2, (RandomSeed() >= 0) ); --num_of_neighbors; if ( !Tie2 ){ delete ResultDist; bestResult.addDisposable( ResultDist2, Res2 ); Res = Res2; } else { delete ResultDist2; bestResult.addDisposable( ResultDist, Res ); } } else { bestResult.addDisposable( ResultDist, Res ); } SubTree->CleanPartition( true ); Distance = getBestDistance(); } } if ( confusionInfo ){ confusionInfo->Increment( Inst.TV, Res ); } bool correct = Inst.TV && ( Res == Inst.TV ); if ( correct ){ stats.addCorrect(); if ( Tie ){ stats.addTieCorrect(); } } else if ( Tie ){ stats.addTieFailure(); } exact = exact || (fabs(Distance) < Epsilon ); if ( exact ){ stats.addExact(); } return Res; } bool TRIBL_Experiment::checkLine( const icu::UnicodeString& line ){ if ( !TimblExperiment::checkLine( line ) ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the TRIBL algorithm on a pruned " " Instance Base" ); return false; } return true; } bool TRIBL2_Experiment::checkLine( const icu::UnicodeString& line ){ if ( !TimblExperiment::checkLine( line ) ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the TRIBL2 algorithm on a pruned " " Instance Base" ); return false; } return true; } const TargetValue *TRIBL2_Experiment::LocalClassify( const Instance& Inst, double& Distance, bool& exact ){ const TargetValue *Res = NULL; exact = false; if ( !bestResult.reset( beamSize, normalisation, norm_factor, targets ) ){ Warning( "no normalisation possible because a BeamSize is specified\n" "output is NOT normalized!" ); } bool Tie = false; const ClassDistribution *ExResultDist = ExactMatch( Inst ); if ( ExResultDist ){ Distance = 0.0; Res = ExResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); bestResult.addConstant( ExResultDist, Res ); exact = Do_Exact(); } else { size_t level = 0; const ClassDistribution *TrResultDist = 0; IB_InstanceBase *SubTree = InstanceBase->TRIBL2_test( Inst, TrResultDist, level ); if ( SubTree ){ testInstance( Inst, SubTree, level ); bestArray.initNeighborSet( nSet ); WClassDistribution *ResultDist1 = getBestDistribution(); Res = ResultDist1->BestTarget( Tie, (RandomSeed() >= 0) ); if ( Tie ){ ++num_of_neighbors; testInstance( Inst, SubTree, level ); bestArray.addToNeighborSet( nSet, num_of_neighbors ); WClassDistribution *ResultDist2 = getBestDistribution(); bool Tie2 = false; const TargetValue *Res2 = ResultDist2->BestTarget( Tie2, (RandomSeed() >= 0) ); --num_of_neighbors; if ( !Tie2 ){ delete ResultDist1; bestResult.addDisposable( ResultDist2, Res2 ); Res = Res2; } else { delete ResultDist2; bestResult.addDisposable( ResultDist1, Res ); } } else { bestResult.addDisposable( ResultDist1, Res ); } SubTree->CleanPartition( true ); match_depth = level; Distance = getBestDistance(); } else { // an exact match Distance = 0.0; Res = TrResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); bestResult.addConstant( TrResultDist, Res ); bestArray.init( num_of_neighbors, MaxBests, Verbosity(NEAR_N), Verbosity(DISTANCE), Verbosity(DISTRIB) ); bestArray.addResult( Distance, TrResultDist, get_org_input() ); bestArray.initNeighborSet( nSet ); } } if ( confusionInfo ){ confusionInfo->Increment( Inst.TV, Res ); } bool correct = Inst.TV && ( Res == Inst.TV ); if ( correct ){ stats.addCorrect(); if ( Tie ){ stats.addTieCorrect(); } } else if ( Tie ){ stats.addTieFailure(); } exact = exact || ( fabs(Distance) < Epsilon ); if ( exact ){ stats.addExact(); } return Res; } void TRIBL_Experiment::showTestingInfo( ostream& os ){ if ( !Verbosity(SILENT) ){ if ( Verbosity(OPTIONS) ){ ShowSettings( os ); } os << endl << "Starting to test, Testfile: " << testStreamName << endl << "Writing output in: " << outStreamName << endl << "Algorithm : TRIBL, q = " << TRIBL_offset() << endl; show_metric_info( os ); show_weight_info( os ); os << decay << endl; } } bool TRIBL_Experiment::GetInstanceBase( istream& is ){ bool result = false; bool Pruned; bool Hashed; int Version; string range_buf; size_t numF = get_IB_Info( is, Pruned, Version, Hashed, range_buf ); if ( numF == 0 ){ return false; } else if ( Pruned ){ Error( "Instance-base is Pruned!, NOT valid for " + TiCC::toString(algorithm) + " Algorithm" ); } else { TreeOrder = DataFile; Initialize( numF ); if ( !get_ranges( range_buf ) ){ Warning( "couldn't retrieve ranges..." ); } else { srand( RandomSeed() ); InstanceBase = new TRIBL_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), KeepDistributions() ); int pos=0; for ( size_t i=0; i < NumOfFeatures(); ++i ){ features[i]->SetWeight( 1.0 ); if ( features[features.permutation[i]]->Ignore() ){ features.perm_feats[i] = NULL; } else { features.perm_feats[pos++] = features[features.permutation[i]]; } } if ( Hashed ){ result = InstanceBase->ReadIB_hashed( is, features, targets, Version ); } else { result = InstanceBase->ReadIB( is, features, targets, Version ); } } } return result; } bool TRIBL2_Experiment::GetInstanceBase( istream& is ){ bool result = false; bool Pruned; bool Hashed; int Version; string range_buf; size_t numF = get_IB_Info( is, Pruned, Version, Hashed, range_buf ); if ( numF == 0 ){ return false; } else if ( Pruned ){ Error( "Instance-base is Pruned!, NOT valid for " + TiCC::toString(algorithm) + " Algorithm" ); } else { TreeOrder = DataFile; Initialize( numF ); if ( !get_ranges( range_buf ) ){ Warning( "couldn't retrieve ranges..." ); } else { srand( RandomSeed() ); InstanceBase = new TRIBL2_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0), KeepDistributions() ); int pos=0; for ( size_t i=0; i < NumOfFeatures(); ++i ){ features[i]->SetWeight( 1.0 ); if ( features[features.permutation[i]]->Ignore() ){ features.perm_feats[i] = NULL; } else { features.perm_feats[pos++] = features[features.permutation[i]]; } } if ( Hashed ){ result = InstanceBase->ReadIB_hashed( is, features, targets, Version ); } else { result = InstanceBase->ReadIB( is, features, targets, Version ); } } } return result; } } LanguageMachines-timbl-642727d/src/Targets.cxx000066400000000000000000000531341451477526200212420ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include // for find_if() #include // for accumulate() #include #include #include "ticcutils/StringOps.h" #include "ticcutils/PrettyPrint.h" #include "ticcutils/UniHash.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Targets.h" using namespace std; using namespace icu; namespace Timbl { using namespace Common; using TiCC::operator<<; size_t Vfield::Index() { return value->Index(); } ostream& operator<<(ostream& os, const Vfield *vd ) { return vd->put( os ); } ostream& operator<<(ostream& os, const Vfield& vd ) { return vd.put( os ); } ostream& Vfield::put( ostream& os ) const { os << value << " " << weight; return os; } inline int random_number( int Min, int Max ){ // calculate a random integer within the interval [min,max] if ( Min == Max ){ return Min; } double randnum = (double)rand()/(double)RAND_MAX; randnum *= (Max-Min); randnum += Min; return (int)floor(randnum+0.5); } void ClassDistribution::clear(){ for ( const auto& d : distribution ){ delete d.second; } distribution.clear(); total_items = 0; } double ClassDistribution::Confidence( const TargetValue *tv ) const { auto it = find_if( distribution.begin(), distribution.end(), [tv]( const std::pair& v ){ return v.second->Value() == tv ; } ); if ( it != distribution.end() ){ return it->second->Weight(); } return 0.0; } void ClassDistribution::DistToString( string& DistStr, double minf ) const { ostringstream oss; oss.setf(ios::showpoint); bool first = true; oss << "{ "; for ( const auto& it : distribution ){ const Vfield *f = it.second; if ( f->frequency >= minf ){ if ( !first ){ oss << ", "; } oss << f->value << " " << double(f->frequency); first = false; } } oss << " }"; DistStr = oss.str(); } void WClassDistribution::DistToString( string& DistStr, double minw ) const { ostringstream oss; oss.setf(ios::showpoint); bool first = true; oss << "{ "; for( const auto& it : distribution ){ const Vfield *f = it.second; if ( abs(f->weight) < minw ){ continue; } if ( abs(f->weight) < Epsilon ){ continue; } if ( !first ){ oss << ", "; } oss << f->value << " " << f->weight; first = false; } oss << " }"; DistStr = oss.str(); } class dblCmp { public: bool operator() ( const double d1, const double d2 ) const { return d1 - d2 > Epsilon; } }; void ClassDistribution::DistToStringWW( string& DistStr, int beam ) const { double minw = 0.0; if ( beam > 0 ){ set freqs; for ( const auto& it : distribution ){ const Vfield *f = it.second; freqs.insert( f->frequency ); } int cnt=0; for ( const auto& rit : freqs ){ if ( ++cnt == beam ) { minw = rit; break; } } } DistToString( DistStr, minw ); } void WClassDistribution::DistToStringWW( string& DistStr, int beam ) const { double minw = 0.0; if ( beam > 0 ){ set wgths; for ( const auto& it : distribution ){ const Vfield *f = it.second; wgths.insert( f->weight ); } int cnt=0; for ( const auto& rit : wgths ){ if ( ++cnt == beam ) { minw = rit; break; } } } DistToString( DistStr, minw ); } const string ClassDistribution::DistToString() const { string result; DistToString( result ); return result; } const string ClassDistribution::DistToStringW( int beam ) const { string result; DistToStringWW( result, beam ); return result; } double ClassDistribution::Entropy() const { double entropy = 0.0; size_t TotalVals = total_items; if ( TotalVals > 0 ){ // Loop over the classes in the distribution for ( const auto& it : distribution ){ size_t Freq = it.second->Freq(); if ( Freq > 0 ){ double Prob = Freq / (double)TotalVals; entropy += Prob * Log2(Prob); } } } return fabs(entropy); } void WClassDistribution::Normalize() { double sum = accumulate( distribution.begin(), distribution.end(), 0.0, []( double r, const std::pair& v ){ return r + v.second->Weight(); } ); for ( auto& it : distribution ){ it.second->SetWeight( it.second->Weight() / sum ); } } void WClassDistribution::Normalize_1( double factor, const Targets& targ ) { for ( const auto& val : targ.values_array ){ // search for val, if not there: add entry with frequency factor; // otherwise increment the ExamplarWeight size_t id = val->Index(); auto const& it = distribution.find( id ); if ( it != distribution.end() ){ it->second->SetWeight( it->second->Weight() + factor ); } else { distribution[id] = new Vfield( val, 1, factor ); } } total_items += targ.num_of_values(); Normalize(); } void WClassDistribution::Normalize_2( ) { for ( const auto& d : distribution ){ d.second->SetWeight( log1p( d.second->Weight() ) ); } Normalize(); } ClassDistribution *ClassDistribution::to_VD_Copy( ) const { ClassDistribution *res = new ClassDistribution(); for ( const auto& d : distribution ){ size_t key = d.first; const Vfield *vdf = d.second; res->distribution[key] = new Vfield( vdf->Value(), vdf->Freq(), vdf->Freq() ); } res->total_items = total_items; return res; } WClassDistribution *ClassDistribution::to_WVD_Copy() const { WClassDistribution *res = new WClassDistribution(); for ( const auto& d : distribution ){ size_t key = d.first; const Vfield *vdf = d.second; res->distribution[key] = new Vfield( vdf->Value(), vdf->Freq(), vdf->Freq() ); } res->total_items = total_items; return res; } WClassDistribution *WClassDistribution::to_WVD_Copy( ) const { WClassDistribution *result = new WClassDistribution(); for ( const auto& d : distribution ){ size_t key = d.first; const Vfield *vdf = d.second; result->distribution[key] = new Vfield( vdf->Value(), vdf->Freq(), vdf->Weight() ); } result->total_items = total_items; return result; } // // special functions to serialize distibutions including both frequency // AND weight information. Needed for store/retrieve InstanceBases // // First hashed variant: // const string ClassDistribution::SaveHashed() const{ ostringstream oss; oss << "{ "; bool first = true; for ( const auto& it : distribution ){ const Vfield *f = it.second; if ( f->frequency > 0 ){ if ( !first ){ oss << ", "; } oss << f->value->Index() << " " << f->frequency; first = false; } } oss << " }"; return oss.str(); } const string WClassDistribution::SaveHashed() const{ ostringstream oss; bool first = true; oss << "{ "; for ( const auto& it : distribution ){ const Vfield *f = it.second; if ( f->frequency > 0 ){ if ( !first ){ oss << ", "; } oss << f->Value()->Index() << " " << f->frequency << " " << f->weight; first = false; } } oss << " }"; return oss.str(); } // // non-hashed variant: // const string ClassDistribution::Save() const{ ostringstream oss; oss << "{ "; bool first = true; for ( const auto& it : distribution ){ const Vfield *f = it.second; if ( f->frequency > 0 ){ if ( !first ){ oss << ", "; } oss << f->value << " " << f->frequency; first = false; } } oss << " }"; return oss.str(); } const string WClassDistribution::Save() const{ ostringstream oss; oss << "{ "; bool first = true; for ( const auto& it : distribution ){ const Vfield *f = it.second; if ( f->frequency > 0 ){ if ( !first ){ oss << ", "; } oss.setf(ios::showpoint); oss << f->value << " " << f->frequency << " " << f->weight; first = false; } } oss << " }"; return oss.str(); } void ClassDistribution::SetFreq( const TargetValue *val, const int freq, double ){ // add entry with frequency freq; Vfield *temp = new Vfield( val, freq, freq ); distribution[val->Index()] = temp; total_items += freq; } void WClassDistribution::SetFreq( const TargetValue *val, const int freq, double sw ){ // add entry with frequency freq; // also sets the sample_weight Vfield *temp = new Vfield( val, freq, sw ); distribution[val->Index()] = temp; total_items += freq; } bool ClassDistribution::IncFreq( const TargetValue *val, size_t occ, double ){ // search for val, if not there: add entry with frequency 'occ'; // otherwise increment the freqency size_t id = val->Index(); auto const& it = distribution.find( id ); if ( it != distribution.end() ){ it->second->IncFreq( occ ); } else { distribution[id] = new Vfield( val, occ, 1.0 ); } total_items += occ; return true; } bool WClassDistribution::IncFreq( const TargetValue *val, size_t occ, double sw ){ // search for val, if not there: add entry with frequency 'occ'; // otherwise increment the freqency // also set sample weight size_t id = val->Index(); auto const& it = distribution.find( id ); if ( it != distribution.end() ){ it->second->IncFreq( occ ); } else { distribution[id] = new Vfield( val, occ, sw ); } total_items += occ; return fabs( distribution[id]->Weight() - sw ) > Epsilon; } void ClassDistribution::DecFreq( const TargetValue *val ){ // search for val, if not there, just forget // otherwise decrement the freqency auto const& it = distribution.find( val->Index() ); if ( it != distribution.end() ){ it->second->DecFreq(); total_items -= 1; } } void ClassDistribution::Merge( const ClassDistribution& VD ){ for ( const auto& it : VD.distribution ){ size_t key = it.first; const Vfield *vd = it.second; if ( distribution.find(key) != distribution.end() ){ // the key is already present, increment the frequency distribution[key]->AddFreq( vd->Freq() ); } else { // add a key // VD might be weighted. But we don't need/want that info here // Weight == Freq is more convenient distribution[key] = new Vfield( vd->Value(), vd->Freq(), vd->Freq() ); } } total_items += VD.total_items; } void WClassDistribution::MergeW( const ClassDistribution& VD, double Weight ){ for ( const auto& it : VD.distribution ){ size_t key = it.first; const Vfield *vd = it.second; if ( distribution.find(key) != distribution.end() ){ distribution[key]->SetWeight( distribution[key]->Weight() + vd->Weight() *Weight ); } else { distribution[key] = new Vfield( vd->Value(), 1, vd->Weight() * Weight); } } total_items += VD.total_items; } const TargetValue *ClassDistribution::BestTarget( bool& tie, bool do_rand ) const { // get the most frequent target from the distribution. // In case of a tie take the one which is GLOBALLY the most frequent, // OR (if do_rand) take random one of the most frequents // and signal if this ties also! const TargetValue *best = NULL; tie = false; auto It = distribution.begin(); if ( It != distribution.end() ){ Vfield *pnt = It->second; size_t Max = pnt->Freq(); if ( do_rand ){ int nof_best=1, pick=1; ++It; while ( It != distribution.end() ){ pnt = It->second; if ( pnt->Freq() > Max ){ Max = pnt->Freq(); nof_best = 1; } else { if ( pnt->Freq() == Max ){ nof_best++; } } ++It; } tie = ( nof_best > 1 ); pick = random_number( 1, nof_best ); It = distribution.begin(); nof_best = 0; while ( It != distribution.end() ){ pnt = It->second; if ( pnt->Freq() == Max ){ if ( ++nof_best == pick ){ return pnt->Value(); } } ++It; } return NULL; } else { best = pnt->Value(); ++It; while ( It != distribution.end() ){ pnt = It->second; if ( pnt->Freq() > Max ){ tie = false; best = pnt->Value(); Max = pnt->Freq(); } else { if ( pnt->Freq() == Max ) { tie = true; if ( pnt->Value()->ValFreq() > best->ValFreq() ){ best = pnt->Value(); } } } ++It; } return