Abydos Documentation
Release 0.5.0

Christopher C. Little

Jan 10, 2020

CONTENTS:

1

Introduction
1.1 Abydos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Testing & Contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Badges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 License . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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FAQ
2.1 Why is the library licensed under GPL3+? Can you change the license? . . . . . . . . . . . . . . . .
2.2 What is the purpose of this library? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Can you add this new feature? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Can I contribute to the project? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5 Will you add Metaphone 3? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6 Why have you included algorithm X when it is already a part of NLTK/SciPy/...? . . . . . . . . . . .
2.7 Are there similar projects for languages other than Python? . . . . . . . . . . . . . . . . . . . . . .
2.8 What is the process for adding a new class to the library? . . . . . . . . . . . . . . . . . . . . . . . .
2.9 Are these really Frequently Asked Questions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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abydos
3.1 abydos package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1
Subpackages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.1 abydos.compression package . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.2 abydos.corpus package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.3 abydos.distance package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.4 abydos.fingerprint package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.5 abydos.phones package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.6 abydos.phonetic package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.7 abydos.stats package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.8 abydos.stemmer package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.9 abydos.tokenizer package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.10 abydos.util package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Release History
4.1 0.5.0 (2020-01-10) ecgtheow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 0.4.1 (2020-01-07) distant dietrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 0.4.0 (2019-05-30) dietrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 0.3.6 (2018-11-17) classy carl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 0.3.5 (2018-10-31) cantankerous carl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 0.3.0 (2018-10-15) carl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 0.2.0 (2015-05-27) berthold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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0.1.1 (2015-05-12) albrecht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658

Indices

659

Bibliography

661

Python Module Index

679

Index

681

ii

CHAPTERONEINTRODUCTION

1.1 Abydos

Abydos NLP/IR library
Copyright 2014-2020 by Christopher C. Little

Abydos is a library of phonetic algorithms, string distance measures & metrics, stemmers, and string fingerprinters.

1.2 Installation
Required libraries:• NumPy• deprecation
Optional libraries (all available on PyPI, some available on conda or conda-forge):• SyllabiPy• NLTK• PyLZSS

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Abydos Documentation, Release 0.5.0• paq
To install Abydos (master) from Github source:
git clone https://github.com/chrislit/abydos.git --recursive
cd abydos
python setup install

If your default python command calls Python 2.7 but you want to install for Python 3, you may instead need to call:python3 setup install

To install Abydos (latest release) from PyPI using pip:
pip install abydos

To install from conda-forge:
conda install abydos

It should run on Python 3.5-3.8.

1.3 Testing & Contributing
To run the whole test-suite just call tox:
tox

The tox setup has the following environments: black, py37, doctest, regression, fuzz, pylint, pydocstyle, flake8, doc8,
docs, sloccount, badges, & build. So if you only want to generate documentation (in HTML, EPUB, & PDF formats),
just call:tox -e docs

In order to only run & generate Flake8 reports, call:tox -e flake8

Contributions such as bug reports, PRs, suggestions, desired new features, etc. are welcome through Github Issues &Pull requests.

1.4 Badges
The project's main page has quite a few badges, some seemingly redundant, and a bit of explanation is perhaps
warranted.
• CI & Test Status– Travis-CI is the primary CI used for Linux CI of all supported Python platforms (2.7-3.8-dev). Only thetests in the tests directory are run.
– CircleCI runs only the Python 3.6 tests on Linux and is used for quick tests of each commit.
– Azure DevOps is used to perform tests on Linux, MaxOS, and Windows on Python 2.7, 3.5, 3.6, & 3.7
using pytest.

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Abydos Documentation, Release 0.5.0– Semaphore is used to run the tests in the tests directory, doctests, regression tests, and fuzz tests.
– Coveralls is used to track test coverage.
• Code Quality (some may be removed at a later date)– Code Climate is used to check maintainability, but mostly just complains about McCabe complexity.
– Scrutinizer is used to check complexity and compliance with best practices.
– Codacy is used to check code style, security issues, etc.
– CodeFactor is used to track hotspot files in need of attention.
• Dependencies– Requires.io tracks whether Abydos can be used with the most recent releases of its dependencies.
– Snyk tracks whether there are security vulnerabilities in any dependencies.
– Pyup.io tracks updates and security vulnerabilities in dependencies.
– CII Best Practices identifies compliance with Core Infrastructure Initiative best practices.
• Local Analysis– Pylint score, run locally– flake8 score, run locally, should be 0.
– pydocstyle score, run locally, should be 0.
– SLOCCount shows the total source lines of code.
– Black code style signals that Black is used for code styling.
• Usage– Read the Docs hosts Abydos documentation online.
– Binder provides an online notebook environment for the demo notebooks.
– GPL v3+ is the license used by Abydos.
– Libraries.io assigns a SourceRank to indicate project quality and popularity.
– zenodo publishes the DOI and citation information for Abydos.
• Contribution– OpenHub tracks project activity and KLOC and estimates project value.
– The commit activity shows commit rate.
– The issues badge indicates the number of issues closed.
– The GitHub stars badge indicates the number of stars received.
• PyPI– PyPI hosts the pip installable packages. The pypi badge indicates the most recent pip installable version.
– The downloads badge indicates the number of downloads from PyPI per month.
– The python badge indicates the versions of Python that are supported.
• conda-forge– conda-forge hosts the conda installable packages. The conda-forge badge indicates the most recent conda
installable version.

1.4. Badges

3Abydos Documentation, Release 0.5.0– The downloads badge indicates the number of downloads from conda-forge.
– The platform badge indicates that Abydos is a pure Python project, without platform-specific builds.

1.5 License
Abydos is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License
as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even theimplied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/gpl.txt>.

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CHAPTER

TWO

FAQ

2.1 Why is the library licensed under GPL3+? Can you change thelicense?
GPL3 is the only license compatible with all of the various parts of Abydos that have been ported to Python fromother languages. For example, the Beider-Morse Phonetic Matching algorithm implementation included in Abydos
was ported from their reference implementation in PHP, which is itself licensed under GPL3.
Accordingly, it's not possible to change to a different license without removing parts of the library. However, if you
have a need for a specific part of the library and can't use GPL3+ code, contact us and we may be able to provide it
separately or can give guidance on its underlying licensing status.

2.2 What is the purpose of this library?
A. Abydos is intended to facilitate any manner of string transformation and comparison might be useful for string
matching or record linkage. The two most significant parts of the library are string distance/similarity measures andphonetic algorithms/string fingerprint algorithms, but a large collection of tokenizers, corpus classes, compression
algorithms, & phonetics functions support these and afford greater customization.

2.3 Can you add this new feature?
Maybe. Open an issue at https://github.com/chrislit/abydos/issues and propose your new feature.
Additional string distance/similarity measures, phonetic algorithms, string fingerprint algorithms, and string tokenizers
will certainly be added if possible -- but it's helpful to point them out since we may not be aware of them.

2.4 Can I contribute to the project?
Absolutely. You can take on an unclaimed issue, report bugs, add new classes, or whatever piques your interest. You
are welcome to open an issue at https://github.com/chrislit/abydos/issues proposing what you'd like to work on, or you
can submit a pull request if you have something ready to contribute to the repository.

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2.5 Will you add Metaphone 3?
No. Although Lawrence Philips (author of Metaphone, Double Metaphone, and Metaphone 3) released
Metaphone 3 version 2.1.3 under the BSD 3-clause license as part of Google Refine, which became
OpenRefine (https://github.com/OpenRefine/OpenRefine/blob/master/main/src/com/google/refine/clustering/binning/
Metaphone3.java), he doesn't want that code used for ports to other languages or used in any way outside of OpenRefine. In accordance with his wishes, no one has released Metaphone 3 ports to other languages or included it otherlibraries.

2.6 Why have you included algorithm X when it is already a part of
NLTK/SciPy/...?
Abydos is a collection of algorithms with common class & function interfaces and options. So, while NLTK has
Levenshtein & Jaccard string similarity measures, they don't allow for tunable edit costs or using the tokenizer of yourchoice.

2.7 Are there similar projects for languages other than Python?
Yes, there are libraries such as:• Talisman for JavaScript• Phonics for R (phonetic algorithms)• stringmetric for Scala

2.8 What is the process for adding a new class to the library?
The process of adding a new class follows roughly the following steps:• Discover that a new (unimplemented) measure/algorithm/method exists• Locate the original source of the algorithm (a journal article, a reference implementation, etc.). And save thereference to it in docs/abydos.bib.
– If the original source cannot be located for reference, use an adequate secondary source and add its reference info to docs/abydos.bib.
• Implement the class based on its description/reference implementation.
• Create a test class and add all examples and test cases from the original source. Add other reliable test cases
from other sources, if they are available.
• Ensure that the class passes all test cases.
• Add test cases, as necessary, until test coverage reaches 100%, or as close to 100% as possible.

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2.9 Are these really Frequently Asked Questions?
No. Most of these questions have never been explicitly asked.

2.9. Are these really Frequently Asked Questions?

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CHAPTERTHREE

ABYDOS

3.1 abydos package
abydos.
Abydos NLP/IR library by Christopher C. Little
There are nine major packages that make up Abydos:• compression for string compression classes• corpus for document corpus classes• distance for string distance measure & metric classes• fingerprint for string fingerprint classes• phones for functions relating to phones and phonemes• phonetic for phonetic algorithm classes• stats for statistical functions and a confusion table class• stemmer for stemming classes• tokenizer for tokenizer classes
Classes with each package have consistent method names, as discussed below. A tenth package, util, contains
functions not intended for end-user use.

3.1.1 Subpackages
3.1.1.1 abydos.compression package
abydos.compression.
The compression package defines compression and compression-related functions for use within Abydos, including
implementations of the following:• Arithmetic for arithmetic coding• BWT for Burrows-Wheeler Transform• RLE for Run-Length Encoding

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Each class exposes encode and decode methods for performing and reversing its encoding. For example, theBurrows-Wheeler Transform can be performed by creating a BWT object and then calling BWT.encode() on astring:>>> bwt = BWT()>>> bwt.encode('^BANANA')'ANNB^AA\x00'

class abydos.compression.Arithmetic(text=None)
Bases: object
Arithmetic Coder.
This is based on Andrew Dalke's public domain implementation [Dal05]. It has been ported to use the fractions.Fraction class.
New in version 0.3.6.
Initialize arithmetic coder object.
Parameters text (str) -- The training text
New in version 0.3.6.
decode(longval, nbits)
Decode the number to a string using the given statistics.
Parameters• longval (int) -- The first part of an encoded tuple from encode• nbits (int) -- The second part of an encoded tuple from encode
Returns The arithmetically decoded text
Return type str
Example>>> ac = Arithmetic('the quick brown fox jumped over the lazy dog')>>> ac.decode(16720586181, 34)'align'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode(text)
Encode a text using arithmetic coding.
Text and the 0-order probability statistics -> longval, nbits
The encoded number is Fraction(longval, 2**nbits)
Parameters text (str) -- A string to encode
Returns The arithmetically coded text
Return type tuple

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Example>>> ac = Arithmetic('the quick brown fox jumped over the lazy dog')>>> ac.encode('align')(16720586181, 34)

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
get_probs()
Return the probs dictionary.
Returns The dictionary of probabilities
Return type dict
New in version 0.3.6.
set_probs(probs)
Set the probs dictionary.
Parameters probs (dict) -- The dictionary of probabilities
New in version 0.3.6.
train(text)
Generate a probability dict from the provided text.
Text to 0-order probability statistics as a dictParameters text (str) -- The text data over which to calculate probability statistics. This
must not contain the NUL (0x00) character because that is used to indicate the end of data.
Example>>> ac = Arithmetic()>>> ac.train('the quick brown fox jumped over the lazy dog')>>> ac.get_probs(){' ': (Fraction(0, 1), Fraction(8, 45)),'o': (Fraction(8, 45), Fraction(4, 15)),'e': (Fraction(4, 15), Fraction(16, 45)),'u': (Fraction(16, 45), Fraction(2, 5)),'t': (Fraction(2, 5), Fraction(4, 9)),'r': (Fraction(4, 9), Fraction(22, 45)),'h': (Fraction(22, 45), Fraction(8, 15)),'d': (Fraction(8, 15), Fraction(26, 45)),'z': (Fraction(26, 45), Fraction(3, 5)),'y': (Fraction(3, 5), Fraction(28, 45)),'x': (Fraction(28, 45), Fraction(29, 45)),'w': (Fraction(29, 45), Fraction(2, 3)),'v': (Fraction(2, 3), Fraction(31, 45)),'q': (Fraction(31, 45), Fraction(32, 45)),'p': (Fraction(32, 45), Fraction(11, 15)),'n': (Fraction(11, 15), Fraction(34, 45)),'m': (Fraction(34, 45), Fraction(7, 9)),'l': (Fraction(7, 9), Fraction(4, 5)),'k': (Fraction(4, 5), Fraction(37, 45)),'j': (Fraction(37, 45), Fraction(38, 45)),(continues on next page)

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11Abydos Documentation, Release 0.5.0(continued from previous page)'i': (Fraction(38, 45), Fraction(13, 15)),'g': (Fraction(13, 15), Fraction(8, 9)),'f': (Fraction(8, 9), Fraction(41, 45)),'c': (Fraction(41, 45), Fraction(14, 15)),'b': (Fraction(14, 15), Fraction(43, 45)),'a': (Fraction(43, 45), Fraction(44, 45)),'\x00': (Fraction(44, 45), Fraction(1, 1))}

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.compression.ac_decode(longval, nbits, probs)
Decode the number to a string using the given statistics.
This is a wrapper for Arithmetic.decode().
Parameters• longval (int) -- The first part of an encoded tuple from ac_encode• nbits (int) -- The second part of an encoded tuple from ac_encode• probs (dict) -- A probability statistics dictionary generated by Arithmetic.
train()
Returns The arithmetically decoded text
Return type str
Example>>> pr = ac_train('the quick brown fox jumped over the lazy dog')>>> ac_decode(16720586181, 34, pr)'align'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Arithmetic.decode method instead.
abydos.compression.ac_encode(text, probs)
Encode a text using arithmetic coding with the provided probabilities.
This is a wrapper for Arithmetic.encode().
Parameters• text (str) -- A string to encode• probs (dict) -- A probability statistics dictionary generated by Arithmetic.
train()
Returns The arithmetically coded text
Return type tuple

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Example>>> pr = ac_train('the quick brown fox jumped over the lazy dog')>>> ac_encode('align', pr)(16720586181, 34)

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Arithmetic.encode method instead.
abydos.compression.ac_train(text)
Generate a probability dict from the provided text.
This is a wrapper for Arithmetic.train().
Parameters text (str) -- The text data over which to calculate probability statistics. This must
not contain the NUL (0x00) character because that's used to indicate the end of data.
Returns A probability dictReturn type dict
Example>>> ac_train('the quick brown fox jumped over the lazy dog'){' ': (Fraction(0, 1), Fraction(8, 45)),'o': (Fraction(8, 45), Fraction(4, 15)),'e': (Fraction(4, 15), Fraction(16, 45)),'u': (Fraction(16, 45), Fraction(2, 5)),'t': (Fraction(2, 5), Fraction(4, 9)),'r': (Fraction(4, 9), Fraction(22, 45)),'h': (Fraction(22, 45), Fraction(8, 15)),'d': (Fraction(8, 15), Fraction(26, 45)),'z': (Fraction(26, 45), Fraction(3, 5)),'y': (Fraction(3, 5), Fraction(28, 45)),'x': (Fraction(28, 45), Fraction(29, 45)),'w': (Fraction(29, 45), Fraction(2, 3)),'v': (Fraction(2, 3), Fraction(31, 45)),'q': (Fraction(31, 45), Fraction(32, 45)),'p': (Fraction(32, 45), Fraction(11, 15)),'n': (Fraction(11, 15), Fraction(34, 45)),'m': (Fraction(34, 45), Fraction(7, 9)),'l': (Fraction(7, 9), Fraction(4, 5)),'k': (Fraction(4, 5), Fraction(37, 45)),'j': (Fraction(37, 45), Fraction(38, 45)),'i': (Fraction(38, 45), Fraction(13, 15)),'g': (Fraction(13, 15), Fraction(8, 9)),'f': (Fraction(8, 9), Fraction(41, 45)),'c': (Fraction(41, 45), Fraction(14, 15)),'b': (Fraction(14, 15), Fraction(43, 45)),'a': (Fraction(43, 45), Fraction(44, 45)),'\x00': (Fraction(44, 45), Fraction(1, 1))}

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Arithmetic.train method instead.
class abydos.compression.BWT(terminator='x00')
Bases: object

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Burrows-Wheeler Transform.
The Burrows-Wheeler transform is an attempt at placing similar characters together to improve compression.
Cf. [BW94].
New in version 0.3.6.
Initialize BWT instance.
Parameters terminator (str) -- A character added to signal the end of the string
New in version 0.4.0.
decode(code)
Return a word decoded from BWT form.
Parameters• code (str) -- The word to transform from BWT form
• terminator (str) -- A character added to signal the end of the string
Returns Word decoded by BWTReturn type str
Raises ValueError -- Specified terminator absent from code.
Examples>>> bwt = BWT()>>> bwt.decode('n\x00ilag')'align'>>> bwt.decode('annb\x00aa')'banana'>>> bwt = BWT('@')>>> bwt.decode('annb@aa')'banana'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode(word)
Return the Burrows-Wheeler transformed form of a word.
Parameters word (str) -- The word to transform using BWTReturns Word encoded by BWTReturn type str
Raises ValueError -- Specified terminator absent from code.

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Examples>>> bwt = BWT()>>> bwt.encode('align')'n\x00ilag'>>> bwt.encode('banana')'annb\x00aa'>>> bwt = BWT('@')>>> bwt.encode('banana')'annb@aa'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.compression.bwt_decode(code, terminator='\x00')
Return a word decoded from BWT form.
This is a wrapper for BWT.decode().
Parameters• code (str) -- The word to transform from BWT form
• terminator (str) -- A character added to signal the end of the string
Returns Word decoded by BWTReturn type str
Examples>>> bwt_decode('n\x00ilag')'align'>>> bwt_decode('annb\x00aa')'banana'>>> bwt_decode('annb@aa', '@')
'banana'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the BWT.decode method instead.
abydos.compression.bwt_encode(word, terminator='\x00')
Return the Burrows-Wheeler transformed form of a word.
This is a wrapper for BWT.encode().
Parameters• word (str) -- The word to transform using BWT• terminator (str) -- A character added to signal the end of the string
Returns Word encoded by BWTReturn type str

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Examples>>> bwt_encode('align')'n\x00ilag'>>> bwt_encode('banana')'annb\x00aa'>>> bwt_encode('banana', '@')
'annb@aa'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the BWT.encode method instead.
class abydos.compression.RLE
Bases: object
Run-Length Encoding.
Cf. [RC67].
Based on http://rosettacode.org/wiki/Run-length_encoding#Python [Cod18b]. This is licensed GFDL 1.2.
Digits 0-9 cannot be in text.
New in version 0.3.6.
decode(text)
Perform decoding of run-length-encoding (RLE).
Parameters text (str) -- A text string to decode
Returns Word decoded by RLE
Return type str
Examples>>> rle = RLE()>>> bwt = BWT()>>> bwt.decode(rle.decode('n\x00ilag'))'align'>>> rle.decode('align')'align'>>> bwt.decode(rle.decode('annb\x00aa'))'banana'>>> rle.decode('banana')'banana'>>> bwt.decode(rle.decode('ab\x00abbab5a'))'aaabaabababa'>>> rle.decode('3abaabababa')'aaabaabababa'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode(text)
Perform encoding of run-length-encoding (RLE).

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Parameters text (str) -- A text string to encode
Returns Word decoded by RLE
Return type str
Examples>>> rle = RLE()>>> bwt = BWT()>>> rle.encode(bwt.encode('align'))'n\x00ilag'>>> rle.encode('align')'align'>>> rle.encode(bwt.encode('banana'))'annb\x00aa'>>> rle.encode('banana')'banana'>>> rle.encode(bwt.encode('aaabaabababa'))'ab\x00abbab5a'>>> rle.encode('aaabaabababa')'3abaabababa'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.compression.rle_decode(text, use_bwt=True)
Perform decoding of run-length-encoding (RLE).
This is a wrapper for RLE.decode().
Parameters• text (str) -- A text string to decode• use_bwt (bool) -- Indicates whether to perform BWT decoding after RLE decoding
Returns Word decoded by RLE
Return type str
Examples>>> rle_decode('n\x00ilag')'align'>>> rle_decode('align', use_bwt=False)'align'>>> rle_decode('annb\x00aa')'banana'>>> rle_decode('banana', use_bwt=False)'banana'

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Abydos Documentation, Release 0.5.0>>> rle_decode('ab\x00abbab5a')'aaabaabababa'>>> rle_decode('3abaabababa', False)'aaabaabababa'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RLE.decode method instead.
abydos.compression.rle_encode(text, use_bwt=True)
Perform encoding of run-length-encoding (RLE).
This is a wrapper for RLE.encode().
Parameters• text (str) -- A text string to encode• use_bwt (bool) -- Indicates whether to perform BWT encoding before RLE encoding
Returns Word decoded by RLE
Return type str
Examples>>> rle_encode('align')'n\x00ilag'>>> rle_encode('align', use_bwt=False)'align'>>> rle_encode('banana')'annb\x00aa'>>> rle_encode('banana', use_bwt=False)'banana'>>> rle_encode('aaabaabababa')'ab\x00abbab5a'>>> rle_encode('aaabaabababa', False)'3abaabababa'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RLE.encode method instead.
3.1.1.2 abydos.corpus package
abydos.corpus.
The corpus package includes basic and n-gram corpus classes:• Corpus• NGramCorpus• UnigramCorpus
As a quick example of Corpus:

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Abydos Documentation, Release 0.5.0>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n\n'>>> tqbf += 'And then it slept.\n\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.docs()[[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog.']],[['And', 'then', 'it', 'slept.']], [['And', 'the', 'dog', 'ran', 'off.']]]>>> round(corp.idf('dog'), 10)
1.0986122887>>> round(corp.idf('the'), 10)0.4054651081

Here, each sentence is a separate "document". We can retrieve IDF values from the Corpus. The same Corpus canbe used to initialize an NGramCorpus and calculate TF values:>>> ngcorp = NGramCorpus(corp)>>> ngcorp.get_count('the')2>>> ngcorp.get_count('fox')
1class abydos.corpus.Corpus(corpus_text='',doc_split='nn',
sent_split='n',stop_words=None, word_tokenizer=None)
Bases: object

filter_chars='',

Corpus class.
Internally, this is a list of lists or lists. The corpus itself is a list of documents. Each document is an ordered listof sentences in those documents. And each sentence is an ordered list of words that make up that sentence.
New in version 0.1.0.
Initialize Corpus.
By default, when importing a corpus:• two consecutive newlines divide documents• single newlines divide sentences• other whitespace divides words
Parameters• corpus_text (str) -- The corpus text as a single string• doc_split (str) -- A character or string used to split corpus_text into documents• sent_split (str) -- A character or string used to split documents into sentences• filter_chars (list) -- A list of characters (as a string, tuple, set, or list) to filter outof the corpus text
• stop_words (list) -- A list of words (as a tuple, set, or list) to filter out of the corpus
text• word_tokenizer (_Tokenizer) -- A tokenizer to apply to each sentence in order toretrieve the individual "word" tokens. If set to none, str.split() will be used.

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Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)

New in version 0.1.0.
docs()
Return the docs in the corpus.
Each list within a doc represents the sentences in that doc, each of which is in turn a list of words withinthat sentence.
Returns The docs in the corpus as a list of lists of lists of strs
Return type [[[str]]]
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.docs()[[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.'], ['And', 'then', 'it', 'slept.'], ['And', 'the', 'dog','ran', 'off.']]]>>> len(corp.docs())
1

New in version 0.1.0.
docs_of_words()
Return the docs in the corpus, with sentences flattened.
Each list within the corpus represents all the words of that document. Thus the sentence level of lists has
been flattened.
Returns The docs in the corpus as a list of list of strs
Return type [[str]]
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.docs_of_words()[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.', 'And', 'then', 'it', 'slept.', 'And', 'the', 'dog', 'ran','off.']]>>> len(corp.docs_of_words())
1

New in version 0.1.0.
idf(term, transform=None)
Calculate the Inverse Document Frequency of a term in the corpus.
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Parameters• term (str) -- The term to calculate the IDF of• transform (function) -- A function to apply to each document term before checking
for the presence of term
Returns The IDF
Return type float
Examples>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n\n'>>> tqbf += 'And then it slept.\n\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> print(corp.docs())[[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.']],[['And', 'then', 'it', 'slept.']],[['And', 'the', 'dog', 'ran', 'off.']]]>>> round(corp.idf('dog'), 10)
1.0986122887>>> round(corp.idf('the'), 10)0.4054651081

New in version 0.1.0.
paras()
Return the paragraphs in the corpus.
Each list within a paragraph represents the sentences in that doc, each of which is in turn a list of words
within that sentence. This is identical to the docs() member function and exists only to mirror part of
NLTK's API for corpora.
Returns The paragraphs in the corpus as a list of lists of lists of strs
Return type [[[str]]]
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.paras()[[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.'], ['And', 'then', 'it', 'slept.'], ['And', 'the', 'dog','ran', 'off.']]]>>> len(corp.paras())
1

New in version 0.1.0.
raw()
Return the raw corpus.
This is reconstructed by joining sub-components with the corpus' split characters
Returns The raw corpus

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Return type str
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> print(corp.raw())
The quick brown fox jumped over the lazy dog.
And then it slept.
And the dog ran off.
>>> len(corp.raw())
85

New in version 0.1.0.
sents()
Return the sentences in the corpus.
Each list within a sentence represents the words within that sentence.
Returns The sentences in the corpus as a list of lists of strs
Return type [[str]]
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.sents()[['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.'], ['And', 'then', 'it', 'slept.'], ['And', 'the', 'dog','ran', 'off.']]>>> len(corp.sents())
3

words()
Return the words in the corpus as a single list.
Returns The words in the corpus as a list of strs
Return type [str]Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = Corpus(tqbf)>>> corp.words()['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy','dog.', 'And', 'then', 'it', 'slept.', 'And', 'the', 'dog', 'ran','off.']>>> len(corp.words())
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New in version 0.1.0.
class abydos.corpus.NGramCorpus(corpus=None)
Bases: object
The NGramCorpus class.
Internally, this is a set of recursively embedded dicts, with n layers for a corpus of n-grams. E.g. for a trigram
corpus, this will be a dict of dicts of dicts. More precisely, collections.Counter is used in place of dict,making multiset operations valid and allowing unattested n-grams to be queried.
The key at each level is a word. The value at the most deeply embedded level is a numeric value representing
the frequency of the trigram. E.g. the trigram frequency of 'colorless green ideas' would be the value stored inself.ngcorpus['colorless']['green']['ideas'][None].
New in version 0.3.0.
Initialize Corpus.
Parameters corpus (Corpus) -- The Corpus from which to initialize the n-gram corpus. By
default, this is None, which initializes an empty NGramCorpus. This can then be populatedusing NGramCorpus methods.
Raises TypeError -- Corpus argument must be None or of type abydos.Corpus
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> ngcorp = NGramCorpus(Corpus(tqbf))

New in version 0.3.0.
corpus_importer(corpus, n_val=1, bos='_START_', eos='_END_')
Fill in self.ngcorpus from a Corpus argument.
Parameters• corpus (Corpus) -- The Corpus from which to initialize the n-gram corpus• n_val (int) -- Maximum n value for n-grams• bos (str) -- String to insert as an indicator of beginning of sentence• eos (str) -- String to insert as an indicator of end of sentence
Raises TypeError -- Corpus argument of the Corpus class required.
Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> ngcorp = NGramCorpus()>>> ngcorp.corpus_importer(Corpus(tqbf))

New in version 0.3.0.
get_count(ngram, corpus=None)
Get the count of an n-gram in the corpus.
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Abydos Documentation, Release 0.5.0• ngram (str) -- The n-gram to retrieve the count of from the n-gram corpus• corpus (Corpus) -- The corpus
Returns The n-gram count
Return type int
Examples>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> ngcorp = NGramCorpus(Corpus(tqbf))>>> ngcorp.get_count('the')2>>> ngcorp.get_count('fox')1

New in version 0.3.0.
gng_importer(corpus_file)
Fill in self.ngcorpus from a Google NGram corpus file.
Parameters corpus_file (file) -- The Google NGram file from which to initialize then-gram corpus
New in version 0.3.0.
class abydos.corpus.UnigramCorpus(corpus_text='', documents=0,
word_tokenizer=None)
Bases: object

word_transform=None,

Unigram corpus class.
Largely intended for calculating inverse document frequence (IDF) from a large corpus of unigram (or smaller)
tokens, this class encapsulates a dict object. Each key is a unigram token whose value is a tuple consisting of
the number of times a term appeared and the number of distinct documents in which it appeared.
New in version 0.4.0.
Initialize UnigramCorpus.
Parameters• corpus_text (str) -- The corpus text as a single string• documents (int) -- The number of documents in the corpus. If equal to 0 (the default)then the maximum from the internal dictionary's distinct documents count.
• word_transform (function) -- A function to apply to each term before term tokenization and addition to the corpus. One might use this, for example, to apply Soundex
encoding to each term.
• word_tokenizer (_Tokenizer) -- A tokenizer to apply to each sentence in order toretrieve the individual "word" tokens. If set to none, str.split() will be used.

