pax_global_header 0000666 0000000 0000000 00000000064 15101347240 0014507 g ustar 00root root 0000000 0000000 52 comment=de93cb71d7253b4d6fa73fe03f55f557e5bddba6
rapidfuzz-Levenshtein-de93cb7/ 0000775 0000000 0000000 00000000000 15101347240 0016511 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/.clang-format 0000664 0000000 0000000 00000001431 15101347240 0021063 0 ustar 00root root 0000000 0000000 ColumnLimit: 110
IndentWidth: 4
AccessModifierOffset: -4
AllowShortIfStatementsOnASingleLine: true
PointerAlignment: Left
AllowShortBlocksOnASingleLine: Always
AllowShortFunctionsOnASingleLine: None
AllowShortLambdasOnASingleLine: None
BreakBeforeBraces: Custom
AlwaysBreakTemplateDeclarations: true
BraceWrapping:
SplitEmptyFunction: false
AfterCaseLabel: true
AfterClass: false
AfterControlStatement: MultiLine
AfterEnum: false
AfterFunction: true
AfterNamespace: false
AfterStruct: false
AfterUnion: false
BeforeCatch: true
BeforeElse: true
SplitEmptyRecord: false
SplitEmptyNamespace: false
AllowAllConstructorInitializersOnNextLine: true
ConstructorInitializerAllOnOneLineOrOnePerLine: true
AllowShortCaseLabelsOnASingleLine: true
IndentPPDirectives: AfterHash
rapidfuzz-Levenshtein-de93cb7/.gitattributes 0000664 0000000 0000000 00000000046 15101347240 0021404 0 ustar 00root root 0000000 0000000 src/c_levenshtein.c linguist-vendored
rapidfuzz-Levenshtein-de93cb7/.github/ 0000775 0000000 0000000 00000000000 15101347240 0020051 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/.github/FUNDING.yml 0000664 0000000 0000000 00000000136 15101347240 0021666 0 ustar 00root root 0000000 0000000 github: maxbachmann
custom: ["https://www.paypal.com/donate/?hosted_button_id=VGWQBBD5CTWJU"]
rapidfuzz-Levenshtein-de93cb7/.github/dependabot.yml 0000664 0000000 0000000 00000001102 15101347240 0022673 0 ustar 00root root 0000000 0000000 # Keep GitHub Actions up to date with GitHub's Dependabot...
# https://docs.github.com/en/code-security/dependabot/working-with-dependabot/keeping-your-actions-up-to-date-with-dependabot
# https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#package-ecosystem
version: 2
updates:
- package-ecosystem: github-actions
directory: /
groups:
github-actions:
patterns:
- "*" # Group all Actions updates into a single larger pull request
schedule:
interval: weekly rapidfuzz-Levenshtein-de93cb7/.github/workflows/ 0000775 0000000 0000000 00000000000 15101347240 0022106 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/.github/workflows/docs.yml 0000664 0000000 0000000 00000002066 15101347240 0023565 0 ustar 00root root 0000000 0000000 name: Build docs
on:
push:
branches:
- main
workflow_dispatch:
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write
# Allow one concurrent deployment
concurrency:
group: "pages"
cancel-in-progress: true
jobs:
build:
runs-on: [ubuntu-latest]
steps:
- uses: actions/checkout@v5
with:
submodules: 'true'
- uses: actions/setup-python@v6
- name: Install dependencies
run: |
python -m pip install -r docs/requirements.txt
python -m pip install .
- name: Build Site
run: sphinx-build -b html docs build/html
- name: Upload artifact
uses: actions/upload-pages-artifact@v4
with:
path: "build/html"
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
needs: build
steps:
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
rapidfuzz-Levenshtein-de93cb7/.github/workflows/pythonbuild.yml 0000664 0000000 0000000 00000012233 15101347240 0025173 0 ustar 00root root 0000000 0000000 name: Build
on:
push:
pull_request:
release:
types:
- published
jobs:
build_sdist:
name: Build source distribution
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v5
with:
submodules: 'true'
- uses: actions/setup-python@v6
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install pytest hypothesis mypy Cython==3.1.6
# The cythonized files allow installation from the sdist without cython
- name: Generate cython
run: |
chmod +x ./src/Levenshtein/generate.sh
./src/Levenshtein/generate.sh
- name: Build sdist
run: |
git apply ./tools/sdist.patch
pip install build
python -m build --sdist
# test whether tarball contains all files required for compiling
pip install dist/levenshtein-*.tar.gz -v
- name: Test type stubs
run: |
python -m mypy.stubtest Levenshtein --ignore-missing-stub
- name: Test with pytest and backtrace in case of SegFault
run: |
tools/seg_wrapper.sh pytest tests
- uses: actions/upload-artifact@v5
with:
name: artifact-sdist
path: dist/*.tar.gz
build_wheels_windows:
name: Build wheel on ${{ matrix.os }}/auto
needs: [build_sdist]
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [windows-latest, windows-11-arm]
env:
CIBW_TEST_SKIP: "*-win32"
CIBW_BUILD_VERBOSITY: 3
steps:
- uses: actions/download-artifact@v6
with:
name: artifact-sdist
path: dist
- uses: actions/setup-python@v6
- name: Copy wheel
run: cp dist/*.tar.gz levenshtein.tar.gz
- name: Build wheels
uses: pypa/cibuildwheel@v3.2.1
with:
package-dir: levenshtein.tar.gz
output-dir: wheelhouse
- name: Upload wheels
uses: actions/upload-artifact@v5
with:
name: artifact-${{ github.job }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
build_wheels_macos:
name: Build wheel on ${{ matrix.os }}/auto
needs: [build_sdist]
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [macos-15-intel, macos-14]
env:
CIBW_TEST_SKIP: "pp*-macosx_*"
CIBW_BUILD_VERBOSITY: 3
steps:
- uses: actions/download-artifact@v6
with:
name: artifact-sdist
path: dist
- uses: actions/setup-python@v6
- name: Copy wheel
run: cp dist/*.tar.gz levenshtein.tar.gz
- name: Build wheels
uses: pypa/cibuildwheel@v3.2.1
with:
package-dir: levenshtein.tar.gz
output-dir: wheelhouse
- name: Upload wheels
uses: actions/upload-artifact@v5
with:
name: artifact-${{ github.job }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
build_wheels_linux:
name: Build wheels on ubuntu-latest/auto
needs: [build_sdist]
runs-on: ubuntu-latest
env:
CIBW_TEST_SKIP: "*musllinux_*"
CIBW_BUILD_VERBOSITY: 3
steps:
- uses: actions/download-artifact@v6
with:
name: artifact-sdist
path: dist
- uses: actions/setup-python@v6
- name: Copy wheel
run: cp dist/*.tar.gz levenshtein.tar.gz
- name: Build wheel
uses: pypa/cibuildwheel@v3.2.1
with:
package-dir: levenshtein.tar.gz
output-dir: wheelhouse
- name: Upload wheels
uses: actions/upload-artifact@v5
with:
name: artifact-${{ github.job }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
build_wheels_linux_arm:
name: Build wheels on ubuntu-24.04-arm/${{matrix.arch}}
needs: [build_sdist]
runs-on: ubuntu-24.04-arm
strategy:
fail-fast: false
matrix:
arch: [aarch64, armv7l]
env:
CIBW_ARCHS_LINUX: ${{matrix.arch}}
CIBW_TEST_SKIP: "{*_{aarch64},*musllinux_*}"
CIBW_BUILD_VERBOSITY: 3
steps:
- uses: actions/download-artifact@v6
with:
name: artifact-sdist
path: dist
- uses: actions/setup-python@v6
- name: Copy wheel
run: cp dist/*.tar.gz levenshtein.tar.gz
- name: Build wheel
uses: pypa/cibuildwheel@v3.2.1
with:
package-dir: levenshtein.tar.gz
output-dir: wheelhouse
- name: Upload wheels
uses: actions/upload-artifact@v5
with:
name: artifact-${{ github.job }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
deploy-wheels:
if: github.event_name == 'release' && github.event.action == 'published'
needs: [build_wheels_windows, build_wheels_macos, build_wheels_linux, build_wheels_linux_arm, build_sdist]
name: deploy wheels to pypi
runs-on: ubuntu-latest
environment: pypi-release
permissions:
id-token: write
steps:
- uses: actions/download-artifact@v6
with:
path: dist
pattern: artifact-*
merge-multiple: true
- uses: pypa/gh-action-pypi-publish@v1.13.0
rapidfuzz-Levenshtein-de93cb7/.gitignore 0000664 0000000 0000000 00000004060 15101347240 0020501 0 ustar 00root root 0000000 0000000 # Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# vscode
.vscode/
_skbuild/
# Cython generated files
*.cxx
rapidfuzz-Levenshtein-de93cb7/.gitmodules 0000664 0000000 0000000 00000000151 15101347240 0020663 0 ustar 00root root 0000000 0000000 [submodule "extern/rapidfuzz-cpp"]
path = extern/rapidfuzz-cpp
url = ../../rapidfuzz/rapidfuzz-cpp.git
rapidfuzz-Levenshtein-de93cb7/.pre-commit-config.yaml 0000664 0000000 0000000 00000005634 15101347240 0023002 0 ustar 00root root 0000000 0000000 # To use:
#
# pre-commit run -a
#
# Or:
#
# pre-commit install # (runs every time you commit in git)
#
# To update this file:
#
# pre-commit autoupdate
#
# See https://github.com/pre-commit/pre-commit
exclude: |
(?x)(
.*\.patch
)
repos:
# Standard hooks
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: "v4.6.0"
hooks:
- id: check-added-large-files
- id: check-case-conflict
- id: check-docstring-first
- id: check-merge-conflict
- id: check-symlinks
- id: check-toml
- id: check-yaml
- id: debug-statements
- id: end-of-file-fixer
- id: mixed-line-ending
- id: requirements-txt-fixer
- id: trailing-whitespace
# Black, the code formatter, natively supports pre-commit
- repo: https://github.com/psf/black
rev: "24.8.0" # Keep in sync with blacken-docs
hooks:
- id: black
# Also code format the docs
- repo: https://github.com/asottile/blacken-docs
rev: "1.18.0"
hooks:
- id: blacken-docs
additional_dependencies:
- black==22.8.0 # keep in sync with black hook
# Changes tabs to spaces
- repo: https://github.com/Lucas-C/pre-commit-hooks
rev: "v1.5.5"
hooks:
- id: remove-tabs
- repo: https://github.com/sirosen/texthooks
rev: "0.6.7"
hooks:
- id: fix-ligatures
- id: fix-smartquotes
# Checking for common mistakes
- repo: https://github.com/pre-commit/pygrep-hooks
rev: "v1.10.0"
hooks:
- id: rst-backticks
- id: rst-directive-colons
- id: rst-inline-touching-normal
# PyLint has native support - not always usable, but works for us
- repo: https://github.com/PyCQA/pylint
rev: "v3.2.7"
hooks:
- id: pylint
files: ^pybind11
# CMake formatting
- repo: https://github.com/cheshirekow/cmake-format-precommit
rev: "v0.6.13"
hooks:
- id: cmake-format
additional_dependencies: [pyyaml]
types: [file]
files: (\.cmake|CMakeLists.txt)(.in)?$
# Check static types with mypy
#- repo: https://github.com/pre-commit/mirrors-mypy
# rev: "v0.971"
# hooks:
# - id: mypy
# args: []
# exclude: ^(tests|docs)/
# additional_dependencies: [nox, rich]
- repo: https://github.com/charliermarsh/ruff-pre-commit
rev: v0.6.5
hooks:
- id: ruff
args: ["--fix", "--show-fixes"]
# Check for spelling
- repo: https://github.com/codespell-project/codespell
rev: "v2.3.0"
hooks:
- id: codespell
exclude: ".*/test_.*.py"
#args: ["-x", ".codespell-ignore-lines"]
# Check for common shell mistakes
#- repo: https://github.com/shellcheck-py/shellcheck-py
# rev: "v0.9.0.6"
# hooks:
# - id: shellcheck
# Disallow some common capitalization mistakes
- repo: local
hooks:
- id: disallow-caps
name: Disallow improper capitalization
language: pygrep
entry: PyBind|Numpy|Cmake|CCache|PyTest
exclude: ^\.pre-commit-config.yaml$
# Clang format the codebase automatically
- repo: https://github.com/pre-commit/mirrors-clang-format
rev: "v18.1.8"
hooks:
- id: clang-format
types_or: [c++, c]
rapidfuzz-Levenshtein-de93cb7/CMakeLists.txt 0000664 0000000 0000000 00000003507 15101347240 0021256 0 ustar 00root root 0000000 0000000 cmake_minimum_required(VERSION 3.15...3.26)
cmake_policy(SET CMP0054 NEW)
set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
set(SKBUILD_LINK_LIBRARIES_KEYWORD PRIVATE)
set(Python_FIND_IMPLEMENTATIONS CPython PyPy)
set(THREADS_PREFER_PTHREAD_FLAG ON)
if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
set(CMAKE_OSX_DEPLOYMENT_TARGET
"10.9"
CACHE STRING "Minimum OS X deployment version")
endif()
project(Levenshtein LANGUAGES C CXX)
if(MSVC)
add_compile_options(/W4)
else()
add_compile_options(-Wall -Wextra -pedantic)
endif()
if(CMAKE_VERSION VERSION_LESS 3.18)
find_package(
Python
COMPONENTS Interpreter Development
REQUIRED)
else()
set(Python_ARTIFACTS_INTERACTIVE TRUE)
find_package(
Python
COMPONENTS Interpreter Development.Module
REQUIRED)
endif()
if(CMAKE_VERSION VERSION_LESS 3.17)
execute_process(
COMMAND
"${Python_EXECUTABLE}" -c
"import sysconfig; print(sysconfig.get_config_var('EXT_SUFFIX').split('.')[1])"
OUTPUT_VARIABLE Python_SOABI
OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ECHO STDOUT)
message(STATUS "Corrected SOABI: ${Python_SOABI}")
elseif("${Python_INTERPRETER_ID}" STREQUAL "PyPy")
message(STATUS "PyPy SOABI: ${Python_SOABI}")
execute_process(
COMMAND
"${Python_EXECUTABLE}" -c
"import sysconfig; print(sysconfig.get_config_var('EXT_SUFFIX').split('.')[1])"
OUTPUT_VARIABLE Python_SOABI
OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ECHO STDOUT)
message(STATUS "Corrected SOABI: ${Python_SOABI}")
endif()
find_package(rapidfuzz 3.3.3 QUIET)
if(rapidfuzz_FOUND)
message(STATUS "Using system supplied version of rapidfuzz-cpp")
else()
message(STATUS "Using packaged version of rapidfuzz-cpp")
add_subdirectory(extern/rapidfuzz-cpp)
endif()
set(LEV_BASE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/Levenshtein)
add_subdirectory(src/Levenshtein)
rapidfuzz-Levenshtein-de93cb7/HISTORY.md 0000664 0000000 0000000 00000021402 15101347240 0020173 0 ustar 00root root 0000000 0000000 ## Changelog
### v0.27.3
#### Fixed
- add missing pypy and freethreaded linux wheels
#### Removed
- drop s390x and ppc64le wheels since they are virtually unused and require extremly long to build under emulation
### v0.27.2
#### Changed
- fix build with ``scikit-build-core`` ``v0.11``
- upgrade to `Cython==3.1.6`
- upgrade to `rapidfuzz-cpp==3.3.3`
#### Added
- add support for free threaded Python
- add python 3.14 wheels
#### Removed
- drop support for Python 3.9
- drop 32 bit linux wheels
### v0.27.1
#### Changed
- fix build with ``scikit-build-core`` ``v0.11``
- upgrade to `Cython==3.0.12`
### v0.27.0
#### Fixed
- avoid instantiation of basic_string for unsupported types
#### Changed
- upgrade ``rapidfuzz-cpp`` to ``v3.2.0``
### v0.26.1
#### Fixed
- add missing mac os wheels
### v0.26.0
#### Changed
- add support for Python 3.13
- drop support for Python 3.8
- switch build system to `scikit-build-core`
- upgrade to `Cython==3.0.11`
### v0.25.1
#### Fixed
- fix potentially incorrect results of `jaro_winkler` when using high prefix weights
### v0.25.0
#### Changed
- improve type hints
### v0.24.0
#### Changed
- upgrade ``rapidfuzz-cpp`` to ``v3.0.0``
- drop support for Python 3.7
### v0.23.0
#### Changed
- added keyword argument `pad` to Hamming distance. This controls whether sequences of different
length should be padded or lead to a `ValueError`
- upgrade to `Cython==3.0.3`
### v0.22.0
#### Changed
- add support for Python 3.12
- drop support for Python 3.6
#### Added
- add wheels for windows arm64
### v0.21.1
#### Changed
- upgrade ``rapidfuzz-cpp`` to ``v2.0.0``
### v0.21.0
#### Changed
- relax dependency requirement on ``rapidfuzz``
### v0.20.9
#### Fixed
- fix function signature of `get_requires_for_build_wheel`
### v0.20.8
#### Fixed
- type hints for `editops`/`opcoded`/`matching_blocks` did not allow any
hashable sequence
### v0.20.7
#### Fixed
- type hints did not get installed
### v0.20.6
#### Fixed
- fix incorrect result normalization in `setratio` and `seqratio`
### v0.20.5
#### Fixed
- fix support for cmake versions below 3.17
- fix version requirement for `rapidfuzz-cpp` when building against a previously installed version
### v0.20.4
#### Changed
- modernize cmake build to fix most conda-forge builds
### v0.20.3
#### Changed
- Added support for Python 3.11
### v0.20.2
#### Fixed
- fix matching_blocks conversion for empty editops
#### Changed
- added in-tree build backend to install cmake and ninja only when it is not installed yet and only when wheels are available
### v0.20.1
#### Fixed
- fix broken matching_blocks conversion
### v0.20.0
#### Changed
- use `matching_blocks`/`apply`/`remove_subsequence`/`inverse` implementation from RapidFuzz
#### Fixed
- stop adding data to wheels
- fix segmentation fault on some invalid editop sequences in subtract_edit
- detect duplicated entries in editops validation
### v0.19.3
#### Added
- add musllinux wheels
### v0.19.2
#### Added
- add missing type hints
### v0.19.1
#### Added
- Add type hints
### v0.19.0
#### Changed
- implement all Python wrappers mostly with cython
- replace usage of deprecated Python APIs
#### Fixed
- fix behavior of median and median_improve
### v0.18.2
#### Changed
- Allow installation from system installed versions of `rapidfuzz-cpp`
### v0.18.1
#### Fixed
- Indel.normalized_similarity was broken in RapidFuzz v2.0.0 (see #20)
### v0.18.0
#### Fixed
* Fixed memory leak in error path of setratio
* Fixed out of bound reads due to uninitialized variable in median
* e.g. quickmedian(["test", "teste"], [0, 0]) caused out of bound reads
#### Changed
* Use a faster editops implementation provided by RapidFuzz
* Reduce code duplication
* reuse implementations from rapidfuzz-cpp
* Transition to scikit-build
### v0.17.0
* Removed support for Python 3.5
### v0.16.1
* Add support for RapidFuzz v1.9.*
### v0.16.0
* Add support for Python 3.10
### v0.15.0
* Update SequenceMatcher interface to support the autojunk parameter
### v0.14.0
* Drop Python 2 support
* Fixed free of non heap object due caused by zero offset on a heap object
* Fixed warnings about missing type conversions
* Fix segmentation fault in subtract_edit when incorrect input types are used
* Fixed unchecked memory allocations
* Implement distance/ratio/hamming/jaro/jaro_winkler
using rapidfuzz instead of providing a own implementation
* Implement Wrapper for inverse/editops/opcodes/matching_blocks/subtract_edit/apply_edit
using Cython to simplify support for new Python versions
### v0.13.0
* Maintainership passed to Max Bachmann
* use faster bitparallel implementations for distance and ratio
* avoid string copies in distance, ratio and hamming
* Fix usage of deprecated Unicode APIs in distance, ratio and hamming
* Fixed incorrect window size inside Jaro and Jaro-Winkler implementation
* Fixed incorrect exception messages
* Removed unused functions and compiler specific hacks
* Split the Python and C implementations to simplify building of
the C library
* Fixed multiple bugs which prevented the use as C library, since some functions
only got defined when compiling for Python
* Build and deliver python wheels for the library
* Fixed incorrect allocation size in lev_editops_matching_blocks and
lev_opcodes_matching_blocks
### v0.12.1
* Fixed handling of numerous possible wraparounds in calculating the size
of memory allocations; incorrect handling of which could cause denial
of service or even possible remote code execution in previous versions
of the library.
### v0.12.0
* Fixed a bug in StringMatcher.StringMatcher.get_matching_blocks /
extract_editops for Python 3; now allow only `str` editops on
both Python 2 and Python 3, for simpler and working code.
* Added documentation in the source distribution and in GIT
* Fixed the package layout: renamed the .so/.dll to _levenshtein,
and made it reside inside a package, along with the StringMatcher
class.
* Fixed spelling errors.
### v0.11.2
* Fixed a bug in setup.py: installation would fail on Python 3 if the locale
did not specify UTF-8 charset (Felix Yan).
* Added COPYING, StringMatcher.py, gendoc.sh and NEWS in MANIFEST.in, as they
were missing from source distributions.
### v0.11.1
* Added Levenshtein.h to MANIFEST.in
### v0.11.0
* Python 3 support, maintainership passed to Antti Haapala
### v0.10.2
* Made python-Lehvenstein Git compatible and use setuptools for PyPi upload
* Created HISTORY.txt and made README reST compatible
### v0.10.1
* apply_edit() broken for Unicodes was fixed (thanks to Radovan Garabik)
* subtract_edit() function was added
### v0.10.0
* Hamming distance, Jaro similarity metric and Jaro-Winkler similarity
metric were added
* ValueErrors raised on wrong argument types were fixed to TypeErrors
### v0.9.0
* a poor-but-fast generalized median method quickmedian() was added
* some auxiliary functions added to the C api (lev_set_median_index,
lev_editops_normalize, ...)
### v0.8.2
* fixed missing `static' in the method list
### v0.8.1
* some compilation problems with non-gcc were fixed
v0.8.0
* median_improve(), a generalized median improving function, was added
* an arbitrary length limitation imposed on greedy median() result was
removed
* out of memory should be handled more gracefully (on systems w/o memory
overcomitting)
* the documentation now passes doctest
### v0.7.0
* fixed greedy median() for Unicode characters > U+FFFF, it's now usable
with whatever integer type wchar_t happens to be
* added missing MANIFEST
* renamed exported C functions, all public names now have lev_, LEV_ or
Lev prefix; defined lev_byte, lev_wchar, and otherwise santinized
the (still unstable) C interface
* added edit-ops group of functions, with two interfaces: native, useful
for string averaging, and difflib-like for interoperability
* added an example SequenceMatcher-like class StringMatcher
### v0.6.0
* a segfault in seqratio()/setratio() on invalid input has been fixed
to an exception
* optimized ratio() and distance() (about 20%)
* Levenshtein.h header file was added to make it easier to actually use
it as a C library
### v0.5.0
* a segfault in setratio() was fixed
* median() handles all empty strings situation more gracefully
### v0.4.0
* new functions seqratio() and setratio() computing similarity between
string sequences and sets
* Levenshtein optimizations (affects all routines except median())
* all Sequence objects are accepted, not just Lists
### v0.3.0
* setmedian() finding set median was added
* median() initial overhead for Unicodes was reduced
### v0.2.0
* ratio() and distance() now accept both Strings and Unicodes
* removed uratio() and udistance()
* Levenshtein.c is now compilable as a C library (with -DNO_PYTHON)
* a median() function finding approximate weighted median of a string
set was added
### v0.1.0
* Initial release
rapidfuzz-Levenshtein-de93cb7/LICENSE 0000664 0000000 0000000 00000037204 15101347240 0017524 0 ustar 00root root 0000000 0000000 Copyright (C) 2010-2013 Mikko Ohthamaa
2013-2021 Antti Haapala
2021-2025 Max Bachmann
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License
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rapidfuzz-Levenshtein-de93cb7/README.md 0000664 0000000 0000000 00000005013 15101347240 0017767 0 ustar 00root root 0000000 0000000 # Levenshtein
## Introduction
The Levenshtein Python C extension module contains functions for fast
computation of:
* Levenshtein (edit) distance, and edit operations
* string similarity
* approximate median strings, and generally string averaging
* string sequence and set similarity
## Requirements
* Python 3.10 or later
## Installation
```bash
pip install levenshtein
```
## Documentation
The documentation for the current version can be found at [https://rapidfuzz.github.io/Levenshtein/](https://rapidfuzz.github.io/Levenshtein/)
## Support the project
If you are using Levenshtein for your work and feel like giving a bit of your own benefit back to support the project, consider sending us money through GitHub Sponsors or PayPal that we can use to buy us free time for the maintenance of this great library, to fix bugs in the software, review and integrate code contributions, to improve its features and documentation, or to just take a deep breath and have a cup of tea every once in a while. Thank you for your support.
Support the project through [GitHub Sponsors](https://github.com/sponsors/maxbachmann) or via [PayPal](https://www.paypal.com/donate/?hosted_button_id=VGWQBBD5CTWJU):
[](https://www.paypal.com/donate/?hosted_button_id=VGWQBBD5CTWJU).
## License
Levenshtein 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.
See the file [LICENSE](https://github.com/rapidfuzz/Levenshtein/blob/main/LICENSE) for the full text of GNU General Public License version 2.
rapidfuzz-Levenshtein-de93cb7/SECURITY.md 0000664 0000000 0000000 00000001733 15101347240 0020306 0 ustar 00root root 0000000 0000000 ## Reporting Security Issues
If you believe you have found a security vulnerability in the project, please report it to us through coordinated disclosure.