best; } } return best; } const TargetValue *WClassDistribution::BestTarget( bool& tie, bool do_rand ) const { // get the most frequent target from the distribution. // In case of a tie take the one which is GLOBALLY the most frequent, // OR (if do_rand) take random one of the most frequents // and signal if this ties also! const TargetValue *best = NULL; auto It = distribution.begin(); tie = false; if ( It != distribution.end() ){ double Max = It->second->Weight(); if ( do_rand ){ int nof_best=1, pick=1; ++It; while ( It != distribution.end() ){ if ( It->second->Weight() > Max ){ Max = It->second->Weight(); nof_best = 1; } else { if ( abs(It->second->Weight()- Max) < Epsilon ){ nof_best++; } } ++It; } tie = ( nof_best > 1 ); pick = random_number( 1, nof_best ); It = distribution.begin(); nof_best = 0; while ( It != distribution.end() ){ if ( abs(It->second->Weight() - Max) < Epsilon ){ if ( ++nof_best == pick ){ return It->second->Value(); } } ++It; } return NULL; } else { best = It->second->Value(); ++It; while ( It != distribution.end() ){ if ( It->second->Weight() > Max ){ tie = false; best = It->second->Value(); Max = It->second->Weight(); } else { if ( abs(It->second->Weight() - Max) < Epsilon ) { tie = true; if ( It->second->Value()->ValFreq() > best->ValFreq() ){ best = It->second->Value(); } } } ++It; } return best; } } return best; } ostream& operator<<(ostream& os, const ClassDistribution& vd ) { string tmp; vd.DistToString( tmp ); os << tmp; return os; } ostream& operator<<(ostream& os, const ClassDistribution *vd ) { string tmp = "{null}"; if ( vd ){ vd->DistToString( tmp ); } os << tmp; return os; } ClassDistribution *ClassDistribution::read_distribution( istream &is, Targets& Targ, bool do_fr ){ // read a distribution from stream is into Target // if do_f we also adjust the value of Frequency of the Target, which is // otherwise 1. Special case when reading the TopDistribution. // ClassDistribution *result = 0; char nextCh; is >> nextCh; // skip { if ( nextCh != '{' ){ throw runtime_error( "missing '{' in distribution string." ); } else { int next; do { size_t freq; UnicodeString buf; is >> ws >> buf; is >> freq; TargetValue *target; if ( do_fr ){ target = Targ.add_value( buf, freq ); } else { target = Targ.Lookup( buf ); } if ( !target ){ delete result; result = 0; break; } next = look_ahead(is); if ( next == ',' ){ if ( !result ) { result = new ClassDistribution(); } result->SetFreq( target, freq ); is >> nextCh; next = look_ahead(is); } else if ( next == '}' ){ if ( !result ){ result = new ClassDistribution(); } result->SetFreq( target, freq ); } else if ( isdigit(next) ){ if ( !result ){ result = new WClassDistribution(); } double sw; is >> sw; result->SetFreq( target, freq, sw ); next = look_ahead(is); if ( next == ',' ){ is >> nextCh; next = look_ahead(is); } } } while ( is && next != '}' ); if ( is ){ is >> nextCh; // skip } } else { delete result; throw runtime_error( "missing '}' in distribution string." ); } } return result; } ClassDistribution *ClassDistribution::read_distribution_hashed( istream &is, Targets& Targ, bool do_fr ){ ClassDistribution *result = 0; // read a distribution from stream is into Target // if do_f we also adjust the value of Frequency of the Target, which is // otherwise 1. Special case when reading the TopDistribution. // char nextCh; is >> nextCh; // skip { if ( nextCh != '{' ){ throw runtime_error( "missing '{' in distribution string." ); } else { int next; do { unsigned int index; size_t freq; is >> index; is >> freq; TargetValue *target; if ( do_fr ){ target = Targ.add_value( index, freq ); } else { target = Targ.ReverseLookup( index ); } if ( !target ){ delete result; result = 0; break; } next = look_ahead(is); if ( next == ',' ){ if ( !result ){ result = new ClassDistribution(); } result->SetFreq( target, freq ); is >> nextCh; next = look_ahead(is); } else if ( next == '}' ){ if ( !result ){ result = new ClassDistribution(); } result->SetFreq( target, freq ); } else if ( isdigit(next) ){ double sw; is >> sw; if ( !result ){ result = new WClassDistribution(); } result->SetFreq( target, freq, sw ); next = look_ahead(is); if ( next == ',' ){ is >> nextCh; next = look_ahead(is); } } } while ( is && next != '}' ); if ( is ){ is >> nextCh; // skip thr '}' } else { delete result; throw runtime_error( "missing '}' in distribution string" ); } } return result; } ostream& operator<<( std::ostream& os, ValueClass const *vc ){ if ( vc ){ os << vc->name(); } else { os << "*FV-NF*"; } return os; } TargetValue::TargetValue( const UnicodeString& value, size_t value_hash ): ValueClass( value, value_hash ){} size_t Targets::EffectiveValues() const { return count_if( values_array.begin(), values_array.end(), [&]( const TargetValue* v ){ return (v->ValFreq() > 0); } ); } size_t Targets::TotalValues() const { return accumulate( values_array.begin(), values_array.end(), 0, [&]( size_t r, const TargetValue *v ){ return r + v->ValFreq(); } ); } Targets &Targets::operator=( const Targets& t ){ if ( this != &t ){ values_array = t.values_array; reverse_values = t.reverse_values; target_hash = t.target_hash; // shared ?? is_reference =true; } return *this; } Targets::~Targets() { if ( !is_reference ){ for ( const auto& it : values_array ){ delete it; } delete target_hash; } reverse_values.clear(); } void Targets::init(){ assert( target_hash == 0 ); // Safeguard init() may only called once target_hash = new Hash::UnicodeHash(); } TargetValue *Targets::Lookup( const UnicodeString& str ) const { TargetValue *result = 0; size_t index = target_hash->lookup( str ); if ( index ) { auto const& it = reverse_values.find( index ); result = it->second; } return result; } TargetValue *Targets::ReverseLookup( size_t index ) const { auto const& it = reverse_values.find( index ); return it->second; } TargetValue *Targets::add_value( const UnicodeString& valstr, int freq ){ unsigned int hash_val = target_hash->hash( valstr ); // cerr << "target hash(" << valstr << ") geeft: " << hash_val << endl; return add_value( hash_val, freq ); } TargetValue *Targets::add_value( size_t index, int freq ){ auto const& it = reverse_values.find( index ); if ( it == reverse_values.end() ){ const UnicodeString& name = target_hash->reverse_lookup( index ); // cerr << "target lookup(" << index << ") geeft: " << name << endl; // we want to store the singleton value for this index // so we MUST reverse lookup the index TargetValue *tv = new TargetValue( name, index ); tv->ValFreq( freq ); reverse_values[index] = tv; values_array.push_back( tv ); } else { it->second->IncValFreq( freq ); } return reverse_values[index]; } TargetValue *Targets::MajorityClass() const { TargetValue *result = 0; size_t freq = 0; for ( const auto& it : values_array ){ if ( it->ValFreq() > freq ){ result = it; freq = result->ValFreq(); } } return result; } bool Targets::increment_value( TargetValue *TV ){ bool result = false; if ( TV ){ TV->incr_val_freq(); result = true; } return result; } bool Targets::decrement_value( TargetValue *TV ){ bool result = false; if ( TV ){ TV->decr_val_freq(); result = true; } return result; } } LanguageMachines-timbl-642727d/src/Testers.cxx000066400000000000000000000167461451477526200212720ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/Metrics.h" #include "timbl/Testers.h" using namespace std; using Common::Epsilon; using Common::Log2; namespace Timbl{ //#define DBGTEST //#define DBGTEST_DOT double overlapTestFunction::test( const FeatureValue *F, const FeatureValue *G, const Feature *Feat ) const { #ifdef DBGTEST cerr << "overlap_distance(" << F << "," << G << ") = "; #endif double result = Feat->fvDistance( F, G ); #ifdef DBGTEST cerr << result; #endif result *= Feat->Weight(); #ifdef DBGTEST cerr << " gewogen " << result << endl; #endif return result; } double valueDiffTestFunction::test( const FeatureValue *F, const FeatureValue *G, const Feature *Feat ) const { #ifdef DBGTEST cerr << TiCC::toString(Feat->getMetricType()) << "_distance(" << F << "," << G << ") = "; #endif double result = Feat->fvDistance( F, G, threshold ); #ifdef DBGTEST cerr << result; #endif result *= Feat->Weight(); #ifdef DBGTEST cerr << " gewogen " << result << endl; #endif return result; } TesterClass* getTester( MetricType m, const Feature_List& features, int mvdThreshold ){ if ( m == Cosine ){ return new CosineTester( features ); } else if ( m == DotProduct ){ return new DotProductTester( features ); } else { return new DistanceTester( features, mvdThreshold ); } } TesterClass::TesterClass( const Feature_List& features ): _size(features.feats.size()), effSize(_size), offSet(0), FV(0), features(features.feats), permutation(features.permutation) { permFeatures.resize(_size,0); #ifdef DBGTEST cerr << "created TesterClass(" << _size << ")" << endl; #endif for ( size_t j=0; j < _size; ++j ){ permFeatures[j] = features.feats[features.permutation[j]]; } distances.resize(_size+1, 0.0); } void TesterClass::init( const Instance& inst, size_t effective, size_t oset ){ #ifdef DBGTEST cerr << "tester Initialized!" << endl; #endif effSize = effective-oset; offSet = oset; FV = &inst.FV; } DistanceTester::~DistanceTester(){ for ( const auto& it : metricTest ){ delete it; } } DistanceTester::DistanceTester( const Feature_List& features, int mvdmThreshold ): TesterClass( features ){ #ifdef DBGTEST cerr << "create a tester with threshold = " << mvdmThreshold << endl; #endif metricTest.resize(_size,0); for ( size_t i=0; i < _size; ++i ){ #ifdef DBGTEST cerr << "set metric[" << i+1 << "]=" << TiCC::toString(features.feats[i]->getMetricType()) << endl; #endif if ( features[i]->Ignore() ) continue; if ( features[i]->isStorableMetric() ){ #ifdef DBGTEST cerr << "created valueDiffTestFunction " << endl; #endif metricTest[i] = new valueDiffTestFunction( mvdmThreshold ); } else { #ifdef DBGTEST cerr << "created overlapFunction " << endl; #endif metricTest[i] = new overlapTestFunction(); } } } size_t DistanceTester::test( const vector& G, size_t CurPos, double Threshold ) { size_t i; size_t TrueF; for ( i=CurPos, TrueF = i + offSet; i < effSize; ++i,++TrueF ){ #ifdef DBGTEST cerr << "feature " << TrueF << " (perm=" << permutation[TrueF] << ")" << endl; #endif double result = metricTest[permutation[TrueF]]->test( (*FV)[TrueF], G[i], permFeatures[TrueF] ); distances[i+1] = distances[i] + result; if ( distances[i+1] > Threshold ){ #ifdef DBGTEST cerr << "threshold reached at " << i << " distance=" << distances[i+1] << endl; #endif return i; } } #ifdef DBGTEST cerr << "threshold reached at end, distance=" << distances[effSize] << endl; #endif return effSize; } double DistanceTester::getDistance( size_t pos ) const{ return distances[pos]; } inline bool FV_to_real( const FeatureValue *FV, double &result ){ if ( FV ){ if ( TiCC::stringTo( FV->name(), result ) ){ return true; } } return false; } double innerProduct( const FeatureValue *FV, const FeatureValue *G ) { double r1=0, r2=0, result; #ifdef DBGTEST_DOT cerr << "innerproduct " << FV << " x " << G << endl; #endif if ( FV_to_real( FV, r1 ) && FV_to_real( G, r2 ) ){ #ifdef DBGTEST_DOT cerr << "innerproduct " << r1 << " x " << r2 << endl; #endif result = r1 * r2; } else { result = 0.0; } #ifdef DBGTEST_DOT cerr << " resultaat == " << result << endl; #endif return result; } size_t CosineTester::test( const vector& G, size_t, double ){ double denom1 = 0.0; double denom2 = 0.0; double result = 0.0; size_t TrueF; size_t i; for ( i=0, TrueF = i + offSet; i < effSize; ++i,++TrueF ){ double W = permFeatures[TrueF]->Weight(); denom1 += innerProduct( (*FV)[TrueF], (*FV)[TrueF] ) * W; denom2 += innerProduct( G[i], G[i] ) * W; result += innerProduct( (*FV)[TrueF], G[i] ) * W; } double denom = sqrt( denom1 * denom2 ); distances[effSize] = result/ (denom + Common::Epsilon); #ifdef DBGTEST cerr << "denom1 " << denom1 << endl; cerr << "denom2 " << denom2 << endl; cerr << "denom " << denom << endl; cerr << "result " << result << endl; cerr << "cosine::test() distance " << distances[effSize] << endl; #endif return effSize; } size_t DotProductTester::test( const vector& G, size_t, double ) { size_t TrueF; size_t i; for ( i=0, TrueF = i + offSet; i < effSize; ++i,++TrueF ){ double result = innerProduct( (*FV)[TrueF], G[i] ); result *= permFeatures[TrueF]->Weight(); distances[i+1] = distances[i] + result; #ifdef DBGTEST cerr << "gewogen result " << result << endl; cerr << "dot::test() distance[" << i+1 << "]=" << distances[i+1] << endl; #endif } return effSize; } double CosineTester::getDistance( size_t pos ) const{ #ifdef DBGTEST cerr << "getDistance, maxSim = " << 1.0 << endl; cerr << " distances[" << pos << "]= " << distances[pos] << endl; #endif return 1.0 - distances[pos]; } double DotProductTester::getDistance( size_t pos ) const{ #ifdef DBGTEST_DOT cerr << "getDistance, maxSim = " << std::numeric_limits::max() << endl; cerr << " distances[" << pos << "]= " << distances[pos] << endl; #endif return (std::numeric_limits::max() - distances[pos])/std::numeric_limits::max();; } } LanguageMachines-timbl-642727d/src/Timbl.cxx000066400000000000000000000664231451477526200207050ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include #include "config.h" #include "ticcutils/CommandLine.h" #include "ticcutils/Timer.h" #include "timbl/TimblAPI.h" using namespace std; using namespace Timbl; static list ind_lines; Algorithm algorithm; bool Do_CV = false; bool Do_LOO = false; bool Do_NS = false; bool Do_Indirect = false; bool Do_Save_Perc = false; bool Do_Limit = false; size_t limit_val = 0; string I_Path = ""; string O_Path = ""; string Q_value = ""; string dataFile = ""; string TestFile = ""; string OutputFile = ""; string PercFile = ""; string MatrixInFile = ""; string MatrixOutFile = ""; string TreeInFile = ""; string TreeOutFile = ""; string levelTreeOutFile = ""; int levelTreeLevel = 0; string XOutFile = ""; string WgtInFile = ""; Weighting WgtType = UNKNOWN_W; string WgtOutFile = ""; string ProbInFile = ""; string ProbOutFile = ""; string NamesFile = ""; inline void usage_full(void){ cerr << "usage: timbl -f data-file {-t test-file} [options]" << endl; cerr << "Algorithm and Metric options:" << endl; cerr << "-a n : algorithm" << endl; cerr << " 0 or IB1 : IB1 (default)" << endl; cerr << " 1 or IG : IGTree" << endl; cerr << " 2 or TRIBL : TRIBL" << endl; cerr << " 3 or IB2 : IB2" << endl; cerr << " 4 or TRIBL2 : TRIBL2" << endl; cerr << "-m s : use feature metrics as specified in string s:" << endl << " format: GlobalMetric:MetricRange:MetricRange" << endl << " e.g.: mO:N3:I2,5-7" << endl; cerr << " C: Cosine distance. (Global only. numeric features implied)" << endl; cerr << " D: Dot product. (Global only. numeric features implied)" << endl; cerr << " DC: Dice Coefficient" << endl; cerr << " O: weighted Overlap (default)" << endl; cerr << " L: Levenshtein distance" << endl; cerr << " E: Euclidean Distance" << endl; cerr << " M: Modified value difference" << endl; cerr << " J: Jeffrey Divergence" << endl; cerr << " S: Jensen-Shannon Divergence" << endl; cerr << " N: numeric values" << endl; cerr << " I: Ignore named values" << endl; cerr << "-w n : Weighting" << endl; cerr << " 0 or nw: No Weighting" << endl; cerr << " 1 or gr: Weight using GainRatio (default)" << endl; cerr << " 2 or ig: Weight using InfoGain" << endl; cerr << " 3 or x2: Weight using Chi-square" << endl; cerr << " 4 or sv: Weight using Shared Variance" << endl; cerr << " 5 or sd: Weight using Standard Deviation. (all features must be numeric)" << endl; cerr << "-w f : read Weights from file 'f'" << endl; cerr << "-w f:n : read Weight n from file 'f'" << endl; cerr << "-b n : number of lines used for bootstrapping (IB2 only)" << endl; #ifdef HAVE_OPENMP cerr << "--clones= : use 'n' threads for parallel testing" << endl; #endif cerr << "--Diversify: rescale weight (see docs)" << endl; cerr << "-d val : weight neighbors as function of their distance:" << endl; cerr << " Z : equal weights to all (default)" << endl; cerr << " ID : Inverse Distance" << endl; cerr << " IL : Inverse Linear" << endl; cerr << " ED:a : Exponential Decay with factor a (no whitespace!)" << endl; cerr << " ED:a:b : Exponential Decay with factor a and b (no whitespace!)" << endl; cerr << "-k n : k nearest neighbors (default n = 1)" << endl; cerr << "-q n : TRIBL threshold at level n" << endl; cerr << "-L n : MVDM threshold at level n" << endl; cerr << "-R n : solve ties at random with seed n" << endl; cerr << "-t f : test using file 'f'" << endl; cerr << "-t leave_one_out:" << " test with Leave One Out,using IB1" << endl; cerr << " you may add -sloppy to speed up Leave One Out testing (see docs)" << endl; cerr << "-t cross_validate:" << " Cross Validate Test,using IB1" << endl; cerr << " @f : test using files and options described in file 'f'" << endl; cerr << " Supported options: d e F k m o p q R t