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Example>>> tqbf = 'The quick brown fox jumped over the lazy dog.\n'>>> tqbf += 'And then it slept.\n And the dog ran off.'>>> corp = UnigramCorpus(tqbf)

New in version 0.4.0.
add_document(doc)
Add a new document to the corpus.
Parameters doc (str) -- A string, representing the document to be added.
New in version 0.4.0.
gng_importer(corpus_file)
Fill in self.corpus from a Google NGram corpus file.
Parameters corpus_file (file) -- The Google NGram file from which to initialize then-gram corpus
New in version 0.4.0.
idf(term)
Calculate the Inverse Document Frequency of a term in the corpus.
Parameters term (str) -- The term to calculate the IDF of
Returns The IDF
Return type float
Examples>>> tqbf = 'the quick brown fox jumped over the lazy dog\n\n'>>> tqbf += 'and then it slept\n\n and the dog ran off'>>> corp = UnigramCorpus(tqbf)>>> round(corp.idf('dog'), 10)0.6931471806>>> round(corp.idf('the'), 10)0.6931471806

New in version 0.4.0.
load_corpus(filename)Load the corpus from a file.
This employs pickle to load the corpus (a defaultdict). Other parameters of the corpus, such as its
word_tokenizer, will not be affected and should be set during initialization.
Parameters filename (str) -- The filename to load the corpus from.
New in version 0.4.0.
save_corpus(filename)
Save the corpus to a file.
This employs pickle to save the corpus (a defaultdict). Other parameters of the corpus, such as its
word_tokenizer, will not be affected and should be set during initialization.
Parameters filename (str) -- The filename to save the corpus to.
New in version 0.4.0.
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3.1.1.3 abydos.distance package
abydos.distance.
The distance package implements string distance measure and metric classes:
These include traditional Levenshtein edit distance and related algorithms:• Levenshtein distance (Levenshtein)• Optimal String Alignment distance (Levenshtein with mode='osa')• Damerau-Levenshtein distance (DamerauLevenshtein)• Yujian-Bo normalized edit distance (YujianBo)• Higuera-Micó contextual normalized edit distance (HigueraMico)• Indel distance (Indel)• Syllable Alignment Pattern Searching similarity (distance.SAPS)• Meta-Levenshtein distance (MetaLevenshtein)• Covington distance (Covington)• ALINE distance (ALINE)• FlexMetric distance (FlexMetric)• BI-SIM similarity (BISIM )• Discounted Levenshtein distance (DiscountedLevenshtein)• Phonetic edit distance (PhoneticEditDistance)
Hamming distance (Hamming), Relaxed Hamming distance (RelaxedHamming), and the closely related Modified
Language-Independent Product Name Search distance (MLIPNS) are provided.
Block edit distances:• Tichy edit distance (Tichy)• Levenshtein distance with block operations (BlockLevenshtein)• Rees-Levenshtein distance (ReesLevenshtein)• Cormode's LZ distance (CormodeLZ)• Shapira-Storer I edit distance with block moves, greedy algorithm (ShapiraStorerI)
Distance metrics developed for the US Census or derived from them are included:• Jaro distance (JaroWinkler with mode='Jaro')• Jaro-Winkler distance (JaroWinkler)• Strcmp95 distance (Strcmp95)• Iterative-SubString (I-Sub) correlation (IterativeSubString)
A large set of multi-set token-based distance metrics are provided, including:• AMPLE similarity (AMPLE)• AZZOO similarity (AZZOO)• Anderberg's D similarity (Anderberg)• Andres & Marzo's Delta correlation (AndresMarzoDelta)

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Abydos Documentation, Release 0.5.0• Baroni-Urbani & Buser I similarity (BaroniUrbaniBuserI)• Baroni-Urbani & Buser II correlation (BaroniUrbaniBuserII)• Batagelj & Bren similarity (BatageljBren)• Baulieu I distance (BaulieuI)• Baulieu II distance (BaulieuII)• Baulieu III distance (BaulieuIII)• Baulieu IV distance (BaulieuIV )• Baulieu V distance (BaulieuV )• Baulieu VI distance (BaulieuVI)• Baulieu VII distance (BaulieuVII)• Baulieu VIII distance (BaulieuVIII)• Baulieu IX distance (BaulieuIX)• Baulieu X distance (BaulieuX)• Baulieu XI distance (BaulieuXI)• Baulieu XII distance (BaulieuXII)• Baulieu XIII distance (BaulieuXIII)• Baulieu XIV distance (BaulieuXIV )• Baulieu XV distance (BaulieuXV )• Benini I correlation (BeniniI)• Benini II correlation (BeniniII)• Bennet's S correlation (Bennet)• Braun-Blanquet similarity (BraunBlanquet)• Canberra distance (Canberra)• Cao similarity (Cao)• Chao's Dice similarity (ChaoDice)• Chao's Jaccard similarity (ChaoJaccard)• Chebyshev distance (Chebyshev)• Chord distance (Chord)• Clark distance (Clark)• Clement similarity (Clement)• Cohen's Kappa similarity (CohenKappa)• Cole correlation (Cole)• Consonni & Todeschini I similarity (ConsonniTodeschiniI)• Consonni & Todeschini II similarity (ConsonniTodeschiniII)• Consonni & Todeschini III similarity (ConsonniTodeschiniIII)• Consonni & Todeschini IV similarity (ConsonniTodeschiniIV )3.1. abydos package

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Abydos Documentation, Release 0.5.0• Consonni & Todeschini V correlation (ConsonniTodeschiniV )• Cosine similarity (Cosine)• Dennis similarity (Dennis)• Dice's Asymmetric I similarity (DiceAsymmetricI)• Dice's Asymmetric II similarity (DiceAsymmetricII)• Digby correlation (Digby)• Dispersion correlation (Dispersion)• Doolittle similarity (Doolittle)• Dunning similarity (Dunning)• Euclidean distance (Euclidean)• Eyraud similarity (Eyraud)• Fager & McGowan similarity (FagerMcGowan)• Faith similarity (Faith)• Fidelity similarity (Fidelity)• Fleiss correlation (Fleiss)• Fleiss-Levin-Paik similarity (FleissLevinPaik)• Forbes I similarity (ForbesI)• Forbes II correlation (ForbesII)• Fossum similarity (Fossum)• Generalized Fleiss correlation (GeneralizedFleiss)• Gilbert correlation (Gilbert)• Gilbert & Wells similarity (GilbertWells)• Gini I correlation (GiniI)• Gini II correlation (GiniII)• Goodall similarity (Goodall)• Goodman & Kruskal's Lambda similarity (GoodmanKruskalLambda)• Goodman & Kruskal's Lambda-r correlation (GoodmanKruskalLambdaR)• Goodman & Kruskal's Tau A similarity (GoodmanKruskalTauA)• Goodman & Kruskal's Tau B similarity (GoodmanKruskalTauB)• Gower & Legendre similarity (GowerLegendre)• Guttman Lambda A similarity (GuttmanLambdaA)• Guttman Lambda B similarity (GuttmanLambdaB)• Gwet's AC correlation (GwetAC)• Hamann correlation (Hamann)• Harris & Lahey similarity (HarrisLahey)• Hassanat distance (Hassanat)

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Abydos Documentation, Release 0.5.0• Hawkins & Dotson similarity (HawkinsDotson)• Hellinger distance (Hellinger)• Henderson-Heron similarity (HendersonHeron)• Horn-Morisita similarity (HornMorisita)• Hurlbert correlation (Hurlbert)• Jaccard similarity (Jaccard) & Tanimoto coefficient (Jaccard.tanimoto_coeff())• Jaccard-NM similarity (JaccardNM )• Johnson similarity (Johnson)• Kendall's Tau correlation (KendallTau)• Kent & Foster I similarity (KentFosterI)• Kent & Foster II similarity (KentFosterII)• Köppen I correlation (KoppenI)• Köppen II similarity (KoppenII)• Kuder & Richardson correlation (KuderRichardson)• Kuhns I correlation (KuhnsI)• Kuhns II correlation (KuhnsII)• Kuhns III correlation (KuhnsIII)• Kuhns IV correlation (KuhnsIV )• Kuhns V correlation (KuhnsV )• Kuhns VI correlation (KuhnsVI)• Kuhns VII correlation (KuhnsVII)• Kuhns VIII correlation (KuhnsVIII)• Kuhns IX correlation (KuhnsIX)• Kuhns X correlation (KuhnsX)• Kuhns XI correlation (KuhnsXI)• Kuhns XII similarity (KuhnsXII)• Kulczynski I similarity (KulczynskiI)• Kulczynski II similarity (KulczynskiII)• Lorentzian distance (Lorentzian)• Maarel correlation (Maarel)• Manhattan distance (Manhattan)• Morisita similarity (Morisita)• marking distance (Marking)• marking metric (MarkingMetric)• MASI similarity (MASI)• Matusita distance (Matusita)

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Abydos Documentation, Release 0.5.0• Maxwell & Pilliner correlation (MaxwellPilliner)• McConnaughey correlation (McConnaughey)• McEwen & Michael correlation (McEwenMichael)• mean squared contingency correlation (MSContingency)• Michael similarity (Michael)• Michelet similarity (Michelet)• Millar distance (Millar)• Minkowski distance (Minkowski)• Mountford similarity (Mountford)• Mutual Information similarity (MutualInformation)• Overlap distance (Overlap)• Pattern difference (Pattern)• Pearson & Heron II correlation (PearsonHeronII)• Pearson II similarity (PearsonII)• Pearson III correlation (PearsonIII)• Pearson's Chi-Squared similarity (PearsonChiSquared)• Pearson's Phi correlation (PearsonPhi)• Peirce correlation (Peirce)• q-gram distance (QGram)• Raup-Crick similarity (RaupCrick)• Rogers & Tanimoto similarity (RogersTanimoto)• Rogot & Goldberg similarity (RogotGoldberg)• Russell & Rao similarity (RussellRao)• Scott's Pi correlation (ScottPi)• Shape difference (Shape)• Size difference (Size)• Sokal & Michener similarity (SokalMichener)• Sokal & Sneath I similarity (SokalSneathI)• Sokal & Sneath II similarity (SokalSneathII)• Sokal & Sneath III similarity (SokalSneathIII)• Sokal & Sneath IV similarity (SokalSneathIV )• Sokal & Sneath V similarity (SokalSneathV )• Sørensen–Dice coefficient (Dice)• Sorgenfrei similarity (Sorgenfrei)• Steffensen similarity (Steffensen)• Stiles similarity (Stiles)

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Abydos Documentation, Release 0.5.0• Stuart's Tau correlation (StuartTau)• Tarantula similarity (Tarantula)• Tarwid correlation (Tarwid)• Tetrachoric correlation coefficient (Tetrachronic)• Tulloss' R similarity (TullossR)• Tulloss' S similarity (TullossS)• Tulloss' T similarity (TullossT)• Tulloss' U similarity (TullossU )• Tversky distance (Tversky)• Weighted Jaccard similarity (WeightedJaccard)• Unigram subtuple similarity (UnigramSubtuple)• Unknown A correlation (UnknownA)• Unknown B similarity (UnknownB)• Unknown C similarity (UnknownC)• Unknown D similarity (UnknownD)• Unknown E correlation (UnknownE)• Unknown F similarity (UnknownF)• Unknown G similarity (UnknownG)• Unknown H similarity (UnknownH)• Unknown I similarity (UnknownI)• Unknown J similarity (UnknownJ)• Unknown K distance (UnknownK)• Unknown L similarity (UnknownL)• Unknown M similarity (UnknownM )• Upholt similarity (Upholt)• Warrens I correlation (WarrensI)• Warrens II similarity (WarrensII)• Warrens III correlation (WarrensIII)• Warrens IV similarity (WarrensIV )• Warrens V similarity (WarrensV )• Whittaker distance (Whittaker)• Yates' Chi-Squared similarity (YatesChiSquared)• Yule's Q correlation (YuleQ)• Yule's Q II distance (YuleQII)• Yule's Y correlation (YuleY)• YJHHR distance (YJHHR)

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Abydos Documentation, Release 0.5.0• Bhattacharyya distance (Bhattacharyya)• Brainerd-Robinson similarity (BrainerdRobinson)• Quantitative Cosine similarity (QuantitativeCosine)• Quantitative Dice similarity (QuantitativeDice)• Quantitative Jaccard similarity (QuantitativeJaccard)• Roberts similarity (Roberts)• Average linkage distance (AverageLinkage)• Single linkage distance (SingleLinkage)• Complete linkage distance (CompleteLinkage)• Bag distance (Bag)• Soft cosine similarity (SoftCosine)• Monge-Elkan distance (MongeElkan)• TF-IDF similarity (TFIDF)• SoftTF-IDF similarity (SoftTFIDF)• Jensen-Shannon divergence (JensenShannon)• Simplified Fellegi-Sunter distance (FellegiSunter)• MinHash similarity (MinHash)• BLEU similarity (BLEU )• Rouge-L similarity (RougeL)• Rouge-W similarity (RougeW )• Rouge-S similarity (RougeS)• Rouge-SU similarity (RougeSU )• Positional Q-Gram Dice distance (PositionalQGramDice)• Positional Q-Gram Jaccard distance (PositionalQGramJaccard)• Positional Q-Gram Overlap distance (PositionalQGramOverlap)
Three popular sequence alignment algorithms are provided:• Needleman-Wunsch score (NeedlemanWunsch)• Smith-Waterman score (SmithWaterman)• Gotoh score (Gotoh)
Classes relating to substring and subsequence distances include:• Longest common subsequence (LCSseq)• Longest common substring (LCSstr)• Ratcliff-Obserhelp distance (RatcliffObershelp)
A number of simple distance classes provided in the package include:• Identity distance (Ident)• Length distance (Length)

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Abydos Documentation, Release 0.5.0• Prefix distance (Prefix)• Suffix distance (Suffix)
Normalized compression distance classes for a variety of compression algorithms are provided:• zlib (NCDzlib)• bzip2 (NCDbz2)• lzma (NCDlzma)• LZSS (NCDlzss)• arithmetic coding (NCDarith)• PAQ9A (NCDpaq9a)• BWT plus RLE (NCDbwtrle)• RLE (NCDrle)
Three similarity measures from SeatGeek's FuzzyWuzzy:• FuzzyWuzzy Partial String similarity (FuzzyWuzzyPartialString)• FuzzyWuzzy Token Sort similarity (FuzzyWuzzyTokenSort)• FuzzyWuzzy Token Set similarity (FuzzyWuzzyTokenSet)
A convenience class, allowing one to pass a list of string transforms (phonetic algorithms, string transforms, and/or
stemmers) and, optionally, a string distance measure to compute the similarity/distance of two strings that have undergone each transform, is provided in:• Phonetic distance (PhoneticDistance)
The remaining distance measures & metrics include:• Western Airlines' Match Rating Algorithm comparison (distance.MRA)• Editex (Editex)• Bavarian Landesamt für Statistik distance (Baystat)• Eudex distance (distance.Eudex)• Sift4 distance (Sift4, Sift4Simplest, Sift4Extended)• Typo distance (Typo)• Synoname (Synoname)• Ozbay metric (Ozbay)• Indice de Similitude-Guth (ISG)• INClusion Programme (Inclusion)• Guth (Guth)• Victorian Panel Study (VPS)• LIG3 (LIG3)• String subsequence kernel (SSK) (SSK)
Most of the distance and similarity measures have sim and dist methods, which return a measure that is normalized
to the range [0, 1]. The normalized distance and similarity are always complements, so the normalized distance will
always equal 1 - the similarity for a particular measure supplied with the same input. Some measures have an absolute
distance method dist_abs that is not limited to any range.

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All three methods can be demonstrated using the DamerauLevenshtein class:>>> dl = DamerauLevenshtein()>>> dl.dist_abs('orange', 'strange')2>>> dl.dist('orange', 'strange')
0.2857142857142857>>> dl.sim('orange', 'strange')
0.7142857142857143

abydos.distance.sim(src, tar, method=<function sim_levenshtein>)
Return a similarity of two strings.
This is a generalized function for calling other similarity functions.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• method (function) -- Specifies the similarity metric (sim_levenshtein() by default)
Returns Similarity according to the specified function
Return type float
Raises AttributeError -- Unknown distance function
Examples>>> round(sim('cat', 'hat'), 12)
0.666666666667>>> round(sim('Niall', 'Neil'), 12)
0.4>>> sim('aluminum', 'Catalan')
0.125>>> sim('ATCG', 'TAGC')
0.25

New in version 0.1.0.
abydos.distance.dist(src, tar, method=<function sim_levenshtein>)
Return a distance between two strings.
This is a generalized function for calling other distance functions.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• method (function) -- Specifies the similarity metric (sim_levenshtein() by default) -- Note that this takes a similarity metric function, not a distance metric function.
Returns Distance according to the specified function
Return type float
Raises AttributeError -- Unknown distance function
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Examples>>> round(dist('cat', 'hat'), 12)0.333333333333>>> round(dist('Niall', 'Neil'), 12)0.6>>> dist('aluminum', 'Catalan')
0.875>>> dist('ATCG', 'TAGC')
0.75

New in version 0.1.0.
class abydos.distance.Levenshtein(mode='lev', cost=(1, 1, 1, 1), normalizer=<built-in functionmax>, taper=False, **kwargs)
Bases: abydos.distance._distance._DistanceLevenshtein distance.
This is the standard edit distance measure. Cf. [Lev65][Lev66].
Optimal string alignment (aka restricted Damerau-Levenshtein distance) [Boy11] is also supported.
The ordinary Levenshtein & Optimal String Alignment distance both employ the Wagner-Fischer dynamic programming algorithm [WF74].
Levenshtein edit distance ordinarily has unit insertion, deletion, and substitution costs.
New in version 0.3.6.
Changed in version 0.4.0: Added taper option
Initialize Levenshtein instance.
Parameters• mode (str) -- Specifies a mode for computing the Levenshtein distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• taper (bool) -- Enables cost tapering. Following [ZD96], it causes edits at the start of thestring to "just [exceed] twice the minimum penalty for replacement or deletion at the end of
the string".
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
alignment(src, tar)
Return the Levenshtein alignment of two strings.
Parameters• src (str) -- Source string for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison
Returns A tuple containing the Levenshtein distance and the two strings, aligned.
Return type tuple
Examples>>> cmp = Levenshtein()>>> cmp.alignment('cat', 'hat')(1.0, 'cat', 'hat')>>> cmp.alignment('Niall', 'Neil')(3.0, 'N-iall', 'Nei-l-')>>> cmp.alignment('aluminum', 'Catalan')(7.0, '-aluminum', 'Catalan--')>>> cmp.alignment('ATCG', 'TAGC')(3.0, 'ATCG-', '-TAGC')>>> cmp = Levenshtein(mode='osa')>>> cmp.alignment('ATCG', 'TAGC')(2.0, 'ATCG', 'TAGC')>>> cmp.alignment('ACTG', 'TAGC')(4.0, 'ACT-G-', '--TAGC')

New in version 0.4.1.
dist(src, tar)
Return the normalized Levenshtein distance between two strings.
The Levenshtein distance is normalized by dividing the Levenshtein distance (calculated by either of thetwo supported methods) by the greater of the number of characters in src times the cost of a delete and thenumber of characters in tar times the cost of an insert. For the case in which all operations have 𝑐𝑜𝑠𝑡 = 1,
this is equivalent to the greater of the length of the two strings src & tar.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Levenshtein distance between src & tar
Return type float
Examples>>> cmp = Levenshtein()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.6>>> cmp.dist('aluminum', 'Catalan')
0.875>>> cmp.dist('ATCG', 'TAGC')
0.75

New in version 0.1.0.
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dist_abs(src, tar)
Return the Levenshtein distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Levenshtein distance between src & tar
Return type int (may return a float if cost has float values)
Examples>>> cmp = Levenshtein()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')7>>> cmp.dist_abs('ATCG', 'TAGC')
3>>> cmp = Levenshtein(mode='osa')>>> cmp.dist_abs('ATCG', 'TAGC')
2>>> cmp.dist_abs('ACTG', 'TAGC')4

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.levenshtein(src, tar, mode='lev', cost=(1, 1, 1, 1))
Return the Levenshtein distance between two strings.
This is a wrapper of Levenshtein.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• mode (str) -- Specifies a mode for computing the Levenshtein distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The Levenshtein distance between src & tar
Return type int (may return a float if cost has float values)

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Examples>>> levenshtein('cat', 'hat')1>>> levenshtein('Niall', 'Neil')3>>> levenshtein('aluminum', 'Catalan')7>>> levenshtein('ATCG', 'TAGC')
3>>> levenshtein('ATCG', 'TAGC', mode='osa')
2>>> levenshtein('ACTG', 'TAGC', mode='osa')
4

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Levenshtein.dist_abs method instead.
abydos.distance.dist_levenshtein(src, tar, mode='lev', cost=(1, 1, 1, 1))
Return the normalized Levenshtein distance between two strings.
This is a wrapper of Levenshtein.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• mode (str) -- Specifies a mode for computing the Levenshtein distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The Levenshtein distance between src & tar
Return type float
Examples>>> round(dist_levenshtein('cat', 'hat'), 12)0.333333333333>>> round(dist_levenshtein('Niall', 'Neil'), 12)0.6>>> dist_levenshtein('aluminum', 'Catalan')
0.875>>> dist_levenshtein('ATCG', 'TAGC')
0.75

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Levenshtein.dist method instead.

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abydos.distance.sim_levenshtein(src, tar, mode='lev', cost=(1, 1, 1, 1))
Return the Levenshtein similarity of two strings.
This is a wrapper of Levenshtein.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• mode (str) -- Specifies a mode for computing the Levenshtein distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The Levenshtein similarity between src & tar
Return type float
Examples>>> round(sim_levenshtein('cat', 'hat'), 12)
0.666666666667>>> round(sim_levenshtein('Niall', 'Neil'), 12)
0.4>>> sim_levenshtein('aluminum', 'Catalan')
0.125>>> sim_levenshtein('ATCG', 'TAGC')
0.25

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Levenshtein.sim method instead.
class abydos.distance.DamerauLevenshtein(cost=(1, 1, 1, 1), normalizer=<built-in functionmax>, **kwargs)
Bases: abydos.distance._distance._Distance
Damerau-Levenshtein distance.
This computes the Damerau-Levenshtein distance [Dam64]. Damerau-Levenshtein code is based on Java codeby Kevin L. Stern [Ste14], under the MIT license: https://github.com/KevinStern/software-and-algorithms/blob/master/src/main/java/blogspot/software_and_algorithms/stern_library/string/DamerauLevenshteinAlgorithm.
java
Initialize Levenshtein instance.
Parameters• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.

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New in version 0.4.0.
dist(src, tar)
Return the Damerau-Levenshtein similarity of two strings.
Damerau-Levenshtein distance normalized to the interval [0, 1].
The Damerau-Levenshtein distance is normalized by dividing the Damerau-Levenshtein distance by thegreater of the number of characters in src times the cost of a delete and the number of characters in tar
times the cost of an insert. For the case in which all operations have 𝑐𝑜𝑠𝑡 = 1, this is equivalent to thegreater of the length of the two strings src & tar.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Damerau-Levenshtein distance
Return type float
Examples>>> cmp = DamerauLevenshtein()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.6>>> cmp.dist('aluminum', 'Catalan')
0.875>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the Damerau-Levenshtein distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Damerau-Levenshtein distance between src & tar
Return type int (may return a float if cost has float values)
Raises ValueError -- Unsupported cost assignment; the cost of two transpositions must not
be less than the cost of an insert plus a delete.

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Examples>>> cmp = DamerauLevenshtein()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')7>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.damerau_levenshtein(src, tar, cost=(1, 1, 1, 1))
Return the Damerau-Levenshtein distance between two strings.
This is a wrapper of DamerauLevenshtein.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The Damerau-Levenshtein distance between src & tar
Return type int (may return a float if cost has float values)
Examples>>> damerau_levenshtein('cat', 'hat')1>>> damerau_levenshtein('Niall', 'Neil')3>>> damerau_levenshtein('aluminum', 'Catalan')7>>> damerau_levenshtein('ATCG', 'TAGC')2

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the DamerauLevenshtein.dist_abs method
instead.
abydos.distance.dist_damerau(src, tar, cost=(1, 1, 1, 1))
Return the Damerau-Levenshtein similarity of two strings.
This is a wrapper of DamerauLevenshtein.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Abydos Documentation, Release 0.5.0• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The normalized Damerau-Levenshtein distance
Return type float
Examples>>> round(dist_damerau('cat', 'hat'), 12)0.333333333333>>> round(dist_damerau('Niall', 'Neil'), 12)0.6>>> dist_damerau('aluminum', 'Catalan')
0.875>>> dist_damerau('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the DamerauLevenshtein.dist method instead.
abydos.distance.sim_damerau(src, tar, cost=(1, 1, 1, 1))
Return the Damerau-Levenshtein similarity of two strings.
This is a wrapper of DamerauLevenshtein.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 1))
Returns The normalized Damerau-Levenshtein similarity
Return type float
Examples>>> round(sim_damerau('cat', 'hat'), 12)
0.666666666667>>> round(sim_damerau('Niall', 'Neil'), 12)
0.4>>> sim_damerau('aluminum', 'Catalan')
0.125>>> sim_damerau('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the DamerauLevenshtein.sim method instead.
class abydos.distance.ShapiraStorerI(cost=(1, 1), prime=False, **kwargs)
Bases: abydos.distance._distance._Distance

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Shapira & Storer I edit distance with block moves, greedy algorithm.
Shapira & Storer's greedy edit distance [SS07] is similar to Levenshtein edit distance, but with two importantdistinctions:• It considers blocks of characters, if they occur in both the source and target strings, so the edit distance
between 'abcab' and 'abc' is only 1, since the substring 'ab' occurs in both and can be inserted as a block
into 'abc'.
• It allows three edit operations: insert, delete, and move (but not substitute). Thus the distance between'abcde' and 'deabc' is only 1 because the block 'abc' can be moved in 1 move operation, rather than being
deleted and inserted in 2 separate operations.
If prime is set to True at initialization, this employs the greedy' algorithm, which limits replacements of blocks
in the two strings to matching occurrences of the LCS.
New in version 0.4.0.
Initialize ShapiraStorerI instance.
Parameters• prime (bool) -- If True, employs the greedy' algorithm rather than greedy• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Shapira & Storer I distance.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Shapira & Storer I distance between src & tar
Return type float
Examples>>> cmp = ShapiraStorerI()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.333333333333>>> cmp.dist('aluminum', 'Catalan')
0.6>>> cmp.dist('ATCG', 'TAGC')
0.25

New in version 0.4.0.
dist_abs(src, tar)
Return the Shapira & Storer I edit distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns The Shapira & Storer I edit distance between src & tar
Return type int
Examples>>> cmp = ShapiraStorerI()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')9>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.4.0.
class abydos.distance.Marking(**kwargs)
Bases: abydos.distance._distance._Distance
Ehrenfeucht & Haussler's marking distance.
This edit distance [EH88] is the number of marked characters in one word that must be masked in order for thatword to consist entirely of substrings of another word.
It is normalized by the length of the first word.
New in version 0.4.0.
Initialize Marking instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized marking distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns marking distance
Return type float
Examples>>> cmp = Marking()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.6>>> cmp.dist('aluminum', 'Catalan')
0.625>>> cmp.dist('ATCG', 'TAGC')
0.5(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> cmp.dist('cbaabdcb', 'abcba')
0.25

New in version 0.4.0.
dist_abs(src, tar)
Return the marking distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns marking distance
Return type int
Examples>>> cmp = Marking()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')5>>> cmp.dist_abs('ATCG', 'TAGC')
2>>> cmp.dist_abs('cbaabdcb', 'abcba')
2

New in version 0.4.0.
class abydos.distance.MarkingMetric(**kwargs)
Bases: abydos.distance._marking.Marking
Ehrenfeucht & Haussler's marking metric.
This metric [EH88] is the base 2 logarithm of the product of the marking distances between each term plus 1
computed in both orders. For strings x and y, this is:

𝑑𝑖𝑠𝑡𝑀 𝑎𝑟𝑘𝑖𝑛𝑔𝑀 𝑒𝑡𝑟𝑖𝑐 (𝑥, 𝑦) = 𝑙𝑜𝑔2 ((𝑑𝑖𝑓 𝑓 (𝑥, 𝑦) + 1)(𝑑𝑖𝑓 𝑓 (𝑦, 𝑥) + 1))
The function diff is Ehrenfeucht & Haussler's marking distance Marking.
New in version 0.4.0.
Initialize MarkingMetric instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized marking distance of two strings.
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Abydos Documentation, Release 0.5.0• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns marking distance
Return type float
Examples>>> cmp = Marking()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.6>>> cmp.dist('aluminum', 'Catalan')
0.625>>> cmp.dist('ATCG', 'TAGC')
0.5>>> cmp.dist('cbaabdcb', 'abcba')
0.25

New in version 0.4.0.
dist_abs(src, tar)
Return the marking distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns marking distance
Return type int
Examples>>> cmp = MarkingMetric()>>> cmp.dist_abs('cat', 'hat')
2.0>>> cmp.dist_abs('Niall', 'Neil')
3.584962500721156>>> cmp.dist_abs('aluminum', 'Catalan')
4.584962500721156>>> cmp.dist_abs('ATCG', 'TAGC')
3.169925001442312>>> cmp.dist_abs('cbaabdcb', 'abcba')
2.584962500721156

New in version 0.4.0.
class abydos.distance.YujianBo(cost=(1, 1, 1, 1), **kwargs)
Bases: abydos.distance._levenshtein.Levenshtein
Yujian-Bo normalized Levenshtein distance.
Yujian-Bo's normalization of Levenshtein distance [YB07], given Levenshtein distance 𝐺𝐿𝐷(𝑋, 𝑌 ) between
two strings X and Y, is
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𝑑𝑖𝑠𝑡𝑁 −𝐺𝐿𝐷 (𝑋, 𝑌 ) =2 · 𝐺𝐿𝐷(𝑋, 𝑌 )|𝑋| + |𝑌 | + 𝐺𝐿𝐷(𝑋, 𝑌 )New in version 0.4.0.
Initialize YujianBo instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the Yujian-Bo normalized edit distance between strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Yujian-Bo normalized edit distance between src & tar
Return type float
Examples>>> cmp = YujianBo()>>> round(cmp.dist('cat', 'hat'), 12)0.285714285714>>> round(cmp.dist('Niall', 'Neil'), 12)0.5>>> cmp.dist('aluminum', 'Catalan')
0.6363636363636364>>> cmp.dist('ATCG', 'TAGC')
0.5454545454545454

New in version 0.4.0.
dist_abs(src, tar)
Return the Yujian-Bo normalized edit distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Yujian-Bo normalized edit distance between src & tar
Return type int

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Examples>>> cmp = YujianBo()>>> cmp.dist_abs('cat', 'hat')
0.2857142857142857>>> cmp.dist_abs('Niall', 'Neil')
0.5>>> cmp.dist_abs('aluminum', 'Catalan')
0.6363636363636364>>> cmp.dist_abs('ATCG', 'TAGC')
0.5454545454545454

New in version 0.4.0.
class abydos.distance.HigueraMico(**kwargs)
Bases: abydos.distance._distance._Distance
The Higuera-Micó contextual normalized edit distance.
This is presented in [delHigueraMico08].
This measure is not normalized to a particular range. Indeed, for an string of infinite length as and a string of 0
length, the contextual normalized edit distance would be infinity. But so long as the relative difference in string
lengths is not too great, the distance will generally remain below 1.0
Notes
The "normalized" version of this distance, implemented in the dist method is merely the minimum of the distanceand 1.0.
New in version 0.4.0.
Initialize Levenshtein instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the bounded Higuera-Micó distance between two strings.
This is the distance bounded to the range [0, 1].
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The bounded Higuera-Micó distance between src & tar
Return type float

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Examples>>> cmp = HigueraMico()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.5333333333333333>>> cmp.dist('aluminum', 'Catalan')
0.7916666666666667>>> cmp.dist('ATCG', 'TAGC')
0.6000000000000001

New in version 0.4.0.
dist_abs(src, tar)
Return the Higuera-Micó distance between two strings.
This is a straightforward implementation of Higuera & Micó pseudocode from [delHigueraMico08], ported
to Numpy.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Higuera-Micó distance between src & tar
Return type float
Examples>>> cmp = HigueraMico()>>> cmp.dist_abs('cat', 'hat')
0.3333333333333333>>> cmp.dist_abs('Niall', 'Neil')
0.5333333333333333>>> cmp.dist_abs('aluminum', 'Catalan')
0.7916666666666667>>> cmp.dist_abs('ATCG', 'TAGC')
0.6000000000000001

New in version 0.4.0.
class abydos.distance.Indel(**kwargs)
Bases: abydos.distance._levenshtein.Levenshtein
Indel distance.
This is equivalent to Levenshtein distance, when only inserts and deletes are possible.
New in version 0.3.6.
Initialize Levenshtein instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized indel distance between two strings.

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This is equivalent to normalized Levenshtein distance, when only inserts and deletes are possible.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized indel distance
Return type float
Examples>>> cmp = Indel()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.333333333333>>> round(cmp.dist('Colin', 'Cuilen'), 12)0.454545454545>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.3.6.
abydos.distance.indel(src, tar)
Return the indel distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Indel distance
Return type int
Examples>>> indel('cat', 'hat')2>>> indel('Niall', 'Neil')3>>> indel('Colin', 'Cuilen')5>>> indel('ATCG', 'TAGC')4

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Indel.dist_abs method instead.
abydos.distance.dist_indel(src, tar)
Return the normalized indel distance between two strings.
This is equivalent to normalized Levenshtein distance, when only inserts and deletes are possible.
Parameters

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Abydos Documentation, Release 0.5.0• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized indel distance
Return type float
Examples>>> round(dist_indel('cat', 'hat'), 12)0.333333333333>>> round(dist_indel('Niall', 'Neil'), 12)0.333333333333>>> round(dist_indel('Colin', 'Cuilen'), 12)0.454545454545>>> dist_indel('ATCG', 'TAGC')
0.5

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Indel.dist method instead.
abydos.distance.sim_indel(src, tar)
Return the normalized indel similarity of two strings.
This is equivalent to normalized Levenshtein similarity, when only inserts and deletes are possible.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized indel similarity
Return type float
Examples>>> round(sim_indel('cat', 'hat'), 12)
0.666666666667>>> round(sim_indel('Niall', 'Neil'), 12)
0.666666666667>>> round(sim_indel('Colin', 'Cuilen'), 12)0.545454545455>>> sim_indel('ATCG', 'TAGC')
0.5

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Indel.sim method instead.
class abydos.distance.SAPS(cost=(1, -1, -4, 6, -2, -1, -3), normalizer=<built-in function max>, tokenizer=None, **kwargs)
Bases: abydos.distance._distance._Distance
Syllable Alignment Pattern Searching tokenizer.
This is the alignment and similarity calculation described on p. 917-918 of [RY05].

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New in version 0.4.0.
Initialize SAPS instance.
Parameters• cost (tuple) -- A 7-tuple representing the cost of the four possible matches:– syllable-internal match– syllable-internal mis-match– syllable-initial match or mismatch with syllable-internal– syllable-initial match– syllable-initial mis-match– syllable-internal gap– syllable-initial gap(by default: (1, -1, -4, 6, -2, -1, -3))
• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the normalized SAPS similarity between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized SAPS similarity between src & tar
Return type float
Examples>>> cmp = SAPS()>>> round(cmp.sim('cat', 'hat'), 12)
0.0>>> round(cmp.sim('Niall', 'Neil'), 12)0.2>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the SAPS similarity between two strings.
Parameters• src (str) -- Source string for comparison

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Returns The SAPS similarity between src & tar
Return type int
Examples>>> cmp = SAPS()>>> cmp.sim_score('cat', 'hat')0>>> cmp.sim_score('Niall', 'Neil')3>>> cmp.sim_score('aluminum', 'Catalan')-11>>> cmp.sim_score('ATCG', 'TAGC')-1>>> cmp.sim_score('Stevenson', 'Stinson')16

New in version 0.4.0.
class abydos.distance.MetaLevenshtein(tokenizer=None,
corpus=None,
metric=None,
normalizer=<built-in function max>, **kwargs)
Bases: abydos.distance._distance._DistanceMeta-Levenshtein distance.
Meta-Levenshtein distance [MYCappe08] combines Soft-TFIDF with Levenshtein alignment.
New in version 0.4.0.
Initialize MetaLevenshtein instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• corpus (UnigramCorpus) -- A unigram corpus UnigramCorpus. If None, a corpus
will be created from the two words when a similarity function is called.
• metric (_Distance) -- A string distance measure class for making soft matches, by
default Jaro-Winkler.
• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized Levenshtein distance between two strings.
The Levenshtein distance is normalized by dividing the Levenshtein distance (calculated by any of thethree supported methods) by the greater of the number of characters in src times the cost of a delete andthe number of characters in tar times the cost of an insert. For the case in which all operations have𝑐𝑜𝑠𝑡 = 1, this is equivalent to the greater of the length of the two strings src & tar.
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Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Levenshtein distance between src & tar
Return type float
Examples>>> cmp = MetaLevenshtein()>>> round(cmp.dist('cat', 'hat'), 12)0.205186754296>>> round(cmp.dist('Niall', 'Neil'), 12)
0.507780131444>>> cmp.dist('aluminum', 'Catalan')
0.8675933954313434>>> cmp.dist('ATCG', 'TAGC')
0.8077801314441113

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the Meta-Levenshtein distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Meta-Levenshtein distance
Return type float
Examples>>> cmp = MetaLevenshtein()>>> cmp.dist_abs('cat', 'hat')
0.6155602628882225>>> cmp.dist_abs('Niall', 'Neil')
2.538900657220556>>> cmp.dist_abs('aluminum', 'Catalan')
6.940747163450747>>> cmp.dist_abs('ATCG', 'TAGC')
3.2311205257764453

New in version 0.4.0.
class abydos.distance.Covington(weights=(0, 5, 10, 30, 60, 100, 40, 50), **kwargs)
Bases: abydos.distance._distance._Distance
Covington distance.
Covington distance [Cov96]
New in version 0.4.0.
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Initialize Covington instance.
Parameters• weights (tuple) -- An 8-tuple of costs for each kind of match or mismatch described in
Covington's paper:– exact consonant or glide match– exact vowel match– vowel-vowel length mismatch or i and y or u and w– vowel-vowel mismatch– consonant-consonant mismatch– consonant-vowel mismatch– skip preceded by a skip
– skip not preceded by a skip
The weights used in Covington's first approximation can be used by supplying the tuple (0.0,
0.0, 0.5, 0.5, 0.5, 1.0, 0.5, 0.5)
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
alignment(src, tar)
Return the top Covington alignment of two strings.
This returns only the top alignment in a standard (score, source alignment, target alignment) tuple format.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Covington score & alignment
Return type tuple(float, str, str)
Examples>>> cmp = Covington()>>> cmp.alignment('hart', 'kordis')(240, 'hart--', 'kordis')>>> cmp.alignment('niy', 'genu')(170, '--niy', 'genu-')

New in version 0.4.1.
alignments(src, tar, top_n=None)
Return the Covington alignments of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• top_n (int) -- The number of alignments to return. If None, all alignments will be
returned. If 0, all alignments with the top score will be returned.
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Returns Covington alignments
Return type list
Examples>>> cmp = Covington()>>> cmp.alignments('hart', 'kordis', top_n=1)[0]Alignment(src='hart--', tar='kordis', score=240)>>> cmp.alignments('niy', 'genu', top_n=1)[0]Alignment(src='--niy', tar='genu-', score=170)

New in version 0.4.0.
dist(src, tar)
Return the normalized Covington distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Covington distance
Return type float
Examples>>> cmp = Covington()>>> cmp.dist('cat', 'hat')
0.19117647058823528>>> cmp.dist('Niall', 'Neil')
0.25555555555555554>>> cmp.dist('aluminum', 'Catalan')
0.43333333333333335>>> cmp.dist('ATCG', 'TAGC')
0.45454545454545453

New in version 0.4.0.
dist_abs(src, tar)
Return the Covington distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Covington distance
Return type float

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Examples>>> cmp = Covington()>>> cmp.dist_abs('cat', 'hat')65>>> cmp.dist_abs('Niall', 'Neil')115>>> cmp.dist_abs('aluminum', 'Catalan')325>>> cmp.dist_abs('ATCG', 'TAGC')200

New in version 0.4.0.
class abydos.distance.ALINE(epsilon=0, c_skip=-10, c_sub=35, c_exp=45, c_vwl=10,
mode='local', phones='aline', normalizer=<built-in functionmax>, **kwargs)
Bases: abydos.distance._distance._Distance
ALINE alignment, similarity, and distance.
ALINE alignment was developed by [Kon00][Kon02][DHC+08], and establishes an alignment algorithm basedon multivalued phonetic features and feature salience weights. Along with the alignment itself, the algorithm
produces a term similarity score.
[DHC+08] develops ALINE's similarity score into a similarity measure & distance measure:

𝑠𝑖𝑚𝐴𝐿𝐼𝑁 𝐸 =2𝑠𝑐𝑜𝑟𝑒˙
𝐴𝐿𝐼𝑁 𝐸 (𝑠𝑟𝑐, 𝑡𝑎𝑟)𝑠𝑐𝑜𝑟𝑒𝐴𝐿𝐼𝑁 𝐸 (𝑠𝑟𝑐, 𝑠𝑟𝑐) + 𝑠𝑐𝑜𝑟𝑒𝐴𝐿𝐼𝑁 𝐸 (𝑡𝑎𝑟, 𝑡𝑎𝑟)

However, because the average of the two self-similarity scores is not guaranteed to be greater than or equal to
the similarity score between the two strings, by default, this formula is not used here in order to guarantee thatthe similarity measure is bounded to [0, 1]. Instead, Kondrak's similarity measure is employed:

𝑠𝑖𝑚𝐴𝐿𝐼𝑁 𝐸 =𝑠𝑐𝑜𝑟𝑒𝐴𝐿𝐼𝑁 𝐸 (𝑠𝑟𝑐, 𝑡𝑎𝑟)𝑚𝑎𝑥(𝑠𝑐𝑜𝑟𝑒𝐴𝐿𝐼𝑁 𝐸 (𝑠𝑟𝑐, 𝑠𝑟𝑐), 𝑠𝑐𝑜𝑟𝑒𝐴𝐿𝐼𝑁 𝐸 (𝑡𝑎𝑟, 𝑡𝑎𝑟))

New in version 0.4.0.
Initialize ALINE instance.
Parameters• epsilon (float) -- The portion (out of 1.0) of the maximum ALINE score, above whichalignments are returned. If set to 0, only the alignments matching the maximum alignmentscore are returned. If set to 1, all alignments scoring 0 or higher are returned.
• c_skip (int) -- The cost of an insertion or deletion• c_sub (int) -- The cost of a substitution• c_exp (int) -- The cost of an expansion or contraction• c_vwl (int) -- The additional cost of a vowel substitution, expansion, or contraction• mode (str) -- Alignment mode, which can be local (default), global, half-local,
or semi-global• phones (str) -3.1. abydos package

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Phonetic symbol set, which can be:– aline selects Kondrak's original symbols set– ipa selects IPA symbols• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). For the normalization proposedby Downey, et al. (2008), set this to: lambda x: sum(x)/len(x)• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
alignment(src, tar)
Return the top ALINE alignment of two strings.
The top ALINE alignment is the first alignment with the best score. The purpose of this function is to havea single tuple as a return value.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns ALINE alignment and its score
Return type tuple(float, str, str)
Examples>>> cmp = ALINE()>>> cmp.alignment('cat', 'hat')(50.0, 'c || a t ||', 'h || a t ||')>>> cmp.alignment('niall', 'neil')(90.0, '|| n i a ll ||', '|| n e i l ||')>>> cmp.alignment('aluminum', 'catalan')(81.5, '|| a l u m || inum', 'cat || a l a n ||')>>> cmp.alignment('atcg', 'tagc')(65.0, '|| a t c || g', 't || a g c ||')New in version 0.4.1.
alignments(src, tar, score_only=False)
Return the ALINE alignments of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• score_only (bool) -- Return the score only, not the alignments
Returns ALINE alignments and their scores or the top score
Return type list(tuple(float, str, str) or float

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Examples>>> cmp = ALINE()>>> cmp.alignments('cat', 'hat')[(50.0, 'c || a t ||', 'h || a t ||')]>>> cmp.alignments('niall', 'neil')[(90.0, '|| n i a ll ||', '|| n e i l ||')]>>> cmp.alignments('aluminum', 'catalan')[(81.5, '|| a l u m || inum', 'cat || a l a n ||')]>>> cmp.alignments('atcg', 'tagc')[(65.0, '|| a t c || g', 't || a g c ||'), (65.0, 'a || tc - g ||','|| t a g || c')]

New in version 0.4.0.
Changed in version 0.4.1: Renamed from .alignment to .alignments

c_features = {'aspirated', 'lateral', 'manner', 'nasal', 'place', 'retroflex', 'syllabi
feature_weights = {'affricate':

0.9, 'alveolar':

0.85, 'approximant':

0.6, 'back':

minus', 'place': phones_ipa'retroflex','retroflex':'plus','minus','syllabic':'minus','voice':'minus'},'': {'as= {'a':{'aspirated':'back':'front','high':'low', 'lateral'phones_kondrak = {'A': {'aspirated':'plus', 'supplemental':salience = {'aspirated':

5, 'high':5, 'back':

5, 'lateral':True}, 'B': {'back':
10, 'long':sim(src, tar)
Return the normalized ALINE similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized ALINE similarity
Return type float
Examples>>> cmp = ALINE()>>> cmp.dist('cat', 'hat')
0.4117647058823529>>> cmp.dist('niall', 'neil')
0.33333333333333337>>> cmp.dist('aluminum', 'catalan')
0.5925>>> cmp.dist('atcg', 'tagc')
0.45833333333333337

New in version 0.4.0.
sim_score(src, tar)
Return the ALINE alignment score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns ALINE alignment score
Return type float
Examples>>> cmp = ALINE()>>> cmp.sim_score('cat', 'hat')
50.0>>> cmp.sim_score('niall', 'neil')
90.0>>> cmp.sim_score('aluminum', 'catalan')
81.5>>> cmp.sim_score('atcg', 'tagc')
65.0

New in version 0.4.0.
v_features = {'back', 'high', 'long', 'nasal', 'retroflex', 'round', 'syllabic'}
class abydos.distance.FlexMetric(normalizer=<built-in function max>, indel_costs=None,
subst_costs=None, **kwargs)
Bases: abydos.distance._distance._Distance
FlexMetric distance.
FlexMetric distance [Kem05]
New in version 0.4.0.
Initialize FlexMetric instance.
Parameters• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• indel_costs (list of tuples) -- A list of insertion and deletion costs. Each list
element should be a tuple consisting of an iterable (sets are best) and a float value. The
iterable consists of those letters whose insertion or deletion has a cost equal to the float
value.
• subst_costs (list of tuples) -- A list of substitution costs. Each list element
should be a tuple consisting of an iterable (sets are best) and a float value. The iterable
consists of the letters in each letter class, which may be substituted for each other at costequal to the float value.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized FlexMetric distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized FlexMetric distance

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Return type float
Examples>>> cmp = FlexMetric()>>> cmp.dist('cat', 'hat')
0.26666666666666666>>> cmp.dist('Niall', 'Neil')
0.3>>> cmp.dist('aluminum', 'Catalan')
0.8375>>> cmp.dist('ATCG', 'TAGC')
0.5499999999999999

New in version 0.4.0.
dist_abs(src, tar)
Return the FlexMetric distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns FlexMetric distance
Return type float
Examples>>> cmp = FlexMetric()>>> cmp.dist_abs('cat', 'hat')
0.8>>> cmp.dist_abs('Niall', 'Neil')
1.5>>> cmp.dist_abs('aluminum', 'Catalan')
6.7>>> cmp.dist_abs('ATCG', 'TAGC')
2.1999999999999997

New in version 0.4.0.
class abydos.distance.BISIM(qval=2, **kwargs)
Bases: abydos.distance._distance._Distance
BI-SIM similarity.
BI-SIM similarity [KD03] is an n-gram based, edit-distance derived similarity measure.
New in version 0.4.0.
Initialize BISIM instance.
Parameters• qval (int) -- The number of characters to consider in each n-gram (q-gram). By default
this is 2, hence BI-SIM. But TRI-SIM can be calculated by setting this to 3.
• **kwargs -- Arbitrary keyword arguments

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New in version 0.4.0.
sim(src, tar)
Return the BI-SIM similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns BI-SIM similarity
Return type float
Examples>>> cmp = BISIM()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.4>>> cmp.sim('aluminum', 'Catalan')
0.3125>>> cmp.sim('ATCG', 'TAGC')
0.375

New in version 0.4.0.
class abydos.distance.DiscountedLevenshtein(mode='lev', normalizer=<built-in function
max>,
discount_from=1,
discount_func='log',
vowels='aeiou',**kwargs)
Bases: abydos.distance._levenshtein.Levenshtein
Discounted Levenshtein distance.
This is a variant of Levenshtein distance for which edits later in a string have discounted cost, on the theory thatearlier edits are less likely than later ones.
New in version 0.4.1.
Initialize DiscountedLevenshtein instance.
Parameters• mode (str) -- Specifies a mode for computing the discounted Levenshtein distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once
• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• discount_from (int or str) -- If an int is supplied, this is the first character whose
edit cost will be discounted. If the str coda is supplied, discounting will start with the firstnon-vowel after the first vowel (the first syllable coda).

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Abydos Documentation, Release 0.5.0• discount_func (str or function) -- The two supported str arguments are log,
for a logarithmic discount function, and exp for a exponential discount function. See notes
below for information on how to supply your own discount function.
• vowels (str) -- These are the letters to consider as vowels when discount_from is set tocoda. It defaults to the English vowels 'aeiou', but it would be reasonable to localize this toother languages or to add orthographic semi-vowels like 'y', 'w', and even 'h'.
• **kwargs -- Arbitrary keyword arguments
Notes
This class is highly experimental and will need additional tuning.
The discount function can be passed as a callable function. It should expect an integer as its only argument andreturn a float, ideally less than or equal to 1.0. The argument represents the degree of discounting to apply.
New in version 0.4.1.
dist(src, tar)
Return the normalized Levenshtein distance between two strings.
The Levenshtein distance is normalized by dividing the Levenshtein distance (calculated by any of thethree supported methods) by the greater of the number of characters in src times the cost of a delete andthe number of characters in tar times the cost of an insert. For the case in which all operations have𝑐𝑜𝑠𝑡 = 1, this is equivalent to the greater of the length of the two strings src & tar.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Levenshtein distance between src & tar
Return type float
Examples>>> cmp = DiscountedLevenshtein()>>> cmp.dist('cat', 'hat')
0.3513958291799864>>> cmp.dist('Niall', 'Neil')
0.5909885886270658>>> cmp.dist('aluminum', 'Catalan')
0.8348163322045603>>> cmp.dist('ATCG', 'TAGC')
0.7217609721523955

New in version 0.4.1.
dist_abs(src, tar)
Return the Levenshtein distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Levenshtein distance between src & tar

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Return type float (may return a float if cost has float values)
Examples>>> cmp = DiscountedLevenshtein()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')
2.526064024369237>>> cmp.dist_abs('aluminum', 'Catalan')
5.053867269967515>>> cmp.dist_abs('ATCG', 'TAGC')
2.594032108779918>>> cmp = DiscountedLevenshtein(mode='osa')>>> cmp.dist_abs('ATCG', 'TAGC')
1.7482385137517997>>> cmp.dist_abs('ACTG', 'TAGC')
3.342270622531718

New in version 0.4.1.
class abydos.distance.PhoneticEditDistance(mode='lev', cost=(1, 1, 1, 0.33333),
normalizer=<built-infunctionmax>,
weights=None, **kwargs)
Bases: abydos.distance._levenshtein.Levenshtein
Phonetic edit distance.
This is a variation on Levenshtein edit distance, intended for strings in IPA, that compares individual phones
based on their featural similarity.
New in version 0.4.1.
Initialize PhoneticEditDistance instance.
Parameters• mode (str) -- Specifies a mode for computing the edit distance:– lev (default) computes the ordinary Levenshtein distance, in which edits may include
inserts, deletes, and substitutions– osa computes the Optimal String Alignment distance, in which edits may include inserts,
deletes, substitutions, and transpositions but substrings may only be edited once• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and transpositions, respectively (by default: (1, 1, 1, 0.33333)). Note thattranspositions cost a relatively low 0.33333. If this were 1.0, no phones would ever betransposed under the normal weighting, since even quite dissimilar phones such as [a] and[p] still agree in nearly 63% of their features.
• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• weights (None or list or tuple or dict) -- If None, all features are of equalsignificance and a simple normalized hamming distance of the features is calculated. If a list
or tuple of numeric values is supplied, the values are inferred as the weights for each feature,in order of the features listed in abydos.phones._phones._FEATURE_MASK. If a dict is

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supplied, its key values should match keys in abydos.phones._phones._FEATURE_MASK
to which each weight (value) should be assigned. Missing values in all cases are assigned aweight of 0 and will be omitted from the comparison.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(src, tar)
Return the normalized phonetic edit distance between two strings.
The edit distance is normalized by dividing the edit distance (calculated by either of the two supported
methods) by the greater of the number of characters in src times the cost of a delete and the number ofcharacters in tar times the cost of an insert. For the case in which all operations have 𝑐𝑜𝑠𝑡 = 1, this isequivalent to the greater of the length of the two strings src & tar.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Levenshtein distance between src & tar
Return type float
Examples>>> cmp = PhoneticEditDistance()>>> round(cmp.dist('cat', 'hat'), 12)0.059139784946>>> round(cmp.dist('Niall', 'Neil'), 12)0.232258064516>>> cmp.dist('aluminum', 'Catalan')
0.3084677419354839>>> cmp.dist('ATCG', 'TAGC')
0.2983870967741935

New in version 0.4.1.
dist_abs(src, tar)
Return the phonetic edit distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The phonetic edit distance between src & tar
Return type int (may return a float if cost has float values)

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Examples>>> cmp = PhoneticEditDistance()>>> cmp.dist_abs('cat', 'hat')
0.17741935483870974>>> cmp.dist_abs('Niall', 'Neil')
1.161290322580645>>> cmp.dist_abs('aluminum', 'Catalan')
2.467741935483871>>> cmp.dist_abs('ATCG', 'TAGC')
1.193548387096774>>> cmp = PhoneticEditDistance(mode='osa')>>> cmp.dist_abs('ATCG', 'TAGC')
0.46236225806451603>>> cmp.dist_abs('ACTG', 'TAGC')
1.2580645161290323

New in version 0.4.1.
class abydos.distance.Hamming(diff_lens=True, **kwargs)
Bases: abydos.distance._distance._DistanceHamming distance.
Hamming distance [Ham50] equals the number of character positions at which two strings differ. For strings ofunequal lengths, it is not normally defined. By default, this implementation calculates the Hamming distanceof the first n characters where n is the lesser of the two strings' lengths and adds to this the difference in string
lengths.
New in version 0.3.6.
Initialize Hamming instance.
Parameters• diff_lens (bool) -- If True (default), this returns the Hamming distance for those characters that have a matching character in both strings plus the difference in the strings' lengths.
This is equivalent to extending the shorter string with obligatorily non-matching characters.
If False, an exception is raised in the case of strings of unequal lengths.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Hamming distance between two strings.
Hamming distance normalized to the interval [0, 1].
The Hamming distance is normalized by dividing it by the greater of the number of characters in src & tar(unless diff_lens is set to False, in which case an exception is raised).
The arguments are identical to those of the hamming() function.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Hamming distance

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Return type float
Examples>>> cmp = Hamming()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> cmp.dist('Niall', 'Neil')
0.6>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the Hamming distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Hamming distance between src & tar
Return type int
Raises ValueError -- Undefined for sequences of unequal length; set diff_lens to True for
Hamming distance between strings of unequal lengths.
Examples>>> cmp = Hamming()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')8>>> cmp.dist_abs('ATCG', 'TAGC')4

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.hamming(src, tar, diff_lens=True)
Return the Hamming distance between two strings.
This is a wrapper for Hamming.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Abydos Documentation, Release 0.5.0• diff_lens (bool) -- If True (default), this returns the Hamming distance for those characters that have a matching character in both strings plus the difference in the strings' lengths.
This is equivalent to extending the shorter string with obligatorily non-matching characters.
If False, an exception is raised in the case of strings of unequal lengths.
Returns The Hamming distance between src & tar
Return type int
Examples>>> hamming('cat', 'hat')1>>> hamming('Niall', 'Neil')3>>> hamming('aluminum', 'Catalan')8>>> hamming('ATCG', 'TAGC')4

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Hamming.dist_abs method instead.
abydos.distance.dist_hamming(src, tar, diff_lens=True)
Return the normalized Hamming distance between two strings.
This is a wrapper for Hamming.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• diff_lens (bool) -- If True (default), this returns the Hamming distance for those characters that have a matching character in both strings plus the difference in the strings' lengths.
This is equivalent to extending the shorter string with obligatorily non-matching characters.
If False, an exception is raised in the case of strings of unequal lengths.
Returns The normalized Hamming distance
Return type float
Examples>>> round(dist_hamming('cat', 'hat'), 12)0.333333333333>>> dist_hamming('Niall', 'Neil')
0.6>>> dist_hamming('aluminum', 'Catalan')
1.0>>> dist_hamming('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Hamming.dist method instead.