**Please do not report security vulnerabilities through public GitHub issues, discussions, or pull requests.**
Instead, please send an email to oss@maxbachmann.de.
Please include as much of the information listed below as you can to help us better understand and resolve the issue:
* The type of issue (e.g., buffer overflow, SQL injection, or cross-site scripting)
* Full paths of source file(s) related to the manifestation of the issue
* The location of the affected source code (tag/branch/commit or direct URL)
* Any special configuration required to reproduce the issue
* Step-by-step instructions to reproduce the issue
* Proof-of-concept or exploit code (if possible)
* Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.
rapidfuzz-Levenshtein-de93cb7/docs/ 0000775 0000000 0000000 00000000000 15101347240 0017441 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/docs/changelog.rst 0000664 0000000 0000000 00000000034 15101347240 0022117 0 ustar 00root root 0000000 0000000 .. include:: ../HISTORY.txt
rapidfuzz-Levenshtein-de93cb7/docs/conf.py 0000664 0000000 0000000 00000004214 15101347240 0020741 0 ustar 00root root 0000000 0000000 # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
from __future__ import annotations
import Levenshtein
project = "Levenshtein"
copyright = "2021, Max Bachmann"
author = "Max Bachmann"
# The full version, including alpha/beta/rc tags
release = Levenshtein.__version__
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.napoleon"
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "furo"
pygments_style = "monokai"
pygments_dark_style = "monokai"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
# html_static_path = ["_static"]
napoleon_google_docstring = False
# -- Extension configuration -------------------------------------------------
rapidfuzz-Levenshtein-de93cb7/docs/index.rst 0000664 0000000 0000000 00000001630 15101347240 0021302 0 ustar 00root root 0000000 0000000 Welcome to Levenshtein's documentation!
=======================================
A C extension module for fast computation of:
- Levenshtein (edit) distance and edit sequence manipulation
- string similarity
- approximate median strings, and generally string averaging
- string sequence and set similarity
Levenshtein has a some overlap with difflib (SequenceMatcher). It
supports only strings, not arbitrary sequence types, but on the
other hand it's much faster.
It supports both normal and Unicode strings, but can't mix them, all
arguments to a function (method) have to be of the same type (or its
subclasses).
.. toctree::
:maxdepth: 2
:caption: Installation:
installation
.. toctree::
:maxdepth: 2
:caption: Usage:
levenshtein
.. toctree::
:maxdepth: 2
:caption: Changelog:
changelog
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
rapidfuzz-Levenshtein-de93cb7/docs/installation.rst 0000664 0000000 0000000 00000001127 15101347240 0022675 0 ustar 00root root 0000000 0000000 Installation
============
The recommended method to install Levenshtein is by using ``pip`` (the Python package manager)
using pip
---------
Levenshtein can be installed with ``pip``:
.. code-block:: sh
pip install levenshtein
There are pre-built binaries (wheels) of Levenshtein for MacOS (10.9 and later), Linux x86_64 and Windows.
from git
--------
Levenshtein can be directly used from GitHub by cloning the
repository which might be useful when you want to work on it:
.. code-block:: sh
git clone https://github.com/rapidfuzz/Levenshtein.git
cd Levenshtein
pip install .
rapidfuzz-Levenshtein-de93cb7/docs/levenshtein.rst 0000664 0000000 0000000 00000002055 15101347240 0022521 0 ustar 00root root 0000000 0000000 Levenshtein module
==================
distance
--------
.. autofunction:: Levenshtein.distance
ratio
-----
.. autofunction:: Levenshtein.ratio
hamming
-------
.. autofunction:: Levenshtein.hamming
jaro
----
.. autofunction:: Levenshtein.jaro
jaro_winkler
------------
.. autofunction:: Levenshtein.jaro_winkler
median
------
.. autofunction:: Levenshtein.median
median_improve
--------------
.. autofunction:: Levenshtein.median_improve
quickmedian
-----------
.. autofunction:: Levenshtein.quickmedian
setmedian
---------
.. autofunction:: Levenshtein.setmedian
seqratio
--------
.. autofunction:: Levenshtein.seqratio
setratio
--------
.. autofunction:: Levenshtein.setratio
editops
-------
.. autofunction:: Levenshtein.editops
opcodes
-------
.. autofunction:: Levenshtein.opcodes
inverse
-------
.. autofunction:: Levenshtein.inverse
apply_edit
----------
.. autofunction:: Levenshtein.apply_edit
matching_blocks
---------------
.. autofunction:: Levenshtein.matching_blocks
subtract_edit
-------------
.. autofunction:: Levenshtein.subtract_edit
rapidfuzz-Levenshtein-de93cb7/docs/requirements.txt 0000664 0000000 0000000 00000000061 15101347240 0022722 0 ustar 00root root 0000000 0000000 docutils==0.18.1
furo
Sphinx
sphinxcontrib-bibtex rapidfuzz-Levenshtein-de93cb7/extern/ 0000775 0000000 0000000 00000000000 15101347240 0020016 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/extern/rapidfuzz-cpp/ 0000775 0000000 0000000 00000000000 15101347240 0022614 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/make.bat 0000664 0000000 0000000 00000001430 15101347240 0020114 0 ustar 00root root 0000000 0000000 @ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=docs
set BUILDDIR=build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
rapidfuzz-Levenshtein-de93cb7/pyproject.toml 0000664 0000000 0000000 00000007513 15101347240 0021433 0 ustar 00root root 0000000 0000000 [build-system]
requires = [
"scikit-build-core>=0.11",
"Cython>=3.1.6,<3.2.0"
]
build-backend = "scikit_build_core.build"
[project]
name = "Levenshtein"
dynamic = ["version"]
dependencies = [
"rapidfuzz >= 3.9.0, < 4.0.0"
]
requires-python = ">= 3.10"
authors = [
{name = "Max Bachmann", email = "pypi@maxbachmann.de"},
{name = "David Necas"},
{name = "Mikko Ohtamaa"},
{name = "Antti Haapala"},
]
maintainers = [
{name = "Max Bachmann", email = "pypi@maxbachmann.de"},
]
description = "Python extension for computing string edit distances and similarities."
readme = "README.md"
license = "GPL-2.0-or-later"
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
]
[project.urls]
Homepage = "https://github.com/rapidfuzz/Levenshtein"
Documentation = "https://rapidfuzz.github.io/Levenshtein/"
Repository = "https://github.com/rapidfuzz/Levenshtein.git"
Issues = "https://github.com/rapidfuzz/Levenshtein/issues"
Changelog = "https://github.com/rapidfuzz/Levenshtein/blob/main/HISTORY.md"
[tool.scikit-build]
sdist.include = [
"src/Levenshtein/*.cxx"
]
sdist.exclude = [
".github"
]
wheel.exclude = [
"**.pyx",
"**.cxx",
"**.cpp",
"**.hpp",
"CMakeLists.txt",
"generate.sh"
]
[tool.scikit-build.metadata.version]
provider = "scikit_build_core.metadata.regex"
input = "src/Levenshtein/__init__.py"
[tool.black]
line-length = 120
[tool.mypy]
files = ["src"]
python_version = "3.10"
warn_unused_configs = true
show_error_codes = true
enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"]
strict = true
disallow_untyped_defs = false
[tool.pytest.ini_options]
minversion = "6.0"
testpaths = ["tests"]
addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
norecursedirs = ["_skbuild"]
xfail_strict = true
log_cli_level = "info"
[tool.pylint]
py-version = "3.10"
[tool.pylint.reports]
output-format = "colorized"
[tool.pylint.messages_control]
disable = [
"design",
"fixme",
"imports",
"line-too-long",
"imports",
"invalid-name",
"protected-access",
"missing-module-docstring",
]
[tool.ruff]
select = [
"E", "F", "W", # flake8
"B", # flake8-bugbear
"I", # isort
"ARG", # flake8-unused-arguments
"C4", # flake8-comprehensions
"EM", # flake8-errmsg
"ICN", # flake8-import-conventions
"ISC", # flake8-implicit-str-concat
"G", # flake8-logging-format
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PL", # pylint
"PT", # flake8-pytest-style
"PTH", # flake8-use-pathlib
"RET", # flake8-return
"RUF", # Ruff-specific
"SIM", # flake8-simplify
"T20", # flake8-print
"UP", # pyupgrade
"YTT", # flake8-2020
"EXE", # flake8-executable
"NPY", # NumPy specific rules
"PD", # pandas-vet
]
extend-ignore = [
"PLR", # Design related pylint codes
"E501", # Line too long
"PT004", # Use underscore for non-returning fixture (use usefixture instead)
"PTH123", # use pathlib instead of builtin open
"PLC1901", # simply not always correct
]
target-version = "py39"
src = ["src"]
unfixable = [
"T20", # Removes print statements
"F841", # Removes unused variables
]
exclude = []
flake8-unused-arguments.ignore-variadic-names = true
isort.required-imports = ["from __future__ import annotations"]
[tool.ruff.per-file-ignores]
"tests/**" = ["T20"]
"bench/**" = ["T20"]
"_custom_build/backend.py" = ["T20"]
"setup.py" = ["T20"]
[tool.cibuildwheel]
enable = ["cpython-freethreading", "pypy"]
test-requires = ["pytest", "hypothesis"]
test-command = "pytest {package}/tests" rapidfuzz-Levenshtein-de93cb7/src/ 0000775 0000000 0000000 00000000000 15101347240 0017300 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/ 0000775 0000000 0000000 00000000000 15101347240 0021564 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/CMakeLists.txt 0000664 0000000 0000000 00000002776 15101347240 0024340 0 ustar 00root root 0000000 0000000 function(create_cython_target _name)
if(EXISTS ${CMAKE_CURRENT_LIST_DIR}/${_name}.cxx)
set(${_name}
${CMAKE_CURRENT_LIST_DIR}/${_name}.cxx
PARENT_SCOPE)
else()
add_custom_command(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${_name}.cxx"
MAIN_DEPENDENCY "${CMAKE_CURRENT_LIST_DIR}/${_name}.pyx"
VERBATIM
COMMAND
Python::Interpreter -m cython "${CMAKE_CURRENT_LIST_DIR}/${_name}.pyx"
--output-file "${CMAKE_CURRENT_BINARY_DIR}/${_name}.cxx")
set(${_name}
${CMAKE_CURRENT_BINARY_DIR}/${_name}.cxx
PARENT_SCOPE)
endif()
endfunction(create_cython_target)
function(rf_add_library name)
if(CMAKE_VERSION VERSION_LESS 3.17)
python_add_library(${name} MODULE ${ARGV})
get_property(
suffix
TARGET ${name}
PROPERTY SUFFIX)
if(NOT suffix)
set(suffix "${CMAKE_SHARED_MODULE_SUFFIX}")
endif()
set_property(TARGET ${name} PROPERTY SUFFIX ".${Python_SOABI}${suffix}")
else()
python_add_library(${name} MODULE WITH_SOABI ${ARGV})
endif()
endfunction(rf_add_library)
create_cython_target(levenshtein_cpp)
rf_add_library(levenshtein_cpp ${levenshtein_cpp}
${LEV_BASE_DIR}/Levenshtein-c/_levenshtein.cpp)
target_compile_features(levenshtein_cpp PUBLIC cxx_std_17)
target_include_directories(levenshtein_cpp
PRIVATE ${LEV_BASE_DIR}/Levenshtein-c)
target_link_libraries(levenshtein_cpp PRIVATE rapidfuzz::rapidfuzz)
install(TARGETS levenshtein_cpp DESTINATION Levenshtein/)
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/Levenshtein-c/ 0000775 0000000 0000000 00000000000 15101347240 0024270 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/Levenshtein-c/_levenshtein.cpp 0000664 0000000 0000000 00000027265 15101347240 0027473 0 ustar 00root root 0000000 0000000 #include
#include
#include
#include
#include
#include "_levenshtein.hpp"
#define LEV_EPSILON 1e-14
/****************************************************************************
*
* Quick (voting) medians
*
****************************************************************************/
/* {{{ */
struct HQItem {
uint32_t c;
double s;
HQItem* n;
};
/* compute the sets of symbols each string contains, and the set of symbols
* in any of them (symset). meanwhile, count how many different symbols
* there are (used below for symlist).