u v w x % -" << endl; cerr << " -t is mandatory" << endl; cerr << "Input options:" << endl; cerr << "-f f : read from Datafile 'f'" << endl; cerr << "-f f : OR: use filenames from 'f' for CV test" << endl; cerr << "-F format : Assume the specified inputformat" << endl; cerr << " (Compact, C4.5, ARFF, Columns, Tabbed, Binary, Sparse )" << endl; cerr << "-l n : length of Features (Compact format only)" << endl; cerr << "-i f : read the InstanceBase from file 'f' " << "(skips phase 1 & 2 )" << endl; cerr << "--matrixin= read ValueDifference Matrices from file 'f'" << endl; cerr << "-u f : read value_class probabilities from file 'f'" << endl; cerr << "--occurrences=train|test|both assume occurrence info in the files." << endl; cerr << " (train: in the train file, test: in the test file, both: in both)" << endl; cerr << "-P d : read data using path 'd'" << endl; cerr << "-s : use exemplar weights from the input file" << endl; cerr << "-s0 : silently ignore the exemplar weights from the input file" << endl; cerr << "-T n : use input field 'n' as the target. (default is: the last field)" << endl; cerr << "Output options:" << endl; cerr << "-e n : estimate time until n patterns tested" << endl; cerr << "--Beam= : limit +v db output to n highest-vote classes" << endl; cerr << "-I f : dump the InstanceBase in file 'f'" << endl; cerr << "--matrixout= store ValueDifference Matrices in file 'f'" << endl; cerr << "-X f : dump the InstanceBase as XML in file 'f'" << endl; cerr << "-n f : create names file 'f'" << endl; cerr << "-p n : show progress every n lines (default p = 100,000)" << endl; cerr << "-U f : save value_class probabilities in file 'f'" << endl; cerr << "-V or --version : Show VERSION" << endl; cerr << "+v or -v level : set or unset verbosity level, where level is" << endl; cerr << " s: work silently" << endl; cerr << " o: show all options set" << endl; cerr << " b: show node/branch count and branching factor" << endl; cerr << " f: show Calculated Feature Weights (default)" << endl; cerr << " p: show Value Difference matrices" << endl; cerr << " e: show exact matches" << endl; cerr << " as: show advanced statistics (memory consuming)" << endl; cerr << " cm: show Confusion Matrix (implies +vas)" << endl; cerr << " cs: show per Class Statistics (implies +vas)" << endl; cerr << " cf: add confidence to the output file. (needs -G)" << endl; cerr << " di: add distance to output file" << endl; cerr << " db: add distribution of best matched to output file" << endl; cerr << " md: add matching depth to output file." << endl; cerr << " k: add a summary for all k neighbors to output file" << " (sets -x)" << endl; cerr << " n: add nearest neighbors to output file (sets -x)" << endl; cerr << " You may combine levels using '+' e.g. +v p+db or -v o+di" << endl; cerr << "-G : normalize distibutions (+vdb option only)" << endl << " Probability : normalize between 0 and 1" << endl << " 0 : does the same " << endl << " addFactor: : add f to all possible targets" << endl << " then normalize between 0 and 1 (default f=1.0)" << endl << " 1: : does the same" << endl << " logProbability : Add 1 to the target Weight, take the 10Log and" << endl << " then normalize between 0 and 1." << endl << " 2 : does the same" << endl; cerr << "-W f : calculate and save all Weights in file 'f'" << endl; cerr << "+% or -% : do or don't save test result (%) to file" << endl; cerr << "-o s : use s as output filename" << endl; cerr << "-O d : save output using path 'd'" << endl; cerr << "Internal representation options:" << endl; cerr << "-B n : number of bins used for discretization of numeric " << "feature values (default B=20)" << endl; cerr << "-c n : clipping frequency for prestoring MVDM matrices" << endl; cerr << "+D : store distributions on all nodes" << endl << " (necessary for using +v db with IGTree, but wastes memory otherwise)" << endl; cerr << "+H or -H : write hashed trees (default +H)" << endl; cerr << "-M n : size of MaxBests Array" << endl; cerr << "-N n : Number of features (default " << TimblAPI::Default_Max_Feats() << ")" << endl; cerr << "--limit l : limit the number of features used to the 'l' with the highest weights." << endl; cerr << " (will restart Timbl with an adapted -m option)" << endl; cerr << "--Treeorder= : ordering of the Tree :" << endl; cerr << " DO: none" << endl; cerr << " GRO: using GainRatio" << endl; cerr << " IGO: using InformationGain" << endl; cerr << " 1/V: using 1/# of Vals" << endl; cerr << " G/V: using GainRatio/# of Vals" << endl; cerr << " I/V: using InfoGain/# of Vals" << endl; cerr << " X2O: using X-square" << endl; cerr << " X/V: using X-square/# of Vals" << endl; cerr << " SVO: using Shared Variance" << endl; cerr << " S/V: using Shared Variance/# of Vals" << endl; cerr << " SDO: using Standard Deviation" << endl; cerr << " SD/V: using Standard Deviation/# of Vals" << endl; cerr << " GxE: using GainRatio * SplitInfo" << endl; cerr << " IxE: using InformationGain * SplitInfo" << endl; cerr << " 1/S: using 1/SplitInfo" << endl; cerr << "+x or -x : Do or don't use the exact match shortcut " << endl << " (IB1 and IB2 only, default is -x)" << endl; } inline void usage(void){ cerr << "usage: timbl -f data-file {-t test-file}" << endl; cerr << "or see: timbl -h" << endl; cerr << " for all possible options" << endl; cerr << endl; } void get_command_lines( const string& value, list& result ){ result.clear(); ifstream ind( value.substr(1) ); // skip @ if ( ind.bad() ){ cerr << "Problem reading command-lines from file '" << value << "'" << endl; throw( "command line failure" ); } string Buf; while ( getline( ind, Buf ) ){ if ( Buf.empty() ){ continue; } result.push_back( Buf ); } } class softExit : public exception {}; class hardExit : public exception {}; void Preset_Values( TiCC::CL_Options& opts ){ string value; if ( opts.is_present( 'h' ) || opts.is_present( "help" ) ){ usage_full(); throw( softExit() ); } if ( opts.is_present( 'V' ) || opts.is_present( "version" ) ){ cerr << "TiMBL " << Timbl::BuildInfo() << endl; throw( softExit() ); } if ( opts.is_present( 'S' ) ){ cerr << "Server mode is no longer available in timbl" << endl; cerr << "Please use the 'timblserver' command instead." << endl; throw( hardExit() ); } if ( opts.extract( 'a', value ) ){ // the user gave an algorithm if ( !string_to( value, algorithm ) ){ cerr << "illegal -a value: " << value << endl; throw( hardExit() ); // no chance to proceed } } else { algorithm = IB1; // general default } opts.insert( 'a', to_string( algorithm ), false ); if ( opts.extract( 'Z', value ) ){ // Special case // spitting neighborSets only Do_NS = true; } if ( opts.is_present( 't', value ) ){ if ( value == "cross_validate" ){ // Special case // running Cross Validation Do_CV = true; } else if ( value == "leave_one_out" ){ // Special case // running Leave_one_out Do_LOO = true; } else if ( value != "" && value[0] == '@' ){ Do_Indirect = true; opts.remove( 't' ); get_command_lines( value, ind_lines ); } if ( Do_LOO || Do_CV ){ if ( algorithm != IB1 ){ cerr << "Invalid Algorithm: Only IB1 possible for LOO and CV " << endl; throw( hardExit() ); // no chance to proceed } } } if ( opts.extract( "limit", value ) ){ Do_Limit = true; if ( !TiCC::stringTo( value, limit_val ) ){ cerr << "illegal --limit value: " << value << endl; throw( hardExit() ); // no chance to proceed } if ( Do_CV ){ cerr << "--limit is not implemented for --cross-validation" << endl; throw( hardExit() ); // no chance to proceed } } if ( opts.extract( 'P', value ) ){ I_Path = value; } if ( opts.is_present( 'O', value ) ){ // output path is needed for CV testing O_Path = value; } if ( opts.extract( 'f', value ) ){ dataFile = correct_path( value, I_Path ); } opts.is_present( 'q', Q_value ); opts.insert( 'v', "F", true ); opts.insert( 'v', "S", false ); Weighting W = GR; // default Weighting = GainRatio if ( opts.is_present( 'w', value ) ){ // user specified weighting if ( !string_to( value, W ) ){ // no valid weight, hopefully a filename return; } else { // valid Weight, but maybe a number, so replace opts.remove( 'w' ); } } opts.insert( 'w', to_string(W), false ); } void Adjust_Default_Values( TiCC::CL_Options& opts ){ if ( !opts.is_present( 'm' ) ){ opts.insert( 'm', "O", false ); // Default Metric = Overlap } if ( opts.extract( '%' ) ){ Do_Save_Perc = true; } } bool next_test( string& line ){ bool result = false; line = ""; if ( !ind_lines.empty() ){ line = ind_lines.front(); ind_lines.pop_front(); result = true; } return result; } bool get_file_names( TiCC::CL_Options& opts ){ TestFile = ""; OutputFile = ""; PercFile = ""; MatrixInFile = ""; MatrixOutFile = ""; TreeInFile = ""; TreeOutFile = ""; levelTreeOutFile = ""; levelTreeLevel = 0; XOutFile = ""; WgtInFile = ""; WgtType = UNKNOWN_W; WgtOutFile = ""; ProbInFile = ""; ProbOutFile = ""; NamesFile = ""; string value; if ( opts.extract( 'P', value ) || opts.extract( 'f', value ) ){ cerr << "illegal option, value = " << value << endl; return false; } if ( Do_LOO ){ if ( dataFile == "" ){ cerr << "Missing datafile name for Leave One Out test" << endl; return false; } TestFile = dataFile; } else if ( Do_CV ){ if ( dataFile == "" ){ cerr << "Missing datafile name for Cross Validation test" << endl; return false; } TestFile = dataFile; } else if ( opts.extract( 't', value ) ){ TestFile = correct_path( value, I_Path ); } if ( opts.extract( 'n', value ) ){ NamesFile = correct_path( value, O_Path ); } if ( opts.extract( "matrixout", value ) ){ MatrixOutFile = correct_path( value, O_Path ); } if ( opts.extract( "matrixin", value ) ){ MatrixInFile = correct_path( value, I_Path ); } if ( opts.extract( 'o', value ) ){ if ( Do_CV ){ cerr << "-o option not possible for Cross Validation testing" << endl; return false; } OutputFile = correct_path( value, O_Path ); } if ( opts.extract( "IL", value ) ){ vector vec = TiCC::split_at( value, ":" ); if ( vec.size() > 1 ){ levelTreeOutFile = correct_path( vec[0], O_Path ); levelTreeLevel = TiCC::stringTo( vec[1] ); } else { levelTreeOutFile = correct_path( value, O_Path ); } } if ( opts.extract( 'I', value ) ){ TreeOutFile = correct_path( value, O_Path ); } if ( opts.extract( 'X', value ) ){ XOutFile = correct_path( value, O_Path ); } if ( opts.extract( 'i', value ) ){ TreeInFile = correct_path( value, I_Path ); } if ( opts.extract( 'U', value ) ){ ProbOutFile = correct_path( value, O_Path ); } if ( opts.extract( 'u', value ) ){ if ( algorithm == IGTREE ){ cerr << "-u option is useless for IGtree" << endl; return false; } ProbInFile = correct_path( value, I_Path ); } if ( opts.is_present( 'W', value ) ){ WgtOutFile = correct_path( value, O_Path ); // leave the option, to signal that we need ALL feature weights } if ( opts.is_present( 'w', value ) ){ Weighting W; if ( !string_to( value, W ) ){ // No valid weighting, so assume it also has a filename vector parts = TiCC::split_at( value, ":" ); size_t num = parts.size(); if ( num == 2 ){ if ( !string_to( parts[1], W ) ){ cerr << "invalid weighting option: " << value << endl; return false; } WgtInFile = correct_path( parts[0], I_Path ); WgtType = W; opts.remove( 'w' ); } else if ( num == 1 ){ WgtInFile = correct_path( value, I_Path ); opts.remove( 'w' ); } else { cerr << "invalid weighting option: " << value << endl; return false; } } } return true; } bool Default_Output_Names( TiCC::CL_Options& opts ){ if ( OutputFile == "" && TestFile != "" ){ string value; string temp = correct_path( TestFile, O_Path, false ); temp += "."; switch ( algorithm ){ case IB1: if ( Do_LOO ){ temp += "LOO"; } else if ( Do_CV ){ temp += "CV"; } else { temp += "IB1"; } break; case IB2: temp +="IB2"; break; case IGTREE: temp += "IGTree"; break; case TRIBL: temp += "TRIBL"; if ( Q_value != "" ){ temp += "-"; temp += Q_value; } else { temp += "-0"; } break; case TRIBL2: temp += "TRIBL2"; break; case LOO: temp += "LOO"; break; case CV: temp += "CV"; break; default: temp += "ERROR"; } if ( algorithm != IGTREE ){ temp += "."; if ( opts.is_present( 'm', value ) ){ temp += value; } else { temp += "ErRoR"; } if ( opts.is_present( 'L', value ) ){ temp += ".L"; temp += value; } } temp += "."; if ( opts.is_present( 'w', value ) ){ temp += value; } else if ( !WgtInFile.empty() ){ temp += "ud"; } else { temp += "gr"; } if ( algorithm != IGTREE ){ if ( opts.is_present( 'k', value ) ){ temp += ".k"; temp += value; } else { temp += ".k1"; } if ( opts.is_present( 'd', value ) ){ temp += "."; temp += value; } } bool mood; if ( opts.is_present( 'x', value, mood ) ){ if ( mood ){ temp += ".X"; } } OutputFile = temp + ".out"; if ( Do_Save_Perc ){ PercFile = temp + ".%"; } } else if ( OutputFile != "" ){ if ( Do_Save_Perc ){ PercFile = OutputFile; string::size_type pos = PercFile.rfind( '.' ); if ( pos != string::npos ){ PercFile.resize( pos ); } PercFile += ".%"; } } return true; } void Do_Test( TimblAPI *Run ){ if ( WgtInFile != "" ) { Run->GetWeights( WgtInFile, WgtType ); } if ( ind_lines.empty() ){ // just one test... if ( ProbInFile != "" ){ Run->GetArrays( ProbInFile ); } if ( MatrixInFile != "" ) { Run->GetMatrices( MatrixInFile ); } if ( Do_NS ) { Run->NS_Test( TestFile, OutputFile ); } else { Run->Test( TestFile, OutputFile, PercFile ); } } else { // multiple tests from indirect file string tmp_line; while ( next_test( tmp_line) ){ TiCC::CL_Options opts( timbl_indirect_opts, "" ); try { opts.init( tmp_line ); Adjust_Default_Values( opts ); } catch ( TiCC::OptionError& e ){ cerr << e.what() << endl; cerr << "Warning: Skipped a line from indirect testfile:\n'" << tmp_line << "'" << endl; continue; } if ( !get_file_names( opts ) || TestFile == "" ){ cerr << "Warning: Skipped a line from indirect testfile:\n'" << tmp_line << "'" << endl; if ( TestFile == "" ){ cerr << "missing a Testfile name (-t option)" << endl; } } else if ( Run->SetIndirectOptions( opts ) ){ Default_Output_Names( opts ); if ( WgtInFile != "" ) { if ( !Run->GetWeights( WgtInFile, WgtType ) ){ continue; } } if ( ProbInFile != "" ){ Run->GetArrays( ProbInFile ); } if ( MatrixInFile != "" ) { Run->GetMatrices( MatrixInFile ); } if ( Do_NS ){ Run->NS_Test( TestFile, OutputFile ); } else { Run->Test( TestFile, OutputFile, PercFile ); } } else { cerr << "Warning: Skipped a line from indirect testfile:\n'" << tmp_line << "'" << endl; } } } } bool checkInputFile( const string& name ){ if ( !name.empty() ){ ifstream is( name ); if ( !is.good() ){ cerr << "unable to find or use input file '" << name << "'" << endl; return false; } } return true; } bool checkOutputFile( const string& name ){ if ( !name.empty() ){ ofstream os( name ); if ( !os.good() ) { cerr << "unable to find or use output file" << name << "'" << endl; return false; } } return true; } int main(int argc, char *argv[]){ try { // Start. // cerr << "TiMBL " << TimblAPI::VersionInfo() << " (c) CLST/ILK/CLIPS 1998 - 2023.\n" << "Tilburg Memory Based Learner\n" << "Centre for Language and Speech Technology, Radboud University\n" << "Induction of Linguistic Knowledge Research Group, Tilburg University\n" << "CLiPS Computational Linguistics Group, University of Antwerp" << endl; cerr << TiCC::Timer::now() << endl << endl; if ( argc <= 1 ){ usage(); return 1; } TiCC::CL_Options opts( timbl_short_opts, timbl_long_opts ); try { opts.init( argc, argv ); } catch ( TiCC::OptionError& e ){ cerr << e.what() << endl; usage(); return 666; } Preset_Values( opts ); Adjust_Default_Values( opts ); if ( !get_file_names( opts ) ){ return 2; } TimblAPI *Run = new TimblAPI( opts ); if ( !Run->isValid() ){ delete Run; usage(); return 3; } Default_Output_Names( opts ); vector mas = opts.getMassOpts(); if ( !mas.empty() ){ cerr << "unknown value in option string: " << mas[0] << endl; usage(); return 33; } if ( Do_CV ){ if ( checkInputFile( TestFile ) ){ Run->CVprepare( WgtInFile, WgtType, ProbInFile ); Run->Test( TestFile, "" ); } delete Run; } else { bool do_test = false; if ( !checkInputFile( TreeInFile ) || !checkInputFile( dataFile ) || !checkInputFile( TestFile ) || !checkInputFile( WgtInFile ) || !checkInputFile( MatrixInFile ) || !checkInputFile( ProbInFile ) || !checkOutputFile( TreeOutFile ) || !checkOutputFile( levelTreeOutFile ) || !checkOutputFile( XOutFile ) || !checkOutputFile( NamesFile ) || !checkOutputFile( WgtOutFile ) || !checkOutputFile( MatrixOutFile ) || !checkOutputFile( ProbOutFile ) ){ delete Run; return 3; } // normal cases.... if ( TreeInFile == "" ){ // normal case // learning and maybe a testing phase if ( WgtOutFile != "" ) { Run->SetOptions( "ALL_WEIGHTS: true" ); } if ( Run->Prepare( dataFile ) ){ if ( Do_Limit ){ if ( Run->NumOfFeatures() < limit_val ){ cerr << "value of --limit is larger then the number of features!" << endl; return 32; } string m_val = Run->extract_limited_m( limit_val ); // cerr << endl << endl << "NEW M: " << m_val << endl << endl; opts.extract( 'm' ); opts.insert( 'm', m_val, true ); cerr << "\t--limit=" << limit_val << " is specified, so we retrain " << "the data with option: -m" << m_val << endl; delete Run; Run = new TimblAPI( opts ); } if ( WgtOutFile != "" ) { Run->SaveWeights( WgtOutFile ); } // If we want to create a namesfile, do it here. // if ( NamesFile != "" ) { Run->WriteNamesFile( NamesFile ); } if ( ProbOutFile != "" ){ Run->WriteArrays( ProbOutFile ); } do_test = TestFile != "" || Do_Indirect; if ( do_test || // something to test ? MatrixOutFile != "" || // or at least to produce TreeOutFile != "" || // or at least to produce levelTreeOutFile != "" || // or at least to produce XOutFile != "" ){ // or at least to produce bool ok = true; if ( WgtInFile != "" ){ if ( Run->GetWeights( WgtInFile, WgtType ) ){ cerr << "Calculated weights replaced by:" << endl; Run->ShowWeights( cerr ); } else { cerr << "problems reading weights" << endl; ok = do_test = false; } } if ( ok && Run->Learn( dataFile ) ){ if ( TreeOutFile != "" ){ Run->WriteInstanceBase( TreeOutFile ); } if ( levelTreeOutFile != "" ){ Run->WriteInstanceBaseLevels( levelTreeOutFile, levelTreeLevel ); } } else { do_test = false; // no testing because of problems } } } } else if ( !dataFile.empty() && !