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abydos.distance.sim_hamming(src, tar, diff_lens=True)
Return the normalized Hamming similarity of two strings.
This is a wrapper for Hamming.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• diff_lens (bool) -- If True (default), this returns the Hamming distance for those characters that have a matching character in both strings plus the difference in the strings' lengths.
This is equivalent to extending the shorter string with obligatorily non-matching characters.
If False, an exception is raised in the case of strings of unequal lengths.
Returns The normalized Hamming similarity
Return type float
Examples>>> round(sim_hamming('cat', 'hat'), 12)
0.666666666667>>> sim_hamming('Niall', 'Neil')
0.4>>> sim_hamming('aluminum', 'Catalan')
0.0>>> sim_hamming('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Hamming.sim method instead.
class abydos.distance.MLIPNS(threshold=0.25, max_mismatches=2, **kwargs)
Bases: abydos.distance._distance._Distance
MLIPNS similarity.
Modified Language-Independent Product Name Search (MLIPNS) is described in [SA10]. This function returns
only 1.0 (similar) or 0.0 (not similar). LIPNS similarity is identical to normalized Hamming similarity.
New in version 0.3.6.
Initialize MLIPNS instance.
Parameters• threshold (float) -- A number [0, 1] indicating the maximum similarity score, below
which the strings are considered 'similar' (0.25 by default)• max_mismatches (int) -- A number indicating the allowable number of mismatches toremove before declaring two strings not similar (2 by default)• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the MLIPNS similarity of two strings.
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Abydos Documentation, Release 0.5.0• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns MLIPNS similarity
Return type float
Examples>>> sim_mlipns('cat', 'hat')
1.0>>> sim_mlipns('Niall', 'Neil')
0.0>>> sim_mlipns('aluminum', 'Catalan')
0.0>>> sim_mlipns('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_mlipns(src, tar, threshold=0.25, max_mismatches=2)
Return the MLIPNS distance between two strings.
This is a wrapper for MLIPNS.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• threshold (float) -- A number [0, 1] indicating the maximum similarity score, below
which the strings are considered 'similar' (0.25 by default)• max_mismatches (int) -- A number indicating the allowable number of mismatches toremove before declaring two strings not similar (2 by default)
Returns MLIPNS distance
Return type float
Examples>>> dist_mlipns('cat', 'hat')
0.0>>> dist_mlipns('Niall', 'Neil')
1.0>>> dist_mlipns('aluminum', 'Catalan')
1.0>>> dist_mlipns('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MLIPNS.dist method instead.

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abydos.distance.sim_mlipns(src, tar, threshold=0.25, max_mismatches=2)
Return the MLIPNS similarity of two strings.
This is a wrapper for MLIPNS.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• threshold (float) -- A number [0, 1] indicating the maximum similarity score, below
which the strings are considered 'similar' (0.25 by default)• max_mismatches (int) -- A number indicating the allowable number of mismatches toremove before declaring two strings not similar (2 by default)
Returns MLIPNS similarity
Return type float
Examples>>> sim_mlipns('cat', 'hat')
1.0>>> sim_mlipns('Niall', 'Neil')
0.0>>> sim_mlipns('aluminum', 'Catalan')
0.0>>> sim_mlipns('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MLIPNS.sim method instead.
class abydos.distance.RelaxedHamming(tokenizer=None, maxdist=2, discount=0.2, **kwargs)
Bases: abydos.distance._distance._Distance
Relaxed Hamming distance.
This is a variant of Hamming distance in which positionally close matches are considered partially matching.
New in version 0.4.1.
Initialize DiscountedHamming instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• maxdist (int) -- The maximum distance to consider for discounting.
• discount (float) -- The discount factor multiplied by the distance from the source
string position.
• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.1.

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dist(src, tar)
Return the normalized relaxed Hamming distance between strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized relaxed Hamming distance
Return type float
Examples>>> cmp = RelaxedHamming()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> cmp.dist('Niall', 'Neil')
0.27999999999999997>>> cmp.dist('aluminum', 'Catalan')
0.8>>> cmp.dist('ATCG', 'TAGC')
0.2

New in version 0.4.1.
dist_abs(src, tar)
Return the discounted Hamming distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Relaxed Hamming distance
Return type float
Examples>>> cmp = RelaxedHamming()>>> cmp.dist_abs('cat', 'hat')
1.0>>> cmp.dist_abs('Niall', 'Neil')
1.4>>> cmp.dist_abs('aluminum', 'Catalan')
6.4>>> cmp.dist_abs('ATCG', 'TAGC')
0.8

New in version 0.4.1.
class abydos.distance.Tichy(cost=(1, 1), **kwargs)
Bases: abydos.distance._distance._Distance
Tichy edit distance.
Tichy described an algorithm, implemented below, in [Tic84]. Following this, [Cor03] identifies an interpretation of this algorithm's output as a distance measure, which is largely followed by the methods below.
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Tichy's algorithm locates substrings of a string S to be copied in order to create a string T. The only otheroperation used by his algorithms for string reconstruction are add operations.
Notes
While [Cor03] counts only move operations to calculate distance, I give the option (enabled by default) ofcounting add operations as part of the distance measure. To ignore the cost of add operations, set the cost valueto (1, 0), for example, when initializing the object. Further, in the case that S and T are identical, a distance of
0 will be returned, even though this would still be counted as a single move operation spanning the whole ofstring S.
New in version 0.4.0.
Initialize Tichy instance.
Parameters• cost (tuple) -- A 2-tuple representing the cost of the two possible edits: block moves
and adds (by default: (1, 1))• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Tichy edit distance between two strings.
The Tichy distance is normalized by dividing the distance by the length of the tar string.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Tichy distance between src & tar
Return type float
Examples>>> cmp = Tichy()>>> round(cmp.dist('cat', 'hat'), 12)
0.666666666667>>> round(cmp.dist('Niall', 'Neil'), 12)
1.0>>> cmp.dist('aluminum', 'Catalan')
0.8571428571428571>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Tichy distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns The Tichy distance between src & tar
Return type int (may return a float if cost has float values)
Examples>>> cmp = Tichy()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')4>>> cmp.dist_abs('aluminum', 'Catalan')6>>> cmp.dist_abs('ATCG', 'TAGC')4

New in version 0.4.0.
class abydos.distance.BlockLevenshtein(cost=(1, 1, 1, 1), normalizer=<built-in functionmax>, **kwargs)
Bases: abydos.distance._levenshtein.Levenshtein
Levenshtein distance with block operations.
In addition to character-level insert, delete, and replace operations, this version of the Levenshtein distance
supports block-level insert, delete, and replace, provided that the block occurs in both input strings.
New in version 0.4.0.
Initialize BlockLevenshtein instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized block Levenshtein distance between strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Levenshtein distance with blocks between src & tar
Return type float
Examples>>> cmp = BlockLevenshtein()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.6>>> cmp.dist('aluminum', 'Catalan')
0.875>>> cmp.dist('ATCG', 'TAGC')
0.75

New in version 0.4.0.
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dist_abs(src, tar)
Return the block Levenshtein edit distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The block Levenshtein edit distance between src & tar
Return type int
Examples>>> cmp = BlockLevenshtein()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')7>>> cmp.dist_abs('ATCG', 'TAGC')3

New in version 0.4.0.
class abydos.distance.CormodeLZ(**kwargs)
Bases: abydos.distance._distance._DistanceCormode's LZ distance.
Cormode's LZ distance [CPSV00][Cor03]
New in version 0.4.0.
Initialize CormodeLZ instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Cormode's LZ distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Cormode's LZ distance
Return type float

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Examples>>> cmp = CormodeLZ()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.8>>> cmp.dist('aluminum', 'Catalan')
0.625>>> cmp.dist('ATCG', 'TAGC')
0.75

New in version 0.4.0.
dist_abs(src, tar)
Return the Cormode's LZ distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Cormode's LZ distance
Return type float
Examples>>> cmp = CormodeLZ()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')5>>> cmp.dist_abs('aluminum', 'Catalan')6>>> cmp.dist_abs('ATCG', 'TAGC')4

New in version 0.4.0.
class abydos.distance.JaroWinkler(qval=1,
mode='winkler',
long_strings=False,
boost_threshold=0.7, scaling_factor=0.1, **kwargs)
Bases: abydos.distance._distance._DistanceJaro-Winkler distance.
Jaro(-Winkler) distance is a string edit distance initially proposed by Jaro and extended by Winkler[Jar89][Win90].
This is Python based on the C code for strcmp95: http://web.archive.org/web/20110629121242/http://www.
census.gov/geo/msb/stand/strcmp.c [WMJL94]. The above file is a US Government publication and, accordingly, in the public domain.
New in version 0.3.6.
Initialize JaroWinkler instance.
Parameters• qval (int) -- The length of each q-gram (defaults to 1: character-wise matching)76

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Abydos Documentation, Release 0.5.0• mode (str) -- Indicates which variant of this distance metric to compute:– winkler -- computes the Jaro-Winkler distance (default) which increases the score formatches near the start of the word
– jaro -- computes the Jaro distance• long_strings (bool) -- Set to True to "Increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixed length fields such as
phone and social security numbers." (Used in 'winkler' mode only.)• boost_threshold (float) -- A value between 0 and 1, below which the Winkler boost
is not applied (defaults to 0.7). (Used in 'winkler' mode only.)• scaling_factor (float) -- A value between 0 and 0.25, indicating by how much toboost scores for matching prefixes (defaults to 0.1). (Used in 'winkler' mode only.)
New in version 0.4.0.
sim(src, tar)
Return the Jaro or Jaro-Winkler similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Jaro or Jaro-Winkler similarity
Return type float
Raises• ValueError -- Unsupported boost_threshold assignment; boost_threshold must be between 0 and 1.
• ValueError -- Unsupported scaling_factor assignment; scaling_factor must be between
0 and 0.25.'
Examples>>> round(sim_jaro_winkler('cat', 'hat'), 12)0.777777777778>>> round(sim_jaro_winkler('Niall', 'Neil'), 12)0.805>>> round(sim_jaro_winkler('aluminum', 'Catalan'), 12)0.60119047619>>> round(sim_jaro_winkler('ATCG', 'TAGC'), 12)0.833333333333>>> round(sim_jaro_winkler('cat', 'hat', mode='jaro'), 12)0.777777777778>>> round(sim_jaro_winkler('Niall', 'Neil', mode='jaro'), 12)0.783333333333>>> round(sim_jaro_winkler('aluminum', 'Catalan', mode='jaro'), 12)0.60119047619>>> round(sim_jaro_winkler('ATCG', 'TAGC', mode='jaro'), 12)0.833333333333

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New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_jaro_winkler(src, tar, qval=1, mode='winkler', long_strings=False,
boost_threshold=0.7, scaling_factor=0.1)
Return the Jaro or Jaro-Winkler distance between two strings.
This is a wrapper for JaroWinkler.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• qval (int) -- The length of each q-gram (defaults to 1: character-wise matching)• mode (str) -- Indicates which variant of this distance metric to compute:– winkler -- computes the Jaro-Winkler distance (default) which increases the score formatches near the start of the word
– jaro -- computes the Jaro distance• long_strings (bool) -- Set to True to "Increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixedlength fields such as
phone and social security numbers." (Used in 'winkler' mode only.)• boost_threshold (float) -- A value between 0 and 1, below which the Winkler boost
is not applied (defaults to 0.7). (Used in 'winkler' mode only.)• scaling_factor (float) -- A value between 0 and 0.25, indicating by how much toboost scores for matching prefixes (defaults to 0.1). (Used in 'winkler' mode only.)
Returns Jaro or Jaro-Winkler distance
Return type float
Examples>>> round(dist_jaro_winkler('cat', 'hat'), 12)0.222222222222>>> round(dist_jaro_winkler('Niall', 'Neil'), 12)
0.195>>> round(dist_jaro_winkler('aluminum', 'Catalan'), 12)0.39880952381>>> round(dist_jaro_winkler('ATCG', 'TAGC'), 12)0.166666666667>>> round(dist_jaro_winkler('cat', 'hat', mode='jaro'), 12)0.222222222222>>> round(dist_jaro_winkler('Niall', 'Neil', mode='jaro'), 12)0.216666666667>>> round(dist_jaro_winkler('aluminum', 'Catalan', mode='jaro'), 12)0.39880952381>>> round(dist_jaro_winkler('ATCG', 'TAGC', mode='jaro'), 12)0.166666666667

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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the JaroWinkler.dist method instead.
abydos.distance.sim_jaro_winkler(src, tar, qval=1, mode='winkler', long_strings=False,
boost_threshold=0.7, scaling_factor=0.1)
Return the Jaro or Jaro-Winkler similarity of two strings.
This is a wrapper for JaroWinkler.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• qval (int) -- The length of each q-gram (defaults to 1: character-wise matching)• mode (str) -- Indicates which variant of this distance metric to compute:– winkler -- computes the Jaro-Winkler distance (default) which increases the score formatches near the start of the word
– jaro -- computes the Jaro distance• long_strings (bool) -- Set to True to "Increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixedlength fields such as
phone and social security numbers." (Used in 'winkler' mode only.)• boost_threshold (float) -- A value between 0 and 1, below which the Winkler boost
is not applied (defaults to 0.7). (Used in 'winkler' mode only.)• scaling_factor (float) -- A value between 0 and 0.25, indicating by how much toboost scores for matching prefixes (defaults to 0.1). (Used in 'winkler' mode only.)
Returns Jaro or Jaro-Winkler similarity
Return type float
Examples>>> round(sim_jaro_winkler('cat', 'hat'), 12)0.777777777778>>> round(sim_jaro_winkler('Niall', 'Neil'), 12)0.805>>> round(sim_jaro_winkler('aluminum', 'Catalan'), 12)0.60119047619>>> round(sim_jaro_winkler('ATCG', 'TAGC'), 12)0.833333333333>>> round(sim_jaro_winkler('cat', 'hat', mode='jaro'), 12)0.777777777778>>> round(sim_jaro_winkler('Niall', 'Neil', mode='jaro'), 12)0.783333333333>>> round(sim_jaro_winkler('aluminum', 'Catalan', mode='jaro'), 12)0.60119047619>>> round(sim_jaro_winkler('ATCG', 'TAGC', mode='jaro'), 12)0.833333333333

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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the JaroWinkler.sim method instead.
class abydos.distance.Strcmp95(long_strings=False, **kwargs)
Bases: abydos.distance._distance._Distance
Strcmp95.
This is a Python translation of the C code for strcmp95: http://web.archive.org/web/20110629121242/http://www.census.gov/geo/msb/stand/strcmp.c [WMJL94]. The above file is a US Government publication and,accordingly, in the public domain.
This is based on the Jaro-Winkler distance, but also attempts to correct for some common typos and frequently
confused characters. It is also limited to uppercase ASCII characters, so it is appropriate to American names,
but not much else.
New in version 0.3.6.
Initialize Strcmp95 instance.
Parameters• long_strings (bool) -- Set to True to increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixed length fields such as
phone and social security numbers.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the strcmp95 similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Strcmp95 similarity
Return type float
Examples>>> cmp = Strcmp95()>>> cmp.sim('cat', 'hat')
0.7777777777777777>>> cmp.sim('Niall', 'Neil')
0.8454999999999999>>> cmp.sim('aluminum', 'Catalan')
0.6547619047619048>>> cmp.sim('ATCG', 'TAGC')
0.8333333333333334

New in version 0.1.0.
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abydos.distance.dist_strcmp95(src, tar, long_strings=False)
Return the strcmp95 distance between two strings.
This is a wrapper for Strcmp95.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• long_strings (bool) -- Set to True to increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixed length fields such as
phone and social security numbers.
Returns Strcmp95 distance
Return type float
Examples>>> round(dist_strcmp95('cat', 'hat'), 12)0.222222222222>>> round(dist_strcmp95('Niall', 'Neil'), 12)0.1545>>> round(dist_strcmp95('aluminum', 'Catalan'), 12)0.345238095238>>> round(dist_strcmp95('ATCG', 'TAGC'), 12)0.166666666667

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Strcmp95.dist method instead.
abydos.distance.sim_strcmp95(src, tar, long_strings=False)
Return the strcmp95 similarity of two strings.
This is a wrapper for Strcmp95.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• long_strings (bool) -- Set to True to increase the probability of a match when thenumber of matched characters is large. This option allows for a little more tolerance when
the strings are large. It is not an appropriate test when comparing fixed length fields such as
phone and social security numbers.
Returns Strcmp95 similarity
Return type float

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Examples>>> sim_strcmp95('cat', 'hat')
0.7777777777777777>>> sim_strcmp95('Niall', 'Neil')
0.8454999999999999>>> sim_strcmp95('aluminum', 'Catalan')
0.6547619047619048>>> sim_strcmp95('ATCG', 'TAGC')
0.8333333333333334

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Strcmp95.sim method instead.
class abydos.distance.IterativeSubString(hamacher=0.6,**kwargs)
Bases: abydos.distance._distance._Distancenormalize_strings=False,

Iterative-SubString correlation.
Iterative-SubString (I-Sub) correlation [SSK05]
This is a straightforward port of the primary author's Java implementation: http://www.image.ece.ntua.gr/~gstoil/software/I_Sub.java
New in version 0.4.0.
Initialize IterativeSubString instance.
Parameters• hamacher (float) -- The constant factor for the Hamacher product• normalize_strings (bool) -- Normalize the strings by removing the characters in '._' and lower casing• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
corr(src, tar)
Return the Iterative-SubString correlation of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Iterative-SubString correlation
Return type float

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Examples>>> cmp = IterativeSubString()>>> cmp.corr('cat', 'hat')-1.0>>> cmp.corr('Niall', 'Neil')-0.9>>> cmp.corr('aluminum', 'Catalan')-1.0>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Iterative-SubString similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Iterative-SubString similarity
Return type float
Examples>>> cmp = IterativeSubString()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.04999999999999999>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.AMPLE(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

AMPLE similarity.
The AMPLE similarity [DLZ05][AZvanGemund07] is defined in getAverageSequenceWeight() in the AverageSequenceWeightEvaluator.java file of AMPLE's source code. For two sets X and Y and a population N, it
is⃒ |𝑋 ∩ 𝑌 ||𝑌 ∖ 𝑋| ⃒⃒𝑠𝑖𝑚𝐴𝑀 𝑃 𝐿𝐸 (𝑋, 𝑌 ) = ⃒−|𝑋||𝑁 ∖ 𝑋|
In 2x2 confusion table terms, where a+b+c+d=n, this is⃒ 𝑎𝑐 ⃒⃒𝑠𝑖𝑚𝐴𝑀 𝑃 𝐿𝐸 = ⃒−𝑎+𝑏 𝑐+𝑑3.1. abydos package

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NotesThis measure is asymmetric. The first ratio considers how similar the two strings are, while the second considers
how dissimilar the second string is. As a result, both very similar and very dissimilar strings will score high on
this measure, provided the unique aspects are present chiefly in the latter string.
New in version 0.4.0.
Initialize AMPLE instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the AMPLE similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns AMPLE similarity
Return type float
Examples>>> cmp = AMPLE()>>> cmp.sim('cat', 'hat')
0.49743589743589745>>> cmp.sim('Niall', 'Neil')
0.32947729220222793>>> cmp.sim('aluminum', 'Catalan')
0.10209049255441008>>> cmp.sim('ATCG', 'TAGC')
0.006418485237483954

New in version 0.4.0.

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class abydos.distance.AZZOO(sigma=0.5,
alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-AZZOO similarity.
For two sets X and Y, and alphabet N, and a parameter 𝜎, AZZOO similarity [CTY06] is𝑠𝑖𝑚𝐴𝑍𝑍𝑂𝑂𝜎 (𝑋, 𝑌 ) =∑︁𝑠𝑖where 𝑠𝑖 = 1 if 𝑋𝑖 = 𝑌𝑖 = 1, 𝑠𝑖 = 𝜎 if 𝑋𝑖 = 𝑌𝑖 = 0, and 𝑠𝑖 = 0 otherwise.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐴𝑍𝑍𝑂𝑂 = 𝑎 + 𝜎 · 𝑑
New in version 0.4.0.
Initialize AZZOO instance.
Parameters• sigma (float) -- Sigma designates the contribution to similarity given by the 0-0 samples
in the set.
• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the AZZOO similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns AZZOO similarity
Return type float
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Examples>>> cmp = AZZOO()>>> cmp.sim('cat', 'hat')
0.9923857868020305>>> cmp.sim('Niall', 'Neil')
0.9860759493670886>>> cmp.sim('aluminum', 'Catalan')
0.9710327455919395>>> cmp.sim('ATCG', 'TAGC')
0.9809885931558935

New in version 0.4.0.
sim_score(src, tar)
Return the AZZOO similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns AZZOO similarity
Return type float
Examples>>> cmp = AZZOO()>>> cmp.sim_score('cat', 'hat')
391.0>>> cmp.sim_score('Niall', 'Neil')389.5>>> cmp.sim_score('aluminum', 'Catalan')385.5>>> cmp.sim_score('ATCG', 'TAGC')
387.0

New in version 0.4.0.
class abydos.distance.Anderberg(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceAnderberg's D.
For two sets X and Y and a population N, Anderberg's D [And73] is

𝑡1 = 𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑋 ∖ 𝑌 |) + 𝑚𝑎𝑥(|𝑌 ∖ 𝑋|, |(𝑁 ∖ 𝑋) ∖ 𝑌 |)+𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑌 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |(𝑁 ∖ 𝑋) ∖ 𝑌 |)𝑡2 = 𝑚𝑎𝑥(|𝑌 |, |𝑁 ∖ 𝑌 |) + 𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|)

𝑠𝑖𝑚𝐴𝑛𝑑𝑒𝑟𝑏𝑒𝑟𝑔 (𝑋, 𝑌 ) =86

𝑡1 − 𝑡2
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐴𝑛𝑑𝑒𝑟𝑏𝑒𝑟𝑔 =(𝑚𝑎𝑥(𝑎, 𝑏) + 𝑚𝑎𝑥(𝑐, 𝑑) + 𝑚𝑎𝑥(𝑎, 𝑐) + 𝑚𝑎𝑥(𝑏, 𝑑)) − (𝑚𝑎𝑥(𝑎 + 𝑏, 𝑏 + 𝑑) + 𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑))
2𝑛

Notes
8𝑎
, but I cannot substanThere are various references to another "Anderberg similarity", 𝑠𝑖𝑚𝐴𝑛𝑑𝑒𝑟𝑏𝑒𝑟𝑔 = 8𝑎+𝑏+𝑐
tiate the claim that this appears in [And73]. In any case, if you want to use this measure, you may instatiate
WeightedJaccard with weight=8.

Anderberg states that "[t]his quantity is the actual reduction in the error probability (also the actual increase in
the correct prediction) as a consequence of using predictor information" [And73]. It ranges [0, 0.5] so a sim
method ranging [0, 1] is provided in addition to sim_score, which gives the value D itself.
It is difficult to term this measure a similarity score. Identical strings often fail to gain high scores. Also, strings
that would otherwise be considered quite similar often earn lower scores than those that are less similar.
New in version 0.4.0.
Initialize Anderberg instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Anderberg's D similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Anderberg similarity
Return type float

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Examples>>> cmp = Anderberg()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Anderberg's D similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Anderberg similarity
Return type float
Examples>>> cmp = Anderberg()>>> cmp.sim_score('cat', 'hat')
0.0>>> cmp.sim_score('Niall', 'Neil')
0.0>>> cmp.sim_score('aluminum', 'Catalan')
0.0>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.AndresMarzoDelta(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-Andres & Marzo's Delta correlation.
For two sets X and Y and a population N, Andres & Marzo's ∆ correlation [AndresM04] is√︀|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | − 2 |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
𝑐𝑜𝑟𝑟𝐴𝑛𝑑𝑟𝑒𝑠𝑀 𝑎𝑟𝑧𝑜Δ (𝑋, 𝑌 ) = ∆ =|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is√𝑎+𝑑−2 𝑏·𝑐
𝑐𝑜𝑟𝑟𝐴𝑛𝑑𝑟𝑒𝑠𝑀 𝑎𝑟𝑧𝑜Δ = ∆ =𝑛88Chapter 3. abydos

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New in version 0.4.0.
Initialize AndresMarzoDelta instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Andres & Marzo's Delta correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Andres & Marzo's Delta correlation
Return type float
Examples>>> cmp = AndresMarzoDelta()>>> cmp.corr('cat', 'hat')
0.9897959183673469>>> cmp.corr('Niall', 'Neil')
0.9822344346552608>>> cmp.corr('aluminum', 'Catalan')
0.9618259496215341>>> cmp.corr('ATCG', 'TAGC')
0.9744897959183674

New in version 0.4.0.
sim(src, tar)
Return the Andres & Marzo's Delta similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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89Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Andres & Marzo's Delta similarity
Return type float
Examples>>> cmp = AndresMarzoDelta()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.9911172173276304>>> cmp.sim('aluminum', 'Catalan')
0.980912974810767>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837

New in version 0.4.0.
class abydos.distance.BaroniUrbaniBuserI(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Baroni-Urbani & Buser I similarity.
For two sets X and Y and a population N, the Baroni-Urbani & Buser I similarity [BUB76] is√︀
𝑠𝑖𝑚𝐵𝑎𝑟𝑜𝑛𝑖𝑈 𝑟𝑏𝑎𝑛𝑖𝐵𝑢𝑠𝑒𝑟𝐼 (𝑋, 𝑌 ) = √︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∩ 𝑌 ||𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

This is the second, but more commonly used and referenced of the two similarities proposed by Baroni-Urbani& Buser.
In 2x2 confusion table terms, where a+b+c+d=n, this is√

𝑠𝑖𝑚𝐵𝑎𝑟𝑜𝑛𝑖𝑈 𝑟𝑏𝑎𝑛𝑖𝐵𝑢𝑠𝑒𝑟𝐼 = √𝑎𝑑 + 𝑎𝑎𝑑 + 𝑎 + 𝑏 + 𝑐

New in version 0.4.0.
Initialize BaroniUrbaniBuserI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Baroni-Urbani & Buser I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baroni-Urbani & Buser I similarity
Return type float
Examples>>> cmp = BaroniUrbaniBuserI()>>> cmp.sim('cat', 'hat')
0.9119837740878104>>> cmp.sim('Niall', 'Neil')
0.8552823175014205>>> cmp.sim('aluminum', 'Catalan')
0.656992712054851>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.BaroniUrbaniBuserII(alphabet=None, tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Baroni-Urbani & Buser II correlation.
For two sets X and Y and a population N, the Baroni-Urbani & Buser II correlation [BUB76] is√︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∩ 𝑌 | − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
𝑐𝑜𝑟𝑟𝐵𝑎𝑟𝑜𝑛𝑖𝑈 𝑟𝑏𝑎𝑛𝑖𝐵𝑢𝑠𝑒𝑟𝐼𝐼 (𝑋, 𝑌 ) = √︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
This is the first, but less commonly used and referenced of the two similarities proposed by Baroni-Urbani &Buser.
In 2x2 confusion table terms, where a+b+c+d=n, this is√
𝑐𝑜𝑟𝑟𝐵𝑎𝑟𝑜𝑛𝑖𝑈 𝑟𝑏𝑎𝑛𝑖𝐵𝑢𝑠𝑒𝑟𝐼𝐼 = √3.1. abydos package

𝑎𝑑 + 𝑎 − 𝑏 − 𝑐𝑎𝑑 + 𝑎 + 𝑏 + 𝑐

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New in version 0.4.0.
Initialize BaroniUrbaniBuserII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Baroni-Urbani & Buser II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baroni-Urbani & Buser II correlation
Return type float
Examples>>> cmp = BaroniUrbaniBuserII()>>> cmp.corr('cat', 'hat')
0.8239675481756209>>> cmp.corr('Niall', 'Neil')
0.7105646350028408>>> cmp.corr('aluminum', 'Catalan')
0.31398542410970204>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Baroni-Urbani & Buser II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baroni-Urbani & Buser II similarity
Return type float
Examples>>> cmp = BaroniUrbaniBuserII()>>> cmp.sim('cat', 'hat')
0.9119837740878105>>> cmp.sim('Niall', 'Neil')
0.8552823175014204>>> cmp.sim('aluminum', 'Catalan')
0.656992712054851>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.BatageljBren(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Batagelj & Bren distance.
For two sets X and Y and a population N, the Batagelj & Bren distance [BB95], Batagelj & Bren's 𝑄0 , is

𝑑𝑖𝑠𝑡𝐵𝑎𝑡𝑎𝑔𝑒𝑙𝑗𝐵𝑟𝑒𝑛 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑡𝑎𝑔𝑒𝑙𝑗𝐵𝑟𝑒𝑛 =𝑏𝑐𝑎𝑑

New in version 0.4.0.
Initialize BatageljBren instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.