* the symset is passed as an argument to avoid its allocation and
* deallocation when it's used in the caller too */
class SymMap {
std::unique_ptr symmap;
public:
SymMap(const std::vector& strings)
{
symmap = std::make_unique(0x100);
/* this is an ugly memory allocation avoiding hack: most hash elements
* will probably contain none or one symbols only so, when p->n is equal
* to symmap, it means there're no symbols yet, afters inserting the
* first one, p->n becomes normally NULL and then it behaves like an
* usual singly linked list */
for (size_t i = 0; i < 0x100; i++)
symmap[i].n = &symmap[0];
for (size_t i = 0; i < strings.size(); i++) {
visit(strings[i], [&](auto s1) {
for (auto c : s1) {
int key = ((int)c + ((int)c >> 7)) & 0xff;
HQItem* p = symmap.get() + key;
if (p->n == symmap.get()) {
p->c = c;
p->n = NULL;
continue;
}
while (p->c != c && p->n != NULL)
p = p->n;
if (p->c != c) {
p->n = new HQItem;
p = p->n;
p->n = NULL;
p->c = c;
}
}
});
}
}
~SymMap()
{
for (size_t j = 0; j < 0x100; j++) {
HQItem* p = &symmap[j];
if (p->n == symmap.get() || p->n == NULL) continue;
p = p->n;
while (p) {
HQItem* q = p;
p = p->n;
delete q;
}
}
}
void clear()
{
for (size_t i = 0; i < 0x100; i++) {
HQItem* p = &symmap[i];
if (p->n == symmap.get()) continue;
while (p) {
p->s = 0.0;
p = p->n;
}
}
}
HQItem* get()
{
return symmap.get();
}
};
std::vector lev_quick_median(const std::vector& strings,
const std::vector& weights)
{
std::vector median; /* the resulting string */
/* first check whether the result would be an empty string
* and compute resulting string length */
double ml = 0;
double wl = 0;
for (size_t i = 0; i < strings.size(); i++) {
ml += weights[i] * strings[i].length;
wl += weights[i];
}
if (wl == 0.0) return median;
ml = floor(ml / wl + 0.499999);
median.resize((size_t)ml);
if (median.empty()) return median;
/* find the symbol set;
* now an empty symbol set is really a failure */
SymMap symmap(strings);
for (size_t j = 0; j < median.size(); j++) {
/* clear the symbol probabilities */
symmap.clear();
/* let all strings vote */
for (size_t i = 0; i < strings.size(); i++) {
visit(strings[i], [&](auto s1) {
double weighti = weights[i];
size_t lengthi = (size_t)s1.size();
double start = (double)lengthi / ml * (double)j;
double end = start + (double)lengthi / ml;
size_t istart = (size_t)floor(start);
size_t iend = (size_t)ceil(end);
/* rounding errors can overflow the buffer */
if (iend > lengthi) iend = lengthi;
/* the inner part, including the complete last character */
for (size_t k = istart + 1; k < iend; k++) {
uint32_t c = static_cast(s1[k]);
int key = (c + (c >> 7)) & 0xff;
HQItem* p = symmap.get() + key;
while (p->c != c)
p = p->n;
p->s += weighti;
}
/* the initial fraction */
{
uint32_t c = static_cast(s1[istart]);
int key = (c + (c >> 7)) & 0xff;
HQItem* p = symmap.get() + key;
while (p->c != c)
p = p->n;
p->s += weighti * ((double)(1 + istart) - start);
}
/* subtract what we counted from the last character but doesn't
* actually belong here.
* this strategy works also when istart+1 == iend (i.e., everything
* happens inside a one character) */
{
uint32_t c = static_cast(s1[iend - 1]);
int key = (c + (c >> 7)) & 0xff;
HQItem* p = symmap.get() + key;
while (p->c != c)
p = p->n;
p->s -= weighti * ((double)iend - end);
}
});
}
/* find the elected symbol */
{
HQItem* max = NULL;
for (size_t i = 0; i < 0x100; i++) {
HQItem* p = symmap.get() + i;
if (p->n == symmap.get()) continue;
while (p) {
if (!max || p->s > max->s) max = p;
p = p->n;
}
}
median[j] = max->c;
}
}
return median;
}
/* }}} */
/****************************************************************************
*
* Set, sequence distances
*
****************************************************************************/
/* {{{ */
/*
* Munkres-Blackman algorithm.
*/
std::vector munkres_blackman(size_t n1, size_t n2, double* dists)
{
size_t row = 0;
/* allocate memory */
/* 1 if column is covered */
std::vector covc(n1, 0);
/* row of a z* in given column (1-base indices, so we can use zero as `none')*/
std::vector zstarc(n1, 0);
/* 1 if row is covered */
std::vector covr(n2, 0);
/* column of a z* in given row (1-base indices, so we can use zero as `none')*/
std::vector zstarr(n2, 0);
/* column of a z' in given row (1-base indices, so we can use zero as `none')*/
std::vector zprimer(n2, 0);
/* step 0 (subtract minimal distance) and step 1 (find zeroes) => [2] */
auto step1 = [&]() {
for (size_t j = 0; j < n1; j++) {
size_t minidx = 0;
double* col = dists + j;
double min = *col;
double* p = col + n1;
for (size_t i = 1; i < n2; i++) {
if (min > *p) {
minidx = i;
min = *p;
}
p += n1;
}
/* subtract */
p = col;
for (size_t i = 0; i < n2; i++) {
*p -= min;
if (*p < LEV_EPSILON) *p = 0.0;
p += n1;
}
/* star the zero, if possible */
if (!zstarc[j] && !zstarr[minidx]) {
zstarc[j] = minidx + 1;
zstarr[minidx] = j + 1;
}
else {
/* otherwise try to find some other */
p = col;
for (size_t i = 0; i < n2; i++) {
if (i != minidx && *p == 0.0 && !zstarc[j] && !zstarr[i]) {
zstarc[j] = i + 1;
zstarr[i] = j + 1;
break;
}
p += n1;
}
}
}
return 2;
};
/* step 2 (cover columns containing z*) => [0, 3] */
auto step2 = [&]() {
size_t nc = 0;
for (size_t j = 0; j < n1; j++)
if (zstarc[j]) {
covc[j] = 1;
nc++;
}
return (nc == n1) ? 0 : 3;
};
/* step 3 (find uncovered zeroes) => [3, 4, 5] */
auto step3 = [&]() {
/* search uncovered matrix entries */
for (size_t j = 0; j < n1; j++) {
double* p = dists + j;
if (covc[j]) continue;
for (size_t i = 0; i < n2; i++) {
if (!covr[i] && *p == 0.0) {
/* when a zero is found, prime it */
zprimer[i] = j + 1;
if (zstarr[i]) {
/* if there's a z* in the same row,
* uncover the column, cover the row and redo */
covr[i] = 1;
covc[zstarr[i] - 1] = 0;
return 3;
}
/* if there's no z*,
* we are at the end of our path an can convert z'
* to z* */
row = i;
return 4;
}
p += n1;
}
}
return 5;
};
/* step 4 (increment the number of z*)
* i is the row number (we get it from step 3) => [2] */
auto step4 = [&]() {
row++;
do {
size_t x = row;
row--;
size_t j = zprimer[row] - 1; /* move to z' in the same row */
zstarr[row] = j + 1; /* mark it as z* in row buffer */
row = zstarc[j]; /* move to z* in the same column */
zstarc[j] = x; /* mark the z' as being new z* */
} while (row);
std::fill(std::begin(zprimer), std::end(zprimer), 0);
std::fill(std::begin(covr), std::end(covr), 0);
std::fill(std::begin(covc), std::end(covc), 0);
return 2;
};
/* step 5 (new zero manufacturer)
* we can't get here, unless no zero is found at all => [3] */
auto step5 = [&]() {
/* find the smallest uncovered entry */
double min = std::numeric_limits::max();
for (size_t j = 0; j < n1; j++) {
double* p = dists + j;
if (covc[j]) continue;
for (size_t i = 0; i < n2; i++) {
if (!covr[i] && min > *p) {
min = *p;
}
p += n1;
}
}
/* add it to all covered rows */
for (size_t i = 0; i < n2; i++) {
double* p = dists + i * n1;
if (!covr[i]) continue;
for (size_t j = 0; j < n1; j++)
*(p++) += min;
}
/* subtract if from all uncovered columns */
for (size_t j = 0; j < n1; j++) {
double* p = dists + j;
if (covc[j]) continue;
for (size_t i = 0; i < n2; i++) {
*p -= min;
if (*p < LEV_EPSILON) *p = 0.0;
p += n1;
}
}
return 3;
};
/* main */
int next_step = 1;
while (next_step) {
switch (next_step) {
case 1: next_step = step1(); break;
case 2: next_step = step2(); break;
case 3: next_step = step3(); break;
case 4: next_step = step4(); break;
case 5: next_step = step5(); break;
default: next_step = 0; break;
}
}
for (size_t j = 0; j < n1; j++)
zstarc[j]--;
return zstarc;
}
/* }}} */
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/Levenshtein-c/_levenshtein.hpp 0000664 0000000 0000000 00000062574 15101347240 0027502 0 ustar 00root root 0000000 0000000 #pragma once
#include "Python.h"
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#define PYTHON_VERSION(major, minor, micro) ((major << 24) | (minor << 16) | (micro << 8))
using rapidfuzz::detail::Range;
enum RF_StringType {
RF_UINT8, /* uint8_t */
RF_UINT16, /* uint16_t */
RF_UINT32 /* uint32_t */
};
typedef struct _RF_String {
/* members */
RF_StringType kind;
void* data;
int64_t length;
} RF_String;
#define LIST_OF_CASES() \
X_ENUM(RF_UINT8, uint8_t) \
X_ENUM(RF_UINT16, uint16_t) \
X_ENUM(RF_UINT32, uint32_t)
template
auto visit(const RF_String& str, Func&& f, Args&&... args)
{
switch (str.kind) {
#define X_ENUM(kind, type) \
case kind: return f(Range((type*)str.data, (type*)str.data + str.length), std::forward(args)...);
LIST_OF_CASES()
#undef X_ENUM
default: throw std::logic_error("Invalid string type");
}
}
template
auto visitor(const RF_String& str1, const RF_String& str2, Func&& f, Args&&... args)
{
return visit(str2, [&](auto s2) {
return visit(str1, std::forward(f), s2, std::forward(args)...);
});
}
static inline bool is_valid_string(PyObject* py_str)
{
bool is_string = false;
if (PyBytes_Check(py_str))
is_string = true;
else if (PyUnicode_Check(py_str)) {
// PEP 623 deprecates legacy strings and therefore
// deprecates e.g. PyUnicode_READY in Python 3.10
#if PY_VERSION_HEX < PYTHON_VERSION(3, 10, 0)
if (PyUnicode_READY(py_str))
// cython will use the exception set by PyUnicode_READY
throw std::runtime_error("");
#endif
is_string = true;
}
return is_string;
}
static inline RF_String convert_string(PyObject* py_str)
{
if (PyBytes_Check(py_str))
return {RF_UINT8, PyBytes_AS_STRING(py_str), static_cast(PyBytes_GET_SIZE(py_str))};
else {
RF_StringType kind;
switch (PyUnicode_KIND(py_str)) {
case PyUnicode_1BYTE_KIND: kind = RF_UINT8; break;
case PyUnicode_2BYTE_KIND: kind = RF_UINT16; break;
default: kind = RF_UINT32; break;
}
return {kind, PyUnicode_DATA(py_str), static_cast(PyUnicode_GET_LENGTH(py_str))};
}
}
/* Edit operation type
* DON'T CHANGE! used as array indices and the bits are occasionally used
* as flags */
enum LevEditType {
LEV_EDIT_KEEP = 0,
LEV_EDIT_REPLACE = 1,
LEV_EDIT_INSERT = 2,
LEV_EDIT_DELETE = 3,
LEV_EDIT_LAST /* sometimes returned when an error occurs */
};
/* compute the sets of symbols each string contains, and the set of symbols
* in any of them (symset). meanwhile, count how many different symbols
* there are (used below for symlist). */
static inline std::vector make_symlist(const std::vector& strings)
{
std::vector symlist;
auto is_empty_str = [](const auto& x) {
return x.length == 0;
};
if (std::all_of(std::begin(strings), std::end(strings), is_empty_str)) return symlist;
std::set symmap;
for (const auto& string : strings)
visit(string, [&](auto s1) {
for (auto ch : s1)
symmap.insert(ch);
});
/* create dense symbol table, so we can easily iterate over only characters
* present in the strings */
symlist.insert(std::end(symlist), std::begin(symmap), std::end(symmap));
return symlist;
}
/**
* lev_greedy_median:
* @n: The size of @lengths, @strings, and @weights.
* @lengths: The lengths of @strings.
* @strings: An array of strings, that may contain NUL characters.
* @weights: The string weights (they behave exactly as multiplicities, though
* any positive value is allowed, not just integers).
* @medlength: Where the length of the median should be stored.
*
* Finds a generalized median string of @strings using the greedy algorithm.
*
* Note it's considerably more efficient to give a string with weight 2
* than to store two identical strings in @strings (with weights 1).
*
* Returns: The generalized median, as a newly allocated string; its length
* is stored in @medlength.