( TestFile.empty() && TreeOutFile.empty() && levelTreeOutFile.empty() ) ){ // it seems we want to expand our tree do_test = false; if ( Run->GetInstanceBase( TreeInFile ) ) { if ( Run->Expand( dataFile ) ){ if ( !TreeOutFile.empty() ){ Run->WriteInstanceBase( TreeOutFile ); } if ( levelTreeOutFile != "" ){ Run->WriteInstanceBaseLevels( levelTreeOutFile, levelTreeLevel ); } do_test = !TestFile.empty(); } } } else { // normal case // running a testing phase from recovered tree if ( TestFile.empty() && XOutFile == "" && !Do_Indirect ){ cerr << "reading an instancebase(-i option) without a testfile (-t option) is useless" << endl; do_test = false; } else { do_test = true; } if ( do_test ){ do_test = Run->GetInstanceBase( TreeInFile ); } } if ( do_test ){ Do_Test( Run ); } if ( Run->isValid() ) { if ( XOutFile != "" ){ Run->WriteInstanceBaseXml( XOutFile ); } if ( MatrixOutFile != "" ) { Run->WriteMatrices( MatrixOutFile ); } } if ( !do_test || !Run->isValid() ){ delete Run; return EXIT_FAILURE; } delete Run; } return EXIT_SUCCESS; } catch( const softExit& e ){ return EXIT_SUCCESS; } catch( const std::string& what ){ cerr << what << ", sorry" << endl; } catch( const std::bad_alloc&){ cerr << "ran out of memory somewhere" << endl; cerr << "timbl terminated, Sorry for that" << endl; } catch( const std::exception& e ){ cerr << e.what() << ", sorry" << endl; } return EXIT_FAILURE; } LanguageMachines-timbl-642727d/src/TimblAPI.cxx000066400000000000000000000405031451477526200212260ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include "timbl/Common.h" #include "timbl/MsgClass.h" #include "timbl/Types.h" #include "timbl/Options.h" #include "timbl/Instance.h" #include "timbl/neighborSet.h" #include "timbl/BestArray.h" #include "timbl/Statistics.h" #include "timbl/MBLClass.h" #include "ticcutils/CommandLine.h" #include "timbl/GetOptClass.h" using namespace std; using namespace icu; #include "timbl/TimblAPI.h" #include "timbl/TimblExperiment.h" namespace Timbl { TimblExperiment *Create_Pimpl( AlgorithmType algo, const string& ex_name, GetOptClass *opt ){ TimblExperiment *result = NULL; switch ( algo ){ case IB1_a: result = new IB1_Experiment( opt->MaxFeatures(), ex_name ); break; case IB2_a: result = new IB2_Experiment( opt->MaxFeatures(), ex_name ); break; case IGTREE_a: result = new IG_Experiment( opt->MaxFeatures(), ex_name ); break; case TRIBL_a: result = new TRIBL_Experiment( opt->MaxFeatures(), ex_name ); break; case TRIBL2_a: result = new TRIBL2_Experiment( opt->MaxFeatures(), ex_name ); break; case LOO_a: result = new LOO_Experiment( opt->MaxFeatures(), ex_name ); break; case CV_a: result = new CV_Experiment( opt->MaxFeatures(), ex_name ); break; default: cerr << "wrong algorithm to create TimblAPI" << endl; return NULL; } result->setOptParams( opt ); return result; } TimblAPI::TimblAPI( const TimblAPI& exp ): pimpl( exp.pimpl->splitChild() ), i_am_fine(true) { } TimblAPI::TimblAPI( ): pimpl( 0 ), i_am_fine(false) { } TimblAPI::TimblAPI( const TiCC::CL_Options& opts, const string& name ): pimpl(), i_am_fine(false) { GetOptClass *OptPars = new GetOptClass( opts ); if ( !OptPars->parse_options( opts ) ){ delete OptPars; } else if ( OptPars->Algo() != Unknown_a ){ pimpl = Create_Pimpl( OptPars->Algo(), name, OptPars ); } else { pimpl = Create_Pimpl( IB1_a, name, OptPars ); } i_am_fine = (pimpl != NULL); } TimblAPI::TimblAPI( const string& pars, const string& name ): pimpl(), i_am_fine(false){ TiCC::CL_Options Opts; Opts.init( pars ); GetOptClass *OptPars = new GetOptClass( Opts ); if ( OptPars->parse_options( Opts ) ){ if ( OptPars->Algo() != Unknown_a ){ pimpl = Create_Pimpl( OptPars->Algo(), name, OptPars ); } else { pimpl = Create_Pimpl( IB1_a, name, OptPars ); } } i_am_fine = (pimpl != NULL); } TimblAPI::~TimblAPI(){ delete pimpl; } bool TimblAPI::Valid() const { return i_am_fine && pimpl && !pimpl->ExpInvalid(); } bool TimblAPI::isValid() const { return i_am_fine && pimpl && !pimpl->ExpInvalid(false); } const string to_string( const Algorithm A ) { string result; switch ( A ){ case IB1: result = "IB1"; break; case IB2: result = "IB2"; break; case IGTREE: result = "IGTREE"; break; case TRIBL: result = "TRIBL"; break; case TRIBL2: result = "TRIBL2"; break; case LOO: result = "LOO"; break; case CV: result = "CV"; break; default: cerr << "invalid algorithm in switch " << endl; result = "Unknown Algorithm"; } return result; } bool string_to( const string& s, Algorithm& A ){ A = UNKNOWN_ALG; AlgorithmType tmp; if ( TiCC::stringTo( s, tmp ) ){ switch ( tmp ){ case IB1_a: A = IB1; break; case IB2_a: A = IB2; break; case IGTREE_a: A = IGTREE; break; case TRIBL_a: A = TRIBL; break; case TRIBL2_a: A = TRIBL2; break; case LOO_a: A = LOO; break; case CV_a: A = CV; break; default: return false; } return true; } return false; } const string to_string( const Weighting W ) { string result; switch ( W ){ case UD: result = "ud"; break; case NW: result = "nw"; break; case GR: result = "gr"; break; case IG: result = "ig"; break; case X2: result = "x2"; break; case SV: result = "sv"; break; case SD: result = "sd"; break; default: cerr << "invalid Weighting in switch " << endl; result = "Unknown Weight"; } return result; } inline Weighting WT_to_W( WeightType wt ){ Weighting w; switch ( wt ){ case UserDefined_w: w = UD; break; case No_w: w = NW; break; case GR_w: w = GR; break; case IG_w: w = IG; break; case X2_w: w = X2; break; case SV_w: w = SV; break; case SD_w: w = SD; break; default: w = UNKNOWN_W; } return w; } bool string_to( const string& s, Weighting& w ){ w = UNKNOWN_W; WeightType tmp; if ( TiCC::stringTo( s, tmp ) ){ w = WT_to_W( tmp ); if ( w == UNKNOWN_W ){ return false; } return true; } return false; } Algorithm TimblAPI::Algo() const { Algorithm result = UNKNOWN_ALG; if ( pimpl ){ switch ( pimpl->Algorithm() ){ case IB1_a: result = IB1; break; case IB2_a: result = IB2; break; case IGTREE_a: result = IGTREE; break; case TRIBL_a: result = TRIBL; break; case TRIBL2_a: result = TRIBL2; break; case LOO_a: result = LOO; break; case CV_a: result = CV; break; default: cerr << "invalid algorithm in switch " << endl; break; } } return result; } bool TimblAPI::Learn( const string& s ){ if ( Valid() ){ return pimpl->Learn( s ); } else { return false; } } bool TimblAPI::Prepare( const string& s ){ if ( Valid() ){ return pimpl->Prepare( s ); } else { return false; } } bool TimblAPI::CVprepare( const string& wf, Weighting w, const string& pf ){ if ( Valid() ){ WeightType tmp; switch ( w ){ case UNKNOWN_W: tmp = Unknown_w; break; case NW: tmp = No_w; break; case GR: tmp = GR_w; break; case IG: tmp = IG_w; break; case X2: tmp = X2_w; break; case SV: tmp = SV_w; break; case SD: tmp = SD_w; break; default: return false; } return pimpl->CVprepare( wf, tmp, pf ); } else { return false; } } bool TimblAPI::Increment_u( const UnicodeString& us ){ return Valid() && pimpl->Increment( us ); } bool TimblAPI::Increment( const string& s ){ return Valid() && pimpl->Increment( TiCC::UnicodeFromUTF8(s) ); } bool TimblAPI::Decrement_u( const UnicodeString& us ){ return Valid() && pimpl->Decrement( us ); } bool TimblAPI::Decrement( const string& s ){ return Valid() && pimpl->Decrement( TiCC::UnicodeFromUTF8(s) ); } bool TimblAPI::Expand( const string& s ){ return Valid() && pimpl->Expand( s ); } bool TimblAPI::Remove( const string& s ){ return Valid() && pimpl->Remove( s ); } bool TimblAPI::Test( const string& in, const string& out, const string& p ){ if ( !Valid() ){ return false; } else { if ( in.empty() ){ return false; } if ( out.empty() && Algo() != CV ){ return false; } if ( !pimpl->Test( in, out ) ){ return false; } return pimpl->createPercFile( p ); } } bool TimblAPI::NS_Test( const string& in, const string& out ){ if ( !Valid() ){ return false; } else { if ( in.empty() ){ return false; } if ( out.empty() && Algo() != CV ){ return false; } return pimpl->NS_Test( in, out ); } } const TargetValue *TimblAPI::Classify( const string& s, const ClassDistribution *& db, double& di ){ if ( Valid() ){ return pimpl->Classify( TiCC::UnicodeFromUTF8(s), db, di ); } else { db = NULL; di = DBL_MAX; } return NULL; } const TargetValue *TimblAPI::Classify( const icu::UnicodeString& s, const ClassDistribution *& db, double& di ){ if ( Valid() ){ return pimpl->Classify( s, db, di ); } else { db = NULL; di = DBL_MAX; } return NULL; } const TargetValue *TimblAPI::Classify( const string& s ){ if ( Valid() ){ return pimpl->Classify( TiCC::UnicodeFromUTF8(s) ); } return NULL; } const TargetValue *TimblAPI::Classify( const icu::UnicodeString& s ){ if ( Valid() ){ return pimpl->Classify( s ); } return NULL; } const TargetValue *TimblAPI::Classify( const string& s, const ClassDistribution *& db ){ if ( Valid() ){ return pimpl->Classify( TiCC::UnicodeFromUTF8(s), db ); } else { db = NULL; } return NULL; } const TargetValue *TimblAPI::Classify( const icu::UnicodeString& s, const ClassDistribution *& db ){ if ( Valid() ){ return pimpl->Classify( s, db ); } else { db = NULL; } return NULL; } const TargetValue *TimblAPI::Classify( const string& s, double& di ){ if ( Valid() ){ return pimpl->Classify( TiCC::UnicodeFromUTF8(s), di ); } else { di = DBL_MAX; } return NULL; } const TargetValue *TimblAPI::Classify( const icu::UnicodeString& s, double& di ){ if ( Valid() ){ return pimpl->Classify( s, di ); } else { di = DBL_MAX; } return NULL; } const neighborSet *TimblAPI::classifyNS( const icu::UnicodeString& s ){ const neighborSet *ns = 0; if ( Valid() ){ ns = pimpl->NB_Classify( s ); } return ns; } bool TimblAPI::classifyNS( const icu::UnicodeString& s, neighborSet& ns ){ const neighborSet *b = classifyNS( s ); if ( b != 0 ){ ns = *b; return true; } return false; } const Instance *TimblAPI::lastHandledInstance() const { if ( Valid() ){ return &pimpl->CurrInst; } return 0; } const Targets& TimblAPI::myTargets() const{ if ( Valid() ){ return pimpl->targets; } abort(); } bool TimblAPI::Classify( const string& s, string& cls ){ string dummy; double f; return Valid() && pimpl->Classify( s, cls, dummy, f ); } bool TimblAPI::Classify( const icu::UnicodeString& s, icu::UnicodeString& cls ){ return Valid() && pimpl->Classify( s, cls ); } bool TimblAPI::Classify( const string& s, string& cls, double &f ) { string dummy; return Valid() && pimpl->Classify( s, cls, dummy, f ); } bool TimblAPI::Classify( const string& s, string& cls, string& dist, double &f ){ return Valid() && pimpl->Classify( s, cls, dist, f ); } size_t TimblAPI::matchDepth() const { if ( Valid() ){ return pimpl->matchDepth(); } else { return -1; } } double TimblAPI::confidence() const { if ( Valid() ){ return pimpl->confidence(); } else { return -1; } } bool TimblAPI::matchedAtLeaf() const { return Valid() && pimpl->matchedAtLeaf(); } bool TimblAPI::initExperiment( ){ if ( Valid() ){ pimpl->initExperiment( true ); return true; } else { return false; } } InputFormatType TimblAPI::getInputFormat() const { if ( Valid() ){ return pimpl->InputFormat(); } else { return UnknownInputFormat; } } bool TimblAPI::SaveWeights( const string& f ){ if ( Valid() ){ return pimpl->SaveWeights( f ); } else { return false; } } bool TimblAPI::GetWeights( const string& f, Weighting w ){ if ( Valid() ){ WeightType tmp; switch ( w ){ case UNKNOWN_W: tmp = Unknown_w; break; case NW: tmp = No_w; break; case GR: tmp = GR_w; break; case IG: tmp = IG_w; break; case X2: tmp = X2_w; break; case SV: tmp = SV_w; break; case SD: tmp = SD_w; break; default: return false; } return pimpl->GetWeights( f, tmp ); } else { return false; } } double TimblAPI::GetAccuracy() { if (Valid()) { return pimpl->stats.testedCorrect()/(double) pimpl->stats.dataLines(); } else { return -1; } } Weighting TimblAPI::CurrentWeighting() const{ if ( Valid() ){ return WT_to_W( pimpl->CurrentWeighting() ); } else { return UNKNOWN_W; } } Weighting TimblAPI::GetCurrentWeights( std::vector& res ) const { res.clear(); if ( Valid() && pimpl->GetCurrentWeights( res ) ){ return CurrentWeighting(); } return UNKNOWN_W; } bool TimblAPI::SetOptions( const string& argv ){ return Valid() && pimpl->SetOptions( argv ); } bool TimblAPI::SetIndirectOptions( const TiCC::CL_Options& opts ){ return Valid() && pimpl->IndirectOptions( opts ); } string TimblAPI::ExpName() const { if ( pimpl ) { // return the name, even when !Valid() return pimpl->ExpName(); } else { return "ERROR"; } } bool TimblAPI::WriteNamesFile( const string& f ){ if ( Valid() ) { return pimpl->WriteNamesFile( f ); } else { return false; } } bool TimblAPI::WriteInstanceBase( const string& f ){ if ( Valid() ){ return pimpl->WriteInstanceBase( f ); } else { return false; } } bool TimblAPI::WriteInstanceBaseXml( const string& f ){ if ( Valid() ){ return pimpl->WriteInstanceBaseXml( f ); } else { return false; } } bool TimblAPI::WriteInstanceBaseLevels( const string& f, unsigned int l ){ if ( Valid() ){ return pimpl->WriteInstanceBaseLevels( f, l ); } else { return false; } } bool TimblAPI::GetInstanceBase( const string& f ){ if ( Valid() ){ if ( !pimpl->ReadInstanceBase( f ) ){ i_am_fine = false; } return Valid(); } else { return false; } } bool TimblAPI::WriteArrays( const string& f ){ if ( Valid() ){ return pimpl->WriteArrays( f ); } else { return false; } } bool TimblAPI::GetArrays( const string& f ){ if ( Valid() ){ return pimpl->GetArrays( f ); } else { return false; } } bool TimblAPI::WriteMatrices( const string& f ){ return Valid() && pimpl->WriteMatrices( f ); } bool TimblAPI::GetMatrices( const string& f ){ return Valid() && pimpl->GetMatrices( f ); } bool TimblAPI::ShowBestNeighbors( ostream& os ) const{ return Valid() && pimpl->showBestNeighbors( os ); } bool TimblAPI::ShowWeights( ostream& os ) const{ return Valid() && pimpl->ShowWeights( os ); } bool TimblAPI::ShowOptions( ostream& os ) const{ return Valid() && pimpl->ShowOptions( os ); } bool TimblAPI::ShowSettings( ostream& os ) const{ return Valid() && pimpl->ShowSettings( os ); } bool TimblAPI::ShowIBInfo( ostream& os ) const{ if ( Valid() ){ pimpl->IBInfo( os ); return true; } else { return false; } } bool TimblAPI::ShowStatistics( ostream& os ) const{ return Valid() && pimpl->showStatistics( os ); } string TimblAPI::extract_limited_m( int lim ) const { if ( Valid() ){ return pimpl->extract_limited_m( lim ); } else { return "error"; } } string TimblAPI::VersionInfo( bool full ){ return Common::VersionInfo( full ); } size_t TimblAPI::Default_Max_Feats(){ return Common::DEFAULT_MAX_FEATS; } size_t TimblAPI::NumOfFeatures() const { if ( Valid() ){ return pimpl->NumOfFeatures(); } else { return -1; } } } LanguageMachines-timbl-642727d/src/TimblExperiment.cxx000066400000000000000000002127021451477526200227370ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include #include #include "config.h" #include "timbl/MsgClass.h" #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Options.h" #include "timbl/Instance.h" #include "timbl/Choppers.h" #include "timbl/Metrics.h" #include "timbl/Statistics.h" #include "timbl/neighborSet.h" #include "timbl/BestArray.h" #include "timbl/IBtree.h" #include "timbl/MBLClass.h" #include "timbl/GetOptClass.h" #include "timbl/TimblExperiment.h" #include "ticcutils/XMLtools.h" #include "ticcutils/Timer.h" #include "ticcutils/PrettyPrint.h" #include "ticcutils/CommandLine.h" #ifdef HAVE_OPENMP #include #endif using namespace std; using namespace icu; using namespace nlohmann; namespace Timbl { using TiCC::operator<<; const string timbl_short_opts = "a:b:B:c:C:d:De:f:F:G::hHi:I:k:l:L:m:M:n:N:o:O:p:P:q:QR:s::t:T:u:U:v:Vw:W:xX:Z%"; const string timbl_long_opts = ",Beam:,clones:,Diversify,occurrences:,sloppy::,silly::,Threshold:,Treeorder:,matrixin:,matrixout:,version,help,limit:"; const