3.1. abydos package

93Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized Batagelj & Bren distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Batagelj & Bren distance
Return type float
Examples>>> cmp = BatageljBren()>>> cmp.dist('cat', 'hat')
3.2789465400556106e-06>>> cmp.dist('Niall', 'Neil')
9.874917709019092e-06>>> cmp.dist('aluminum', 'Catalan')
9.276668350823718e-05>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Batagelj & Bren distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Batagelj & Bren distance
Return type float
Examples>>> cmp = BatageljBren()>>> cmp.dist_abs('cat', 'hat')
0.002570694087403599>>> cmp.dist_abs('Niall', 'Neil')
0.007741935483870968>>> cmp.dist_abs('aluminum', 'Catalan')
0.07282184655396619>>> cmp.dist_abs('ATCG', 'TAGC')
inf

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New in version 0.4.0.
class abydos.distance.BaulieuI(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Baulieu I distance.
For two sets X and Y and a population N, Baulieu I distance [Bau89] is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼 (𝑋, 𝑌 ) =|𝑋| · |𝑌 | − |𝑋 ∩ 𝑌 |2
|𝑋| · |𝑌 |This is Baulieu's 12th dissimilarity coefficient.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼 =(𝑎 + 𝑏)(𝑎 + 𝑐) − 𝑎2
(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize BaulieuI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu I distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu I distance
Return type float
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Examples>>> cmp = BaulieuI()>>> cmp.dist('cat', 'hat')
0.75>>> cmp.dist('Niall', 'Neil')
0.8666666666666667>>> cmp.dist('aluminum', 'Catalan')
0.9861111111111112>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.BaulieuII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu II similarity.
For two sets X and Y and a population N, Baulieu II similarity [Bau89] is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |2 · |(𝑁 ∖ 𝑋) ∖ 𝑌 |2
|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |This is based on Baulieu's 13th dissimilarity coefficient.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝐼 =𝑎2 𝑑2(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize BaulieuII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
sim(src, tar)
Return the Baulieu II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu II similarity
Return type float
Examples>>> cmp = BaulieuII()>>> cmp.sim('cat', 'hat')
0.24871959237343852>>> cmp.sim('Niall', 'Neil')
0.13213719608444902>>> cmp.sim('aluminum', 'Catalan')
0.013621892326789235>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.BaulieuIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu III distance.
For two sets X and Y and a population N, Baulieu III distance [Bau89] is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝐼𝐼 (𝑋, 𝑌 ) =|𝑁 |2 − 4(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)
2 · |𝑁 |2

This is based on Baulieu's 20th dissimilarity coefficient.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝐼𝐼 =𝑛2 − 4(𝑎𝑑 − 𝑏𝑐)
2𝑛2

Notes
It should be noted that this is based on Baulieu's 20th dissimilarity coefficient. This distance is exactly half
Baulieu's 20th dissimilarity. According to [Bau89], the 20th dissimilarity should be a value in the range [0.0,1.0], meeting the article's (P1) property, but the formula given ranges [0.0, 2.0], so dividing by 2 corrects theformula to meet the article's expectations.
New in version 0.4.0.
Initialize BaulieuIII instance.
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Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu III distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu III distance
Return type float
Examples>>> cmp = BaulieuIII()>>> cmp.dist('cat', 'hat')
0.4949500208246564>>> cmp.dist('Niall', 'Neil')
0.4949955747605165>>> cmp.dist('aluminum', 'Catalan')
0.49768591017891195>>> cmp.dist('ATCG', 'TAGC')
0.5000813463140358

New in version 0.4.0.
class abydos.distance.BaulieuIV(alphabet=None, tokenizer=None, intersection_type='crisp',
positive_irrational=2.718281828459045, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu IV distance.
For two sets X and Y, a population N, and a positive irractional number k, Baulieu IV distance [Bau97] is98

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𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝑉 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| − (|𝑋 ∩ 𝑌 | + 21 ) · (|(𝑁 ∖ 𝑋) ∖ 𝑌 | + 21 ) · |(𝑁 ∖ 𝑋) ∖ 𝑌 | · 𝑘|𝑁 |This is Baulieu's 22nd dissimilarity coefficient.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝑉 =𝑏 + 𝑐 − (𝑎 + 21 )(𝑑 + 12 )𝑑𝑘𝑛Notes
The default value of k is Euler's number 𝑒, but other irrationals such as 𝜋 orization.

√2 could be substituted at initial-

New in version 0.4.0.
Initialize BaulieuIV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized Baulieu IV distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Baulieu IV distance
Return type float

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Examples>>> cmp = BaulieuIV()>>> cmp.dist('cat', 'hat')
0.49999799606535283>>> cmp.dist('Niall', 'Neil')
0.49999801148659684>>> cmp.dist('aluminum', 'Catalan')0.49999883126809364>>> cmp.dist('ATCG', 'TAGC')
0.4999996033268451

New in version 0.4.0.
dist_abs(src, tar)
Return the Baulieu IV distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu IV distance
Return type float
Examples>>> cmp = BaulieuIV()>>> cmp.dist_abs('cat', 'hat')-5249.96272285802>>> cmp.dist_abs('Niall', 'Neil')-5209.561726488335>>> cmp.dist_abs('aluminum', 'Catalan')-3073.6070822721244>>> cmp.dist_abs('ATCG', 'TAGC')-1039.2151656463932

New in version 0.4.0.
class abydos.distance.BaulieuV(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Baulieu V distance.
For two sets X and Y and a population N, Baulieu V distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 1|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 1

This is Baulieu's 23rd dissimilarity coefficient. This coefficient fails Baulieu's (P2) property, that 𝐷(𝑎, 0, 0, 0) =0. Rather, 𝐷(𝑎, 0, 0, 0) > 0, but lim𝑎→∞ 𝐷(𝑎, 0, 0, 0) = 0.
In 2x2 confusion table terms, where a+b+c+d=n, this is

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𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 =𝑏+𝑐+1
𝑎+𝑏+𝑐+1

New in version 0.4.0.
Initialize BaulieuV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu V distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu V distance
Return type float
Examples>>> cmp = BaulieuV()>>> cmp.dist('cat', 'hat')
0.7142857142857143>>> cmp.dist('Niall', 'Neil')
0.8>>> cmp.dist('aluminum', 'Catalan')
0.9411764705882353>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.

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class abydos.distance.BaulieuVI(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu VI distance.
For two sets X and Y and a population N, Baulieu VI distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 1

This is Baulieu's 24th dissimilarity coefficient. This coefficient fails Baulieu's (P3) property, that 𝐷(𝑎, 𝑏, 𝑐, 𝑑) =1 for some (a,b,c,d). Rather, 𝐷(𝑎, 𝑏, 𝑐, 𝑑) < 1, but lim𝑏→∞,𝑐→∞ 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0 for 𝑎 = 0.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼 =𝑏+𝑐
𝑎+𝑏+𝑐+1

New in version 0.4.0.
Initialize BaulieuVI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu VI distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu VI distance
Return type float

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Examples>>> cmp = BaulieuVI()>>> cmp.dist('cat', 'hat')
0.5714285714285714>>> cmp.dist('Niall', 'Neil')
0.7>>> cmp.dist('aluminum', 'Catalan')
0.8823529411764706>>> cmp.dist('ATCG', 'TAGC')
0.9090909090909091

New in version 0.4.0.
class abydos.distance.BaulieuVII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu VII distance.
For two sets X and Y and a population N, Baulieu VII distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
|𝑁 | + |𝑋 ∩ 𝑌 | · (|𝑋 ∩ 𝑌 | − 4)2

This is Baulieu's 25th dissimilarity coefficient. This coefficient fails Baulieu's (P4) property, that 𝐷(𝑎 +1, 𝑏, 𝑐, 𝑑) ≤ 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0 with equality holding iff 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼𝐼 =𝑏+𝑐𝑛 + 𝑎 · (𝑎 − 4)2

New in version 0.4.0.
Initialize BaulieuVII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
dist(src, tar)
Return the Baulieu VII distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu VII distance
Return type float
Examples>>> cmp = BaulieuVII()>>> cmp.dist('cat', 'hat')
0.005050505050505051>>> cmp.dist('Niall', 'Neil')
0.008838383838383838>>> cmp.dist('aluminum', 'Catalan')
0.018891687657430732>>> cmp.dist('ATCG', 'TAGC')
0.012755102040816327

New in version 0.4.0.
class abydos.distance.BaulieuVIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu VIII distance.
For two sets X and Y and a population N, Baulieu VIII distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼𝐼𝐼 (𝑋, 𝑌 ) =(|𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|)2
|𝑁 |2

This is Baulieu's 26th dissimilarity coefficient. This coefficient fails Baulieu's (P5) property, that 𝐷(𝑎, 𝑏 +1, 𝑐, 𝑑) ≥ 𝐷(𝑎, 𝑏, 𝑐, 𝑑), with equality holding if 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 1.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑉 𝐼𝐼𝐼 =(𝑏 − 𝑐)2𝑛2

New in version 0.4.0.
Initialize BaulieuVIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package
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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu VIII distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu VIII distance
Return type float
Examples>>> cmp = BaulieuVIII()>>> cmp.dist('cat', 'hat')
0.0>>> cmp.dist('Niall', 'Neil')
1.6269262807163682e-06>>> cmp.dist('aluminum', 'Catalan')
1.6227838857560144e-06>>> cmp.dist('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.BaulieuIX(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu IX distance.
For two sets X and Y and a population N, Baulieu IX distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝑋 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + 2 · |𝑌 ∖ 𝑋|
|𝑁 | + |𝑌 ∖ 𝑋|

This is Baulieu's 27th dissimilarity coefficient. This coefficient fails Baulieu's (P7) property, that 𝐷(𝑎, 𝑏, 𝑐, 𝑑) =𝐷(𝑎, 𝑐, 𝑏, 𝑑).
In 2x2 confusion table terms, where a+b+c+d=n, this is3.1. abydos package

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𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝐼𝑋 =𝑏 + 2𝑐𝑎 + 𝑏 + 2𝑐 + 𝑑

New in version 0.4.0.
Initialize BaulieuIX instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu IX distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu IX distance
Return type float
Examples>>> cmp = BaulieuIX()>>> cmp.dist('cat', 'hat')
0.007633587786259542>>> cmp.dist('Niall', 'Neil')
0.012706480304955527>>> cmp.dist('aluminum', 'Catalan')
0.027777777777777776>>> cmp.dist('ATCG', 'TAGC')
0.019011406844106463

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class abydos.distance.BaulieuX(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Baulieu X distance.
For two sets X and Y and a population N, Baulieu X distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|)|𝑁 | + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|)

This is Baulieu's 28th dissimilarity coefficient. This coefficient fails Baulieu's (P8) property, that 𝐷 is a rational
function whose numerator and denominator are both (total) linear.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋 =𝑏 + 𝑐 + 𝑚𝑎𝑥(𝑏, 𝑐)
𝑛 + 𝑚𝑎𝑥(𝑏, 𝑐)

New in version 0.4.0.
Initialize BaulieuX instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu X distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu X distance
Return type float

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Examples>>> cmp = BaulieuX()>>> cmp.dist('cat', 'hat')
0.007633587786259542>>> cmp.dist('Niall', 'Neil')
0.013959390862944163>>> cmp.dist('aluminum', 'Catalan')
0.029003783102143757>>> cmp.dist('ATCG', 'TAGC')
0.019011406844106463

New in version 0.4.0.
class abydos.distance.BaulieuXI(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu XI distance.
For two sets X and Y and a population N, Baulieu XI distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + |(𝑁 ∖ 𝑋) ∖ 𝑌 |This is Baulieu's 29th dissimilarity coefficient. This coefficient fails Baulieu's (P4) property, that 𝐷(𝑎 +1, 𝑏, 𝑐, 𝑑) ≤ 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0 with equality holding iff 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼 =

𝑏+𝑐
𝑏+𝑐+𝑑

New in version 0.4.0.
Initialize BaulieuXI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
dist(src, tar)
Return the Baulieu XI distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu XI distance
Return type float
Examples>>> cmp = BaulieuXI()>>> cmp.dist('cat', 'hat')
0.005115089514066497>>> cmp.dist('Niall', 'Neil')
0.008951406649616368>>> cmp.dist('aluminum', 'Catalan')
0.01913265306122449>>> cmp.dist('ATCG', 'TAGC')
0.012755102040816327

New in version 0.4.0.
class abydos.distance.BaulieuXII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu XII distance.
For two sets X and Y and a population N, Baulieu XII distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| − 1

This is Baulieu's 30th dissimilarity coefficient. This coefficient fails Baulieu's (P5) property, that 𝐷(𝑎, 𝑏 +1, 𝑐, 𝑑) ≥ 𝐷(𝑎, 𝑏, 𝑐, 𝑑), with equality holding if 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 1.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝐼 =3.1. abydos package

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𝑎+𝑏+𝑐−1109

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Notes
In the special case of comparisons where the intersection (a) contains 0 members, the size of the intersection
is set to 1, resulting in a distance of 1.0. This prevents the distance from exceeding 1.0 and similarity frombecoming negative.
New in version 0.4.0.
Initialize BaulieuXII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu XII distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu XII distance
Return type float
Examples>>> cmp = BaulieuXII()>>> cmp.dist('cat', 'hat')
0.8>>> cmp.dist('Niall', 'Neil')
0.875>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
1.0

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class abydos.distance.BaulieuXIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu XIII distance.
For two sets X and Y and a population N, Baulieu XIII distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + |𝑋 ∩ 𝑌 | · (|𝑋 ∩ 𝑌 | − 4)2

This is Baulieu's 31st dissimilarity coefficient. This coefficient fails Baulieu's (P4) property, that 𝐷(𝑎 +1, 𝑏, 𝑐, 𝑑) ≤ 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0 with equality holding iff 𝐷(𝑎, 𝑏, 𝑐, 𝑑) = 0.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝐼𝐼 =𝑏+𝑐
𝑎 + 𝑏 + 𝑐 + 𝑎 · (𝑎 − 4)2

New in version 0.4.0.
Initialize BaulieuXIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu XIII distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu XIII distance
Return type float

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Examples>>> cmp = BaulieuXIII()>>> cmp.dist('cat', 'hat')
0.2857142857142857>>> cmp.dist('Niall', 'Neil')
0.4117647058823529>>> cmp.dist('aluminum', 'Catalan')
0.6>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.BaulieuXIV(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu XIV distance.
For two sets X and Y and a population N, Baulieu XIV distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝑉 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + 2 · |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + 2 · |𝑌 ∖ 𝑋|

This is Baulieu's 32nd dissimilarity coefficient. This coefficient fails Baulieu's (P7) property, that 𝐷(𝑎, 𝑏, 𝑐, 𝑑) =𝐷(𝑎, 𝑐, 𝑏, 𝑑).
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝐼𝑉 =𝑏 + 2𝑐𝑎 + 𝑏 + 2𝑐

New in version 0.4.0.
Initialize BaulieuXIV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
dist(src, tar)
Return the Baulieu XIV distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu XIV distance
Return type float
Examples>>> cmp = BaulieuXIV()>>> cmp.dist('cat', 'hat')
0.75>>> cmp.dist('Niall', 'Neil')
0.8333333333333334>>> cmp.dist('aluminum', 'Catalan')
0.9565217391304348>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.BaulieuXV(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Baulieu XV distance.
For two sets X and Y and a population N, Baulieu XV distance [Bau97] is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝑉 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|)|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|)

This is Baulieu's 33rd dissimilarity coefficient. This coefficient fails Baulieu's (P8) property, that 𝐷 is a rational
function whose numerator and denominator are both (total) linear.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝐵𝑎𝑢𝑙𝑖𝑒𝑢𝑋𝑉 =𝑏 + 𝑐 + 𝑚𝑎𝑥(𝑏, 𝑐)
𝑎 + 𝑏 + 𝑐 + 𝑚𝑎𝑥(𝑏, 𝑐)

New in version 0.4.0.
Initialize BaulieuXV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package
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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Baulieu XV distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Baulieu XV distance
Return type float
Examples>>> cmp = BaulieuXV()>>> cmp.dist('cat', 'hat')
0.75>>> cmp.dist('Niall', 'Neil')
0.8461538461538461>>> cmp.dist('aluminum', 'Catalan')
0.9583333333333334>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.BeniniI(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

BeniniI correlation.
For two sets X and Y and a population N, Benini I correlation, Benini's Index of Attraction, [Ben01] is𝑐𝑜𝑟𝑟𝐵𝑒𝑛𝑖𝑛𝑖𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑌 | · |𝑁 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐵𝑒𝑛𝑖𝑛𝑖𝐼 =114

𝑎𝑑 − 𝑏𝑐(𝑎 + 𝑐)(𝑐 + 𝑑)
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New in version 0.4.0.
Initialize BeniniI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Benini I correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Benini I correlation
Return type float
Examples>>> cmp = BeniniI()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.3953727506426735>>> cmp.corr('aluminum', 'Catalan')
0.11485180412371133>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Benini I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Benini I similarity
Return type float
Examples>>> cmp = BeniniI()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6976863753213367>>> cmp.sim('aluminum', 'Catalan')
0.5574259020618557>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.BeniniII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

BeniniII correlation.
For two sets X and Y and a population N, Benini II correlation, Benini's Index of Repulsion, [Ben01] is𝑐𝑜𝑟𝑟𝐵𝑒𝑛𝑖𝑛𝑖𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
𝑚𝑖𝑛(|𝑌 | · |𝑁 ∖ 𝑋|, |𝑋| · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐵𝑒𝑛𝑖𝑛𝑖𝐼𝐼 =𝑎𝑑 − 𝑏𝑐𝑚𝑖𝑛((𝑎 + 𝑐)(𝑐 + 𝑑), (𝑎 + 𝑏)(𝑏 + 𝑑))

New in version 0.4.0.
Initialize BeniniII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.

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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Benini II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Benini II correlation
Return type float
Examples>>> cmp = BeniniII()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.3953727506426735>>> cmp.corr('aluminum', 'Catalan')
0.11485180412371133>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Benini II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Benini II similarity
Return type float
Examples>>> cmp = BeniniII()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6976863753213367>>> cmp.sim('aluminum', 'Catalan')
0.5574259020618557>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

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New in version 0.4.0.
class abydos.distance.Bennet(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Bennet's S correlation.
For two sets X and Y and a population N, Bennet's 𝑆 correlation [BAG54] is𝑐𝑜𝑟𝑟𝐵𝑒𝑛𝑛𝑒𝑡 (𝑋, 𝑌 ) = 𝑆 =𝑝𝑜 − 𝑝𝑆𝑒
1 − 𝑝𝑆𝑒

where

𝑝𝑜 =

|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |
|𝑁 |
1
𝑝𝑆𝑒 =
2

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑎+𝑑
𝑛
1
𝑝𝑆𝑒 =
2

𝑝𝑜 =

New in version 0.4.0.
Initialize Bennet instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Bennet's S correlation of two strings.
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Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Bennet's S correlation
Return type float
Examples>>> cmp = Bennet()>>> cmp.corr('cat', 'hat')
0.989795918367347>>> cmp.corr('Niall', 'Neil')
0.9821428571428572>>> cmp.corr('aluminum', 'Catalan')
0.9617834394904459>>> cmp.corr('ATCG', 'TAGC')
0.9744897959183674

New in version 0.4.0.
sim(src, tar)
Return the Bennet's S similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Bennet's S similarity
Return type float
Examples>>> cmp = Bennet()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.9910714285714286>>> cmp.sim('aluminum', 'Catalan')
0.9808917197452229>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837

New in version 0.4.0.
class abydos.distance.BraunBlanquet(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Braun-Blanquet similarity.
For two sets X and Y and a population N, the Braun-Blanquet similarity [BB32] is

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𝑠𝑖𝑚𝐵𝑟𝑎𝑢𝑛𝐵𝑙𝑎𝑛𝑞𝑢𝑒𝑡 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |𝑚𝑎𝑥(|𝑋|, |𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐵𝑟𝑎𝑢𝑛𝐵𝑙𝑎𝑛𝑞𝑢𝑒𝑡 =𝑎𝑚𝑎𝑥(𝑎 + 𝑏, 𝑎 + 𝑐)

New in version 0.4.0.
Initialize BraunBlanquet instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Braun-Blanquet similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Braun-Blanquet similarity
Return type float

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Examples>>> cmp = BraunBlanquet()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.1111111111111111>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Canberra(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Canberra distance.
For two sets X and Y, the Canberra distance [LW66][LW67b] is

𝑠𝑖𝑚𝐶𝑎𝑛𝑏𝑒𝑟𝑟𝑎 (𝑋, 𝑌 ) =|𝑋△𝑌 ||𝑋| + |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑎𝑛𝑏𝑒𝑟𝑟𝑎 =𝑏+𝑐(𝑎 + 𝑏) + (𝑎 + 𝑐)

New in version 0.4.0.
Initialize Canberra instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Canberra distance of two strings.
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Abydos Documentation, Release 0.5.0• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Canberra distance
Return type float
Examples>>> cmp = Canberra()>>> cmp.dist('cat', 'hat')
0.5>>> cmp.dist('Niall', 'Neil')
0.6363636363636364>>> cmp.dist('aluminum', 'Catalan')
0.8823529411764706>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.Cao(**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Cao's CY dissimilarity.
Given 𝑋𝑖𝑗 (the number of individuals of speecies 𝑗 in sample 𝑖), 𝑋𝑘𝑗 (the number of individuals of speecies 𝑗
in sample 𝑘), and 𝑁 (the total number of speecies present in both samples), Cao dissimilarity (CYd) [CBW97]
is:
(︃
)︃
(︀ 𝑋 +𝑋 )︀
1 ∑︁ (𝑋𝑖𝑗 + 𝑋𝑘𝑗 )𝑙𝑜𝑔10 𝑖𝑗 2 𝑘𝑗 − 𝑋𝑖𝑗 𝑙𝑜𝑔10 𝑋𝑘𝑗 − 𝑋𝑘𝑗 𝑙𝑜𝑔10 𝑋𝑖𝑗
𝑑𝑖𝑠𝑡𝐶𝑎𝑜 (𝑋, 𝑌 ) = 𝐶𝑌 𝑑 =𝑁
𝑋𝑖𝑗 + 𝑋𝑘𝑗
In the above formula, whenever 𝑋𝑖𝑗 = 0 or 𝑋𝑘𝑗 = 0, the value 0.1 is substituted.
Since this measure ranges from 0 to ∞, a similarity measure, CYs, ranging from 0 to 1 was also developed.
𝑠𝑖𝑚𝐶𝑎𝑜 (𝑋, 𝑌 ) = 𝐶𝑌 𝑠 = 1 −𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝐶𝑌 𝑑
𝑀 𝑎𝑥𝑖𝑚𝑢𝑚 𝐶𝑌 𝑑

where
(︃
𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝐶𝑌 𝑑 =∑︁(𝑋𝑖𝑗 + 𝑋𝑘𝑗 )𝑙𝑜𝑔10(︀ 𝑋𝑖𝑗 +𝑋𝑘𝑗 )︀
2− 𝑋𝑖𝑗 𝑙𝑜𝑔10 𝑋𝑘𝑗 − 𝑋𝑘𝑗 𝑙𝑜𝑔10 𝑋𝑖𝑗
𝑋𝑖𝑗 + 𝑋𝑘𝑗

)︃and with 𝑎 (the number of species present in both samples), 𝑏 (the number of species present in sample 𝑖 only),and 𝑐 (the number of species present in sample 𝑗 only),
𝑀 𝑎𝑥𝑖𝑚𝑢𝑚 𝐶𝑌 𝑑 = 𝐷1 + 𝐷2 + 𝐷3
with)︃
− 𝑋𝑖𝑗 𝑙𝑜𝑔10 0.1 − 0.1𝑙𝑜𝑔10 𝑋𝑖𝑗
𝐷1 =𝑋𝑖𝑗 + 0.1
𝑗=1
(︃
)︃
(︀
𝑋 +0.1 )︀
𝑐∑︁
(𝑋𝑘𝑗 + 0.1)𝑙𝑜𝑔10 𝑘𝑗2− 𝑋𝑘𝑗 𝑙𝑜𝑔10 0.1 − 0.1𝑙𝑜𝑔10 𝑋𝑘𝑗
𝐷2 =𝑋𝑘𝑗 + 0.1
𝑗=1
(︃
)︃
(︀
)︀
(︀
)︀
𝑎
∑︁
(𝐷𝑘 + 1)𝑙𝑜𝑔10 𝐷𝑘2+1 − 𝑙𝑜𝑔10 𝐷𝑘
𝑎 (𝐷𝑖 + 1)𝑙𝑜𝑔10 𝐷𝑖2+1 − 𝑙𝑜𝑔10 𝐷𝑖
𝐷1 =+2
𝐷𝑖 + 1
𝐷𝑘 + 1
𝑗=1
𝑏
∑︁

122

(︃

(𝑋𝑖𝑗 + 0.1)𝑙𝑜𝑔10(︀ 𝑋𝑖𝑗 +0.1 )︀
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with
∑︀
𝐷𝑖 =

𝑋𝑖𝑗 − 𝑎2
𝑎
2

∑︀
𝐷𝑘 =

𝑋𝑘𝑗 − 𝑎2𝑎2

for𝑋𝑖𝑗 ≥ 1
𝑋𝑘𝑗 ≥ 1
New in version 0.4.1.
Initialize Cao instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist_abs(src, tar)
Return Cao's CY dissimilarity (CYd) of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Cao's CY dissimilarity
Return type float
Examples>>> cmp = Cao()>>> cmp.dist_abs('cat', 'hat')
0.3247267992925765>>> cmp.dist_abs('Niall', 'Neil')
0.4132886536450973>>> cmp.dist_abs('aluminum', 'Catalan')
0.5530666041976232>>> cmp.dist_abs('ATCG', 'TAGC')
0.6494535985851531

New in version 0.4.1.
sim(src, tar)
Return Cao's CY similarity (CYs) of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Cao's CY similarity
Return type float

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Examples>>> cmp = Cao()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.ChaoDice(**kwargs)
Bases: abydos.distance._chao_jaccard.ChaoJaccard
Chao's Dice similarity.
Chao's Dice similarity [CCCS04]
New in version 0.4.1.
Initialize ChaoDice instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
sim(src, tar)
Return the normalized Chao's Dice similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Chao's Dice similarity
Return type float
Examples>>> import random>>> random.seed(0)>>> cmp = ChaoDice()>>> cmp.sim('cat', 'hat')
0.36666666666666664>>> cmp.sim('Niall', 'Neil')
0.27868852459016397>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.1.
sim_score(src, tar)
Return the Chao's Dice similarity of two strings.
Parameters
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Abydos Documentation, Release 0.5.0• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Chao's Dice similarity
Return type float
Examples>>> import random>>> random.seed(0)>>> cmp = ChaoDice()>>> cmp.sim_score('cat', 'hat')
0.36666666666666664>>> cmp.sim_score('Niall', 'Neil')
0.27868852459016397>>> cmp.sim_score('aluminum', 'Catalan')
0.0>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.ChaoJaccard(**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Chao's Jaccard similarity.
Chao's Jaccard similarity [CCCS04]
New in version 0.4.1.
Initialize ChaoJaccard instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
sim(src, tar)
Return normalized Chao's Jaccard similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Chao's Jaccard similarity
Return type float

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Examples>>> import random>>> random.seed(0)>>> cmp = ChaoJaccard()>>> cmp.sim('cat', 'hat')
0.22448979591836735>>> cmp.sim('Niall', 'Neil')
0.1619047619047619>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.1.
sim_score(src, tar)
Return Chao's Jaccard similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Chao's Jaccard similarity
Return type float
Examples>>> import random>>> random.seed(0)>>> cmp = ChaoJaccard()>>> cmp.sim_score('cat', 'hat')
0.22448979591836735>>> cmp.sim_score('Niall', 'Neil')
0.1619047619047619>>> cmp.sim_score('aluminum', 'Catalan')
0.0>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.Chebyshev(alphabet=0,
tokenizer=None,**kwargs)
Bases: abydos.distance._minkowski.Minkowski

intersection_type='crisp',

Chebyshev distance.
Euclidean distance is the chessboard distance, equivalent to Minkowski distance in 𝐿∞ -space.
New in version 0.3.6.
Initialize Euclidean instance.
Parameters• alphabet (collection or int) -- The values or size of the alphabet

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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Chebyshev distanceNew in version 0.3.6.
dist_abs(src, tar)
Return the Chebyshev distance between two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns The Chebyshev distance
Return type float
Examples>>> cmp = Chebyshev()>>> cmp.dist_abs('cat', 'hat')
1.0>>> cmp.dist_abs('Niall', 'Neil')
1.0>>> cmp.dist_abs('Colin', 'Cuilen')
1.0>>> cmp.dist_abs('ATCG', 'TAGC')
1.0>>> cmp = Chebyshev(qval=1)>>> cmp.dist_abs('ATCG', 'TAGC')
0.0(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> cmp.dist_abs('ATCGATTCGGAATTTC', 'TAGCATAATCGCCG')
3.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classsim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Chebyshev distanceNew in version 0.3.6.
abydos.distance.chebyshev(src, tar, qval=2, alphabet=0)
Return the Chebyshev distance between two strings.
This is a wrapper for the Chebyshev.dist_abs().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alphabet (collection or int) -- The values or size of the alphabet
Returns The Chebyshev distance
Return type float
Examples>>> chebyshev('cat', 'hat')
1.0>>> chebyshev('Niall', 'Neil')
1.0>>> chebyshev('Colin', 'Cuilen')
1.0>>> chebyshev('ATCG', 'TAGC')
1.0>>> chebyshev('ATCG', 'TAGC', qval=1)0.0>>> chebyshev('ATCGATTCGGAATTTC', 'TAGCATAATCGCCG', qval=1)
3.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Chebyshev.dist_abs method instead.
class abydos.distance.Chord(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Chord distance.