**/
static inline std::vector lev_greedy_median(const std::vector& strings,
const std::vector& weights)
{
/* find all symbols */
std::vector symlist = make_symlist(strings);
if (symlist.empty()) return {};
/* allocate and initialize per-string matrix rows and a common work buffer */
std::vector> rows(strings.size());
size_t maxlen =
(size_t)std::max_element(std::begin(strings), std::end(strings), [](const auto& a, const auto& b) {
return a.length < b.length;
})->length;
for (size_t i = 0; i < strings.size(); i++) {
size_t leni = (size_t)strings[i].length;
rows[i] = std::make_unique(leni + 1);
std::iota(rows[i].get(), rows[i].get() + leni + 1, 0);
}
size_t stoplen = 2 * maxlen + 1;
auto row = std::make_unique(stoplen + 1);
/* compute final cost of string of length 0 (empty string may be also
* a valid answer) */
std::vector median(stoplen);
/**
* the total distance of the best median string of
* given length. warning! mediandist[0] is total
* distance for empty string, while median[] itself
* is normally zero-based
*/
auto mediandist = std::make_unique(stoplen + 1);
mediandist[0] = 0;
for (size_t i = 0; i < strings.size(); i++)
mediandist[0] += strings[i].length + weights[i];
/* build up the approximate median string symbol by symbol
* XXX: we actually exit on break below, but on the same condition */
for (size_t len = 1; len <= stoplen; len++) {
uint32_t symbol = 0;
double minminsum = std::numeric_limits::max();
row[0] = len;
/* iterate over all symbols we may want to add */
for (size_t j = 0; j < symlist.size(); j++) {
double totaldist = 0.0;
double minsum = 0.0;
symbol = symlist[j];
/* sum Levenshtein distances from all the strings, with given weights */
for (size_t i = 0; i < strings.size(); i++) {
visit(strings[i], [&](auto s1) {
size_t* p = rows[i].get();
size_t min = len;
size_t x = len; /* == row[0] */
/* compute how another row of Levenshtein matrix would look for median
* string with this symbol added */
for (auto ch : s1) {
size_t D = *(p++) + (symbol != ch);
x++;
if (x > D) x = D;
if (x > *p + 1) x = *p + 1;
if (x < min) min = x;
}
minsum += (double)min * weights[i];
totaldist += (double)x * weights[i];
});
}
/* is this symbol better than all the others? */
if (minsum < minminsum) {
minminsum = minsum;
mediandist[len] = totaldist;
median[len - 1] = symbol;
}
}
/* stop the iteration if we no longer need to recompute the matrix rows
* or when we are over maxlen and adding more characters doesn't seem
* useful */
if (len == stoplen || (len > maxlen && mediandist[len] > mediandist[len - 1])) {
stoplen = len;
break;
}
/* now the best symbol is known, so recompute all matrix rows for this
* one */
symbol = median[len - 1];
for (size_t i = 0; i < strings.size(); i++)
visit(strings[i], [&](auto s1) {
size_t* oldrow = rows[i].get();
/* compute a row of Levenshtein matrix */
for (size_t k = 1; k <= (size_t)s1.size(); k++) {
size_t c1 = oldrow[k] + 1;
size_t c2 = row[k - 1] + 1;
size_t c3 = oldrow[k - 1] + (symbol != s1[k - 1]);
row[k] = c2 > c3 ? c3 : c2;
if (row[k] > c1) row[k] = c1;
}
memcpy(oldrow, row.get(), (s1.size() + 1) * sizeof(size_t));
});
}
/* find the string with minimum total distance */
size_t bestlen =
std::distance(mediandist.get(), std::min_element(mediandist.get(), mediandist.get() + stoplen));
/* return result */
median.resize(bestlen);
return median;
}
/*
* Knowing the distance matrices up to some row, finish the distance
* computations.
*
* string1, len1 are already shortened.
*/
static inline double finish_distance_computations(const Range& string1,
const std::vector& strings,
const std::vector& weights,
std::vector>& rows,
std::unique_ptr& row)
{
double distsum = 0.0; /* sum of distances */
/* catch trivial case */
if (string1.empty()) {
for (size_t j = 0; j < strings.size(); j++)
distsum += (double)rows[j][(size_t)strings[j].length] * weights[j];
return distsum;
}
/* iterate through the strings and sum the distances */
for (size_t j = 0; j < strings.size(); j++) {
visit(strings[j], [&](auto s2) {
size_t* rowi = rows[j].get(); /* current row */
auto s1_temp = string1; /* temporary string for suffix stripping */
/* strip common suffix (prefix CAN'T be stripped) */
rapidfuzz::detail::remove_common_suffix(s1_temp, s2);
/* catch trivial cases */
if (s1_temp.empty()) {
distsum += (double)rowi[(size_t)s2.size()] * weights[j];
return;
}
/* row[0]; offset + len1 give together real len of string1 */
size_t offset = rowi[0];
if (s2.empty()) {
distsum += (double)(offset + s1_temp.size()) * weights[j];
return;
}
/* complete the matrix */
std::copy_n(rowi, s2.size() + 1, row.get());
for (size_t i = 0; i < (size_t)s1_temp.size(); i++) {
auto ch1 = s1_temp[i];
size_t* p = row.get() + 1;
size_t D = i + 1 + offset;
size_t x = D;
for (auto ch2 : s2) {
size_t c3 = --D + (ch1 != ch2);
x++;
if (x > c3) x = c3;
D = *p;
D++;
if (x > D) x = D;
*(p++) = x;
}
}
size_t* end = row.get() + s2.size();
distsum += weights[j] * (double)(*end);
});
}
return distsum;
}
/**
* lev_median_improve:
* @len: The length of @s.
* @s: The approximate generalized median string to be improved.
* @n: The size of @lengths, @strings, and @weights.
* @lengths: The lengths of @strings.
* @strings: An array of strings, that may contain NUL characters.
* @weights: The string weights (they behave exactly as multiplicities, though
* any positive value is allowed, not just integers).
*
* Tries to make @s a better generalized median string of @strings with
* small perturbations.
*
* It never returns a string with larger SOD than @s; in the worst case, a
* string identical to @s is returned.
*
* Returns: The improved generalized median
**/
static inline std::vector lev_median_improve(const RF_String& string,
const std::vector& strings,
const std::vector& weights)
{
/* find all symbols */
std::vector symlist = make_symlist(strings);
if (symlist.empty()) return {};
/* allocate and initialize per-string matrix rows and a common work buffer */
std::vector> rows(strings.size());
size_t maxlen = 0;
for (size_t i = 0; i < strings.size(); i++) {
size_t leni = (size_t)strings[i].length;
if (leni > maxlen) maxlen = leni;
rows[i] = std::make_unique(leni + 1);
std::iota(rows[i].get(), rows[i].get() + leni + 1, 0);
}
size_t stoplen = 2 * maxlen + 1;
auto row = std::make_unique(stoplen + 2);
/* initialize median to given string */
auto _median = std::make_unique(stoplen + 1);
uint32_t* median = _median.get() + 1; /* we need -1st element for insertions at pos 0 */
size_t medlen = (size_t)string.length;
visit(string, [&](auto s1) {
std::copy(std::begin(s1), std::end(s1), median);
});
double minminsum =
finish_distance_computations(Range(median, median + medlen), strings, weights, rows, row);
/* sequentially try perturbations on all positions */
for (size_t pos = 0; pos <= medlen;) {
uint32_t orig_symbol, symbol;
LevEditType operation;
double sum;
symbol = median[pos];
operation = LEV_EDIT_KEEP;
/* IF pos < medlength: FOREACH symbol: try to replace the symbol
* at pos, if some lower the total distance, chooste the best */
if (pos < medlen) {
orig_symbol = median[pos];
for (size_t j = 0; j < symlist.size(); j++) {
if (symlist[j] == orig_symbol) continue;
median[pos] = symlist[j];
sum = finish_distance_computations(Range(median + pos, median + medlen), strings, weights,
rows, row);
if (sum < minminsum) {
minminsum = sum;
symbol = symlist[j];
operation = LEV_EDIT_REPLACE;
}
}
median[pos] = orig_symbol;
}
/* FOREACH symbol: try to add it at pos, if some lower the total
* distance, chooste the best (increase medlen)
* We simulate insertion by replacing the character at pos-1 */
orig_symbol = *(median + pos - 1);
for (size_t j = 0; j < symlist.size(); j++) {
*(median + pos - 1) = symlist[j];
sum = finish_distance_computations(Range(median + pos - 1, median + medlen), strings, weights,
rows, row);
if (sum < minminsum) {
minminsum = sum;
symbol = symlist[j];
operation = LEV_EDIT_INSERT;
}
}
*(median + pos - 1) = orig_symbol;
/* IF pos < medlen: try to delete the symbol at pos, if it lowers
* the total distance remember it (decrease medlen) */
if (pos < medlen) {
sum = finish_distance_computations(Range(median + pos + 1, median + medlen), strings, weights,
rows, row);
if (sum < minminsum) {
minminsum = sum;
operation = LEV_EDIT_DELETE;
}
}
/* actually perform the operation */
switch (operation) {
case LEV_EDIT_REPLACE: median[pos] = symbol; break;
case LEV_EDIT_INSERT:
memmove(median + pos + 1, median + pos, (medlen - pos) * sizeof(uint32_t));
median[pos] = symbol;
medlen++;
break;
case LEV_EDIT_DELETE:
memmove(median + pos, median + pos + 1, (medlen - pos - 1) * sizeof(uint32_t));
medlen--;
break;
default: break;
}
assert(medlen <= stoplen);
/* now the result is known, so recompute all matrix rows and move on */
if (operation != LEV_EDIT_DELETE) {
symbol = median[pos];
row[0] = pos + 1;
for (size_t i = 0; i < strings.size(); i++) {
visit(strings[i], [&](auto s1) {
size_t* oldrow = rows[i].get();
/* compute a row of Levenshtein matrix */
for (size_t k = 1; k <= (size_t)s1.size(); k++) {
size_t c1 = oldrow[k] + 1;
size_t c2 = row[k - 1] + 1;
size_t c3 = oldrow[k - 1] + (symbol != s1[k - 1]);
row[k] = c2 > c3 ? c3 : c2;
if (row[k] > c1) row[k] = c1;
}
std::copy_n(row.get(), s1.size() + 1, oldrow);
});
}
pos++;
}
}
return std::vector(median, median + medlen);
}
std::vector lev_quick_median(const std::vector& strings,
const std::vector& weights);
/**
* lev_set_median:
* @n: The size of @lengths, @strings, and @weights.
* @lengths: The lengths of @strings.
* @strings: An array of strings, that may contain NUL characters.
* @weights: The string weights (they behave exactly as multiplicities, though
* any positive value is allowed, not just integers).
*
* Finds the median string of a string set @strings.
*
* Returns: The set median
**/
static inline std::vector lev_set_median(const std::vector& strings,
const std::vector& weights)
{
size_t minidx = 0;
double mindist = std::numeric_limits::max();
std::vector distances(strings.size() * (strings.size() - 1) / 2, 0xff);
for (size_t i = 0; i < strings.size(); i++) {
visit(strings[i], [&](auto s1) {
/* deduction guides are broken here on msvc */
rapidfuzz::CachedLevenshtein scorer(s1);
double dist = 0.0;
/* below diagonal */
size_t j = 0;
for (; j < i && dist < mindist; j++) {
size_t dindex = (i - 1) * (i - 2) / 2 + j;
long int d;
if (distances[dindex] >= 0)
d = distances[dindex];
else
d = (size_t)visit(strings[j], [&](auto s2) {
return scorer.distance(s2);
});
dist += weights[j] * (double)d;
}
j++; /* no need to compare item with itself */
/* above diagonal */
for (; j < strings.size() && dist < mindist; j++) {
size_t dindex = (j - 1) * (j - 2) / 2 + i;
distances[dindex] = visit(strings[j], [&](auto s2) {
return scorer.distance(s2);
});
dist += weights[j] * (double)distances[dindex];
}
if (dist < mindist) {
mindist = dist;
minidx = i;
}
});
}
return visit(strings[minidx], [&](auto s1) {
return std::vector(std::begin(s1), std::end(s1));
});
}
static inline bool is_equal(const RF_String& a, const RF_String& b)
{
return visitor(a, b, [](auto s1, auto s2) {
return s1 == s2;
});
}
/**
* lev_edit_seq_distance:
* @n1: The length of @lengths1 and @strings1.
* @lengths1: The lengths of strings in @strings1.
* @strings1: An array of strings that may contain NUL characters.
* @n2: The length of @lengths2 and @strings2.
* @lengths2: The lengths of strings in @strings2.
* @strings2: An array of strings that may contain NUL characters.
*
* Finds the distance between string sequences @strings1 and @strings2.
*
* In other words, this is a double-Levenshtein algorithm.
*
* The cost of string replace operation is based on string similarity: it's
* zero for identical strings and 2 for completely unsimilar strings.
*
* Returns: The distance of the two sequences.