string timbl_serv_short_opts = "C:d:G::k:l:L:p:Qv:x"; const string timbl_indirect_opts = "d:e:G:k:L:m:o:p:QR:t:v:w:x%"; resultStore::~resultStore( ) { clear(); } bool resultStore::reset( int _beam, normType _norm, double _factor, const Targets& _targets ) { clear(); beam = _beam; norm = _norm; factor = _factor; targets = &_targets; bool result = true; if ( norm != noNorm && beam != 0 ){ norm = noNorm; result = false; } return result; } void resultStore::clear( ) { delete dist; dist = 0; if ( disposable ){ delete rawDist; } best_target = 0; rawDist = 0; beam = 0; isTop = false; resultCache.clear(); } const WClassDistribution *resultStore::getResultDist() { if ( rawDist && !dist ){ prepare(); } return dist; } string resultStore::getResult() { if ( isTop ){ if ( topCache.empty() ){ if ( dist ) { topCache = dist->DistToStringW( beam ); } else { topCache = "{}"; } } resultCache = topCache; } else if ( resultCache.empty() ){ if ( dist ) { resultCache = dist->DistToStringW( beam ); } else { resultCache = "{}"; } } return resultCache; } void resultStore::addConstant( const ClassDistribution *vd, const TargetValue *best_result ) { rawDist = vd; best_target = best_result; disposable = false; } void resultStore::addTop( const ClassDistribution *vd, const TargetValue *best_result ) { rawDist = vd; best_target = best_result; disposable = false; isTop = true; } void resultStore::addDisposable( ClassDistribution *vd, const TargetValue *best_result ) { rawDist = vd; best_target = best_result; disposable = true; } void resultStore::prepare() { if ( isTop && !topCache.empty() ){ return; } if ( !dist && rawDist ){ if ( !disposable ){ dist = rawDist->to_WVD_Copy(); } else { dist = dynamic_cast( const_cast(rawDist) ); rawDist = 0; } } } void resultStore::normalize() { if ( dist ){ switch ( norm ){ case noNorm: break; case probabilityNorm: dist->Normalize(); break; case addFactorNorm: dist->Normalize_1( factor, *targets ); break; case logProbNorm: dist->Normalize_2(); break; default: throw runtime_error( "unimplemented case " + TiCC::toString(norm) ); break; } } // silently do nothing when dist == 0; } void TimblExperiment::normalizeResult(){ bestResult.prepare(); bestResult.normalize(); } TimblExperiment::TimblExperiment( const AlgorithmType Alg, const string& s ): MBLClass( s ), Initialized( false ), OptParams( NULL ), algorithm( Alg ), ibCount( 0 ), confusionInfo( 0 ), match_depth(-1), last_leaf(true), estimate( 0 ), numOfThreads( 1 ) { Weighting = GR_w; } TimblExperiment::~TimblExperiment() { delete OptParams; delete confusionInfo; } TimblExperiment& TimblExperiment::operator=( const TimblExperiment&in ){ if ( this != &in ){ MBLClass::operator=(in); Initialized = false; OptParams = NULL; algorithm = in.algorithm; CurrentDataFile = in.CurrentDataFile; WFileName = in.WFileName; estimate = in.estimate; Weighting = in.Weighting; confusionInfo = 0; numOfThreads = in.numOfThreads; } return *this; } TimblExperiment *TimblExperiment::splitChild( ) const { TimblExperiment *result = 0; switch ( Algorithm() ){ case IB1_a: case TRIBL_a: case TRIBL2_a: case IGTREE_a: result = clone(); break; default: FatalError( "You may not split experiments for Special cases like " + TiCC::toString(algorithm) ); exit(EXIT_FAILURE); // never reached, (FatalError throws) but satisfies // scan-build } *result = *this; if ( OptParams ){ result->OptParams = OptParams->Clone( 0 ); } result->WFileName = WFileName; result->CurrentDataFile.clear(); result->InstanceBase->CleanPartition( false ); result->InstanceBase = 0; result->is_synced = true; return result; } void TimblExperiment::initExperiment( bool all_vd ){ if ( !ExpInvalid() ){ match_depth = NumOfFeatures(); if ( !MBL_init ){ // do this only when necessary stats.clear(); delete confusionInfo; confusionInfo = 0; if ( Verbosity(ADVANCED_STATS) ){ confusionInfo = new ConfusionMatrix( targets.num_of_values() ); } initDecay(); calculate_fv_entropy( true ); if (!is_copy ){ if ( ib2_offset != 0 ){ // // isn't this obsolete for the new IB2 implementation? // You should think so, But I see small differences :{ // So leave this code for now // // invalidate MVDM matrices, they might be changing in size for ( size_t j=0; j < NumOfFeatures(); ++j ){ if ( !features[j]->Ignore() ){ features[j]->clear_matrix(); } } } if ( initProbabilityArrays( all_vd ) ){ calculatePrestored(); } else { Error( "not enough memory for Probability Arrays in (" + string(__FILE__) + "," + TiCC::toString(__LINE__) + ")\n" + "ABORTING now" ); throw std::bad_alloc(); } InitWeights(); if ( do_diversify ){ diverseWeights(); } } srand( random_seed ); initTesters(); MBL_init = true; } } } bool TimblExperiment::skipARFFHeader( istream& is ){ string Buffer; while ( getline( is, Buffer ) && !compare_nocase_n( "@DATA", Buffer) ) stats.addSkipped(); return true; } bool TimblExperiment::nextLine( istream& datafile, UnicodeString& Line ){ int dummy; return nextLine( datafile, Line, dummy ); } bool TimblExperiment::nextLine( istream& datafile, UnicodeString& Line, int& cnt ){ // Function that takes a line from a file, skipping comment // returns true if some line is found // bool found = false; cnt = 0; while ( !found && TiCC::getline( datafile, Line ) ){ ++cnt; if ( empty_line( Line, InputFormat() ) ){ stats.addSkipped(); continue; } else { found = true; } } return found; } bool TimblExperiment::chopLine( const UnicodeString& line ){ if ( !Chop( line ) ){ stats.addSkipped(); return false; } else { stats.addLine(); return true; } } /* First learning Phase: Learning of the names of the FeatureValues and TargetValues also their distribution etc. */ bool TimblExperiment::Prepare( const string& FileName, bool warnOnSingleTarget, bool expand ){ assert( runningPhase == LearnWords ); bool result = false; if ( !FileName.empty() && ConfirmOptions() ){ if ( !ExpInvalid() ){ if ( !expand && ( Options.TableFrozen() || NumOfFeatures() != 0 ) ){ Error( "couldn't learn from file '" + FileName + "'\nInstanceBase already filled" ); } else { size_t Num = examineData( FileName ); if ( Num == 0 ){ Error( "Unable to initialize from file :'" + FileName + "'\n" ); } else { if ( !Verbosity(SILENT) ){ *mylog << "Examine datafile '" << FileName << "' gave the following results:" << endl << "Number of Features: " << Num << endl; showInputFormat( *mylog ); } if ( NumOfFeatures() == 0 ){ Initialize( Num ); } CurrentDataFile = FileName; if ( Verbosity(OPTIONS) ){ ShowSettings( *mylog ); } // Open the file. // ifstream datafile( FileName, ios::in); stats.clear(); UnicodeString Buffer; if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } if ( !nextLine( datafile, Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; } else { TiCC::Timer prepT; prepT.start(); bool go_on = true; if ( !Verbosity(SILENT) ){ Info( "Phase 1: Reading Datafile: " + FileName ); time_stamp( "Start: ", 0 ); } while( go_on ){ chopped_to_instance( LearnWords ); // Progress update. // if ( !Verbosity(SILENT) ){ if ( ( stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Examining: ", stats.dataLines() ); } } bool found = false; while ( !found && nextLine( datafile, Buffer ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } go_on = found; } if ( stats.dataLines() < 1 ){ Error( "no useful data in: " + FileName ); } else { if ( !Verbosity(SILENT) ){ time_stamp( "Finished: ", stats.totalLines() ); time_stamp( "Calculating Entropy " ); if ( Verbosity(FEAT_W) ){ *mylog << "Lines of data : " << stats.dataLines() << endl; if ( stats.skippedLines() != 0 ) { *mylog << "SkippedLines : " << stats.skippedLines() << endl; } LearningInfo( *mylog ); } } else { calculate_fv_entropy( false ); } prepT.stop(); if ( !Verbosity(SILENT) ){ Info( "Preparation took " + prepT.toString() ); } if ( warnOnSingleTarget && targets.EffectiveValues() <=1 ){ Warning( "Training file contains only 1 class." ); } result = true; } } } } } } return result; } bool TimblExperiment::CVprepare( const string&, WeightType, const string& ){ Error( "CVprepare called for NON CV experiment" ); return false; } ostream& operator<< ( ostream& os, const fileIndex& fi ){ for ( const auto& it : fi ){ os << "<"; os << it.first << "," << it.second; os << ">"; } return os; } bool TimblExperiment::learnFromFileIndex( const fileIndex& fi, istream& datafile ){ InstanceBase_base *outInstanceBase = 0; for ( const auto& fit : fi ){ for ( const auto& sit : fit.second ){ datafile.clear(); datafile.seekg( sit ); UnicodeString Buffer; nextLine( datafile, Buffer ); chopLine( Buffer ); // Progress update. // if ( ( stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } chopped_to_instance( TrainWords ); if ( !outInstanceBase ){ outInstanceBase = InstanceBase->clone(); } // cerr << "add instance " << &CurrInst << endl; if ( !outInstanceBase->AddInstance( CurrInst ) ){ Warning( "deviating exemplar weight in:\n" + TiCC::UnicodeToUTF8(Buffer) + "\nIgnoring the new weight" ); } } } if ( outInstanceBase ){ if ( !InstanceBase->MergeSub( outInstanceBase ) ){ FatalError( "Merging InstanceBases failed. PANIC" ); return false; } delete outInstanceBase; } return true; } bool TimblExperiment::ClassicLearn( const string& FileName, bool warnOnSingleTarget ){ bool result = true; if ( is_synced ){ CurrentDataFile = FileName; // assume magic! } if ( CurrentDataFile.empty() ) { if ( FileName.empty() ){ Warning( "unable to build an InstanceBase: No datafile defined yet" ); result = false; } else if ( !Prepare( FileName, warnOnSingleTarget ) || ExpInvalid() ){ result = false; } } else if ( !FileName.empty() && CurrentDataFile != FileName ){ Error( "Unable to Learn from file '" + FileName + "'\n" "while previously instantiated from file '" + CurrentDataFile + "'" ); result = false; } if ( result ) { TiCC::Timer learnT; learnT.start(); InitInstanceBase(); if ( ExpInvalid() ){ return false; } if ( EffectiveFeatures() < 2 ) { fileIndex fmIndex; // TiCC::Timer t; // t.start(); result = build_file_index( CurrentDataFile, fmIndex ); // t.stop(); // cerr << "indexing took " << t << endl; // totalT.start(); if ( result ){ // cerr << "index = " << fmIndex << endl; stats.clear(); if ( !Verbosity(SILENT) ) { Info( "\nPhase 3: Learning from Datafile: " + CurrentDataFile ); time_stamp( "Start: ", 0 ); } // Open the file. // ifstream datafile( CurrentDataFile, ios::in); // learnFromFileIndex( fmIndex, datafile ); } } else { fileDoubleIndex fIndex; // TiCC::Timer t; // t.start(); result = build_file_multi_index( CurrentDataFile, fIndex ); // cerr << "index: " << fIndex << endl; // t.stop(); // cerr << "indexing took " << t << endl; // totalT.start(); if ( result ){ stats.clear(); if ( !Verbosity(SILENT) ) { Info( "\nPhase 3: Learning from Datafile: " + CurrentDataFile ); time_stamp( "Start: ", 0 ); } // Open the file. // ifstream datafile( CurrentDataFile, ios::in); // for ( const auto& mit : fIndex ){ learnFromFileIndex( mit.second, datafile ); } } } if ( !Verbosity(SILENT) ){ time_stamp( "Finished: ", stats.dataLines() ); } learnT.stop(); // cerr << "Endresult " << endl; // cerr << InstanceBase << endl; if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); Info( "Learning took " + learnT.toString() ); } #ifdef IBSTATS cerr << "final mismatches: " << InstanceBase->mismatch << endl; #endif } return result; } bool TimblExperiment::Learn( const std::string& s, bool warnOnSingleTarget ){ if ( ExpInvalid() || !ConfirmOptions() ){ return false; } return ClassicLearn( s, warnOnSingleTarget ); } IB1_Experiment::IB1_Experiment( const size_t N, const string& s, const bool init ): TimblExperiment( IB1_a, s ){ if ( init ) { init_options_table(N); } TreeOrder = GRoverFeature; } /* Increment the Instancebase with one instance (IB1 Class only) */ bool IB1_Experiment::Increment( const icu::UnicodeString& InstanceString ){ bool result = true; if ( ExpInvalid() ){ result = false; } else if ( IBStatus() == Invalid ){ Warning( "unable to Increment, No InstanceBase available" ); result = false; } else if ( !Chop( InstanceString ) ){ Error( "Couldn't convert to Instance: " + TiCC::UnicodeToUTF8(InstanceString) ); result = false; // No more input } else { chopped_to_instance( TrainLearnWords ); MBL_init = false; bool happy = InstanceBase->AddInstance( CurrInst ); if ( !happy ){ Warning( "deviating exemplar weight in:\n" + TiCC::UnicodeToUTF8(InstanceString) + "\nIgnoring the new weight" ); } } return result; } /* Decrement the Instancebase with one instance (IB1 Class only) */ bool IB1_Experiment::Decrement( const UnicodeString& InstanceString ){ bool result = true; if ( ExpInvalid() ){ result = false; } else if ( IBStatus() == Invalid ){ Warning( "unable to Decrement, No InstanceBase available" ); result = false; } else { if ( !Chop( InstanceString ) ){ Error( "Couldn't convert to Instance: " + TiCC::UnicodeToUTF8(InstanceString) ); result = false; // No more input } else { chopped_to_instance( TestWords ); HideInstance( CurrInst ); } } return result; } /* Expand an Instance Base */ bool TimblExperiment::Expand( const string& FileName ){ bool result = true; if ( ExpInvalid() ){ result = false; } else if ( IBStatus() == Invalid ){ Warning( "unable to expand the InstanceBase: Not there" ); result = false; } else if ( FileName.empty() ){ Warning( "unable to expand the InstanceBase: No inputfile specified" ); result = false; } else { if ( InputFormat() == UnknownInputFormat ){ // we may expand from 'nothing' if ( !Prepare( FileName, false, true ) ){ Error( "Unable to expand from file :'" + FileName + "'\n" ); return false; } } UnicodeString Buffer; stats.clear(); // Open the file. // ifstream datafile( FileName, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } if ( !nextLine( datafile, Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; // No more input } else { MBL_init = false; if ( !Verbosity(SILENT) ) { Info( "Phase 2: Expanding from Datafile: " + FileName ); time_stamp( "Start: ", 0 ); } bool found; do { // The next Instance to store. chopped_to_instance( TrainLearnWords ); bool happy = InstanceBase->AddInstance( CurrInst ); if ( !happy ){ Warning( "deviating exemplar weight in line #" + TiCC::toString(stats.totalLines() ) + ":\n" + TiCC::UnicodeToUTF8(Buffer) + "\nIgnoring the new weight" ); } // Progress update. // if ( (stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } found = false; while ( !found && nextLine( datafile, Buffer ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } } while( found ); time_stamp( "Finished: ", stats.dataLines() ); if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); } } } return result; } /* Remove Instances from an Instance Base (IB1 only ) */ bool IB1_Experiment::Remove( const string& FileName ){ bool result = true; if ( ExpInvalid() ){ result = false; } else if ( IBStatus() == Invalid ){ Warning( "unable to remove from InstanceBase: Not there" ); result = false; } else if ( FileName.empty() ){ Warning( "unable to remove from InstanceBase: No input specified" ); result = false; } else { UnicodeString Buffer; stats.clear(); // Open the file. // ifstream datafile( FileName, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } if ( !nextLine( datafile, Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + FileName ); result = false; // No more input } else { if ( !Verbosity(SILENT) ) { Info( "Phase 2: Removing using Datafile: " + FileName ); time_stamp( "Start: ", 0 ); } bool found; do { // The next Instance to remove. chopped_to_instance( TestWords ); HideInstance( CurrInst ); // Progress update. // if ( (stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Removing: ", stats.dataLines() ); } found = false; while ( !found && nextLine( datafile, Buffer ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } } while( found ); time_stamp( "Finished: ", stats.dataLines() ); if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); } } } return result; } void TimblExperiment::showInputFormat( ostream& os ) const { switch ( InputFormat() ){ case C4_5: os << "InputFormat : C4.5"; break; case SparseBin: os << "InputFormat : Sparse Binary"; break; case Sparse: os << "InputFormat : Sparse"; break; case ARFF: os << "InputFormat : ARFF"; break; case Columns: os << "InputFormat : Columns"; break; case Tabbed: os << "InputFormat : Tabbed"; break; case Compact: os << "InputFormat : Compact, (Feature Length = " << F_length << ")"; break; default: os << "InputFormat unknown\n"; } os << endl << endl; } void TimblExperiment::show_progress( ostream& os, time_t start, unsigned int line ){ int local_progress = Progress(); if ( ( (line % local_progress ) == 0) || ( line <= 10 ) || ( line == 100 || line == 1000 || line == 10000 ) ){ time_t Time; time(&Time); if ( line == 1000 ){ // check if we are slow, if so, change progress value if ( Time - start > 120 ) { // more then two minutes for 1,000 // very slow ! Progress( 1000 ); } } else if ( line == 10000 ){ if ( Time - start > 600 ) { // more then ten minutes for 10,000 // quite slow ! Progress( 10000 ); } } if ( !exp_name.empty() ){ os << "-" << exp_name << "-"; } os << "Tested: "; os.width(6); os.setf(ios::right, ios::adjustfield); os << line << " @ " << TiCC::Timer::now(); // Estimate time until Estimate. // if ( Estimate() > 0 && (unsigned int)Estimate() > line ) { time_t SecsUsed = Time - start; if ( SecsUsed > 0 ) { double Estimated = (SecsUsed / (float)line) * (float)Estimate(); time_t EstimatedTime = (long)Estimated + start; string time_string = ctime(&EstimatedTime); time_string.pop_back(); // the newline from ctime os << ", " << Estimate() << ": " << time_string; } } os << endl; } } bool IB2_Experiment::show_learn_progress( ostream& os, time_t start, size_t added ){ int local_progress = Progress(); unsigned int lines = stats.dataLines(); unsigned int line = lines - IB2_offset() ; if ( ( (line % local_progress ) == 0) || ( line <= 10 ) || ( line == 100 || line == 1000 || line == 10000 ) ){ time_t Time; time(&Time); if ( line == 100 ){ // check if we are slow, if so, change progress value if ( Time - start > 120 && local_progress > 100 ){ // very slow ! Progress( 100 ); } } else if ( line == 1000 ){ // check if we are slow, if so, change progress value if ( Time - start > 120 && local_progress > 1000 ){ // very slow ! Progress( 1000 ); } } else if ( line == 10000 ){ if ( Time - start > 120 && local_progress > 10000 ){ // quite slow ! Progress( 10000 ); } } if ( !exp_name.empty() ){ os << "-" << exp_name << "-"; } os << "Learning: "; os.width(6); os.setf(ios::right, ios::adjustfield); os << lines << " @ " << TiCC::Timer::now(); os << "\t added:" << added; // Estime time until Estimate. // if ( Estimate() > 0 && (unsigned int)Estimate() > lines ) { time_t SecsUsed = Time - start; if ( SecsUsed > 0 ) { double Estimated = (SecsUsed / (float)line) * ( (float)Estimate() - IB2_offset() ); time_t EstimatedTime = (long)Estimated + start; string time_string = ctime(&EstimatedTime); time_string.pop_back(); // the newline from ctime os << "\t, " << Estimate() << ": " << time_string; } } os << endl; return true; } else { return false; } } void TimblExperiment::show_speed_summary( ostream& os, const timeval& Start ) const { timeval Time; gettimeofday( &Time, 0 ); long int uSecsUsed = (Time.tv_sec - Start.tv_sec) * 1000000 + (Time.tv_usec - Start.tv_usec); double secsUsed = (double)uSecsUsed / 1000000 + Epsilon; int oldPrec = os.precision(4); os << setprecision(4); os.setf( ios::fixed, ios::floatfield ); os << "Seconds taken: " << secsUsed << " ("; os << setprecision(2); os << stats.dataLines() / secsUsed << " p/s)" << endl; os << setprecision(oldPrec); } bool TimblExperiment::showStatistics( ostream& os ) const { os << endl; if ( confusionInfo ){ confusionInfo->FScore( os, targets, Verbosity(CLASS_STATS) ); } os << "overall accuracy: " << stats.testedCorrect()/(double) stats.dataLines() << " (" << stats.testedCorrect() << "/" << stats.dataLines() << ")" ; if ( stats.exactMatches() != 0 ){ os << ", of which " << stats.exactMatches() << " exact matches " ; } os << endl; int totalTies = stats.tiedCorrect() + stats.tiedFailure(); if ( totalTies > 0 ){ if ( totalTies == 1 ) { os << "There was 1 tie"; } else { os << "There were " << totalTies << " ties"; } double tie_perc = 100 * ( stats.tiedCorrect() / (double)totalTies); int oldPrec = os.precision(2); os << " of which " << stats.tiedCorrect() << " (" << setprecision(2) << tie_perc << setprecision(6) << "%)"; if ( totalTies == 1 ){ os << " was correctly resolved" << endl; } else { os << " were correctly resolved" << endl; } os.precision(oldPrec); } if ( confusionInfo && Verbosity(CONF_MATRIX) ){ os << endl; confusionInfo->Print( os, targets ); } return true; } bool TimblExperiment::createPercFile( const string& fileName ) const { if ( !fileName.empty() ) { ofstream outfile( fileName, ios::out | ios::trunc); if (!outfile) { Warning( "can't open: " + fileName ); return false; } else { outfile << (stats.testedCorrect() / (float)stats.dataLines()) * 100.0 << endl << "tested " << stats.dataLines() << " lines " << endl << "correct " << stats.testedCorrect() << " lines " << endl; outfile.close(); } } return true; } bool TimblExperiment::showBestNeighbors( ostream& outfile ) const { if ( Verbosity( NEAR_N | ALL_K) ){ outfile << bestArray; return true; } else { return false; } } xmlNode *TimblExperiment::bestNeighborsToXML() const { if ( Verbosity( NEAR_N | ALL_K) ){ return bestArray.toXML(); } else { return 0; } } json TimblExperiment::best_neighbors_to_JSON() const { if ( Verbosity( NEAR_N | ALL_K) ){ return bestArray.to_JSON(); } else { return json(); } } void TimblExperiment::show_results( ostream& outfile, const double confidence, const string& dString, const TargetValue *Best, const double Distance ) { outfile << get_org_input() << CodeToStr(Best->name()); if ( Verbosity(CONFIDENCE) ){ outfile << " [" << confidence << "]"; } if ( Verbosity(DISTRIB) ){ outfile << " " << dString; } if ( Verbosity(DISTANCE) ) { int OldPrec = outfile.precision(DBL_DIG-1); outfile.setf(ios::showpoint); outfile.width(8); outfile << " " << Distance; outfile.precision(OldPrec); } if ( Verbosity(MATCH_DEPTH) ){ outfile << " " << matchDepth() << ":" << (matchedAtLeaf()?"L":"N"); } outfile << endl; showBestNeighbors( outfile ); } bool IB2_Experiment::Prepare( const string& FileName, bool, bool expand ){ if ( !ConfirmOptions() || ( IB2_offset() == 0 && InstanceBase == 0 ) ){ Error( "IB2 learning failed, invalid bootstrap option?" ); return false; } else { return TimblExperiment::Prepare( FileName, false, expand ); } } bool IB2_Experiment::Learn( const string& FileName, bool ){ if ( IB2_offset() == 0 ){ Error( "IB2 learning failed, invalid bootstrap option?" ); return false; } else { bool result = true; TiCC::Timer learnT; if ( ExpInvalid() || !ConfirmOptions() ){ result = false; } else { if ( is_synced ){ CurrentDataFile = FileName; // assume magic! } if ( CurrentDataFile == "" ){ if ( FileName == "" ){ Warning( "unable to build an InstanceBase: No datafile defined yet" ); result = false; } else if ( !Prepare( FileName, false ) || ExpInvalid() ){ result = false; } } else if ( FileName != "" && CurrentDataFile != FileName ){ Error( "Unable to Learn from file '" + FileName + "'\n" "while previously instantiated from file '" + CurrentDataFile + "'" ); result = false; } } if ( result ) { UnicodeString Buffer; stats.clear(); // Open the file. // ifstream datafile( CurrentDataFile, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } if ( !nextLine( datafile, Buffer ) ){ Error( "cannot start learning from in: " + CurrentDataFile ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + CurrentDataFile ); result = false; // No more input } else { learnT.start(); InitInstanceBase( ); if ( ExpInvalid() ){ return false; } MBL_init = false; if ( !Verbosity(SILENT) ) { Info( "Phase 2: Learning from Datafile: " + CurrentDataFile ); time_stamp( "Start: ", 0 ); } bool found; bool go_on = ( stats.dataLines() <= IB2_offset() ); while( go_on ){ // The next Instance to store. chopped_to_instance( TrainWords ); bool happy = InstanceBase->AddInstance( CurrInst ); if ( !happy ){ Warning( "deviating exemplar weight in line #" + TiCC::toString(stats.totalLines()) + ":\n" + TiCC::UnicodeToUTF8(Buffer) + "\nIgnoring the new weight" ); } // Progress update. // if ( (stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Learning: ", stats.dataLines() ); } if ( stats.dataLines() >= IB2_offset() ){ go_on = false; } else { found = false; while ( !found && nextLine( datafile, Buffer ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } go_on = found; } } if ( !Verbosity(SILENT) ){ time_stamp( "Finished: ", stats.dataLines() ); } learnT.stop(); if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); Info( "Learning took " + learnT.toString() ); } #ifdef IBSTATS cerr << "IB2 mismatches: " << InstanceBase->mismatch << endl; #endif } if ( result ){ result = Expand_N( FileName ); } } return result; } } bool IB2_Experiment::Expand( const string& FileName ){ bool result = false; if ( CurrentDataFile == "" && InstanceBase == 0 ){ Warning( "IB2, cannot Append data: No datafile bootstrapped yet" ); } else { IB2_offset( 0 ); if ( InputFormat() == UnknownInputFormat ){ // we may expand from 'nothing' if ( !Prepare( FileName, false, true ) ){ Error( "Unable to expand from file :'" + FileName + "'\n" ); return false; } } result = Expand_N( FileName ); } return result; } bool IB2_Experiment::Remove( const string& ){ Warning( "IB2, remove impossible, (ignored) " ); return false; } bool IB2_Experiment::Expand_N( const string& FileName ){ bool result = true; if ( ExpInvalid() ){ result = false; } else if ( CurrentDataFile == "" && InstanceBase == 0 ){ Warning( "IB2, cannot Append data: No datafile bootstrapped yet" ); result = false; } else if ( IBStatus() == Invalid ){ Warning( "unable to expand the InstanceBase: Not there" ); result = false; } else { string file_name; if ( FileName == "" ){ file_name = CurrentDataFile; } else { file_name = FileName; } UnicodeString Buffer; stats.clear(); // Open the file. // ifstream datafile( file_name, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } if ( !nextLine( datafile, Buffer ) ){ Error( "no useful data in: " + file_name ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + file_name ); result = false; // No more input } else { while ( stats.dataLines() <= IB2_offset() ){ if ( !nextLine( datafile, Buffer ) ){ Error( "not enough lines to skip in " + FileName ); result = false; break; } else if ( !chopLine( Buffer ) ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } if ( result ){ time_t lStartTime; time(&lStartTime); if ( !Verbosity(SILENT) ) { Info( "Phase 2: Appending from Datafile: " + FileName + " (starting at line " + TiCC::toString( stats.dataLines() ) + ")" ); time_stamp( "Start: ", stats.dataLines() ); } bool found; size_t TotalAdded = 0; size_t Added = 0; initExperiment(); do { // The next Instance to store. chopped_to_instance( TestWords ); double final_distance; bool dummy = false; StatisticsClass stats_keep = stats; const TargetValue *ResultTarget = LocalClassify( CurrInst, final_distance, dummy ); stats = stats_keep; if ( ResultTarget != CurrInst.TV ) { chopped_to_instance( TrainLearnWords ); bool happy = InstanceBase->AddInstance( CurrInst ); if ( !happy ){ Warning( "deviating exemplar weight in line #" + TiCC::toString(stats.totalLines() ) + ":\n" + TiCC::UnicodeToUTF8(Buffer) + "\nIgnoring the new weight" ); } ++Added; ++TotalAdded; MBL_init = true; // avoid recalculations in LocalClassify } // Progress update. // if ( show_learn_progress( *mylog, lStartTime, Added ) ){ Added = 0; } found = false; while ( !found && nextLine( datafile, Buffer ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } } while( found ); time_stamp( "Finished: ", stats.dataLines() ); *mylog << "in total added " << TotalAdded << " new entries" << endl; if ( !Verbosity(SILENT) ){ IBInfo( *mylog ); LearningInfo( *mylog ); } MBL_init = false; // force recalculations when testing } } } return result; } bool TimblExperiment::initTestFiles( const string& InFileName, const string& OutFileName ){ if ( !ExpInvalid() && ConfirmOptions() ){ testStream.close(); testStream.clear(); // just to be shure. old G++ libraries are in error here testStream.open( InFileName, ios::in); if ( !testStream ) { Error( "can't open: " + InFileName ); } else { outStream.close(); outStream.clear(); // just to be shure. old G++ libraries are in error here // first we check if the outFile is writable. // We don't write it though, because we don't want to have // it mangled when checkTestFile fails outStream.open( OutFileName, ios::app ); if ( !outStream ) { Error( "can't open: " + OutFileName ); } else { testStreamName = InFileName; outStreamName = OutFileName; if ( checkTestFile() ){ outStream.close(); outStream.clear(); // just to be shure. old G++ libraries are in error here outStream.open( OutFileName, ios::out | ios::trunc ); return true; } } } } return false; } bool TimblExperiment::checkTestFile(){ if ( IBStatus() == Invalid ){ Warning( "you tried to apply the " + TiCC::toString( algorithm ) + " algorithm, but no Instance Base is available yet" ); } else { runningPhase = TestWords; size_t numF =0; if ( (numF = examineData( testStreamName )) != NumOfFeatures() ){ if ( numF == 0 ){ Error( "unable to use the data from '" + testStreamName + "', wrong Format?" ); } else { Error( "mismatch between number of features in Testfile " + testStreamName + " and the Instancebase (" + TiCC::toString(numF) + " vs. " + TiCC::toString(NumOfFeatures()) + ")" ); } return false; } if ( !Verbosity(SILENT) ){ *mylog << "Examine datafile '" << testStreamName << "' gave the following results:" << endl << "Number of Features: " << numF << endl; showInputFormat( *mylog ); } } return true; } bool IB1_Experiment::checkTestFile(){ if ( !TimblExperiment::checkTestFile() ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the " + TiCC::toString( algorithm) + " algorithm on a pruned Instance Base" ); return false; } return true; } bool IB2_Experiment::checkTestFile(){ if ( !IB1_Experiment::checkTestFile() ){ return false; } else if ( IB2_offset() == 0 && InstanceBase == 0 ){ Error( "missing bootstrap information for IB2 algorithm." ); return false; } return true; } bool TimblExperiment::checkLine( const UnicodeString& line ){ bool result = false; if ( !ExpInvalid() && ConfirmOptions() ) { runningPhase = TestWords; InputFormatType IF = InputFormat(); if ( IF == UnknownInputFormat ){ IF = getInputFormat( line ); } size_t i = countFeatures( line, IF ); if ( i != NumOfFeatures() ){ if ( i > 0 ){ Warning( "mismatch between number of features in testline '" + TiCC::UnicodeToUTF8(line) + "' and the Instancebase (" + TiCC::toString(i) + " vs. " + TiCC::toString(NumOfFeatures()) + ")" ); } } else if ( Initialized ){ result = true; } else if ( IBStatus() == Invalid ){ Warning( "no Instance Base is available yet" ); } else if ( !setInputFormat( IF ) ){ Error( "Couldn't set input format to " + TiCC::toString( IF ) ); } else { if ( Verbosity(NEAR_N) ){ Do_Exact( false ); } initExperiment(); Initialized = true; result = true; } } return result; } bool IB1_Experiment::checkLine( const UnicodeString& line ){ if ( !TimblExperiment::checkLine( line ) ){ return false; } else if ( IBStatus() == Pruned ){ Warning( "you tried to apply the IB1 algorithm on a pruned" " Instance Base" ); return false; } else if ( TRIBL_offset() != 0 ){ Error( "IB1 algorithm impossible while threshold > 0\n" "Please use TRIBL" ); return false; } return true; } json TimblExperiment::classify_to_JSON( const string& inst ) { json result; double distance = 0.0; const TargetValue *targ = classifyString( TiCC::UnicodeFromUTF8(inst), distance ); if ( targ ){ string cat = targ->name_string(); normalizeResult(); result["category"] = cat; if ( Verbosity(NEAR_N) ){ json tmp = best_neighbors_to_JSON(); if ( !tmp.empty() ){ result["neighbors"] = tmp; } } if ( Verbosity(DISTANCE) ){ result["distance"] = distance; } string distribution = bestResult.getResult(); if ( Verbosity(DISTRIB) ){ result["distribution"] = distribution; } if ( Verbosity(MATCH_DEPTH) ){ result["match_depth"] = matchDepth(); } if ( Verbosity(NEAR_N) ){ json tmp = best_neighbors_to_JSON(); if ( !tmp.empty() ){ result["neighbors"] = tmp; } } if (Verbosity(CONFIDENCE) ){ result["confidence"] = confidence(); } } else { result = last_error; } return result; } json TimblExperiment::classify_to_JSON( const vector& instances ) { json result = json::array(); for ( const auto& i : instances ){ json tmp = classify_to_JSON( i ); result.push_back( tmp ); } if ( result.size() != instances.size() ){ json error; error["status"] = "error"; error["message"] = "total confusion in Timbl"; result = error; } return result; } bool TimblExperiment::Classify( const string& Line, string& Result, string& Dist, double& Distance ){ Result.clear(); Dist.clear(); const TargetValue *targ = classifyString( TiCC::UnicodeFromUTF8(Line), Distance ); if ( targ ){ Result = targ->name_string(); normalizeResult(); Dist = bestResult.getResult(); return true; } return false; } bool TimblExperiment::Classify( const UnicodeString& Line, UnicodeString& Result, UnicodeString& Dist, double& Distance ){ Result.remove(); Dist.remove(); const TargetValue *targ = classifyString( Line, Distance ); if ( targ ){ Result = targ->name(); normalizeResult(); Dist = TiCC::UnicodeFromUTF8(bestResult.getResult()); return true; } return false; } bool TimblExperiment::Classify( const UnicodeString& Line, UnicodeString& Result ) { UnicodeString dist; double dummy; return Classify( Line, Result, dist, dummy ); } void TimblExperiment::testInstance( const Instance& Inst, InstanceBase_base *base, size_t offset ) { initExperiment(); bestArray.init( num_of_neighbors, MaxBests, Verbosity(NEAR_N), Verbosity(DISTANCE), Verbosity(DISTRIB) ); TestInstance( Inst, base, offset ); } const TargetValue *TimblExperiment::LocalClassify( const Instance& Inst, double& Distance, bool& exact ){ bool recurse = true; bool Tie = false; exact = false; if ( !bestResult.reset( beamSize, normalisation, norm_factor, targets ) ){ Warning( "no normalisation possible because a BeamSize is specified\n" "output is NOT normalized!" ); } const ClassDistribution *ExResultDist = ExactMatch( Inst ); WClassDistribution *ResultDist = 0; nSet.clear(); const TargetValue *Res; if ( ExResultDist ){ Distance = 0.0; recurse = !Do_Exact(); // no retesting when exact match and the user ASKED for them.. Res = ExResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); // // add the exact match to bestArray. It should be taken into account // for Tie resolution. this fixes bug 44 // bestArray.init( num_of_neighbors, MaxBests, Verbosity(NEAR_N), Verbosity(DISTANCE), Verbosity(DISTRIB) ); bestArray.addResult( Distance, ExResultDist, get_org_input() ); bestArray.initNeighborSet( nSet ); } else { testInstance( Inst, InstanceBase ); bestArray.initNeighborSet( nSet ); ResultDist = getBestDistribution( ); Res = ResultDist->BestTarget( Tie, (RandomSeed() >= 0) ); Distance = getBestDistance(); } if ( Tie && recurse ){ bool Tie2 = true; ++num_of_neighbors; testInstance( Inst, InstanceBase ); bestArray.addToNeighborSet( nSet, num_of_neighbors ); WClassDistribution *ResultDist2 = getBestDistribution(); const TargetValue *Res2 = ResultDist2->BestTarget( Tie2, (RandomSeed() >= 0) ); --num_of_neighbors; if ( !Tie2 ){ Res = Res2; delete ResultDist; ResultDist = ResultDist2; } else { delete ResultDist2; } } exact = fabs(Distance) < Epsilon ; if ( ResultDist ){ bestResult.addDisposable( ResultDist, Res ); } else { bestResult.addConstant( ExResultDist, Res ); exact = exact || Do_Exact(); } if ( exact ){ stats.addExact(); } if ( confusionInfo ){ confusionInfo->Increment( Inst.TV, Res ); } bool correct = Inst.TV && ( Res == Inst.TV ); if ( correct ){ stats.addCorrect(); if ( Tie ){ stats.addTieCorrect(); } } else if ( Tie ){ stats.addTieFailure(); } return Res; } const TargetValue *TimblExperiment::classifyString( const UnicodeString& Line, double& Distance ){ Distance = -1.0; const TargetValue *BestT = NULL; if ( checkLine( Line ) && chopLine( Line ) ){ chopped_to_instance( TestWords ); bool exact = false; BestT = LocalClassify( CurrInst, Distance, exact ); } return BestT; } const neighborSet *TimblExperiment::NB_Classify( const UnicodeString& line ){ initExperiment(); if ( checkLine( line ) && chopLine( line ) ){ chopped_to_instance( TestWords ); return LocalClassify( CurrInst ); } return 0; } const neighborSet *TimblExperiment::LocalClassify( const Instance& Inst ){ testInstance( Inst, InstanceBase ); bestArray.initNeighborSet( nSet ); nSet.setShowDistance( Verbosity(DISTANCE) ); nSet.setShowDistribution( Verbosity(DISTRIB) ); return &nSet; } double TimblExperiment::sum_remaining_weights( size_t level ) const { double result = 0.0; for ( size_t i = level; i < EffectiveFeatures(); ++i ){ result += features.perm_feats[i]->Weight(); } return result; } void TimblExperiment::show_metric_info( ostream& os ) const { os << "Global metric : " << TiCC::toString( globalMetricOption, true); if ( GlobalMetric->isStorable() ){ os << ", Prestored matrix"; } if ( Do_Exact() ){ os << ", prefering exact matches"; } os << endl; os << "Deviant Feature Metrics:"; int cnt = 0; vector InvPerm( NumOfFeatures() ); for ( size_t i = 0; i < NumOfFeatures(); ++i ){ InvPerm[features.permutation[i]] = i; } for ( size_t i = 0; i < NumOfFeatures(); ++i ){ if ( !features[i]->Ignore() && InvPerm[i]+1 > TRIBL_offset() ){ MetricType mt = features[i]->getMetricType(); if ( mt != globalMetricOption ){ ++cnt; os << endl << " Feature[" << i+1 << "] : " << TiCC::toString( mt, true ); if ( features[i]->isStorableMetric() ){ bool readM = false; if ( features[i]->matrixPresent( readM ) ){ if ( readM ){ os << " (User Defined)"; } else { os << " (Prestored)"; } } else { os << " (Not Prestored)"; } } } } } if ( cnt ){ os << endl; } else { os << "(none)" << endl; } MatrixInfo( os ); show_ignore_info( os ); } void TimblExperiment::show_weight_info( ostream& os ) const { os << "Weighting : " << TiCC::toString(CurrentWeighting(), true); if ( CurrentWeighting() == UserDefined_w ){ if ( WFileName != "" ){ os << " (" << WFileName << ")"; } else { os << " (no weights loaded, using No Weighting)" ; } } os << endl; if ( Verbosity( FEAT_W ) && CurrentWeighting() != No_w ){ ShowWeights( os ); } } void TimblExperiment::show_ignore_info( ostream& os ) const{ bool first = true; for ( size_t i=0; i< NumOfFeatures(); ++i ){ if ( features[i]->Ignore() ){ if ( first ){ first = false; os << "Ignored features : { "; } else { os << ", "; } os << i+1; } } if ( !first ){ os << " } " << endl; } } void TimblExperiment::showTestingInfo( ostream& os ) { if ( !Verbosity(SILENT) ){ if ( Verbosity(OPTIONS ) ){ ShowSettings( os ); } os << endl << "Starting to test, Testfile: " << testStreamName << endl << "Writing output in: " << outStreamName << endl << "Algorithm : " << TiCC::toString( Algorithm() ) << endl; show_metric_info( os ); show_weight_info( os ); os << decay << endl; } } class threadData { public: threadData():exp(0), lineNo(0), resultTarget(0), exact(false), distance(-1), confidence(0) {}; bool exec(); void show( ostream& ) const; TimblExperiment *exp; UnicodeString Buffer; unsigned int lineNo; const TargetValue *resultTarget; bool exact; string distrib; double distance; double confidence; }; bool threadData::exec(){ resultTarget = 0; // #pragma omp critical // cerr << "exec " << lineNo << " '" << Buffer << "'" << endl; if ( Buffer.isEmpty() ){ return false; } if ( !exp->chopLine( Buffer ) ){ exp->Warning( "testfile, skipped line #" + TiCC::toString( lineNo ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); return false; } else { exp->chopped_to_instance( TimblExperiment::TestWords ); exact = false; resultTarget = exp->LocalClassify( exp->CurrInst, distance, exact ); exp->normalizeResult(); distrib = exp->bestResult.getResult(); if ( exp->Verbosity(CONFIDENCE) ){ confidence = exp->confidence(); } else { confidence = 0; } return true; } } void threadData::show( ostream& os ) const { if ( resultTarget != 0 ){ exp->show_results( os, confidence, distrib, resultTarget, distance ); if ( exact ){ // remember that a perfect match may be incorrect! if ( exp->Verbosity(EXACT) ) { *exp->mylog << "Exacte match:\n" << exp->get_org_input() << endl; } } } } class threadBlock { public: explicit threadBlock( TimblExperiment *, int = 1 ); bool readLines( istream& ); void finalize(); vector exps; private: size_t size; }; threadBlock::threadBlock( TimblExperiment *parent, int num ){ if ( num <= 0 ){ throw range_error( "threadBlock size cannot be <=0" ); } size = num; exps.resize( size ); exps[0].exp = parent; for ( size_t i = 1; i < size; ++i ){ exps[i].exp = parent->clone(); *exps[i].exp = *parent; exps[i].exp->initExperiment(); }; } bool threadBlock::readLines( istream& is ){ bool result = true; for ( size_t i=0; i < size; ++i ){ exps[i].Buffer = ""; int cnt; bool goon = exps[0].exp->nextLine( is, exps[i].Buffer, cnt ); exps[i].lineNo += cnt; if ( !goon && i == 0 ){ result = false; } } return result; } void threadBlock::finalize(){ for ( size_t i=1; i < size; ++i ){ exps[0].exp->stats.merge( exps[i].exp->stats ); if ( exps[0].exp->confusionInfo ){ exps[0].exp->confusionInfo->merge( exps[i].exp->confusionInfo ); } delete exps[i].exp; } } #ifdef HAVE_OPENMP bool TimblExperiment::Test( const string& FileName, const string& OutFile ){ bool result = false; if ( initTestFiles( FileName, OutFile ) ){ initExperiment(); stats.clear(); showTestingInfo( *mylog ); if ( numOfThreads > 1 ){ omp_set_num_threads( numOfThreads ); } threadBlock experiments( this, numOfThreads ); // Start time. // time_t lStartTime; time(&lStartTime); timeval startTime; gettimeofday( &startTime, 0 ); if ( InputFormat() == ARFF ){ skipARFFHeader( testStream ); } unsigned int dataCount = stats.dataLines(); while ( experiments.readLines( testStream ) ){ if ( numOfThreads > 1 ){ #pragma omp parallel for shared( experiments, dataCount ) for ( int i=0; i < numOfThreads; ++i ){ if ( experiments.exps[i].exec() && !Verbosity(SILENT) ) // Display progress counter. #pragma omp critical show_progress( *mylog, lStartTime, ++dataCount ); } for ( int i=0; i < numOfThreads; ++i ){ // Write it to the output file for later analysis. experiments.exps[i].show( outStream ); } } else { if ( experiments.exps[0].exec() && !Verbosity(SILENT) ){ // Display progress counter. show_progress( *mylog, lStartTime, ++dataCount ); } // Write it to the output file for later analysis. experiments.exps[0].show( outStream ); } } experiments.finalize(); if ( !Verbosity(SILENT) ){ time_stamp( "Ready: ", stats.dataLines() ); show_speed_summary( *mylog, startTime ); showStatistics( *mylog ); } result = true; } return result; } #else bool TimblExperiment::Test( const string& FileName, const string& OutFile ){ bool result = false; if ( initTestFiles( FileName, OutFile ) ){ initExperiment(); stats.clear(); showTestingInfo( *mylog ); // Start time. // time_t lStartTime; time(&lStartTime); timeval startTime; gettimeofday( &startTime, 0 ); if ( InputFormat() == ARFF ){ skipARFFHeader( testStream ); } UnicodeString Buffer; while ( nextLine( testStream, Buffer ) ){ if ( !chopLine( Buffer ) ) { Warning( "testfile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } else { chopped_to_instance( TestWords ); bool exact = false; string distrib; double distance; double confi = 0; const TargetValue *resultTarget = LocalClassify( CurrInst, distance, exact ); normalizeResult(); distrib = bestResult.getResult(); if ( Verbosity(CONFIDENCE) ){ confi = confidence(); } show_results( outStream, confi, distrib, resultTarget, distance ); if ( exact ){ // remember that a perfect match may be incorrect! if ( Verbosity(EXACT) ) { *mylog << "Exacte match:\n" << get_org_input() << endl; } } if ( !Verbosity(SILENT) ){ // Display progress counter. show_progress( *mylog, lStartTime, stats.dataLines() ); } } } if ( !Verbosity(SILENT) ){ time_stamp( "Ready: ", stats.dataLines() ); show_speed_summary( *mylog, startTime ); showStatistics( *mylog ); } result = true; } return result; } #endif bool TimblExperiment::NS_Test( const string& , const string& ){ FatalError( "wrong algorithm" ); return false; } bool IB1_Experiment::NS_Test( const string& FileName, const string& OutFile ){ bool result = false; if ( initTestFiles( FileName, OutFile ) ){ initExperiment(); stats.clear(); showTestingInfo( *mylog ); // Start time. // time_t lStartTime; time(&lStartTime); timeval startTime; gettimeofday( &startTime, 0 ); if ( InputFormat() == ARFF ){ skipARFFHeader( testStream ); } UnicodeString Buffer; while ( nextLine( testStream, Buffer ) ){ if ( !chopLine( Buffer ) ) { Warning( "testfile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } else { chopped_to_instance( TestWords ); const neighborSet *res = LocalClassify( CurrInst ); outStream << get_org_input() << endl << *res; if ( !Verbosity(SILENT) ){ // Display progress counter. show_progress( *mylog, lStartTime, stats.dataLines() ); } } }// end while. if ( !Verbosity(SILENT) ){ time_stamp( "Ready: ", stats.dataLines() ); show_speed_summary( *mylog, startTime ); } result = true; } return result; } bool TimblExperiment::SetOptions( int argc, const char *argv[] ){ if ( IsClone() ){ TiCC::CL_Options Opts( timbl_serv_short_opts, "" ); try { Opts.init( argc, argv ); } catch( exception& e ){ Error( string(e.what()) + ": valid options: " + timbl_serv_short_opts ); } return SetOptions( Opts ); } else { TiCC::CL_Options Opts( timbl_short_opts, timbl_long_opts); try { Opts.init( argc, argv ); } catch( exception& e ){ Error( string(e.what()) + ": valid options: " + timbl_short_opts + " " + timbl_long_opts ); } return SetOptions( Opts ); } } bool TimblExperiment::SetOptions( const string& arg ){ if ( IsClone() ){ TiCC::CL_Options Opts( timbl_serv_short_opts, "" ); try { Opts.init( arg ); } catch( exception& e ){ Error( string(e.what()) + ": valid options: " + timbl_serv_short_opts ); } return SetOptions( Opts ); } else { TiCC::CL_Options Opts( timbl_short_opts, timbl_long_opts); try { Opts.init( arg ); } catch( exception& e ){ Error( string(e.what()) + ": valid options: " + timbl_short_opts + " " + timbl_long_opts ); } return SetOptions( Opts ); } } bool TimblExperiment::SetOptions( const TiCC::CL_Options& Opts ){ bool result; if ( IsClone() ){ result = OptParams->parse_options( Opts, 2 ); } else { result = OptParams->parse_options( Opts, 0 ); } return result; } bool TimblExperiment::IndirectOptions( const TiCC::CL_Options& Opts ){ OptParams->set_default_options(); return OptParams->parse_options( Opts, 1 ); } bool TimblExperiment::ConfirmOptions(){ return OptParams->definitive_options( this ); } bool TimblExperiment::ShowOptions( ostream& os ){ return ( ConfirmOptions() && MBLClass::ShowOptions( os ) ); } bool TimblExperiment::ShowSettings( ostream& os ){ return ( ConfirmOptions() && MBLClass::ShowSettings( os ) ); } xmlNode *TimblExperiment::settingsToXML(){ if ( ConfirmOptions() ){ return MBLClass::settingsToXml( ); } else { return 0; } } json TimblExperiment::settings_to_JSON(){ if ( ConfirmOptions() ){ return MBLClass::settings_to_JSON( ); } else { return 0; } } xmlNode *TimblExperiment::weightsToXML(){ xmlNode *result = TiCC::XmlNewNode( "currentWeights" ); TiCC::XmlSetAttribute( result, "weighting", TiCC::toString( CurrentWeighting() ) ); vector wghts; GetCurrentWeights( wghts ); for ( unsigned int i=0; i < wghts.size(); ++i ){ xmlNode *n = TiCC::XmlNewTextChild( result, "feature", TiCC::toString(wghts[i]) ); TiCC::XmlSetAttribute( n, "index", TiCC::toString(i+1) ); } return result; } json TimblExperiment::weights_to_JSON(){ json result; result["weighting"] = TiCC::toString( CurrentWeighting() ); json arr = json::array(); vector wghts; GetCurrentWeights( wghts ); copy( wghts.begin(), wghts.end(), back_inserter(arr) ); result["weights"] = arr; return result; } bool TimblExperiment::WriteArrays( const std::string& FileName ){ ofstream out( FileName, ios::out | ios::trunc ); if ( !out ) { Warning( "Problem opening Probability file '" + FileName + "' (not written)" ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Saving Probability Arrays in " + FileName ); } return MBLClass::writeArrays( out ); } } bool TimblExperiment::GetArrays( const std::string& FileName ){ ifstream inf( FileName, ios::in ); if ( !inf ){ Error( "Problem opening Probability file " + FileName ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Reading Probability Arrays from " + FileName ); } if ( !readArrays( inf ) ){ Error( "Errors found in file " + FileName ); return false; } else { return true; } } } bool TimblExperiment::WriteMatrices( const std::string& FileName ){ ofstream out( FileName, ios::out | ios::trunc ); if ( !out ) { Warning( "Problem opening matrices file '" + FileName + "' (not written)" ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Saving Matrices in " + FileName ); } initExperiment( ); return writeMatrices( out ); } } bool TimblExperiment::GetMatrices( const std::string& FileName ){ ifstream inf( FileName, ios::in ); if ( !inf ){ Error( "Problem opening matrices file " + FileName ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Reading matrices from " + FileName ); } if ( !readMatrices( inf ) ){ Error( "Errors found in file " + FileName ); return false; } else { return true; } } } bool TimblExperiment::SaveWeights( const std::string& FileName ){ if ( ConfirmOptions() ){ // Open the output file. // ofstream outfile( FileName, ios::out | ios::trunc); if (!outfile) { Warning( "can't open Weightsfile: " + FileName ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Saving Weights in " + FileName ); } if ( writeWeights( outfile ) ){ return true; } else { Error( "failed to store weights in file " + FileName ); return false; } } } else { return false; } } bool TimblExperiment::GetWeights( const std::string& FileName, WeightType w ){ if ( ConfirmOptions() ){ // Open the file. // ifstream weightsfile( FileName, ios::in); if ( !weightsfile) { Error( "can't open WeightsFile " + FileName ); return false; } else { if ( w == Unknown_w ){ w = GR_w; } if ( !Verbosity(SILENT) ){ Info( "Reading weights from " + FileName ); } if ( readWeights( weightsfile, w ) ){ WFileName = FileName; return true; } else { Warning( "Errors in Weightsfile " + FileName ); return false; } } } else { return false; } } bool TimblExperiment::WriteInstanceBase( const std::string& FileName ){ bool result = false; if ( ConfirmOptions() ){ ofstream outfile( FileName, ios::out | ios::trunc ); if (!outfile) { Warning( "can't open