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For two sets X and Y drawn from an alphabet S, the chord distance [Orloci67] is⎯⎸∑︁ (︁
𝑋⎸√︁∑︀ 𝑖𝑠𝑖𝑚𝑐ℎ𝑜𝑟𝑑 (𝑋, 𝑌 ) = ⎷𝑖∈𝑆2𝑗∈𝑋 𝑋𝑗− √︁∑︀

𝑌𝑖)︁22𝑗∈𝑌 𝑌𝑗New in version 0.4.0.
Initialize Chord instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized Chord distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized chord distance
Return type float
Examples>>> cmp = Chord()>>> cmp.dist('cat', 'hat')
0.707106781186547>>> cmp.dist('Niall', 'Neil')
0.796775770420944>>> cmp.dist('aluminum', 'Catalan')
0.94519820240106>>> cmp.dist('ATCG', 'TAGC')
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dist_abs(src, tar)
Return the Chord distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Chord distance
Return type float
Examples>>> cmp = Chord()>>> cmp.dist_abs('cat', 'hat')
1.0>>> cmp.dist_abs('Niall', 'Neil')
1.126811100699571>>> cmp.dist_abs('aluminum', 'Catalan')
1.336712116966249>>> cmp.dist_abs('ATCG', 'TAGC')
1.414213562373095

New in version 0.4.0.
class abydos.distance.Clark(**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Clark's coefficient of divergence.
For two sets X and Y and a population N, Clark's coefficient of divergence [Cla52] is:⎯⎸ ∑︀|𝑁 | (︀ 𝑥 −𝑦 )︀2𝑖𝑖⎸⎷ 𝑖=0 𝑥𝑖 +𝑦𝑖
𝑑𝑖𝑠𝑡𝐶𝑙𝑎𝑟𝑘 (𝑋, 𝑌 ) =|𝑁 |New in version 0.4.1.
Initialize Clark instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(src, tar)
Return Clark's coefficient of divergence of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Clark's coefficient of divergence
Return type float

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Examples>>> cmp = Clark()>>> cmp.dist('cat', 'hat')
0.816496580927726>>> cmp.dist('Niall', 'Neil')
0.8819171036881969>>> cmp.dist('aluminum', 'Catalan')
0.9660917830792959>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.1.
class abydos.distance.Clement(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Clement similarity.
For two sets X and Y and a population N, Clement similarity [Cle76] is defined as

𝑠𝑖𝑚𝐶𝑙𝑒𝑚𝑒𝑛𝑡 (𝑋, 𝑌 ) =|𝑋| )︁ |(𝑁 ∖ 𝑋) ∖ 𝑌 | (︁|𝑁 ∖ 𝑋| )︁
|𝑋 ∩ 𝑌 | (︁
1−
+
1−
|𝑋|
|𝑁 |
|𝑁 ∖ 𝑋||𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑙𝑒𝑚𝑒𝑛𝑡 =𝑎 + 𝑏 )︁𝑑 (︁𝑐 + 𝑑 )︁
𝑎 (︁
1−
+
1−
𝑎+𝑏
𝑛
𝑐+𝑑
𝑛

New in version 0.4.0.
Initialize Clement instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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sim(src, tar)
Return the Clement similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Clement similarity
Return type float
Examples>>> cmp = Clement()>>> cmp.sim('cat', 'hat')
0.5025379382522239>>> cmp.sim('Niall', 'Neil')
0.33840586363079933>>> cmp.sim('aluminum', 'Catalan')
0.12119877280918714>>> cmp.sim('ATCG', 'TAGC')
0.006336616803332366

New in version 0.4.0.
class abydos.distance.CohenKappa(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Cohen's Kappa similarity.
For two sets X and Y and a population N, Cohen's kappa similarity [Coh60] is

𝑠𝑖𝑚𝐶𝑜ℎ𝑒𝑛𝜅 (𝑋, 𝑌 ) = 𝜅 =𝑝𝑜 − 𝑝𝜅𝑒1 − 𝑝𝜅𝑒

where∖𝑋)∖𝑌 |
𝑝𝑜 = |𝑋∩𝑌 |+|(𝑁
|𝑁 |
|𝑌 |
|𝑁 ∖𝑋| |𝑁 ∖𝑌 |𝑝𝜅𝑒 = |𝑋|
|𝑁 | · |𝑁 | + |𝑁 | · |𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑝𝑜 = 𝑎+𝑑𝑛
𝑎+𝑐
𝑐+𝑑 𝑏+𝑑𝑝𝜅𝑒 = 𝑎+𝑏𝑛 · 𝑛 + 𝑛 · 𝑛

New in version 0.4.0.
Initialize CohenKappa instance.
Parameters
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• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Cohen's Kappa similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Cohen's Kappa similarity
Return type float
Examples>>> cmp = CohenKappa()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9955041746949261>>> cmp.sim('aluminum', 'Catalan')
0.9903412749517064>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.
class abydos.distance.Cole(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Cole correlation.
For two sets X and Y and a population N, the Cole correlation [Col49] has three formulae:• If |𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | ≥ |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑌 | then

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𝑐𝑜𝑟𝑟𝐶𝑜𝑙𝑒 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|(|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 |) · (|𝑋 ∖ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)• If |(𝑁 ∖ 𝑋) ∖ 𝑌 | ≥ |𝑋 ∩ 𝑌 | then𝑐𝑜𝑟𝑟𝐶𝑜𝑙𝑒 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|(|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 |) · (|𝑋 ∩ 𝑌 | + |𝑌 ∖ 𝑋|)• Otherwise

𝑐𝑜𝑟𝑟𝐶𝑜𝑙𝑒 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|(|𝑋 ∖ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |) · (|𝑌 ∖ 𝑋| + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)Cole terms this measurement the Coefficient of Interspecific Association.
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐶𝑜𝑙𝑒 =

⎧
𝑎𝑑−𝑏𝑐
⎪
⎪
(𝑎+𝑏)(𝑏+𝑑)
⎪
⎪
⎪
⎨
𝑎𝑑−𝑏𝑐

(𝑎+𝑏)(𝑎+𝑐)
⎪
⎪
⎪
⎪
⎪
𝑎𝑑−𝑏𝑐
⎩
(𝑏+𝑑)(𝑐+𝑑)if 𝑎𝑑 ≥ 𝑏𝑐
if 𝑑 ≥ 𝑎otherwise

New in version 0.4.0.
Initialize Cole instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Cole correlation of two strings.
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Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Cole correlation
Return type float
Examples>>> cmp = Cole()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.3290543431750107>>> cmp.corr('aluminum', 'Catalan')
0.10195910195910196>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Cole similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for similarity• tar (str) -- Target string (or QGrams/Counter objects) for similarity
Returns Cole similarity
Return type float
Examples>>> cmp = Cole()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6645271715875054>>> cmp.sim('aluminum', 'Catalan')
0.550979550979551>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.ConsonniTodeschiniI(alphabet=None, tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Consonni & Todeschini I similarity.
For two sets X and Y and a population N, Consonni & Todeschini I similarity [CT12] is

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𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼 (𝑋, 𝑌 ) =𝑙𝑜𝑔(1 + |𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)𝑙𝑜𝑔(1 + |𝑁 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼 =𝑙𝑜𝑔(1 + 𝑎 + 𝑑)𝑙𝑜𝑔(1 + 𝑛)

New in version 0.4.0.
Initialize ConsonniTodeschiniI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Consonni & Todeschini I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini I similarity
Return type float

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Examples>>> cmp = ConsonniTodeschiniI()>>> cmp.sim('cat', 'hat')
0.9992336018090547>>> cmp.sim('Niall', 'Neil')
0.998656222829757>>> cmp.sim('aluminum', 'Catalan')
0.9971098629456009>>> cmp.sim('ATCG', 'TAGC')
0.9980766131469967

New in version 0.4.0.
class abydos.distance.ConsonniTodeschiniII(alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Consonni & Todeschini II similarity.
For two sets X and Y and a population N, Consonni & Todeschini II similarity [CT12] is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝐼 (𝑋, 𝑌 ) =𝑙𝑜𝑔(1 + |𝑁 |) − 𝑙𝑜𝑔(1 + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
𝑙𝑜𝑔(1 + |𝑁 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝐼 =𝑙𝑜𝑔(1 + 𝑛) − 𝑙𝑜𝑔(1 + 𝑏 + 𝑐)𝑙𝑜𝑔(1 + 𝑛)

New in version 0.4.0.
Initialize ConsonniTodeschiniII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
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sim(src, tar)
Return the Consonni & Todeschini II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini II similarity
Return type float
Examples>>> cmp = ConsonniTodeschiniII()>>> cmp.sim('cat', 'hat')
0.7585487129939101>>> cmp.sim('Niall', 'Neil')
0.6880377723094788>>> cmp.sim('aluminum', 'Catalan')
0.5841297898633079>>> cmp.sim('ATCG', 'TAGC')
0.640262668568961

New in version 0.4.0.
class abydos.distance.ConsonniTodeschiniIII(alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Consonni & Todeschini III similarity.
For two sets X and Y and a population N, Consonni & Todeschini III similarity [CT12] is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝐼𝐼 (𝑋, 𝑌 ) =𝑙𝑜𝑔(1 + |𝑋 ∩ 𝑌 |)𝑙𝑜𝑔(1 + |𝑁 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝐼𝐼 =𝑙𝑜𝑔(1 + 𝑎)𝑙𝑜𝑔(1 + 𝑛)

New in version 0.4.0.
Initialize ConsonniTodeschiniIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Consonni & Todeschini III similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini III similarity
Return type float
Examples>>> cmp = ConsonniTodeschiniIII()>>> cmp.sim('cat', 'hat')
0.1648161441769704>>> cmp.sim('Niall', 'Neil')
0.1648161441769704>>> cmp.sim('aluminum', 'Catalan')
0.10396755253417303>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.ConsonniTodeschiniIV(alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Consonni & Todeschini IV similarity.
For two sets X and Y and a population N, Consonni & Todeschini IV similarity [CT12] is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝑉 (𝑋, 𝑌 ) =𝑙𝑜𝑔(1 + |𝑋 ∩ 𝑌 |)𝑙𝑜𝑔(1 + |𝑋 ∪ 𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝐼𝑉 =𝑙𝑜𝑔(1 + 𝑎)𝑙𝑜𝑔(1 + 𝑎 + 𝑏 + 𝑐)

New in version 0.4.0.
Initialize ConsonniTodeschiniIV instance.
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Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Consonni & Todeschini IV similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini IV similarity
Return type float
Examples>>> cmp = ConsonniTodeschiniIV()>>> cmp.sim('cat', 'hat')
0.5645750340535796>>> cmp.sim('Niall', 'Neil')
0.4771212547196623>>> cmp.sim('aluminum', 'Catalan')
0.244650542118226>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.ConsonniTodeschiniV(alphabet=None, tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Consonni & Todeschini V correlation.
For two sets X and Y and a population N, Consonni & Todeschini V correlation [CT12] is

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𝑐𝑜𝑟𝑟𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝑉 (𝑋, 𝑌 ) =𝑙𝑜𝑔(1 + |𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |) − 𝑙𝑜𝑔(1 + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)
2

𝑙𝑜𝑔(1 + |𝑁4| )In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐶𝑜𝑛𝑠𝑜𝑛𝑛𝑖𝑇 𝑜𝑑𝑒𝑠𝑐ℎ𝑖𝑛𝑖𝑉 =𝑙𝑜𝑔(1 + 𝑎𝑑) − 𝑙𝑜𝑔(1 + 𝑏𝑐)
2
𝑙𝑜𝑔(1 + 𝑛4 )New in version 0.4.0.
Initialize ConsonniTodeschiniV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Consonni & Todeschini V correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini V correlation
Return type float

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Examples>>> cmp = ConsonniTodeschiniV()>>> cmp.corr('cat', 'hat')
0.48072545510682463>>> cmp.corr('Niall', 'Neil')
0.4003930264973547>>> cmp.corr('aluminum', 'Catalan')
0.21794239483504532>>> cmp.corr('ATCG', 'TAGC')-0.2728145951429799

New in version 0.4.0.
sim(src, tar)
Return the Consonni & Todeschini V similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Consonni & Todeschini V similarity
Return type float
Examples>>> cmp = ConsonniTodeschiniV()>>> cmp.sim('cat', 'hat')
0.7403627275534124>>> cmp.sim('Niall', 'Neil')
0.7001965132486774>>> cmp.sim('aluminum', 'Catalan')
0.6089711974175227>>> cmp.sim('ATCG', 'TAGC')
0.36359270242851005

New in version 0.4.0.
class abydos.distance.Cosine(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Cosine similarity.
For two sets X and Y, the cosine similarity, Otsuka-Ochiai coefficient, or Ochiai coefficient [Ots36][Och57] is|𝑋 ∩ 𝑌 |𝑠𝑖𝑚𝑐𝑜𝑠𝑖𝑛𝑒 (𝑋, 𝑌 ) = √︀
|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑠𝑖𝑚𝑐𝑜𝑠𝑖𝑛𝑒 = √︀142𝑎(𝑎 + 𝑏)(𝑎 + 𝑐)

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NotesThis measure is also known as the Fowlkes-Mallows index [FM83] for two classes and G-measure, the geometricmean of precision & recall.
New in version 0.3.6.
Initialize Cosine instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the cosine similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Cosine similarity
Return type float
Examples>>> cmp = Cosine()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3651483716701107>>> cmp.sim('aluminum', 'Catalan')
0.11785113019775793>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class

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abydos.distance.dist_cosine(src, tar, qval=2)
Return the cosine distance between two strings.
This is a wrapper for Cosine.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Cosine distance
Return type float
Examples>>> dist_cosine('cat', 'hat')
0.5>>> dist_cosine('Niall', 'Neil')
0.6348516283298893>>> dist_cosine('aluminum', 'Catalan')
0.882148869802242>>> dist_cosine('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Cosine.dist method instead.
abydos.distance.sim_cosine(src, tar, qval=2)
Return the cosine similarity of two strings.
This is a wrapper for Cosine.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Cosine similarity
Return type float
Examples>>> sim_cosine('cat', 'hat')
0.5>>> sim_cosine('Niall', 'Neil')
0.3651483716701107>>> sim_cosine('aluminum', 'Catalan')
0.11785113019775793>>> sim_cosine('ATCG', 'TAGC')
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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Cosine.sim method instead.
class abydos.distance.Dennis(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Dennis similarity.
For two sets X and Y and a population N, Dennis similarity [Den65] is𝑠𝑖𝑚𝐷𝑒𝑛𝑛𝑖𝑠 (𝑋, 𝑌 ) =||𝑋 ∩ 𝑌 | − |𝑋|·|𝑌|𝑁 |√︁
|𝑋|·|𝑌 ||𝑁 |This is the fourth of Dennis' association measures, and that which she claims is the best of the four.
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑎 − (𝑎+𝑏)(𝑎+𝑐)𝑛𝑠𝑖𝑚𝐷𝑒𝑛𝑛𝑖𝑠 = √︁(𝑎+𝑏)(𝑎+𝑐)𝑛

New in version 0.4.0.
Initialize Dennis instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Dennis correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
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Returns Dennis correlation
Return type float
Examples>>> cmp = Dennis()>>> cmp.corr('cat', 'hat')
0.494897959183673>>> cmp.corr('Niall', 'Neil')
0.358162114559075>>> cmp.corr('aluminum', 'Catalan')
0.107041854561785>>> cmp.corr('ATCG', 'TAGC')-0.006377551020408

New in version 0.4.0.
sim(src, tar)
Return the normalized Dennis similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Dennis similarity
Return type float
Examples>>> cmp = Dennis()>>> cmp.sim('cat', 'hat')
0.6632653061224487>>> cmp.sim('Niall', 'Neil')
0.5721080763727167>>> cmp.sim('aluminum', 'Catalan')
0.4046945697078567>>> cmp.sim('ATCG', 'TAGC')
0.32908163265306134

New in version 0.4.0.
sim_score(src, tar)
Return the Dennis similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dennis similarity
Return type float

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Examples>>> cmp = Dennis()>>> cmp.sim_score('cat', 'hat')
13.857142857142858>>> cmp.sim_score('Niall', 'Neil')
10.028539207654113>>> cmp.sim_score('aluminum', 'Catalan')
2.9990827802847835>>> cmp.sim_score('ATCG', 'TAGC')-0.17857142857142858

New in version 0.4.0.
class abydos.distance.Dice(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._tversky.Tversky
Sørensen–Dice coefficient.
For two sets X and Y, the Sørensen–Dice coefficient [Dic45][Sorensen48][Cze09][MDobrzanskiZ50] is

𝑠𝑖𝑚𝐷𝑖𝑐𝑒 (𝑋, 𝑌 ) =2 · |𝑋 ∩ 𝑌 ||𝑋| + |𝑌 |This is the complement of Bray & Curtis dissimilarity [BC57], also known as the Lance & Williams dissimilarity[LW67a].
This is identical to the Tanimoto similarity coefficient [Tan58] and the Tversky index [Tve77] for 𝛼 = 𝛽 = 0.5.
In the Ruby text library this is identified as White similarity, after [Whid.].
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐷𝑖𝑐𝑒 =2𝑎2𝑎 + 𝑏 + 𝑐

Notes
In terms of a confusion matrix, this is equivalent to 𝐹1 score ConfusionTable.f1_score().
The multiset variant is termed Gleason similarity [Gle20].
New in version 0.3.6.
Initialize Dice instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters3.1. abydos package

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sørensen–Dice coefficient of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sørensen–Dice similarity
Return type float
Examples>>> cmp = Dice()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352941>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_dice(src, tar, qval=2)
Return the Sørensen–Dice distance between two strings.
This is a wrapper for Dice.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Sørensen–Dice distance
Return type float

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Examples>>> dist_dice('cat', 'hat')
0.5>>> dist_dice('Niall', 'Neil')
0.6363636363636364>>> dist_dice('aluminum', 'Catalan')
0.8823529411764706>>> dist_dice('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Dice.dist method instead.
abydos.distance.sim_dice(src, tar, qval=2)
Return the Sørensen–Dice coefficient of two strings.
This is a wrapper for Dice.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Sørensen–Dice similarity
Return type float
Examples>>> sim_dice('cat', 'hat')
0.5>>> sim_dice('Niall', 'Neil')
0.36363636363636365>>> sim_dice('aluminum', 'Catalan')
0.11764705882352941>>> sim_dice('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Dice.sim method instead.
class abydos.distance.DiceAsymmetricI(tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Dice's Asymmetric I similarity.
For two sets X and Y and a population N, Dice's Asymmetric I similarity [Dic45] is

𝑠𝑖𝑚𝐷𝑖𝑐𝑒𝐴𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑋|

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𝑠𝑖𝑚𝐷𝑖𝑐𝑒𝐴𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝐼 =

𝑎
𝑎+𝑏

Notes
In terms of a confusion matrix, this is equivalent to precision or positive predictive value ConfusionTable.
precision().
New in version 0.4.0.
Initialize DiceAsymmetricI instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Dice's Asymmetric I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dice's Asymmetric I similarity
Return type float
Examples>>> cmp = DiceAsymmetricI()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.1111111111111111>>> cmp.sim('ATCG', 'TAGC')
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New in version 0.4.0.
class abydos.distance.DiceAsymmetricII(tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Dice's Asymmetric II similarity.
For two sets X and Y, Dice's Asymmetric II similarity [Dic45] is

𝑠𝑖𝑚𝐷𝑖𝑐𝑒𝐴𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐷𝑖𝑐𝑒𝐴𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝐼𝐼 =

𝑎
𝑎+𝑐

Notes
In terms of a confusion matrix, this is equivalent to recall, sensitivity, or true positive rate ConfusionTable.
recall().
New in version 0.4.0.
Initialize DiceAsymmetricII instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Dice's Asymmetric II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dice's Asymmetric II similarity
Return type float
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Examples>>> cmp = DiceAsymmetricII()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.4>>> cmp.sim('aluminum', 'Catalan')
0.125>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Digby(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Digby correlation.
For two sets X and Y and a population N, Digby's approximation of the tetrachoric correlation coefficient[Dig83] is𝑐𝑜𝑟𝑟𝐷𝑖𝑔𝑏𝑦 (𝑋, 𝑌 ) =

3

3

3
4

3
4

(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |) 4 − (|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|) 4(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |) + (|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐷𝑖𝑔𝑏𝑦 =

3

3

3

3

𝑎𝑑 4 − 𝑏𝑐 4
𝑎𝑑 4 + 𝑏𝑐 4

New in version 0.4.0.
Initialize Digby instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
corr(src, tar)
Return the Digby correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Digby correlation
Return type float
Examples>>> cmp = Digby()>>> cmp.corr('cat', 'hat')
0.9774244829419212>>> cmp.corr('Niall', 'Neil')
0.9491281473458171>>> cmp.corr('aluminum', 'Catalan')
0.7541039303781305>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Digby similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Digby similarity
Return type float
Examples>>> cmp = Digby()>>> cmp.sim('cat', 'hat')
0.9887122414709606>>> cmp.sim('Niall', 'Neil')
0.9745640736729085>>> cmp.sim('aluminum', 'Catalan')
0.8770519651890653>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Dispersion(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Dispersion correlation.
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For two sets X and Y and a population N, the dispersion correlation [Cor17] is𝑐𝑜𝑟𝑟𝑑𝑖𝑠𝑝𝑒𝑟𝑠𝑖𝑜𝑛 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑁 |2

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑑𝑖𝑠𝑝𝑒𝑟𝑠𝑖𝑜𝑛 =𝑎𝑑 − 𝑏𝑐𝑛2

New in version 0.4.0.
Initialize Dispersion instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Dispersion correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dispersion correlation
Return type float

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Examples>>> cmp = Dispersion()>>> cmp.corr('cat', 'hat')
0.002524989587671803>>> cmp.corr('Niall', 'Neil')
0.002502212619741774>>> cmp.corr('aluminum', 'Catalan')
0.0011570449105440383>>> cmp.corr('ATCG', 'TAGC')-4.06731570179092e-05

New in version 0.4.0.
sim(src, tar)
Return the Dispersion similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dispersion similarity
Return type float
Examples>>> cmp = Dispersion()>>> cmp.sim('cat', 'hat')
0.5012624947938359>>> cmp.sim('Niall', 'Neil')
0.5012511063098709>>> cmp.sim('aluminum', 'Catalan')
0.500578522455272>>> cmp.sim('ATCG', 'TAGC')
0.499979663421491

New in version 0.4.0.
class abydos.distance.Doolittle(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Doolittle similarity.
For two sets X and Y and a population N, the Doolittle similarity [Doo84] is𝑠𝑖𝑚𝐷𝑜𝑜𝑙𝑖𝑡𝑡𝑙𝑒 (𝑋, 𝑌 ) =(|𝑋 ∩ 𝑌 | · |𝑁 | − |𝑋| · |𝑌 |)2|𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 | · |𝑁 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐷𝑜𝑜𝑙𝑖𝑡𝑡𝑙𝑒 =3.1. abydos package(𝑎𝑛 − (𝑎 + 𝑏)(𝑎 + 𝑐))2
(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

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New in version 0.4.0.
Initialize Doolittle instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Doolittle similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Doolittle similarity
Return type float
Examples>>> cmp = Doolittle()>>> cmp.sim('cat', 'hat')
0.24744247205785666>>> cmp.sim('Niall', 'Neil')
0.13009912077202224>>> cmp.sim('aluminum', 'Catalan')
0.011710186806836291>>> cmp.sim('ATCG', 'TAGC')
4.1196952743799446e-05

New in version 0.4.0.
class abydos.distance.Dunning(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Dunning similarity.
For two sets X and Y and a population N, Dunning log-likelihood [Dun93], following [CGHH91], is156

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𝑠𝑖𝑚𝐷𝑢𝑛𝑛𝑖𝑛𝑔 (𝑋, 𝑌 ) = 𝜆 = |𝑋 ∩ 𝑌 | · 𝑙𝑜𝑔2 (|𝑋 ∩ 𝑌 |)+|𝑋 ∖ 𝑌 | · 𝑙𝑜𝑔2 (|𝑋 ∖ 𝑌 |) + |𝑌 ∖ 𝑋| · 𝑙𝑜𝑔2 (|𝑌 ∖ 𝑋|)+|(𝑁 ∖ 𝑋) ∖ 𝑌 | · 𝑙𝑜𝑔2 (|(𝑁 ∖ 𝑋) ∖ 𝑌 |)−(|𝑋| · 𝑙𝑜𝑔2 (|𝑋|) + |𝑌 | · 𝑙𝑜𝑔2 (|𝑌 |)+|𝑁 ∖ 𝑌 | · 𝑙𝑜𝑔2 (|𝑁 ∖ 𝑌 |) + |𝑁 ∖ 𝑋| · 𝑙𝑜𝑔2 (|𝑁 ∖ 𝑋|))
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐷𝑢𝑛𝑛𝑖𝑛𝑔 = 𝜆 = 𝑎 · 𝑙𝑜𝑔2 (𝑎)+𝑏 · 𝑙𝑜𝑔2 (𝑏) + 𝑐 · 𝑙𝑜𝑔2 (𝑐) + 𝑑 · 𝑙𝑜𝑔2 (𝑑)−((𝑎 + 𝑏) · 𝑙𝑜𝑔2 (𝑎 + 𝑏) + (𝑎 + 𝑐) · 𝑙𝑜𝑔2 (𝑎 + 𝑐)+(𝑏 + 𝑑) · 𝑙𝑜𝑔2 (𝑏 + 𝑑) + (𝑐 + 𝑑)𝑙𝑜𝑔2 (𝑐 + 𝑑))
Notes
To avoid NaNs, every logarithm is calculated as the logarithm of 1 greater than the value in question. (Python's
math.log1p function is used.)
New in version 0.4.0.
Initialize Dunning instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Dunning similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison

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Returns Normalized Dunning similarity
Return type float
Examples>>> cmp = Dunning()>>> cmp.sim('cat', 'hat')
0.33462839191969423>>> cmp.sim('Niall', 'Neil')
0.19229445539929793>>> cmp.sim('aluminum', 'Catalan')
0.03220862737070572>>> cmp.sim('ATCG', 'TAGC')
0.0010606026735052122

New in version 0.4.0.
sim_score(src, tar)
Return the Dunning similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Dunning similarity
Return type float
Examples>>> cmp = Dunning()>>> cmp.sim('cat', 'hat')
0.33462839191969423>>> cmp.sim('Niall', 'Neil')
0.19229445539929793>>> cmp.sim('aluminum', 'Catalan')
0.03220862737070572>>> cmp.sim('ATCG', 'TAGC')
0.0010606026735052122

New in version 0.4.0.
class abydos.distance.Euclidean(alphabet=0,
tokenizer=None,**kwargs)
Bases: abydos.distance._minkowski.Minkowski

intersection_type='crisp',

Euclidean distance.
Euclidean distance is the straigh-line or as-the-crow-flies distance, equivalent to Minkowski distance in 𝐿2 space.
New in version 0.3.6.
Initialize Euclidean instance.
Parameters• alphabet (collection or int) -- The values or size of the alphabet
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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized Euclidean distance between two strings.
The normalized Euclidean distance is a distance metric in 𝐿2 -space, normalized to [0, 1].
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns The normalized Euclidean distance
Return type float
Examples>>> cmp = Euclidean()>>> round(cmp.dist('cat', 'hat'), 12)0.57735026919>>> round(cmp.dist('Niall', 'Neil'), 12)0.683130051064>>> round(cmp.dist('Colin', 'Cuilen'), 12)0.727606875109>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, normalized=False)
Return the Euclidean distance between two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• normalized (bool) -- Normalizes to [0, 1] if True
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Return type float
Examples>>> cmp = Euclidean()>>> cmp.dist_abs('cat', 'hat')
2.0>>> round(cmp.dist_abs('Niall', 'Neil'), 12)2.645751311065>>> cmp.dist_abs('Colin', 'Cuilen')
3.0>>> round(cmp.dist_abs('ATCG', 'TAGC'), 12)
3.162277660168

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.euclidean(src, tar, qval=2, normalized=False, alphabet=0)
Return the Euclidean distance between two strings.
This is a wrapper for Euclidean.dist_abs().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• normalized (bool) -- Normalizes to [0, 1] if True
• alphabet (collection or int) -- The values or size of the alphabet
Returns float
Return type The Euclidean distance
Examples>>> euclidean('cat', 'hat')
2.0>>> round(euclidean('Niall', 'Neil'), 12)2.645751311065>>> euclidean('Colin', 'Cuilen')
3.0>>> round(euclidean('ATCG', 'TAGC'), 12)
3.162277660168

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Euclidean.dist_abs method instead.
abydos.distance.dist_euclidean(src, tar, qval=2, alphabet=0)
Return the normalized Euclidean distance between two strings.
This is a wrapper for Euclidean.dist().
Parameters

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Abydos Documentation, Release 0.5.0• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Euclidean distance
Return type float
Examples>>> round(dist_euclidean('cat', 'hat'), 12)0.57735026919>>> round(dist_euclidean('Niall', 'Neil'), 12)0.683130051064>>> round(dist_euclidean('Colin', 'Cuilen'), 12)0.727606875109>>> dist_euclidean('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Euclidean.dist method instead.
abydos.distance.sim_euclidean(src, tar, qval=2, alphabet=0)
Return the normalized Euclidean similarity of two strings.
This is a wrapper for Euclidean.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Euclidean similarity
Return type float
Examples>>> round(sim_euclidean('cat', 'hat'), 12)0.42264973081>>> round(sim_euclidean('Niall', 'Neil'), 12)0.316869948936>>> round(sim_euclidean('Colin', 'Cuilen'), 12)0.272393124891>>> sim_euclidean('ATCG', 'TAGC')
0.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Euclidean.sim method instead.