**/
static inline double lev_edit_seq_distance(const std::vector& strings1,
const std::vector& strings2)
{
if (strings1.size() > strings2.size()) return lev_edit_seq_distance(strings2, strings1);
auto strings1_start = std::begin(strings1);
auto strings1_end = std::end(strings1);
auto strings2_start = std::begin(strings2);
auto strings2_end = std::end(strings2);
/* strip common prefix */
while (strings1_start != strings1_end && strings2_start != strings2_end &&
is_equal(*strings1_start, *strings2_start))
{
strings1_start++;
strings2_start++;
}
/* strip common suffix */
while (strings1_start != strings1_end && strings2_start != strings2_end &&
is_equal(*(strings1_end - 1), *(strings2_end - 1)))
{
strings1_end--;
strings2_end--;
}
/* catch trivial cases */
if (strings1_start == strings1_end) return (double)std::distance(strings2_start, strings2_end);
if (strings2_start == strings2_end) return (double)std::distance(strings1_start, strings1_end);
/* initialize first row */
size_t n1 = std::distance(strings1_start, strings1_end);
size_t n2 = std::distance(strings2_start, strings2_end);
auto row = std::make_unique(n2 + 1);
double* last = row.get() + n2;
double* end = row.get() + n2 + 1;
std::iota(row.get(), end, 0.0);
/* go through the matrix and compute the costs. yes, this is an extremely
* obfuscated version, but also extremely memory-conservative and relatively
* fast. */
for (size_t i = 0; i < n1; i++) {
double* p = row.get() + 1;
auto strings2_it = strings2_start;
double D = (double)i;
double x = (double)i + 1.0;
visit(strings1[i], [&](auto s1) {
/* deduction guides are broken here on msvc */
rapidfuzz::CachedIndel scorer(s1);
while (p != end) {
size_t l = strings1[i].length + strings2_it->length;
double q;
if (l == 0)
q = D;
else {
size_t d = visit(*strings2_it, [&](auto s2) {
return scorer.distance(s2);
});
strings2_it++;
q = D + 2.0 / (double)l * (double)d;
}
x += 1.0;
if (x > q) x = q;
D = *p;
if (x > D + 1.0) x = D + 1.0;
*(p++) = x;
}
});
}
return *last;
}
std::vector munkres_blackman(size_t n1, size_t n2, double* dists);
/**
* lev_set_distance:
* @n1: The length of @lengths1 and @strings1.
* @lengths1: The lengths of strings in @strings1.
* @strings1: An array of strings that may contain NUL characters.
* @n2: The length of @lengths2 and @strings2.
* @lengths2: The lengths of strings in @strings2.
* @strings2: An array of strings that may contain NUL characters.
*
* Finds the distance between string sets @strings1 and @strings2.
*
* The difference from lev_edit_seq_distance() is that order doesn't matter.
* The optimal association of @strings1 and @strings2 is found first and
* the similarity is computed for that.
*
* Uses sequential Munkres-Blackman algorithm.
*
* Returns: The distance of the two sets.
**/
static inline double lev_set_distance(const std::vector& strings1,
const std::vector& strings2)
{
/* catch trivial cases */
if (strings1.empty()) return (double)strings2.size();
if (strings2.empty()) return (double)strings1.size();
/* make the number of columns (n1) smaller than the number of rows */
if (strings1.size() > strings2.size()) return lev_set_distance(strings2, strings1);
/* compute distances from each to each */
if (SIZE_MAX / strings1.size() <= strings2.size()) throw std::bad_alloc();
auto dists = std::make_unique(strings1.size() * strings2.size());
double* r = dists.get();
for (const auto& str2 : strings2)
visit(str2, [&](auto s1) {
/* deduction guides are broken here on msvc */
rapidfuzz::CachedIndel scorer(s1);
for (const auto& str1 : strings1)
*(r++) = visit(str1, [&](auto s2) {
return scorer.normalized_distance(s2);
});
});
/* find the optimal mapping between the two sets */
auto map = munkres_blackman(strings1.size(), strings2.size(), dists.get());
/* sum the set distance */
double sum = (double)(strings2.size() - strings1.size());
for (size_t j = 0; j < strings1.size(); j++) {
size_t i = map[j];
sum += 2.0 * visitor(strings1[j], strings2[i], [](auto s1, auto s2) {
return rapidfuzz::indel_normalized_distance(s1, s2);
});
}
return sum;
}
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/StringMatcher.py 0000664 0000000 0000000 00000004467 15101347240 0024723 0 ustar 00root root 0000000 0000000 from __future__ import annotations
from warnings import warn
from Levenshtein import distance, editops, matching_blocks, opcodes, ratio
class StringMatcher:
"""A SequenceMatcher-like class built on the top of Levenshtein"""
def _reset_cache(self):
self._ratio = self._distance = None
self._opcodes = self._editops = self._matching_blocks = None
def __init__(self, isjunk=None, seq1="", seq2="", autojunk=False):
if isjunk:
warn("isjunk NOT implemented, it will be ignored", stacklevel=1)
if autojunk:
warn("autojunk NOT implemented, it will be ignored", stacklevel=1)
self._str1, self._str2 = seq1, seq2
self._reset_cache()
def set_seqs(self, seq1, seq2):
self._str1, self._str2 = seq1, seq2
self._reset_cache()
def set_seq1(self, seq1):
self._str1 = seq1
self._reset_cache()
def set_seq2(self, seq2):
self._str2 = seq2
self._reset_cache()
def get_opcodes(self):
if not self._opcodes:
if self._editops:
self._opcodes = opcodes(self._editops, self._str1, self._str2)
else:
self._opcodes = opcodes(self._str1, self._str2)
return self._opcodes
def get_editops(self):
if not self._editops:
if self._opcodes:
self._editops = editops(self._opcodes, self._str1, self._str2)
else:
self._editops = editops(self._str1, self._str2)
return self._editops
def get_matching_blocks(self):
if not self._matching_blocks:
self._matching_blocks = matching_blocks(self.get_opcodes(), self._str1, self._str2)
return self._matching_blocks
def ratio(self):
if not self._ratio:
self._ratio = ratio(self._str1, self._str2)
return self._ratio
def quick_ratio(self):
# This is usually quick enough :o)
if not self._ratio:
self._ratio = ratio(self._str1, self._str2)
return self._ratio
def real_quick_ratio(self):
len1, len2 = len(self._str1), len(self._str2)
return 2.0 * min(len1, len2) / (len1 + len2)
def distance(self):
if not self._distance:
self._distance = distance(self._str1, self._str2)
return self._distance
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/__init__.py 0000664 0000000 0000000 00000043015 15101347240 0023700 0 ustar 00root root 0000000 0000000 """
A C extension module for fast computation of:
- Levenshtein (edit) distance and edit sequence manipulation
- string similarity
- approximate median strings, and generally string averaging
- string sequence and set similarity
Levenshtein has a some overlap with difflib (SequenceMatcher). It
supports only strings, not arbitrary sequence types, but on the
other hand it's much faster.
It supports both normal and Unicode strings, but can't mix them, all
arguments to a function (method) have to be of the same type (or its
subclasses).
"""
from __future__ import annotations
__author__: str = "Max Bachmann"
__license__: str = "GPL"
__version__: str = "0.27.3"
import rapidfuzz.distance.Hamming as _Hamming
import rapidfuzz.distance.Indel as _Indel
import rapidfuzz.distance.Jaro as _Jaro
import rapidfuzz.distance.JaroWinkler as _JaroWinkler
import rapidfuzz.distance.Levenshtein as _Levenshtein
from rapidfuzz.distance import (
Editops as _Editops,
)
from rapidfuzz.distance import (
Opcodes as _Opcodes,
)
from Levenshtein.levenshtein_cpp import (
median,
median_improve,
quickmedian,
seqratio,
setmedian,
setratio,
)
__all__ = [
"quickmedian",
"median",
"median_improve",
"setmedian",
"setratio",
"seqratio",
"distance",
"ratio",
"hamming",
"jaro",
"jaro_winkler",
"editops",
"opcodes",
"matching_blocks",
"apply_edit",
"subtract_edit",
"inverse",
]
def distance(s1, s2, *, weights=(1, 1, 1), processor=None, score_cutoff=None, score_hint=None):
"""
Calculates the minimum number of insertions, deletions, and substitutions
required to change one sequence into the other according to Levenshtein with custom
costs for insertion, deletion and substitution
Parameters
----------
s1 : Sequence[Hashable]
First string to compare.
s2 : Sequence[Hashable]
Second string to compare.
weights : Tuple[int, int, int] or None, optional
The weights for the three operations in the form
(insertion, deletion, substitution). Default is (1, 1, 1),
which gives all three operations a weight of 1.
processor: callable, optional
Optional callable that is used to preprocess the strings before
comparing them. Default is None, which deactivates this behaviour.
score_cutoff : int, optional
Maximum distance between s1 and s2, that is
considered as a result. If the distance is bigger than score_cutoff,
score_cutoff + 1 is returned instead. Default is None, which deactivates
this behaviour.
score_hint : int, optional
Expected distance between s1 and s2. This is used to select a
faster implementation. Default is None, which deactivates this behaviour.
Returns
-------
distance : int
distance between s1 and s2
Raises
------
ValueError
If unsupported weights are provided a ValueError is thrown
Examples
--------
Find the Levenshtein distance between two strings:
>>> from Levenshtein import distance
>>> distance("lewenstein", "levenshtein")
2
Setting a maximum distance allows the implementation to select
a more efficient implementation:
>>> distance("lewenstein", "levenshtein", score_cutoff=1)
2
It is possible to select different weights by passing a `weight`
tuple.
>>> distance("lewenstein", "levenshtein", weights=(1,1,2))
3
"""
return _Levenshtein.distance(
s1,
s2,
weights=weights,
processor=processor,
score_cutoff=score_cutoff,
score_hint=score_hint,
)
def ratio(s1, s2, *, processor=None, score_cutoff=None):
"""
Calculates a normalized indel similarity in the range [0, 1].
The indel distance calculates the minimum number of insertions and deletions
required to change one sequence into the other.
This is calculated as ``1 - (distance / (len1 + len2))``
Parameters
----------
s1 : Sequence[Hashable]
First string to compare.
s2 : Sequence[Hashable]
Second string to compare.
processor: callable, optional
Optional callable that is used to preprocess the strings before
comparing them. Default is None, which deactivates this behaviour.
score_cutoff : float, optional
Optional argument for a score threshold as a float between 0 and 1.0.
For norm_sim < score_cutoff 0 is returned instead. Default is 0,
which deactivates this behaviour.
Returns
-------
norm_sim : float
normalized similarity between s1 and s2 as a float between 0 and 1.0
Examples
--------
Find the normalized Indel similarity between two strings:
>>> from Levenshtein import ratio
>>> ratio("lewenstein", "levenshtein")
0.85714285714285
Setting a score_cutoff allows the implementation to select
a more efficient implementation:
>>> ratio("lewenstein", "levenshtein", score_cutoff=0.9)
0.0
When a different processor is used s1 and s2 do not have to be strings
>>> ratio(["lewenstein"], ["levenshtein"], processor=lambda s: s[0])
0.8571428571428572
"""
return _Indel.normalized_similarity(s1, s2, processor=processor, score_cutoff=score_cutoff)
def hamming(s1, s2, *, pad=True, processor=None, score_cutoff=None):
"""
Calculates the Hamming distance between two strings.
The hamming distance is defined as the number of positions
where the two strings differ. It describes the minimum
amount of substitutions required to transform s1 into s2.
Parameters
----------
s1 : Sequence[Hashable]
First string to compare.
s2 : Sequence[Hashable]
Second string to compare.
pad : bool, optional
should strings be padded if there is a length difference.
If pad is False and strings have a different length
a ValueError is thrown instead. Default is True.
processor: callable, optional
Optional callable that is used to preprocess the strings before
comparing them. Default is None, which deactivates this behaviour.
score_cutoff : int or None, optional
Maximum distance between s1 and s2, that is
considered as a result. If the distance is bigger than score_cutoff,
score_cutoff + 1 is returned instead. Default is None, which deactivates
this behaviour.
Returns
-------
distance : int
distance between s1 and s2
Raises
------
ValueError
If s1 and s2 have a different length
"""
return _Hamming.distance(s1, s2, pad=pad, processor=processor, score_cutoff=score_cutoff)
def jaro(s1, s2, *, processor=None, score_cutoff=None) -> float:
"""
Calculates the jaro similarity
Parameters
----------
s1 : Sequence[Hashable]
First string to compare.
s2 : Sequence[Hashable]
Second string to compare.
processor: callable, optional
Optional callable that is used to preprocess the strings before
comparing them. Default is None, which deactivates this behaviour.
score_cutoff : float, optional
Optional argument for a score threshold as a float between 0 and 1.0.
For ratio < score_cutoff 0 is returned instead. Default is None,
which deactivates this behaviour.
Returns
-------
similarity : float
similarity between s1 and s2 as a float between 0 and 1.0
"""
return _Jaro.similarity(s1, s2, processor=processor, score_cutoff=score_cutoff)
def jaro_winkler(s1, s2, *, prefix_weight=0.1, processor=None, score_cutoff=None) -> float:
"""
Calculates the jaro winkler similarity
Parameters
----------
s1 : Sequence[Hashable]
First string to compare.
s2 : Sequence[Hashable]
Second string to compare.
prefix_weight : float, optional
Weight used for the common prefix of the two strings.