outputfile: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Writing Instance-Base in: " + FileName ); } result = PutInstanceBase( outfile ); } } return result; } bool TimblExperiment::WriteInstanceBaseXml( const std::string& FileName ) { bool result = false; if ( ConfirmOptions() ){ ofstream os( FileName, ios::out | ios::trunc ); if (!os) { Warning( "can't open outputfile: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Writing Instance-Base in: " + FileName ); } if ( ExpInvalid() ){ result = false; } else if ( InstanceBase == NULL ){ Warning( "unable to write an Instance Base, nothing learned yet" ); } else { InstanceBase->toXML( os ); } } } return result; } bool TimblExperiment::WriteInstanceBaseLevels( const std::string& FileName, unsigned int levels ) { bool result = false; if ( ConfirmOptions() ){ ofstream os( FileName, ios::out | ios::trunc ); if (!os) { Warning( "can't open outputfile: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Writing Instance-Base in: " + FileName ); } if ( ExpInvalid() ){ result = false; } else if ( InstanceBase == NULL ){ Warning( "unable to write an Instance Base, nothing learned yet" ); } else { InstanceBase->printStatsTree( os, levels ); } } } return result; } bool IB1_Experiment::GetInstanceBase( istream& is ){ bool result = false; bool Pruned; bool Hashed; int Version; string range_buf; size_t numF = get_IB_Info( is, Pruned, Version, Hashed, range_buf ); if ( numF == 0 ){ return false; } else if ( Pruned ){ Error( "Instance-base is Pruned!, NOT valid for " + TiCC::toString(algorithm) + " Algorithm" ); } else { TreeOrder = DataFile; Initialize( numF ); if ( !get_ranges( range_buf ) ){ Warning( "couldn't retrieve ranges..." ); } else { srand( RandomSeed() ); int pos=0; for ( size_t i=0; i < NumOfFeatures(); ++i ){ features[i]->SetWeight( 1.0 ); if ( features[features.permutation[i]]->Ignore() ){ features.perm_feats[i] = NULL; } else { features.perm_feats[pos++] = features[features.permutation[i]]; } } InstanceBase = new IB_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0) ); if ( Hashed ){ result = InstanceBase->ReadIB_hashed( is, features, targets, Version ); } else { result = InstanceBase->ReadIB( is, features, targets, Version ); } } } return result; } bool TimblExperiment::ReadInstanceBase( const string& FileName ){ bool result = false; if ( ConfirmOptions() ){ ifstream infile( FileName, ios::in ); if ( !infile ) { Error( "can't open: " + FileName ); } else { if ( !Verbosity(SILENT) ){ Info( "Reading Instance-Base from: " + FileName ); } if ( GetInstanceBase( infile ) ){ if ( !Verbosity(SILENT) ){ IBInfo( cout ); writePermutation( cout ); } result = true; } } } return result; } bool TimblExperiment::WriteNamesFile( const string& FileName ) const { // Open the file. // ofstream namesfile( FileName, ios::out | ios::trunc); if (!namesfile) { Warning( "can't open NamesFile: '" + FileName + "' (not written)" ); return false; } else { if ( !Verbosity(SILENT) ){ Info( "Saving names in " + FileName ); } MBLClass::writeNamesFile( namesfile ); return true; } } void IB1_Experiment::InitInstanceBase(){ srand( RandomSeed() ); set_order(); runningPhase = TrainWords; InstanceBase = new IB_InstanceBase( EffectiveFeatures(), ibCount, (RandomSeed()>=0) ); } bool TimblExperiment::GetCurrentWeights( vector& res ) { res.clear(); if ( ExpInvalid() ){ return false; } else { initExperiment(); for ( size_t i=0; i< NumOfFeatures(); ++i ){ res.push_back( features[i]->Weight() ); } } return true; } bool TimblExperiment::build_file_index( const string& file_name, fileIndex& fmIndex ){ bool result = true; UnicodeString Buffer; stats.clear(); size_t cur_pos = 0; // Open the file. // ifstream datafile( file_name, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } cur_pos = datafile.tellg(); if ( !nextLine( datafile, Buffer ) ){ Error( "cannot start learning from in: " + file_name ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + file_name ); result = false; // No more input } else { if ( !Verbosity(SILENT) ) { Info( "Phase 2: Building index on Datafile: " + file_name ); time_stamp( "Start: ", 0 ); } bool go_on = true; while ( go_on ){ // The next Instance to store. // cerr << "line at pos " << cur_pos << " : " << Buffer << endl; chopped_to_instance( TrainWords ); // cerr << "gives Instance " << &CurrInst << endl; FeatureValue *fv0 = CurrInst.FV[0]; auto const it = fmIndex.find( fv0 ); if ( it == fmIndex.end() ){ set st; st.insert(cur_pos); fmIndex[fv0] = st; } else { it->second.insert( cur_pos ); } if ( (stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Indexing: ", stats.dataLines() ); } bool found = false; while ( !found && ( cur_pos = datafile.tellg(), nextLine( datafile, Buffer ) ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } go_on = found; } time_stamp( "Finished: ", stats.dataLines() ); } return result; } bool TimblExperiment::build_file_multi_index( const string& file_name, fileDoubleIndex& fmIndex ){ bool result = true; UnicodeString Buffer; stats.clear(); size_t cur_pos = 0; // Open the file. // ifstream datafile( file_name, ios::in); if ( InputFormat() == ARFF ){ skipARFFHeader( datafile ); } cur_pos = datafile.tellg(); if ( !nextLine( datafile, Buffer ) ){ Error( "cannot start learning from in: " + file_name ); result = false; // No more input } else if ( !chopLine( Buffer ) ){ Error( "no useful data in: " + file_name ); result = false; // No more input } else { if ( !Verbosity(SILENT) ){ Info( "Phase 2: Building multi index on Datafile: " + file_name ); time_stamp( "Start: ", 0 ); } bool go_on = true; while( go_on ){ // The next Instance to store. // cerr << "line at pos " << cur_pos << " : " << Buffer << endl; chopped_to_instance( TrainWords ); // cerr << "gives Instance " << &CurrInst << endl; FeatureValue *fv0 = CurrInst.FV[0]; FeatureValue *fv1 = CurrInst.FV[1]; auto const it = fmIndex.find( fv0 ); if ( it != fmIndex.end() ){ it->second[fv1].insert( cur_pos ); } else { fileIndex mi; mi[fv1].insert( cur_pos ); fmIndex[fv0] = mi; } if ( (stats.dataLines() % Progress() ) == 0 ){ time_stamp( "Indexing: ", stats.dataLines() ); } bool found = false; while ( !found && ( cur_pos = datafile.tellg(), nextLine( datafile, Buffer ) ) ){ found = chopLine( Buffer ); if ( !found ){ Warning( "datafile, skipped line #" + TiCC::toString( stats.totalLines() ) + "\n" + TiCC::UnicodeToUTF8(Buffer) ); } } go_on = found; } time_stamp( "Finished: ", stats.dataLines() ); } return result; } } LanguageMachines-timbl-642727d/src/Types.cxx000066400000000000000000000126071451477526200207350ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include #include #include #include #include #include "timbl/Types.h" namespace Timbl { using std::string; using std::vector; // initializers const string AlgorithmName[][2] = { { "Unknown", "Unknown Algorithm" }, { "IB1", "Memory Based Learning" }, { "IB2", "Adapted Memory Based Learning"}, { "IGTree", "Information Gain Tree" }, { "TRIBL", "Tree IB1" }, { "TRIBL2", "Tribl 2" }, { "LOO", "Leave One Out" }, { "CV", "Cross Validate" } }; const string MetricName[][2] = { { "U", "Unknown Metric" }, { "I", "Ignore" }, { "N", "Numeric" }, { "D", "Dot product" }, { "C", "Cosine metric" }, { "O", "Overlap" }, { "L", "Levenshtein" }, { "DC", "Dice coefficient" }, { "M", "Value Difference" }, { "J", "Jeffrey Divergence" }, { "S", "Jensen-Shannon Divergence" }, { "E", "Euclidean Distance" } }; const string WeightName[][2] = { { "un", "Unknown Weighting" }, { "nw", "No Weighting" }, { "gr", "GainRatio" }, { "ig", "InfoGain" }, { "x2", "Chi-square" }, { "sv", "Shared Variance" }, { "sd", "Standard Deviation" }, { "ud", "User Defined"} }; const string DecayName[][2] = { { "Unknown", "Unknown Decay" }, { "Z", "Zero Decay" }, { "ID", "Inverse Distance" }, { "IL", "Inverse Linear Distance" }, { "ED", "Exponential Decay" } }; const string SmoothingName[][2] = { { "Unknown", "Unknown Smoothing" }, { "Default", "Default Smoothing" }, { "L", "Lidstone Smoothing" } }; const string OrdeningName[][2] = { { "Unknown", "Unknown Ordering" }, { "UDO", "Data File Ordering" }, { "DO", "Default Ordering" }, { "GRO", "GainRatio" }, { "IGO", "InformationGain" }, { "1/V", "Inverse Values" }, { "1/S", "Inverse SplitInfo" }, { "G/V", "GainRatio/Values" }, { "I/V", "InformationGain/Values" }, { "GxE", "GainRatio*Entropy" }, { "IxE", "InformationGain*Entropy" }, { "X2O", "Chi-Squared" }, { "SVO", "Shared Variance" }, { "SDO", "Standard Deviation" }, { "X/V", "Chi-Squared/Values" }, { "S/V", "Shared Variance/Values" }, { "SD/V", "Standard Deviation/Values" } }; const string InputFormatName[][2] = { { "Unknown", "Unknown Input Format" }, { "Compact", "Compact" }, { "C45", "C4.5" }, { "Column", "Columns" }, { "Tabbed", "Tabbed" }, { "ARFF", "ARFF" }, { "BINARY", "Sparse Binary" }, { "SPARSE", "Sparse" } }; const string VerbosityName[][2] = { { "Unknown", "erroneous" }, { "S", "Silent" }, { "O", "Options" }, { "F", "Feature_Statistics" }, { "P", "Probability_arrays" }, { "E", "Exact_match" }, { "DI", "Distances" }, { "DB", "Distribution" }, { "N", "Nearest_Neighbours" }, { "AS", "Advanced_Statistics" }, { "CM", "Confusion_Matrix" }, { "CS", "Class_Statistics" }, { "CD", "Client_Debug" }, { "K", "All_K_values" }, { "MD", "MatchingDepth" }, { "B", "BranchingFactor" }, { "CF", "Confidence" }, // Verbosity is special! // should end with "" strings! { "", "" } }; const string NormalisationName[][2] = { { "Unknown", "Unknown normalisation" }, { "None", "No Normalisation" }, { "Probability", "Normalise to 100%" }, { "AddFactor", "Add a factor to all targets, then normalise to 100%" }, { "LogProbability", "Take 10log, then Normalise to 100%" } }; WeightType charToWeig( char w ){ switch ( w ){ case '0': return No_w; case '1': return GR_w; case '2': return IG_w; case '3': return X2_w; case '4': return SV_w; case '5': return SD_w; default: return Unknown_w; } } AlgorithmType charToAlg( char a ){ switch ( a ){ case '0': return IB1_a; case '1': return IGTREE_a; case '2': return TRIBL_a; case '3': return IB2_a; case '4': return TRIBL2_a; default: return Unknown_a; } } normType charToNorm( char a ){ switch ( a ){ case '0': return probabilityNorm; case '1': return addFactorNorm; case '2': return logProbNorm; default: return unknownNorm; } } } LanguageMachines-timbl-642727d/src/neighborSet.cxx000066400000000000000000000145511451477526200221020ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include #include "timbl/Common.h" #include "timbl/Types.h" #include "timbl/Instance.h" #include "timbl/neighborSet.h" namespace Timbl { using namespace std; using namespace Common; neighborSet::neighborSet(): showDistance(false),showDistribution(false){} neighborSet::~neighborSet(){ clear(); } neighborSet::neighborSet( const neighborSet& in ){ showDistance = in.showDistance; showDistribution = in.showDistribution; merge( in ); } neighborSet& neighborSet::operator=( const neighborSet& in ){ if ( this != &in ){ clear(); showDistance = in.showDistance; showDistribution = in.showDistribution; merge( in ); } return *this; } size_t neighborSet::size() const{ return distances.size(); } void neighborSet::clear(){ distances.clear(); for ( auto const& db : distributions ){ delete db; } distributions.clear(); } void neighborSet::reserve( size_t s ){ distances.reserve( s ); distributions.reserve( s ); } void neighborSet::truncate( size_t len ){ if ( len < distributions.size() ){ for ( size_t i=len; i < distributions.size(); ++i ){ delete distributions[i]; } distributions.resize( len ); distances.resize( len); } } void neighborSet::push_back( double d, const ClassDistribution &dist ){ distances.push_back( d ); distributions.push_back( dist.to_VD_Copy() ); } void neighborSet::merge( const neighborSet& s ){ // reserve enough space to avoid reallocations // reallocation invalidates pointers! reserve( size() + s.size() ); auto dit1 = distances.begin(); auto dit2 = s.distances.begin(); auto dis1 = distributions.begin(); auto dis2 = s.distributions.begin(); while ( dit1 != distances.end() ){ if ( dit2 != s.distances.end() ){ if (fabs(*dit1 - *dit2) < Epsilon) { // equal (*dis1)->Merge( **dis2 ); ++dit1; ++dis1; ++dit2; ++dis2; } else if ( *dit1 < *dit2 ){ ++dit1; ++dis1; } else { dit1 = distances.insert( dit1, *dit2 ); ++dit1; ++dit2; dis1 = distributions.insert( dis1, (*dis2)->to_VD_Copy() ); ++dis1; ++dis2; } } else { break; } } while ( dit2 != s.distances.end() ){ distances.push_back( *dit2 ); ++dit2; distributions.push_back( (*dis2)->to_VD_Copy() ); ++dis2; } } double neighborSet::relativeWeight( const decayStruct *d, size_t k ) const{ double result = 1.0; if ( !d ){ return result; } switch ( d->type() ){ case Zero: break; case InvDist: result = 1.0/(distances[k] + Epsilon); break; case InvLinear: if ( k > 0 && size() != 1 ){ double nearest_dist, furthest_dist; nearest_dist = distances[0]; furthest_dist = distances[size()-1]; result = (furthest_dist - distances[k]) / (furthest_dist-nearest_dist); } break; case ExpDecay: result = exp(-d->alpha*pow(distances[k], d->beta)); if ( result == 0 ){ // A result of zero is undesirable. (bug 89) // We optimisticly replace it with Epsilon result = Epsilon; } break; default: throw std::logic_error( "wrong value in switch" ); } return result; } double neighborSet::getDistance( size_t n ) const { if ( size() <= n ){ throw std::range_error( "getDistance() parameter exceeds size of neighborSet" ); } return distances[n]; } const ClassDistribution *neighborSet::getDistribution( size_t n ) const { if ( size() <= n ){ throw std::range_error( "getDistribution() parameter exceeds size of neighborSet" ); } return distributions[n]; } WClassDistribution *neighborSet::bestDistribution( const decayStruct *d, size_t max ) const { // Analyse the set to find THE best ClassDistribution. // For each neighbor, we loop over the number of bests in that // bin, and merge that distribution into the result // WClassDistribution *result = new WClassDistribution(); size_t stop = distributions.size(); stop = ( max > 0 && max < stop ? max : stop ); for ( size_t k = 0; k < stop; ++k ) { result->MergeW( *distributions[k], relativeWeight( d, k ) ); } return result; } ostream& operator<<( ostream& os, const neighborSet& set ){ for ( unsigned int i=0; i < set.size(); ++i ){ os << "# k=" << i+1; if ( set.showDistribution ){ os << "\t" << set.distributions[i]->DistToStringW(0); } if ( set.showDistance ){ int OldPrec = os.precision(DBL_DIG-1); os.setf(ios::showpoint); os << "\t" << set.distances[i]; os.precision(OldPrec); } os << endl; } return os; } ostream& operator<<( ostream& os, const neighborSet *Set ){ os << *Set; return os; } ostream& operator<<( ostream& os, const decayStruct& dc ){ return dc.put( os ); } ostream& zeroDecay::put( ostream& os ) const { return os; } ostream& invLinDecay::put( ostream& os ) const { os << "Decay : " << TiCC::toString( type(), true); return os; } ostream& invDistDecay::put( ostream& os ) const { os << "Decay : " << TiCC::toString( type(), true); return os; } ostream& expDecay::put( ostream& os ) const { os << "Decay : " << TiCC::toString( type(), true); os << " a=" << alpha << " b= " << beta; return os; } ostream& operator<<( ostream& os, const decayStruct *dc ){ if ( dc ){ os << *dc; } return os; } } LanguageMachines-timbl-642727d/src/simpletest.cxx000066400000000000000000000032311451477526200220130ustar00rootroot00000000000000/* Copyright (c) 1998 - 2023 ILK - Tilburg University CLST - Radboud University CLiPS - University of Antwerp This file is part of timbl timbl is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. timbl is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . For questions and suggestions, see: https://github.com/LanguageMachines/timbl/issues or send mail to: lamasoftware (at ) science.ru.nl */ #include "timbl/TimblAPI.h" #include int main(){ std::string path = std::getenv( "topsrcdir" ); std::cerr << path << std::endl; std::cerr << "version: " << Timbl::VersionName() << std::endl; Timbl::AlgorithmType alg = Timbl::charToAlg( '0' ); assert( alg == Timbl::IB1_a ); Timbl::normType nor = Timbl::charToNorm( '0' ); assert( nor == Timbl::probabilityNorm ); Timbl::WeightType w = Timbl::charToWeig( '0' ); assert( w == Timbl::No_w ); Timbl::TimblAPI exp( "+vdi+db", "test1" ); if ( exp.isValid() ){ exp.Learn( path + "/demos/dimin.train" ); if ( exp.isValid() ){ exp.Test( path + "/demos/dimin.test", "dimin.out" ); if ( exp.isValid() ){ return EXIT_SUCCESS; } } } return EXIT_FAILURE; } LanguageMachines-timbl-642727d/timbl.pc.in000066400000000000000000000004401451477526200203460ustar00rootroot00000000000000prefix=@prefix@ exec_prefix=@exec_prefix@ libdir=@libdir@ includedir=@includedir@ openmpflags=@OPENMP_CXXFLAGS@ Name: timbl Version: @VERSION@ Description: timbl library. Requires.private: libxml-2.0 Libs: -L${libdir} -ltimbl Libs.private: @LIBS@ ${openmpflags} Cflags: -I${includedir}