Has to be between 0 and 0.25. Default is 0.1.
processor: callable, optional
Optional callable that is used to preprocess the strings before
comparing them. Default is None, which deactivates this behaviour.
score_cutoff : float, optional
Optional argument for a score threshold as a float between 0 and 1.0.
For ratio < score_cutoff 0 is returned instead. Default is None,
which deactivates this behaviour.
Returns
-------
similarity : float
similarity between s1 and s2 as a float between 0 and 1.0
Raises
------
ValueError
If prefix_weight is invalid
"""
return _JaroWinkler.similarity(
s1,
s2,
prefix_weight=prefix_weight,
processor=processor,
score_cutoff=score_cutoff,
)
# assign attributes to function. This allows rapidfuzz to call them more efficiently
# we can't directly copy the functions + replace the docstrings, since this leads to
# crashes on PyPy
distance._RF_OriginalScorer = distance
ratio._RF_OriginalScorer = ratio
hamming._RF_OriginalScorer = hamming
jaro._RF_OriginalScorer = jaro
jaro_winkler._RF_OriginalScorer = jaro_winkler
distance._RF_ScorerPy = _Levenshtein.distance._RF_ScorerPy
ratio._RF_ScorerPy = _Indel.normalized_similarity._RF_ScorerPy
hamming._RF_ScorerPy = _Hamming.distance._RF_ScorerPy
jaro._RF_ScorerPy = _Jaro.similarity._RF_ScorerPy
jaro_winkler._RF_ScorerPy = _JaroWinkler.similarity._RF_ScorerPy
if hasattr(_Levenshtein.distance, "_RF_Scorer"):
distance._RF_Scorer = _Levenshtein.distance._RF_Scorer
if hasattr(_Indel.normalized_similarity, "_RF_Scorer"):
ratio._RF_Scorer = _Indel.normalized_similarity._RF_Scorer
if hasattr(_Hamming.distance, "_RF_Scorer"):
hamming._RF_Scorer = _Hamming.distance._RF_Scorer
if hasattr(_Jaro.similarity, "_RF_Scorer"):
jaro._RF_Scorer = _Jaro.similarity._RF_Scorer
if hasattr(_JaroWinkler.similarity, "_RF_Scorer"):
jaro_winkler._RF_Scorer = _JaroWinkler.similarity._RF_Scorer
def editops(*args):
"""
Find sequence of edit operations transforming one string to another.
editops(source_string, destination_string)
editops(edit_operations, source_length, destination_length)
The result is a list of triples (operation, spos, dpos), where
operation is one of 'equal', 'replace', 'insert', or 'delete'; spos
and dpos are position of characters in the first (source) and the
second (destination) strings. These are operations on single
characters. In fact the returned list doesn't contain the 'equal',
but all the related functions accept both lists with and without
'equal's.
Examples
--------
>>> editops('spam', 'park')
[('delete', 0, 0), ('insert', 3, 2), ('replace', 3, 3)]
The alternate form editops(opcodes, source_string, destination_string)
can be used for conversion from opcodes (5-tuples) to editops (you can
pass strings or their lengths, it doesn't matter).
"""
# convert: we were called (bops, s1, s2)
if len(args) == 3:
arg1, arg2, arg3 = args
len1 = arg2 if isinstance(arg2, int) else len(arg2)
len2 = arg3 if isinstance(arg3, int) else len(arg3)
return _Editops(arg1, len1, len2).as_list()
# find editops: we were called (s1, s2)
arg1, arg2 = args
return _Levenshtein.editops(arg1, arg2).as_list()
def opcodes(*args):
"""
Find sequence of edit operations transforming one string to another.
opcodes(source_string, destination_string)
opcodes(edit_operations, source_length, destination_length)
The result is a list of 5-tuples with the same meaning as in
SequenceMatcher's get_opcodes() output. But since the algorithms
differ, the actual sequences from Levenshtein and SequenceMatcher
may differ too.
Examples
--------
>>> for x in opcodes('spam', 'park'):
... print(x)
...
('delete', 0, 1, 0, 0)
('equal', 1, 3, 0, 2)
('insert', 3, 3, 2, 3)
('replace', 3, 4, 3, 4)
The alternate form opcodes(editops, source_string, destination_string)
can be used for conversion from editops (triples) to opcodes (you can
pass strings or their lengths, it doesn't matter).
"""
# convert: we were called (ops, s1, s2)
if len(args) == 3:
arg1, arg2, arg3 = args
len1 = arg2 if isinstance(arg2, int) else len(arg2)
len2 = arg3 if isinstance(arg3, int) else len(arg3)
return _Opcodes(arg1, len1, len2).as_list()
# find editops: we were called (s1, s2)
arg1, arg2 = args
return _Levenshtein.opcodes(arg1, arg2).as_list()
def matching_blocks(edit_operations, source_string, destination_string):
"""
Find identical blocks in two strings.
Parameters
----------
edit_operations : list[]
editops or opcodes created for the source and destination string
source_string : str | int
source string or the length of the source string
destination_string : str | int
destination string or the length of the destination string
Returns
-------
matching_blocks : list[]
List of triples with the same meaning as in SequenceMatcher's
get_matching_blocks() output.
Examples
--------
>>> a, b = 'spam', 'park'
>>> matching_blocks(editops(a, b), a, b)
[(1, 0, 2), (4, 4, 0)]
>>> matching_blocks(editops(a, b), len(a), len(b))
[(1, 0, 2), (4, 4, 0)]
The last zero-length block is not an error, but it's there for
compatibility with difflib which always emits it.
One can join the matching blocks to get two identical strings:
>>> a, b = 'dog kennels', 'mattresses'
>>> mb = matching_blocks(editops(a,b), a, b)
>>> ''.join([a[x[0]:x[0]+x[2]] for x in mb])
'ees'
>>> ''.join([b[x[1]:x[1]+x[2]] for x in mb])
'ees'
"""
len1 = source_string if isinstance(source_string, int) else len(source_string)
len2 = destination_string if isinstance(destination_string, int) else len(destination_string)
if not edit_operations or len(edit_operations[0]) == 3:
return _Editops(edit_operations, len1, len2).as_matching_blocks()
return _Opcodes(edit_operations, len1, len2).as_matching_blocks()
def apply_edit(edit_operations, source_string, destination_string):
"""
Apply a sequence of edit operations to a string.
apply_edit(edit_operations, source_string, destination_string)
In the case of editops, the sequence can be arbitrary ordered subset
of the edit sequence transforming source_string to destination_string.
Examples
--------
>>> e = editops('man', 'scotsman')
>>> apply_edit(e, 'man', 'scotsman')
'scotsman'
>>> apply_edit(e[:3], 'man', 'scotsman')
'scoman'
The other form of edit operations, opcodes, is not very suitable for
such a tricks, because it has to always span over complete strings,
subsets can be created by carefully replacing blocks with 'equal'
blocks, or by enlarging 'equal' block at the expense of other blocks
and adjusting the other blocks accordingly.
>>> a, b = 'spam and eggs', 'foo and bar'
>>> e = opcodes(a, b)
>>> apply_edit(inverse(e), b, a)
'spam and eggs'
"""
if len(edit_operations) == 0:
return source_string
len1 = len(source_string)
len2 = len(destination_string)
if len(edit_operations[0]) == 3:
return _Editops(edit_operations, len1, len2).apply(source_string, destination_string)
return _Opcodes(edit_operations, len1, len2).apply(source_string, destination_string)
def subtract_edit(edit_operations, subsequence):
"""
Subtract an edit subsequence from a sequence.
subtract_edit(edit_operations, subsequence)
The result is equivalent to
editops(apply_edit(subsequence, s1, s2), s2), except that is
constructed directly from the edit operations. That is, if you apply
it to the result of subsequence application, you get the same final
string as from application complete edit_operations. It may be not
identical, though (in amibuous cases, like insertion of a character
next to the same character).
The subtracted subsequence must be an ordered subset of
edit_operations.
Note this function does not accept difflib-style opcodes as no one in
his right mind wants to create subsequences from them.
Examples
--------
>>> e = editops('man', 'scotsman')
>>> e1 = e[:3]
>>> bastard = apply_edit(e1, 'man', 'scotsman')
>>> bastard
'scoman'
>>> apply_edit(subtract_edit(e, e1), bastard, 'scotsman')
'scotsman'
"""
str_len = 2**32
return (
_Editops(edit_operations, str_len, str_len)
.remove_subsequence(_Editops(subsequence, str_len, str_len))
.as_list()
)
def inverse(edit_operations):
"""
Invert the sense of an edit operation sequence.
In other words, it returns a list of edit operations transforming the
second (destination) string to the first (source). It can be used
with both editops and opcodes.
Parameters
----------
edit_operations : list[]
edit operations to invert
Returns
-------
edit_operations : list[]
inverted edit operations
Examples
--------
>>> editops('spam', 'park')
[('delete', 0, 0), ('insert', 3, 2), ('replace', 3, 3)]
>>> inverse(editops('spam', 'park'))
[('insert', 0, 0), ('delete', 2, 3), ('replace', 3, 3)]
"""
if len(edit_operations) == 0:
return []
if len(edit_operations[0]) == 3:
len1 = edit_operations[-1][1] + 1
len2 = edit_operations[-1][2] + 1
return _Editops(edit_operations, len1, len2).inverse().as_list()
len1 = edit_operations[-1][2]
len2 = edit_operations[-1][4]
return _Opcodes(edit_operations, len1, len2).inverse().as_list()
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/__init__.pyi 0000664 0000000 0000000 00000005546 15101347240 0024060 0 ustar 00root root 0000000 0000000 from __future__ import annotations
from collections.abc import Callable, Hashable, Sequence
from typing import overload
__author__: str
__license__: str
__version__: str
_EditopsList = list[tuple[str, int, int]]
_OpcodesList = list[tuple[str, int, int, int, int]]
_MatchingBlocks = list[tuple[int, int, int]]
_AnyEditops = _EditopsList | _OpcodesList
def inverse(edit_operations: list) -> list: ...
@overload
def editops(s1: Sequence[Hashable], s2: Sequence[Hashable]) -> _EditopsList: ...
@overload
def editops(
ops: _AnyEditops,
s1: Sequence[Hashable] | int,
s2: Sequence[Hashable] | int,
) -> _EditopsList: ...
@overload
def opcodes(s1: Sequence[Hashable], s2: Sequence[Hashable]) -> _OpcodesList: ...
@overload
def opcodes(
ops: _AnyEditops,
s1: Sequence[Hashable] | int,
s2: Sequence[Hashable] | int,
) -> _OpcodesList: ...
def matching_blocks(
edit_operations: _AnyEditops,
source_string: Sequence[Hashable] | int,
destination_string: Sequence[Hashable] | int,
) -> _MatchingBlocks: ...
def subtract_edit(edit_operations: _EditopsList, subsequence: _EditopsList) -> _EditopsList: ...
def apply_edit(edit_operations: _AnyEditops, source_string: str, destination_string: str) -> str: ...
def median(strlist: list[str | bytes], wlist: list[float] | None = None) -> str: ...
def quickmedian(strlist: list[str | bytes], wlist: list[float] | None = None) -> str: ...
def median_improve(
string: str | bytes,
strlist: list[str | bytes],
wlist: list[float] | None = None,
) -> str: ...
def setmedian(strlist: list[str | bytes], wlist: list[float] | None = None) -> str: ...
def setratio(strlist1: list[str | bytes], strlist2: list[str | bytes]) -> float: ...
def seqratio(strlist1: list[str | bytes], strlist2: list[str | bytes]) -> float: ...
def distance(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: Callable[..., Sequence[Hashable]] | None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
) -> int: ...
def ratio(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
processor: Callable[..., Sequence[Hashable]] | None = None,
score_cutoff: float | None = None,
) -> float: ...
def hamming(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
pad: bool = True,
processor: Callable[..., Sequence[Hashable]] | None = None,
score_cutoff: float | None = None,
) -> int: ...
def jaro(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
processor: Callable[..., Sequence[Hashable]] | None = None,
score_cutoff: float | None = None,
) -> float: ...
def jaro_winkler(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
prefix_weight: float | None = 0.1,
processor: Callable[..., Sequence[Hashable]] | None = None,
score_cutoff: float | None = None,
) -> float: ...
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/generate.sh 0000664 0000000 0000000 00000000320 15101347240 0023705 0 ustar 00root root 0000000 0000000 #!/bin/sh
curdir="${0%/*}"
generate_cython()
{
python -m cython -I "$curdir" --cplus "$curdir"/"$1".pyx -o "$curdir"/"$1".cxx || exit 1
echo "Generated $curdir/$1.cxx"
}
generate_cython levenshtein_cpp
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/levenshtein_cpp.pyx 0000664 0000000 0000000 00000017570 15101347240 0025526 0 ustar 00root root 0000000 0000000 # cython: freethreading_compatible = True
from libc.stdint cimport uint32_t
from libcpp.vector cimport vector
from libcpp cimport bool
from libcpp.utility cimport move
cdef extern from *:
int PyUnicode_4BYTE_KIND
object PyUnicode_FromKindAndData(int kind, const void *buffer, Py_ssize_t size)
cdef extern from "_levenshtein.hpp":
cdef vector[uint32_t] lev_greedy_median(const vector[RF_String]& strings, const vector[double]& weights) except +
cdef vector[uint32_t] lev_median_improve(const RF_String& string, const vector[RF_String]& strings, const vector[double]& weights) except +
cdef vector[uint32_t] lev_quick_median(const vector[RF_String]& strings, const vector[double]& weights) except +
cdef vector[uint32_t] lev_set_median(const vector[RF_String]& strings, const vector[double]& weights) except +
cdef double lev_set_distance(const vector[RF_String]& strings1, const vector[RF_String]& strings2) except +
cdef double lev_edit_seq_distance(const vector[RF_String]& strings1, const vector[RF_String]& strings2) except +
ctypedef struct RF_String:
pass
cdef bool is_valid_string(object)
cdef RF_String convert_string(object)
cdef inline RF_String conv_sequence(seq) except *:
if is_valid_string(seq):
return convert_string(seq)
raise TypeError("Expected string or bytes")
cdef vector[double] extract_weightlist(wlist, size_t n) except *:
cdef size_t i
cdef double weight
cdef vector[double] weights
if wlist is None:
weights.resize(n, 1.0)
else:
weights.resize(n)
for i, w in enumerate(wlist):
weight = w
if w < 0:
raise ValueError(f"weight {weight} is negative")
weights[i] = w
return weights
cdef vector[RF_String] extract_stringlist(strings) except *:
cdef vector[RF_String] strlist
for string in strings:
strlist.push_back(move(conv_sequence(string)))
return move(strlist)
def median(strlist, wlist = None, *):
"""
Find an approximate generalized median string using greedy algorithm.
You can optionally pass a weight for each string as the second
argument. The weights are interpreted as item multiplicities,
although any non-negative real numbers are accepted. Use them to
improve computation speed when strings often appear multiple times
in the sequence.
Examples
--------
>>> median(['SpSm', 'mpamm', 'Spam', 'Spa', 'Sua', 'hSam'])
'Spam'
>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
... 'Leveshtei', 'Lenshtein', 'Lvenstein',
... 'Levenhtin', 'evenshtei']
>>> median(fixme)
'Levenshtein'
Hm. Even a computer program can spell Levenshtein better than me.
"""
if wlist is not None and len(strlist) != len(wlist):
raise ValueError("strlist has a different length than wlist")
weights = extract_weightlist(wlist, len(strlist))
strings = extract_stringlist(strlist)
median = lev_greedy_median(strings, weights)
return PyUnicode_FromKindAndData(PyUnicode_4BYTE_KIND, median.data(), median.size())
def quickmedian(strlist, wlist = None, *):
"""
Find a very approximate generalized median string, but fast.
See median() for argument description.
This method is somewhere between setmedian() and picking a random
string from the set; both speedwise and quality-wise.
Examples
--------
>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
... 'Leveshtei', 'Lenshtein', 'Lvenstein',
... 'Levenhtin', 'evenshtei']
>>> quickmedian(fixme)
'Levnshein'
"""
if wlist is not None and len(strlist) != len(wlist):
raise ValueError("strlist has a different length than wlist")
weights = extract_weightlist(wlist, len(strlist))
strings = extract_stringlist(strlist)
median = lev_quick_median(strings, weights)
return PyUnicode_FromKindAndData(PyUnicode_4BYTE_KIND, median.data(), median.size())
def median_improve(string, strlist, wlist = None, *):
"""
Improve an approximate generalized median string by perturbations.
The first argument is the estimated generalized median string you
want to improve, the others are the same as in median(). It returns
a string with total distance less or equal to that of the given string.
Note this is much slower than median(). Also note it performs only
one improvement step, calling median_improve() again on the result
may improve it further, though this is unlikely to happen unless the
given string was not very similar to the actual generalized median.
Examples
--------
>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
... 'Leveshtei', 'Lenshtein', 'Lvenstein',
... 'Levenhtin', 'evenshtei']
>>> median_improve('spam', fixme)
'enhtein'
>>> median_improve(median_improve('spam', fixme), fixme)
'Levenshtein'
It takes some work to change spam to Levenshtein.
"""
if wlist is not None and len(strlist) != len(wlist):
raise ValueError("strlist has a different length than wlist")
weights = extract_weightlist(wlist, len(strlist))
query = conv_sequence(string)
strings = extract_stringlist(strlist)
median = lev_median_improve(query, strings, weights)
return PyUnicode_FromKindAndData(PyUnicode_4BYTE_KIND, median.data(), median.size())
def setmedian(strlist, wlist = None, *):
"""
Find set median of a string set (passed as a sequence).
See median() for argument description.
The returned string is always one of the strings in the sequence.
Examples
--------
>>> setmedian(['ehee', 'cceaes', 'chees', 'chreesc',
... 'chees', 'cheesee', 'cseese', 'chetese'])
'chees'
You haven't asked me about Limburger, sir.
"""
if wlist is not None and len(strlist) != len(wlist):
raise ValueError("strlist has a different length than wlist")
weights = extract_weightlist(wlist, len(strlist))
strings = extract_stringlist(strlist)
median = lev_set_median(strings, weights)
return PyUnicode_FromKindAndData(PyUnicode_4BYTE_KIND, median.data(), median.size())
def setratio(strlist1, strlist2, *):
"""
Compute similarity ratio of two strings sets (passed as sequences).
The best match between any strings in the first set and the second
set (passed as sequences) is attempted. I.e., the order doesn't
matter here.
Examples
--------
>>> setratio(['newspaper', 'litter bin', 'tinny', 'antelope'],
... ['caribou', 'sausage', 'gorn', 'woody'])
0.281845
No, even reordering doesn't help the tinny words to match the
woody ones.
"""
strings1 = extract_stringlist(strlist1)
strings2 = extract_stringlist(strlist2)
lensum = strings1.size() + strings2.size()
if lensum == 0:
return 1.0
if strings1.empty():
dist = strings2.size()
elif strings2.empty():
dist = strings1.size()
else:
dist = lev_set_distance(strings1, strings2)
return (lensum - dist) / lensum
def seqratio(strlist1, strlist2, *):
"""
Compute similarity ratio of two sequences of strings.
This is like ratio(), but for string sequences. A kind of ratio()
is used to to measure the cost of item change operation for the
strings.
Examples
--------
>>> seqratio(['newspaper', 'litter bin', 'tinny', 'antelope'],
... ['caribou', 'sausage', 'gorn', 'woody'])
0.215178
"""
strings1 = extract_stringlist(strlist1)
strings2 = extract_stringlist(strlist2)
lensum = strings1.size() + strings2.size()
if lensum == 0:
return 1.0
if strings1.empty():
dist = strings2.size()
elif strings2.empty():
dist = strings1.size()
else:
dist = lev_edit_seq_distance(strings1, strings2)
return (lensum - dist) / lensum
rapidfuzz-Levenshtein-de93cb7/src/Levenshtein/py.typed 0000664 0000000 0000000 00000000000 15101347240 0023251 0 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/tests/ 0000775 0000000 0000000 00000000000 15101347240 0017653 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/tests/test_levenshtein_distance.py 0000664 0000000 0000000 00000001573 15101347240 0025470 0 ustar 00root root 0000000 0000000 from __future__ import annotations
import Levenshtein
def test_empty_string():
"""
when both strings are empty this is a perfect match
"""
assert Levenshtein.distance(b"", b"") == 0
assert Levenshtein.distance("", "") == 0
def test_simple():
"""
some very simple tests using supported string types
bytes/unicode
to catch relatively obvious implementation errors
"""
assert Levenshtein.distance(b"ABCD", b"AF") == 3
assert Levenshtein.distance("ABCD", "AF") == 3
assert Levenshtein.distance(b"ABCD", b"ABCD") == 0
assert Levenshtein.distance("ABCD", "ABCD") == 0
def test_simple_unicode_tests():
"""
some very simple tests using unicode
to catch relatively obvious implementation errors
"""
s1 = "ÁÄ"
s2 = "ABCD"
assert Levenshtein.distance(s1, s2) == 4
assert Levenshtein.distance(s1, s1) == 0
rapidfuzz-Levenshtein-de93cb7/tests/test_matching_blocks.py 0000664 0000000 0000000 00000001435 15101347240 0024416 0 ustar 00root root 0000000 0000000 from __future__ import annotations
from rapidfuzz.distance import MatchingBlock
from Levenshtein import editops, matching_blocks
def test_simple():
a, b = "spam", "park"
assert matching_blocks(editops(a, b), a, b) == [
MatchingBlock(1, 0, 2),
MatchingBlock(4, 4, 0),
]
assert matching_blocks(editops(a, b), len(a), len(b)) == [
MatchingBlock(1, 0, 2),
MatchingBlock(4, 4, 0),
]
assert matching_blocks(editops("", ""), 0, 0) == [MatchingBlock(0, 0, 0)]
assert matching_blocks(editops("", "a"), 0, 1) == [MatchingBlock(0, 1, 0)]
assert matching_blocks(editops("a", ""), 1, 0) == [MatchingBlock(1, 0, 0)]
assert matching_blocks(editops("a", "a"), 1, 1) == [
MatchingBlock(0, 0, 1),
MatchingBlock(1, 1, 0),
]
rapidfuzz-Levenshtein-de93cb7/tests/test_median.py 0000664 0000000 0000000 00000002425 15101347240 0022524 0 ustar 00root root 0000000 0000000 from __future__ import annotations
import Levenshtein
def test_weight_zero():
"""
Strings with zero weights should be ignored
"""
assert Levenshtein.quickmedian(["tes", "teste"], [0, 0]) == ""
assert Levenshtein.quickmedian(["tes", "teste"], [1, 0]) == "tes"
assert Levenshtein.quickmedian(["tes", "teste"], [0, 1]) == "teste"
def test_documented():
"""
run tests from documentation
"""
assert Levenshtein.median(["SpSm", "mpamm", "Spam", "Spa", "Sua", "hSam"]) == "Spam"
fixme = [
"Levnhtein",
"Leveshein",
"Leenshten",
"Leveshtei",
"Lenshtein",
"Lvenstein",
"Levenhtin",
"evenshtei",
]
assert Levenshtein.median(fixme) == "Levenshtein"
assert Levenshtein.quickmedian(fixme) == "Levnshein"
assert Levenshtein.median_improve("spam", fixme) == "enhtein"
assert Levenshtein.median_improve(Levenshtein.median_improve("spam", fixme), fixme) == "Levenshtein"
assert (
Levenshtein.setmedian(
[
"ehee",
"cceaes",
"chees",
"chreesc",
"chees",
"cheesee",
"cseese",
"chetese",
]
)
== "chees"
)
rapidfuzz-Levenshtein-de93cb7/tests/test_seq_ratio.py 0000664 0000000 0000000 00000000671 15101347240 0023256 0 ustar 00root root 0000000 0000000 from __future__ import annotations
import Levenshtein
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
def test_documented():
"""
run tests from documentation
"""
ratio = Levenshtein.seqratio(
["newspaper", "litter bin", "tinny", "antelope"],
["caribou", "sausage", "gorn", "woody"],
)
assert isclose(ratio, 0.21517857142857144)
rapidfuzz-Levenshtein-de93cb7/tests/test_set_ratio.py 0000664 0000000 0000000 00000000670 15101347240 0023260 0 ustar 00root root 0000000 0000000 from __future__ import annotations
import Levenshtein
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
def test_documented():
"""
run tests from documentation
"""
ratio = Levenshtein.setratio(
["newspaper", "litter bin", "tinny", "antelope"],
["caribou", "sausage", "gorn", "woody"],
)
assert isclose(ratio, 0.2818452380952381)
rapidfuzz-Levenshtein-de93cb7/tools/ 0000775 0000000 0000000 00000000000 15101347240 0017651 5 ustar 00root root 0000000 0000000 rapidfuzz-Levenshtein-de93cb7/tools/backtrace 0000775 0000000 0000000 00000000017 15101347240 0021514 0 ustar 00root root 0000000 0000000 backtrace
quit
rapidfuzz-Levenshtein-de93cb7/tools/sdist.patch 0000664 0000000 0000000 00000000455 15101347240 0022024 0 ustar 00root root 0000000 0000000 diff --git a/pyproject.toml b/pyproject.toml
index 0a8c033..cf967b9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,6 @@
[build-system]
requires = [
- "scikit-build-core>=0.11",
- "Cython>=3.1.6,<3.2.0"
+ "scikit-build-core>=0.11"
]
build-backend = "scikit_build_core.build"
rapidfuzz-Levenshtein-de93cb7/tools/seg_wrapper.sh 0000775 0000000 0000000 00000000303 15101347240 0022522 0 ustar 00root root 0000000 0000000 #!/bin/bash
SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
ulimit -c unlimited
"$@"
if [[ $? -eq 139 ]]; then
coredumpctl gdb -1 -A "--batch -x $SCRIPTPATH/backtrace"
fi