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class abydos.distance.Eyraud(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Eyraud similarity.
For two sets X and Y and a population N, the Eyraud similarity [Eyr38] is𝑠𝑖𝑚𝐸𝑦𝑟𝑎𝑢𝑑 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | − |𝑋| · |𝑌 |
|𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 | · |𝑁 ∖ 𝑋|

For lack of access to the original, this formula is based on the concurring formulae presented in [Shi93] and[Hubalek08].
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐸𝑦𝑟𝑎𝑢𝑑 =𝑎 − (𝑎 + 𝑏)(𝑎 + 𝑐)
(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize Eyraud instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Eyraud similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Eyraud similarity
Return type float

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Examples>>> cmp = Eyraud()>>> cmp.sim('cat', 'hat')
1.438198553583169e-06>>> cmp.sim('Niall', 'Neil')
1.5399964580081465e-06>>> cmp.sim('aluminum', 'Catalan')
1.6354719962967386e-06>>> cmp.sim('ATCG', 'TAGC')
1.6478781097519779e-06

New in version 0.4.0.
sim_score(src, tar)
Return the Eyraud similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Eyraud similarity
Return type float
Examples>>> cmp = Eyraud()>>> cmp.sim_score('cat', 'hat')-1.438198553583169e-06>>> cmp.sim_score('Niall', 'Neil')-1.5399964580081465e-06>>> cmp.sim_score('aluminum', 'Catalan')-1.6354719962967386e-06>>> cmp.sim_score('ATCG', 'TAGC')-1.6478781097519779e-06

New in version 0.4.0.
class abydos.distance.FagerMcGowan(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceFager & McGowan similarity.
For two sets X and Y, the Fager & McGowan similarity [Fag57][FM63] is|𝑋 ∩ 𝑌 |1
𝑠𝑖𝑚𝐹 𝑎𝑔𝑒𝑟𝑀 𝑐𝐺𝑜𝑤𝑎𝑛 (𝑋, 𝑌 ) = √︀− √︀
|𝑋| · |𝑌 | 2 𝑚𝑎𝑥(|𝑋|, |𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐹 𝑎𝑔𝑒𝑟𝑀 𝑐𝐺𝑜𝑤𝑎𝑛 = √︀

3.1. abydos package

𝑎

1
− √︀
(𝑎 + 𝑏)(𝑎 + 𝑐) 2 𝑚𝑎𝑥(𝑎 + 𝑏, 𝑎 + 𝑐)

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New in version 0.4.0.
Initialize FagerMcGowan instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Fager & McGowan similarity of two strings.
As this similarity ranges from (− inf, 1.0), this normalization simply clamps the value to the range (0.0,
1.0).
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Fager & McGowan similarity
Return type float
Examples>>> cmp = FagerMcGowan()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.16102422643817918>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Fager & McGowan similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fager & McGowan similarity
Return type float
Examples>>> cmp = FagerMcGowan()>>> cmp.sim_score('cat', 'hat')
0.25>>> cmp.sim_score('Niall', 'Neil')
0.16102422643817918>>> cmp.sim_score('aluminum', 'Catalan')-0.048815536468908724>>> cmp.sim_score('ATCG', 'TAGC')-0.22360679774997896

New in version 0.4.0.
class abydos.distance.Faith(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Faith similarity.
For two sets X and Y and a population N, the Faith similarity [Fai83] is𝑠𝑖𝑚𝐹 𝑎𝑖𝑡ℎ (𝑋, 𝑌 ) =||𝑋 ∩ 𝑌 | + |(𝑁 ∖𝑋)∖𝑌2|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐹 𝑎𝑖𝑡ℎ =𝑎 + 𝑑2𝑛

New in version 0.4.0.
Initialize Faith instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.

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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Faith similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Faith similarity
Return type float
Examples>>> cmp = Faith()>>> cmp.sim('cat', 'hat')
0.4987244897959184>>> cmp.sim('Niall', 'Neil')
0.4968112244897959>>> cmp.sim('aluminum', 'Catalan')
0.4910828025477707>>> cmp.sim('ATCG', 'TAGC')
0.49362244897959184

New in version 0.4.0.
class abydos.distance.Fidelity(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceFidelity.
For two multisets X and Y drawn from an alphabet S, fidelity is

𝑠𝑖𝑚𝐹 𝑖𝑑𝑒𝑙𝑖𝑡𝑦 (𝑋, 𝑌 ) =)︃2
𝐴𝑖 𝐵 𝑖|·||𝐴| |𝐵|

√︃

(︃
∑︁
𝑖∈𝑆

New in version 0.4.0.
Initialize Fidelity instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.

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sim(src, tar)
Return the fidelity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns fidelity
Return type float
Examples>>> cmp = Fidelity()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.1333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.013888888888888888>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Fleiss(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Fleiss correlation.
For two sets X and Y and a population N, Fleiss correlation [Fle75] is𝑐𝑜𝑟𝑟𝐹 𝑙𝑒𝑖𝑠𝑠 (𝑋, 𝑌 ) =(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|) · (|𝑋| · |𝑁 ∖ 𝑋| + |𝑌 | · |𝑁 ∖ 𝑌 |)2 · |𝑋| · |𝑁 ∖ 𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐹 𝑙𝑒𝑖𝑠𝑠 =(𝑎𝑑 − 𝑏𝑐)((𝑎 + 𝑏)(𝑐 + 𝑑) + (𝑎 + 𝑐)(𝑏 + 𝑑))2(𝑎 + 𝑏)(𝑐 + 𝑑)(𝑎 + 𝑐)(𝑏 + 𝑑)

This is Fleiss' 𝑀 (𝐴1 ), 𝑎𝑑 − 𝑏𝑐 divided by the harmonic mean of the marginals 𝑝1 𝑞1 = (𝑎 + 𝑏)(𝑐 + 𝑑) and𝑝2 𝑞2 = (𝑎 + 𝑐)(𝑏 + 𝑑).
New in version 0.4.0.
Initialize Fleiss instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Fleiss correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fleiss correlation
Return type float
Examples>>> cmp = Fleiss()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.3621712520061204>>> cmp.corr('aluminum', 'Catalan')
0.10839724112919989>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Fleiss similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fleiss similarity
Return type float

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Examples>>> cmp = Fleiss()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6810856260030602>>> cmp.sim('aluminum', 'Catalan')
0.5541986205645999>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.FleissLevinPaik(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Fleiss-Levin-Paik similarity.
For two sets X and Y and a population N, Fleiss-Levin-Paik similarity [FLP03] is𝑠𝑖𝑚𝐹 𝑙𝑒𝑖𝑠𝑠𝐿𝑒𝑣𝑖𝑛𝑃 𝑎𝑖𝑘 (𝑋, 𝑌 ) =2|(𝑁 ∖ 𝑋) ∖ 𝑌 |2|(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

This is [Mor12]'s 'd Specific Agreement'.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐹 𝑙𝑒𝑖𝑠𝑠𝐿𝑒𝑣𝑖𝑛𝑃 𝑎𝑖𝑘 =2𝑑
2𝑑 + 𝑏 + 𝑐

New in version 0.4.0.
Initialize FleissLevinPaik instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.

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New in version 0.4.0.
sim(src, tar)
Return the Fleiss-Levin-Paik similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fleiss-Levin-Paik similarity
Return type float
Examples>>> cmp = FleissLevinPaik()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9955041746949261>>> cmp.sim('aluminum', 'Catalan')
0.9903412749517064>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.
class abydos.distance.ForbesI(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Forbes I similarity.
For two sets X and Y and a population N, the Forbes I similarity [For07][Moz36] is𝑠𝑖𝑚𝐹 𝑜𝑟𝑏𝑒𝑠𝐼 (𝑋, 𝑌 ) =|𝑁 | · |𝑋 ∩ 𝑌 ||𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐹 𝑜𝑟𝑏𝑒𝑠𝐼 =𝑛𝑎(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize ForbesI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Forbes I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Forbes I similarity
Return type float
Examples>>> cmp = ForbesI()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.11125283446712018>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Forbes I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Forbes I similarity
Return type float

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Examples>>> cmp = ForbesI()>>> cmp.sim_score('cat', 'hat')
98.0>>> cmp.sim_score('Niall', 'Neil')
52.266666666666666>>> cmp.sim_score('aluminum', 'Catalan')
10.902777777777779>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.ForbesII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Forbes II correlation.
For two sets X and Y and a population N, the Forbes II correlation, as described in [For25], is𝑐𝑜𝑟𝑟𝐹 𝑜𝑟𝑏𝑒𝑠𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋| − |𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋| · |𝑌 | − |𝑁 | · 𝑚𝑖𝑛(|𝑋|, |𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐹 𝑜𝑟𝑏𝑒𝑠𝐼𝐼 =𝑏𝑐 − 𝑎𝑑(𝑎 + 𝑏)(𝑎 + 𝑐) − 𝑛 · 𝑚𝑖𝑛(𝑎 + 𝑏, 𝑎 + 𝑐)

New in version 0.4.0.
Initialize ForbesII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
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corr(src, tar)
Return the Forbes II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Forbes II correlation
Return type float
Examples>>> cmp = ForbesII()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.3953727506426735>>> cmp.corr('aluminum', 'Catalan')
0.11485180412371133>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Forbes II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Forbes II similarity
Return type float
Examples>>> cmp = ForbesII()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6976863753213367>>> cmp.sim('aluminum', 'Catalan')
0.5574259020618557>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.Fossum(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Fossum similarity.
For two sets X and Y and a population N, the Fossum similarity [FK66] is
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𝑠𝑖𝑚𝐹 𝑜𝑠𝑠𝑢𝑚 (𝑋, 𝑌 ) =(︁)︁2|𝑁 | · |𝑋 ∩ 𝑌 | − 12
|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐹 𝑜𝑠𝑠𝑢𝑚 =𝑛(𝑎 − 21 )2(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize Fossum instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Fossum similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Fossum similarity
Return type float

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Examples>>> cmp = Fossum()>>> cmp.sim('cat', 'hat')
0.1836734693877551>>> cmp.sim('Niall', 'Neil')
0.08925619834710742>>> cmp.sim('aluminum', 'Catalan')
0.0038927335640138415>>> cmp.sim('ATCG', 'TAGC')
0.01234567901234568

New in version 0.4.0.
sim_score(src, tar)
Return the Fossum similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fossum similarity
Return type float
Examples>>> cmp = Fossum()>>> cmp.sim_score('cat', 'hat')
110.25>>> cmp.sim_score('Niall', 'Neil')58.8>>> cmp.sim_score('aluminum', 'Catalan')
2.7256944444444446>>> cmp.sim_score('ATCG', 'TAGC')
7.84

New in version 0.4.0.
class abydos.distance.GeneralizedFleiss(alphabet=None,
tokenizer=None,intersection_type='crisp',
mean_func='arithmetic',
marginals='a', proportional=False, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Generalized Fleiss correlation.
For two sets X and Y and a population N, Generalized Fleiss correlation is based on observations from [Fle75].

𝑐𝑜𝑟𝑟𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑧𝑒𝑑𝐹 𝑙𝑒𝑖𝑠𝑠 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
𝜇𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 𝑜𝑓 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑠

The mean function 𝜇 may be any of the mean functions in abydos.stats. The products of marginals may
be one of the following:• a : |𝑋| · |𝑁 ∖ 𝑋| & |𝑌 | · |𝑁 ∖ 𝑌 |• b : |𝑋| · |𝑌 | & |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |3.1. abydos package

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Abydos Documentation, Release 0.5.0• c : |𝑋| · |𝑁 | ∖ 𝑌 | & |𝑌 | · |𝑁 ∖ 𝑋|
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑧𝑒𝑑𝐹 𝑙𝑒𝑖𝑠𝑠 =𝑎𝑑 − 𝑏𝑐
𝜇𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 𝑜𝑓 𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑠

And the products of marginals are:• a : 𝑝1 𝑞1 = (𝑎 + 𝑏)(𝑐 + 𝑑) & 𝑝2 𝑞2 = (𝑎 + 𝑐)(𝑏 + 𝑑)
• b : 𝑝1 𝑝2 = (𝑎 + 𝑏)(𝑎 + 𝑐) & 𝑞1 𝑞2 = (𝑐 + 𝑑)(𝑏 + 𝑑)
• c : 𝑝1 𝑞2 = (𝑎 + 𝑏)(𝑏 + 𝑑) & 𝑝2 𝑞1 = (𝑎 + 𝑐)(𝑐 + 𝑑)
New in version 0.4.0.
Initialize GeneralizedFleiss instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• mean_func (str or function) -- Specifies the mean function to use. A function taking a list of numbers as its only required argument may be supplied, or one of the following
strings will select the specified mean function from abydos.stats:– arithmetic employs amean(), and this measure will be identical to
MaxwellPilliner with otherwise default parameters– geometric employs gmean(), and this measure will be identical to PearsonPhi
with otherwise default parameters– harmonic employs hmean(), and this measure will be identical to Fleiss with otherwise default parameters– ag employs the arithmetic-geometric mean agmean()– gh employs the geometric-harmonic mean ghmean()– agh employs the arithmetic-geometric-harmonic mean aghmean()– contraharmonic employs the contraharmonic mean cmean()– identric employs the identric mean imean()– logarithmic employs the logarithmic mean lmean()– quadratic employs the quadratic mean qmean()– heronian employs the Heronian mean heronian_mean()– hoelder employs the Hölder mean hoelder_mean()– lehmer employs the Lehmer mean lehmer_mean()– seiffert employs Seiffert's mean seiffert_mean()

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Abydos Documentation, Release 0.5.0• marginals (str) -- Specifies the pairs of marginals to multiply and calculate the resulting
mean of. Can be:– a : 𝑝1 𝑞1 = (𝑎 + 𝑏)(𝑐 + 𝑑) & 𝑝2 𝑞2 = (𝑎 + 𝑐)(𝑏 + 𝑑)
– b : 𝑝1 𝑝2 = (𝑎 + 𝑏)(𝑎 + 𝑐) & 𝑞1 𝑞2 = (𝑐 + 𝑑)(𝑏 + 𝑑)
– c : 𝑝1 𝑞2 = (𝑎 + 𝑏)(𝑏 + 𝑑) & 𝑝2 𝑞1 = (𝑎 + 𝑐)(𝑐 + 𝑑)
• proportional (bool) -- If true, each of the values, 𝑎, 𝑏, 𝑐, 𝑑 and the marginals will be
divided by the total 𝑎 + 𝑏 + 𝑐 + 𝑑 = 𝑛.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Generalized Fleiss correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Generalized Fleiss correlation
Return type float
Examples>>> cmp = GeneralizedFleiss()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.35921989956790845>>> cmp.corr('aluminum', 'Catalan')
0.10803030303030303>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Generalized Fleiss similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Generalized Fleiss similarity

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Return type float
Examples>>> cmp = GeneralizedFleiss()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6796099497839543>>> cmp.sim('aluminum', 'Catalan')
0.5540151515151515>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.Gilbert(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Gilbert correlation.
For two sets X and Y and a population N, the Gilbert correlation [Gil84] is𝑐𝑜𝑟𝑟𝐺𝑖𝑙𝑏𝑒𝑟𝑡 (𝑋, 𝑌 ) =2(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)|𝑁 |2 − |𝑋 ∩ 𝑌 |2 + |𝑋 ∖ 𝑌 |2 + |𝑌 ∖ 𝑋|2 − |(𝑁 ∖ 𝑋) ∖ 𝑌 |2

For lack of access to the original, this formula is based on the concurring formulae presented in [Pei84] and[Doo84].
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐺𝑖𝑙𝑏𝑒𝑟𝑡 =2(𝑎𝑑 − 𝑐𝑑)𝑛2 − 𝑎2 + 𝑏2 + 𝑐2 − 𝑑2

New in version 0.4.0.
Initialize Gilbert instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
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Abydos Documentation, Release 0.5.0• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Gilbert correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gilbert correlation
Return type float
Examples>>> cmp = Gilbert()>>> cmp.corr('cat', 'hat')
0.3310580204778157>>> cmp.corr('Niall', 'Neil')
0.21890122402504983>>> cmp.corr('aluminum', 'Catalan')
0.057094811018577836>>> cmp.corr('ATCG', 'TAGC')-0.003198976327575176

New in version 0.4.0.
sim(src, tar)
Return the Gilbert similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gilbert similarity
Return type float
Examples>>> cmp = Gilbert()>>> cmp.sim('cat', 'hat')
0.6655290102389079>>> cmp.sim('Niall', 'Neil')
0.6094506120125249>>> cmp.sim('aluminum', 'Catalan')0.5285474055092889>>> cmp.sim('ATCG', 'TAGC')
0.4984005118362124

New in version 0.4.0.

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class abydos.distance.GilbertWells(alphabet=None, tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Gilbert & Wells similarity.
For two sets X and Y and a population N, the Gilbert & Wells similarity [GW66] is

𝑠𝑖𝑚𝐺𝑖𝑙𝑏𝑒𝑟𝑡𝑊 𝑒𝑙𝑙𝑠 (𝑋, 𝑌 ) = 𝑙𝑛

|𝑁 |3
|𝑁 |! · |𝑋 ∩ 𝑌 |! · |𝑋 ∖ 𝑌 |! · |𝑌 ∖ 𝑋|! · |(𝑁 ∖ 𝑋) ∖ 𝑌 |!
+ 2𝑙𝑛
2𝜋|𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 | · |𝑁 ∖ 𝑋|
|𝑋|! · |𝑌 |! · |𝑁 ∖ 𝑌 |! · |𝑁 ∖ 𝑋|!

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐺𝑖𝑙𝑏𝑒𝑟𝑡𝑊 𝑒𝑙𝑙𝑠 = 𝑙𝑛𝑛3
𝑛!𝑎!𝑏!𝑐!𝑑!
+ 2𝑙𝑛
2𝜋(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)
(𝑎 + 𝑏)!(𝑎 + 𝑐)!(𝑏 + 𝑑)!(𝑐 + 𝑑)!

Notes
Most lists of similarity & distance measures, including [Hubalek08][CCT10][Mor12] have a quite different𝑎+𝑐𝑎𝑛formula, which would be 𝑙𝑛 𝑎 − 𝑙𝑛 𝑏 − 𝑙𝑛 𝑎+𝑏
𝑛 − 𝑙𝑛 𝑛 = 𝑙𝑛 (𝑎+𝑏)(𝑎+𝑐) . However, neither this formula noranything similar or equivalent to it appears anywhere within the cited work, [GW66]. See :class:UnknownF for
this, alternative, measure.
New in version 0.4.0.
Initialize GilbertWells instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the normalized Gilbert & Wells similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Gilbert & Wells similarity
Return type float

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Examples>>> cmp = GilbertWells()>>> cmp.sim('cat', 'hat')
0.4116913723876516>>> cmp.sim('Niall', 'Neil')
0.2457247406857589>>> cmp.sim('aluminum', 'Catalan')
0.05800001636414742>>> cmp.sim('ATCG', 'TAGC')
0.028716013247135602

New in version 0.4.0.
sim_score(src, tar)
Return the Gilbert & Wells similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gilbert & Wells similarity
Return type float
Examples>>> cmp = GilbertWells()>>> cmp.sim_score('cat', 'hat')
20.17617447734673>>> cmp.sim_score('Niall', 'Neil')
16.717742356982733>>> cmp.sim_score('aluminum', 'Catalan')5.495096667524002>>> cmp.sim_score('ATCG', 'TAGC')
1.6845961909440712

New in version 0.4.0.
class abydos.distance.GiniI(alphabet=None, tokenizer=None, intersection_type='crisp', normalizer='proportional', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Gini I correlation.
For two sets X and Y and a population N, Gini I correlation [Gin12], using the formula from [GK59], is

𝑐𝑜𝑟𝑟𝐺𝑖𝑛𝑖𝐼 (𝑋, 𝑌 ) = √︂|𝑋∩𝑌 |+|(𝑁 ∖𝑋)∖𝑌 |||𝑁 ∖𝑌 |·|𝑁 ∖𝑋|− |𝑋|·|𝑌|𝑁 ||𝑁 | +
|𝑁 |
2

2

2

2

|𝑌 |
|𝑁 ∖𝑌 |
(1 − ( |𝑋|+ |𝑁|𝑁∖𝑋|))|𝑁 | + |𝑁 | )) · (1 − ( |𝑁 ||In 2x2 confusion table terms, where a+b+c+d=n, after each term has been converted to a proportion by dividing
by n, this is3.1. abydos package

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𝑐𝑜𝑟𝑟𝐺𝑖𝑛𝑖𝐼 = √︀(𝑎 + 𝑑) − (𝑎 + 𝑏)(𝑎 + 𝑐) + (𝑏 + 𝑑)(𝑐 + 𝑑)
(1 − ((𝑎 + 𝑏)2 + (𝑐 + 𝑑)2 )) · (1 − ((𝑎 + 𝑐)2 + (𝑏 + 𝑑)2 ))New in version 0.4.0.
Initialize GiniI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Gini I correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gini I correlation
Return type float
Examples>>> cmp = GiniI()>>> cmp.corr('cat', 'hat')
0.49722814498933254>>> cmp.corr('Niall', 'Neil')
0.39649090262533215>>> cmp.corr('aluminum', 'Catalan')
0.14887105223941113>>> cmp.corr('ATCG', 'TAGC')-0.006418485237489576

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New in version 0.4.0.
sim(src, tar)
Return the normalized Gini I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Gini I similarity
Return type float
Examples>>> cmp = GiniI()>>> cmp.sim('cat', 'hat')
0.7486140724946663>>> cmp.sim('Niall', 'Neil')
0.6982454513126661>>> cmp.sim('aluminum', 'Catalan')
0.5744355261197056>>> cmp.sim('ATCG', 'TAGC')
0.4967907573812552

New in version 0.4.0.
class abydos.distance.GiniII(alphabet=None, tokenizer=None, intersection_type='crisp', normalizer='proportional', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Gini II distance.
For two sets X and Y and a population N, Gini II correlation [Gin15], using the formula from [GK59], is

𝑐𝑜𝑟𝑟𝐺𝑖𝑛𝑖𝐼𝐼 (𝑋, 𝑌 ) =|𝑋∩𝑌 |+|(𝑁 ∖𝑋)∖𝑌 |||𝑁 ∖𝑌 |·|𝑁 ∖𝑋|− ( |𝑋|·|𝑌)|𝑁 ||𝑁 | +|𝑁 ||𝑌 ∖𝑋|−|𝑋∖𝑌 ||𝑋|·|𝑌 ||𝑁 ∖𝑌 |·|𝑁 ∖𝑋|
1−|| − ( |𝑁 | +
)
|𝑁 |
|𝑁 |

In 2x2 confusion table terms, where a+b+c+d=n, after each term has been converted to a proportion by dividing
by n, this is𝑐𝑜𝑟𝑟𝐺𝑖𝑛𝑖𝐼𝐼 =(𝑎 + 𝑑) − ((𝑎 + 𝑏)(𝑎 + 𝑐) + (𝑏 + 𝑑)(𝑐 + 𝑑))
1 − |𝑏 − 𝑐| − ((𝑎 + 𝑏)(𝑎 + 𝑐) + (𝑏 + 𝑑)(𝑐 + 𝑑))

New in version 0.4.0.
Initialize GiniII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package
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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Gini II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gini II correlation
Return type float
Examples>>> cmp = GiniII()>>> cmp.corr('cat', 'hat')
0.49722814498933254>>> cmp.corr('Niall', 'Neil')
0.4240703425535771>>> cmp.corr('aluminum', 'Catalan')
0.15701415701415936>>> cmp.corr('ATCG', 'TAGC')-0.006418485237489576

New in version 0.4.0.
sim(src, tar)
Return the normalized Gini II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Gini II similarity
Return type float

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Examples>>> cmp = GiniII()>>> cmp.sim('cat', 'hat')
0.7486140724946663>>> cmp.sim('Niall', 'Neil')
0.7120351712767885>>> cmp.sim('aluminum', 'Catalan')
0.5785070785070797>>> cmp.sim('ATCG', 'TAGC')
0.4967907573812552

New in version 0.4.0.
class abydos.distance.Goodall(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Goodall similarity.
For two sets X and Y and a population N, Goodall similarity [Goo67][AC77] is an angular transformation ofSokal & Michener's simple matching coefficient(︁2
𝑠𝑖𝑚𝐺𝑜𝑜𝑑𝑎𝑙𝑙 (𝑋, 𝑌 ) = sin−1𝜋√︃|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | )︁|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is(︁2
𝑠𝑖𝑚𝐺𝑜𝑜𝑑𝑎𝑙𝑙 = sin−1𝜋√︂

𝑎 + 𝑑 )︁𝑛

New in version 0.4.0.
Initialize Goodall instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
sim(src, tar)
Return the Goodall similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodall similarity
Return type float
Examples>>> cmp = Goodall()>>> cmp.sim('cat', 'hat')
0.9544884026871964>>> cmp.sim('Niall', 'Neil')
0.9397552079794624>>> cmp.sim('aluminum', 'Catalan')
0.9117156301536503>>> cmp.sim('ATCG', 'TAGC')
0.9279473952929225

New in version 0.4.0.
class abydos.distance.GoodmanKruskalLambda(alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Goodman & Kruskal's Lambda similarity.
For two sets X and Y and a population N, Goodman & Kruskal's lambda [GK54] is

1

𝑠𝑖𝑚𝐺𝐾𝜆 (𝑋, 𝑌 ) = 2(𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑋 ∖ 𝑌 |) + 𝑚𝑎𝑥(|𝑌 ∖ 𝑋|, |(𝑁 ∖ 𝑋) ∖ 𝑌 |) + 𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑌 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌|𝑁 | − 21 (𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑌 |, |𝑁

In 2x2 confusion table terms, where a+b+c+d=n, this is1

𝑠𝑖𝑚𝐺𝐾𝜆 = 2((𝑚𝑎𝑥(𝑎, 𝑏) + 𝑚𝑎𝑥(𝑐, 𝑑) + 𝑚𝑎𝑥(𝑎, 𝑐) + 𝑚𝑎𝑥(𝑏, 𝑑)) − (𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑) + 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑)))𝑛 − 21 (𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑) + 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑))

New in version 0.4.0.
Initialize GoodmanKruskalLambda instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Goodman & Kruskal's Lambda similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodman & Kruskal's Lambda similarity
Return type float
Examples>>> cmp = GoodmanKruskalLambda()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.GoodmanKruskalLambdaR(alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Goodman & Kruskal Lambda-r correlation.
For two sets X and Y and a population N, Goodman & Kruskal 𝜆𝑟 correlation [GK54] is𝑐𝑜𝑟𝑟𝐺𝐾𝜆𝑟 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | − 12 (𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑌 |, |𝑁 ∖ 𝑌 |))|𝑁 | − 12 (𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑌 |, |𝑁 ∖ 𝑌 |))In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐺𝐾𝜆𝑟 =3.1. abydos package

𝑎 + 𝑑 − 12 (𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑) + 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑))𝑛 − 21 (𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑) + 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑))
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New in version 0.4.0.
Initialize GoodmanKruskalLambdaR instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return Goodman & Kruskal Lambda-r correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodman & Kruskal Lambda-r correlation
Return type float
Examples>>> cmp = GoodmanKruskalLambdaR()>>> cmp.corr('cat', 'hat')
0.0>>> cmp.corr('Niall', 'Neil')-0.2727272727272727>>> cmp.corr('aluminum', 'Catalan')-0.7647058823529411>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return Goodman & Kruskal Lambda-r similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodman & Kruskal Lambda-r similarity
Return type float
Examples>>> cmp = GoodmanKruskalLambdaR()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352944>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.GoodmanKruskalTauA(alphabet=None,
tokenizer=None,intersection_type='crisp',
normalizer='proportional',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Goodman & Kruskal's Tau A similarity.
For two sets X and Y and a population N, Goodman & Kruskal's 𝜏𝑎 similarity [GK54], by analogy with 𝜏𝑏 , is𝑠𝑖𝑚𝐺𝐾𝜏𝑎 (𝑋, 𝑌 ) =|𝑋∩𝑌 | 2|𝑌 ∖𝑋| 2+ |𝑁 |
|𝑁 |
|𝑌 |
|𝑁 |

+

|(𝑁 ∖𝑋)∖𝑌 | 2|𝑋∖𝑌 | 2
+
|𝑁 |
|𝑁 |
|𝑁 ∖𝑋|
|𝑁 |

2

2

2

|𝑁 ∖𝑋|)− ( |𝑋||𝑁 | + |𝑁 |2

|𝑁 ∖𝑋|
1 − ( |𝑋|)
|𝑁 | + |𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, after each term has been converted to a proportion by dividing
by n, this is

𝑠𝑖𝑚𝐺𝐾𝜏𝑎 =𝑎2 +𝑐2𝑏2 +𝑑22
2
𝑎+𝑐 + 𝑏+𝑑 − ((𝑎 + 𝑏) + (𝑐 + 𝑑) )1 − ((𝑎 + 𝑏)2 + (𝑐 + 𝑑)2 )New in version 0.4.0.
Initialize GoodmanKruskalTauA instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Goodman & Kruskal's Tau A similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodman & Kruskal's Tau A similarity
Return type float
Examples>>> cmp = GoodmanKruskalTauA()>>> cmp.sim('cat', 'hat')
0.3304969657208484>>> cmp.sim('Niall', 'Neil')
0.22137604585914503>>> cmp.sim('aluminum', 'Catalan')
0.05991264724130685>>> cmp.sim('ATCG', 'TAGC')
4.119695274745721e-05

New in version 0.4.0.
class abydos.distance.GoodmanKruskalTauB(alphabet=None,
tokenizer=None,intersection_type='crisp',
normalizer='proportional',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Goodman & Kruskal's Tau B similarity.
For two sets X and Y and a population N, Goodman & Kruskal's 𝜏𝑏 similarity [GK54] is

𝑠𝑖𝑚𝐺𝐾𝜏𝑏 (𝑋, 𝑌 ) =|𝑋∩𝑌 | 2|𝑋∖𝑌 | 2+ |𝑁 |
|𝑁 |
|𝑋|
|𝑁 |

+

|(𝑁 ∖𝑋)∖𝑌 | 2|𝑌 ∖𝑋| 2
+
|𝑁 |
|𝑁 |
|𝑁 ∖𝑋|
|𝑁 |

2

2

2

|𝑌 |
|𝑁 ∖𝑌 |)
− ( |𝑁| + |𝑁 |2|𝑌 ||𝑁 ∖𝑌 |
1 − ( |𝑁)
| + |𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, after each term has been converted to a proportion by dividing
by n, this is

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𝑠𝑖𝑚𝐺𝐾𝜏𝑏 =𝑎2 +𝑏2𝑐2 +𝑑22
2
𝑎+𝑏 + 𝑐+𝑑 − ((𝑎 + 𝑐) + (𝑏 + 𝑑) )1 − ((𝑎 + 𝑐)2 + (𝑏 + 𝑑)2 )New in version 0.4.0.
Initialize GoodmanKruskalTauB instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Goodman & Kruskal's Tau B similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Goodman & Kruskal's Tau B similarity
Return type float
Examples>>> cmp = GoodmanKruskalTauB()>>> cmp.sim('cat', 'hat')
0.3304969657208484>>> cmp.sim('Niall', 'Neil')
0.2346006486710202>>> cmp.sim('aluminum', 'Catalan')
0.06533810992392582>>> cmp.sim('ATCG', 'TAGC')
4.119695274745721e-05

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New in version 0.4.0.
class abydos.distance.GowerLegendre(alphabet=None,
tokenizer=None,tion_type='crisp', theta=0.5, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Gower & Legendre similarity.
For two sets X and Y and a population N, the Gower & Legendre similarity [GL86] is𝑠𝑖𝑚𝐺𝑜𝑤𝑒𝑟𝐿𝑒𝑔𝑒𝑛𝑑𝑟𝑒 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | + 𝜃 · |𝑋△𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐺𝑜𝑤𝑒𝑟𝐿𝑒𝑔𝑒𝑛𝑑𝑟𝑒 =𝑎+𝑑𝑎 + 𝜃(𝑏 + 𝑐) + 𝑑

New in version 0.4.0.
Initialize GowerLegendre instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• theta (float) -- The weight to place on the symmetric difference.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Gower & Legendre similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gower & Legendre similarity
Return type float

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Examples>>> cmp = GowerLegendre()>>> cmp.sim('cat', 'hat')
0.9974424552429667>>> cmp.sim('Niall', 'Neil')
0.9955156950672646>>> cmp.sim('aluminum', 'Catalan')
0.9903536977491961>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.
class abydos.distance.GuttmanLambdaA(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Guttman's Lambda A similarity.
For two sets X and Y and a population N, Guttman's 𝜆𝑎 similarity [Gut41] is

𝑠𝑖𝑚𝐺𝑢𝑡𝑡𝑚𝑎𝑛𝜆𝑎 (𝑋, 𝑌 ) =𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑌 ∖ 𝑋|) + 𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |(𝑁 ∖ 𝑋) ∖ 𝑌 |) − 𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|)|𝑁 | − 𝑚𝑎𝑥(|𝑋|, |𝑁 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐺𝑢𝑡𝑡𝑚𝑎𝑛𝜆𝑎 =𝑚𝑎𝑥(𝑎, 𝑐) + 𝑚𝑎𝑥(𝑏, 𝑑) − 𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑)𝑛 − 𝑚𝑎𝑥(𝑎 + 𝑏, 𝑐 + 𝑑)

New in version 0.4.0.
Initialize GuttmanLambdaA instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
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sim(src, tar)
Return the Guttman Lambda A similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Guttman's Lambda A similarity
Return type float
Examples>>> cmp = GuttmanLambdaA()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.GuttmanLambdaB(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Guttman's Lambda B similarity.
For two sets X and Y and a population N, Guttman's 𝜆𝑏 similarity [Gut41] is

𝑠𝑖𝑚𝐺𝑢𝑡𝑡𝑚𝑎𝑛𝜆𝑏 (𝑋, 𝑌 ) =𝑚𝑎𝑥(|𝑋 ∩ 𝑌 |, |𝑋 ∖ 𝑌 |) + 𝑚𝑎𝑥(|𝑌 ∖ 𝑋|, |(𝑁 ∖ 𝑋) ∖ 𝑌 |) − 𝑚𝑎𝑥(|𝑌 |, |𝑁 ∖ 𝑌 |)|𝑁 | − 𝑚𝑎𝑥(|𝑌 |, |𝑁 ∖ 𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐺𝑢𝑡𝑡𝑚𝑎𝑛𝜆𝑏 =𝑚𝑎𝑥(𝑎, 𝑏) + 𝑚𝑎𝑥(𝑐, 𝑑) − 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑)𝑛 − 𝑚𝑎𝑥(𝑎 + 𝑐, 𝑏 + 𝑑)

New in version 0.4.0.
Initialize GuttmanLambdaB instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Guttman Lambda B similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Guttman's Lambda B similarity
Return type float
Examples>>> cmp = GuttmanLambdaB()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.GwetAC(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Gwet's AC correlation.
For two sets X and Y and a population N, Gwet's AC correlation [Gwe08] is𝑐𝑜𝑟𝑟𝐺𝑤𝑒𝑡𝐴𝐶 (𝑋, 𝑌 ) = 𝐴𝐶 =𝑝𝑜 − 𝑝𝐴𝐶𝑒
1 − 𝑝𝐴𝐶
𝑒

where

𝑝𝑜

=

𝑝𝐴𝐶
𝑒

=

|𝑋∩𝑌 |+|(𝑁 ∖𝑋)∖𝑌 |
|𝑁 |
(︁
)︁
1 |𝑋|+|𝑌 | |𝑋∖𝑌 |+|𝑌 ∖𝑋|
·
2
|𝑁 |
|𝑁 |

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𝑝𝑜

=

𝑝𝐴𝐶
𝑒

=

𝑎+𝑑
𝑛
(︁
)︁
1 2𝑎+𝑏+𝑐 2𝑑+𝑏+𝑐·2𝑛𝑛

New in version 0.4.0.
Initialize GwetAC instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Gwet's AC correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gwet's AC correlation
Return type float
Examples>>> cmp = GwetAC()>>> cmp.corr('cat', 'hat')
0.9948456319360438>>> cmp.corr('Niall', 'Neil')
0.990945276504824>>> cmp.corr('aluminum', 'Catalan')
0.9804734301840141>>> cmp.corr('ATCG', 'TAGC')
0.9870811678360627

New in version 0.4.0.

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sim(src, tar)
Return the Gwet's AC similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Gwet's AC similarity
Return type float
Examples>>> cmp = GwetAC()>>> cmp.sim('cat', 'hat')
0.9974228159680218>>> cmp.sim('Niall', 'Neil')
0.995472638252412>>> cmp.sim('aluminum', 'Catalan')
0.9902367150920071>>> cmp.sim('ATCG', 'TAGC')
0.9935405839180314

New in version 0.4.0.
class abydos.distance.Hamann(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Hamann correlation.
For two sets X and Y and a population N, the Hamann correlation [Ham61] is

𝑐𝑜𝑟𝑟𝐻𝑎𝑚𝑎𝑛𝑛 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝐻𝑎𝑚𝑎𝑛𝑛 =𝑎+𝑑−𝑏−𝑐𝑛

New in version 0.4.0.
Initialize Hamann instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Hamann correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hamann correlation
Return type float
Examples>>> cmp = Hamann()>>> cmp.corr('cat', 'hat')
0.9897959183673469>>> cmp.corr('Niall', 'Neil')
0.9821428571428571>>> cmp.corr('aluminum', 'Catalan')
0.9617834394904459>>> cmp.corr('ATCG', 'TAGC')
0.9744897959183674

New in version 0.4.0.
sim(src, tar)
Return the normalized Hamann similarity of two strings.
Hamann similarity, which has a range [-1, 1] is normalized to [0, 1] by adding 1 and dividing by 2.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Hamann similarity
Return type float

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Examples>>> cmp = Hamann()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.9910714285714286>>> cmp.sim('aluminum', 'Catalan')
0.9808917197452229>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837

New in version 0.4.0.
class abydos.distance.HarrisLahey(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceHarris & Lahey similarity.
For two sets X and Y and a population N, Harris & Lahey similarity [HL78] is

𝑠𝑖𝑚𝐻𝑎𝑟𝑟𝑖𝑠𝐿𝑎ℎ𝑒𝑦 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | |𝑁 ∖ 𝑌 | + |𝑁 ∖ 𝑋||(𝑁 ∖ 𝑋) ∖ 𝑌 | |𝑋| + |𝑌 |·+·|𝑋 ∪ 𝑌 |2|𝑁 ||𝑁 ∖ (𝑋 ∩ 𝑌 )|2|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐻𝑎𝑟𝑟𝑖𝑠𝐿𝑎ℎ𝑒𝑦 =2𝑑 + 𝑏 + 𝑐𝑑
2𝑎 + 𝑏 + 𝑐
𝑎
·
+
·
𝑎+𝑏+𝑐
2𝑛
𝑑+𝑏+𝑐
2𝑛

Notes
Most catalogs of similarity coefficients [War08][Mor12][Xia13] omit the 𝑛 terms in the denominators, but theworked example in [HL78] makes it clear that this is intended in the original.
New in version 0.4.0.
Initialize HarrisLahey instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
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Abydos Documentation, Release 0.5.0• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Harris & Lahey similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Harris & Lahey similarity
Return type float
Examples>>> cmp = HarrisLahey()>>> cmp.sim('cat', 'hat')
0.3367085964820711>>> cmp.sim('Niall', 'Neil')
0.22761577457069784>>> cmp.sim('aluminum', 'Catalan')
0.07244410503054725>>> cmp.sim('ATCG', 'TAGC')
0.006296204706372345

New in version 0.4.0.
class abydos.distance.Hassanat(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Hassanat distance.
For two multisets X and Y drawn from an alphabet S, Hassanat distance [Has14] is

𝑑𝑖𝑠𝑡𝐻𝑎𝑠𝑠𝑎𝑛𝑎𝑡 (𝑋, 𝑌 ) =∑︁

𝐷(𝑋𝑖 , 𝑌𝑖 )

𝑖∈𝑆

where

𝐷(𝑋𝑖 , 𝑌𝑖 ) =⎧1+𝑚𝑖𝑛(𝑋𝑖 ,𝑌𝑖 )⎪⎨ 1 − 1+𝑚𝑎𝑥(𝑋𝑖 ,𝑌𝑖 ), 𝑚𝑖𝑛(𝑋𝑖 , 𝑌𝑖 ) ≥ 0⎪⎩ 1 − 1+𝑚𝑖𝑛(𝑋𝑖 ,𝑌𝑖 )+|𝑚𝑖𝑛(𝑋𝑖 ,𝑌𝑖 )|, 𝑚𝑖𝑛(𝑋𝑖 , 𝑌𝑖 ) < 0

1+𝑚𝑎𝑥(𝑋𝑖 ,𝑌𝑖 )+|𝑚𝑖𝑛(𝑋𝑖 ,𝑌𝑖 )|New in version 0.4.0.
Initialize Hassanat instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
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Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized Hassanat distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Hassanat distance
Return type float
Examples>>> cmp = Hassanat()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.3888888888888889>>> cmp.dist('aluminum', 'Catalan')
0.4777777777777778>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.4.0.
dist_abs(src, tar)
Return the Hassanat distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hassanat distance
Return type float
Examples>>> cmp = Hassanat()>>> cmp.dist_abs('cat', 'hat')
2.0>>> cmp.dist_abs('Niall', 'Neil')
3.5>>> cmp.dist_abs('aluminum', 'Catalan')
7.166666666666667>>> cmp.dist_abs('ATCG', 'TAGC')
5.0

New in version 0.4.0.

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class abydos.distance.HawkinsDotson(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Hawkins & Dotson similarity.
For two sets X and Y and a population N, Hawkins & Dotson similarity [HD73] is the mean of the occurrenceagreement and non-occurrence agreement

𝑠𝑖𝑚𝐻𝑎𝑤𝑘𝑖𝑛𝑠𝐷𝑜𝑡𝑠𝑜𝑛 (𝑋, 𝑌 ) =1 (︁ |𝑋 ∩ 𝑌 ||(𝑁 ∖ 𝑋) ∖ 𝑌 | )︁·+2
|𝑋 ∪ 𝑌 | |𝑁 ∖ (𝑋 ∩ 𝑌 )|In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐻𝑎𝑤𝑘𝑖𝑛𝑠𝐷𝑜𝑡𝑠𝑜𝑛 =

)︁
1 (︁
𝑎
𝑑
·
+
2
𝑎+𝑏+𝑐 𝑏+𝑐+𝑑

New in version 0.4.0.
Initialize HawkinsDotson instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Hawkins & Dotson similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hawkins & Dotson similarity
Return type float

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Examples>>> cmp = HawkinsDotson()>>> cmp.sim('cat', 'hat')
0.6641091219096334>>> cmp.sim('Niall', 'Neil')
0.606635407786303>>> cmp.sim('aluminum', 'Catalan')
0.5216836734693877>>> cmp.sim('ATCG', 'TAGC')
0.49362244897959184

New in version 0.4.0.
class abydos.distance.Hellinger(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Hellinger distance.
For two multisets X and Y drawn from an alphabet S, Hellinger distance [Hel09] is√︃𝑑𝑖𝑠𝑡𝐻𝑒𝑙𝑙𝑖𝑛𝑔𝑒𝑟 (𝑋, 𝑌 ) =

2·

∑︁ √︀
√︀
( |𝐴𝑖 | − |𝐵𝑖 |)2𝑖∈𝑆

New in version 0.4.0.
Initialize Hellinger instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized Hellinger distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Hellinger distance
Return type float

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Examples>>> cmp = Hellinger()>>> cmp.dist('cat', 'hat')
0.8164965809277261>>> cmp.dist('Niall', 'Neil')
0.881917103688197>>> cmp.dist('aluminum', 'Catalan')
0.9128709291752769>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Hellinger distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hellinger distance
Return type float
Examples>>> cmp = Hellinger()>>> cmp.dist_abs('cat', 'hat')
2.8284271247461903>>> cmp.dist_abs('Niall', 'Neil')
3.7416573867739413>>> cmp.dist_abs('aluminum', 'Catalan')
5.477225575051661>>> cmp.dist_abs('ATCG', 'TAGC')
4.47213595499958

New in version 0.4.0.
class abydos.distance.HendersonHeron(**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Henderson-Heron dissimilarity.
For two sets X and Y and a population N, Henderson-Heron dissimilarity [HH77] is:
New in version 0.4.1.
Initialize HendersonHeron instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(src, tar)
Return the Henderson-Heron dissimilarity of two strings.
Parameters• src (str) -- Source string for comparison

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Returns Henderson-Heron dissimilarity
Return type float
Examples>>> cmp = HendersonHeron()>>> cmp.dist('cat', 'hat')
0.00011668873858680838>>> cmp.dist('Niall', 'Neil')
0.00048123075776606097>>> cmp.dist('aluminum', 'Catalan')
0.08534181060514882>>> cmp.dist('ATCG', 'TAGC')
0.9684367974410505

New in version 0.4.1.
class abydos.distance.HornMorisita(**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceHorn-Morisita index of overlap.
Horn-Morisita index of overlap [Hor66], given two populations X and Y drawn from S species, is:𝑠𝑖𝑚𝐻𝑜𝑟𝑛−𝑀 𝑜𝑟𝑖𝑠𝑖𝑡𝑎 (𝑋, 𝑌 ) = 𝐶𝜆 =∑︀𝑆
2 𝑖=1 𝑥𝑖 𝑦𝑖
ˆ𝑥 + 𝜆ˆ 𝑦 )𝑋𝑌
(𝜆

where
𝑋=

𝑆
∑︁

𝑥𝑖 ; 𝑌 =∑︀𝑆

2
𝑖=1 𝑥𝑖
2
𝑋

𝑦𝑖

𝑖=1

𝑖=1

ˆ𝑥 =
𝜆

𝑆
∑︁

ˆ𝑦 =
; 𝜆

∑︀𝑆

2
𝑖=1 𝑦𝑖2𝑌

Observe that this is identical to Morisita similarity, except for the definition of the 𝜆 values in the denominator.
New in version 0.4.1.
Initialize HornMorisita instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
sim(src, tar)
Return the Horn-Morisita similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Horn-Morisita similarity
Return type float

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Examples>>> cmp = HornMorisita()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3636363636363636>>> cmp.sim('aluminum', 'Catalan')
0.10650887573964497>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.Hurlbert(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Hurlbert correlation.
In 2x2 confusion table terms, where a+b+c+d=n, Hurlbert's coefficient of interspecific association [Hur69] is𝑎𝑑 − 𝑏𝑐
𝑐𝑜𝑟𝑟𝐻𝑢𝑟𝑙𝑏𝑒𝑟𝑡 =|𝑎𝑑 − 𝑏𝑐|√︃

𝑂𝑏𝑠𝜒2 − 𝑀 𝑖𝑛𝜒2
𝑀 𝑎𝑥𝜒2 − 𝑀 𝑖𝑛𝜒2

Where:

2𝑂𝑏𝑠𝜒2(𝑎𝑑−𝑏𝑐) 𝑛= (𝑎+𝑏)(𝑎+𝑐)(𝑏+𝑑)(𝑐+𝑑)𝑀 𝑎𝑥𝜒2= (𝑎+𝑏)(𝑏+𝑑)𝑛(𝑎+𝑐)(𝑐+𝑑)when 𝑎𝑑 ≥ 𝑏𝑐

𝑀 𝑎𝑥𝜒2= (𝑎+𝑏)(𝑎+𝑐)𝑛(𝑏+𝑑)(𝑐+𝑑)= (𝑏+𝑑)(𝑐+𝑑)𝑛(𝑎+𝑏)(𝑎+𝑐)
𝑛3 (^
𝑎−𝑔(^
𝑎))2
= (𝑎+𝑏)(𝑎+𝑐)(𝑐+𝑑)(𝑏+𝑑)= (𝑎+𝑏)(𝑎+𝑐)𝑛when 𝑎𝑑 < 𝑏𝑐 and 𝑎 ≤ 𝑑

𝑀 𝑎𝑥𝜒2
𝑀 𝑖𝑛𝜒2where 𝑎ˆand 𝑔(ˆ𝑎)otherwise 𝑔(ˆ
𝑎)

= ⌊ˆ
𝑎⌋
= ⌈ˆ
𝑎⌉

when 𝑎𝑑 < 𝑏𝑐 and 𝑎 > 𝑑when 𝑎𝑑 < 𝑏𝑐,

New in version 0.4.0.
Initialize Hurlbert instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Hurlbert correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hurlbert correlation
Return type float
Examples>>> cmp = Hurlbert()>>> cmp.corr('cat', 'hat')
0.497416003373807>>> cmp.corr('Niall', 'Neil')
0.32899851514665707>>> cmp.corr('aluminum', 'Catalan')
0.10144329225459262>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Hurlbert similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Hurlbert similarity
Return type float

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Examples>>> cmp = Hurlbert()>>> cmp.sim('cat', 'hat')
0.7487080016869034>>> cmp.sim('Niall', 'Neil')
0.6644992575733285>>> cmp.sim('aluminum', 'Catalan')
0.5507216461272963>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Jaccard(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._tversky.Tversky
Jaccard similarity.
For two sets X and Y, the Jaccard similarity coefficient [Jac01][Rruvzivcka58] is

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |‘.
|𝑋 ∪ 𝑌 |This is identical to the Tanimoto similarity coefficient [Tan58] and the Tversky index [Tve77] for 𝛼 = 𝛽 = 1.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑 =

𝑎
𝑎+𝑏+𝑐

Notes
The multiset variant is termed Ellenberg similarity [Ell56].
New in version 0.3.6.
Initialize Jaccard instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
sim(src, tar)
Return the Jaccard similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Jaccard similarity
Return type float
Examples>>> cmp = Jaccard()>>> cmp.sim('cat', 'hat')
0.3333333333333333>>> cmp.sim('Niall', 'Neil')
0.2222222222222222>>> cmp.sim('aluminum', 'Catalan')
0.0625>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classtanimoto_coeff(src, tar)
Return the Tanimoto distance between two strings.
Tanimoto distance [Tan58] is −𝑙𝑜𝑔2 𝑠𝑖𝑚𝑇 𝑎𝑛𝑖𝑚𝑜𝑡𝑜 (𝑋, 𝑌 ).
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tanimoto distance
Return type float
Examples>>> cmp = Jaccard()>>> cmp.tanimoto_coeff('cat', 'hat')-1.5849625007211563>>> cmp.tanimoto_coeff('Niall', 'Neil')-2.1699250014423126>>> cmp.tanimoto_coeff('aluminum', 'Catalan')-4.0>>> cmp.tanimoto_coeff('ATCG', 'TAGC')-inf

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
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abydos.distance.dist_jaccard(src, tar, qval=2)
Return the Jaccard distance between two strings.
This is a wrapper for Jaccard.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Jaccard distance
Return type float
Examples>>> dist_jaccard('cat', 'hat')
0.6666666666666667>>> dist_jaccard('Niall', 'Neil')
0.7777777777777778>>> dist_jaccard('aluminum', 'Catalan')
0.9375>>> dist_jaccard('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Jaccard.dist method instead.
abydos.distance.sim_jaccard(src, tar, qval=2)
Return the Jaccard similarity of two strings.
This is a wrapper for Jaccard.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Jaccard similarity
Return type float
Examples>>> sim_jaccard('cat', 'hat')
0.3333333333333333>>> sim_jaccard('Niall', 'Neil')
0.2222222222222222>>> sim_jaccard('aluminum', 'Catalan')
0.0625>>> sim_jaccard('ATCG', 'TAGC')
0.0

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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Jaccard.sim method instead.
abydos.distance.tanimoto(src, tar, qval=2)
Return the Tanimoto coefficient of two strings.
This is a wrapper for Jaccard.tanimoto_coeff().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Tanimoto distance
Return type float
Examples>>> tanimoto('cat', 'hat')-1.5849625007211563>>> tanimoto('Niall', 'Neil')-2.1699250014423126>>> tanimoto('aluminum', 'Catalan')-4.0>>> tanimoto('ATCG', 'TAGC')-inf

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Jaccard.tanimoto_coeff method instead.
class abydos.distance.JaccardNM(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Jaccard-NM similarity.
For two sets X and Y and a population N, Jaccard-NM similarity [NMM11] is

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑁 𝑀 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑁 | + |𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑁 𝑀 =𝑎2(𝑎 + 𝑏 + 𝑐) + 𝑑

New in version 0.4.0.
Initialize JaccardNM instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Jaccard-NM similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Jaccard-NM similarity
Return type float
Examples>>> cmp = JaccardNM()>>> cmp.sim('cat', 'hat')
0.005063291139240506>>> cmp.sim('Niall', 'Neil')
0.005044136191677175>>> cmp.sim('aluminum', 'Catalan')
0.0024968789013732834>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Jaccard-NM similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Jaccard-NM similarity
Return type float

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Examples>>> cmp = JaccardNM()>>> cmp.sim_score('cat', 'hat')
0.002531645569620253>>> cmp.sim_score('Niall', 'Neil')
0.0025220680958385876>>> cmp.sim_score('aluminum', 'Catalan')
0.0012484394506866417>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Johnson(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceJohnson similarity.
For two sets X and Y, the Johnson similarity [Joh67] is

𝑠𝑖𝑚𝐽𝑜ℎ𝑛𝑠𝑜𝑛 (𝑋, 𝑌 ) =(|𝑋 ∩ 𝑌 | |𝑌 ∩ 𝑋
+
‘.
|𝑋|
|𝑌 |

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐽𝑜ℎ𝑛𝑠𝑜𝑛 =𝑎𝑎+𝑎+𝑏 𝑎+𝑐

New in version 0.4.0.
Initialize Johnson instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Johnson similarity of two strings.
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Returns Normalized Johnson similarity
Return type float
Examples>>> cmp = Johnson()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3666666666666667>>> cmp.sim('aluminum', 'Catalan')
0.11805555555555555>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Johnson similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Johnson similarity
Return type float
Examples>>> cmp = Johnson()>>> cmp.sim_score('cat', 'hat')
1.0>>> cmp.sim_score('Niall', 'Neil')
0.7333333333333334>>> cmp.sim_score('aluminum', 'Catalan')
0.2361111111111111>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.KendallTau(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Kendall's Tau correlation.
For two sets X and Y and a population N, Kendall's Tau correlation [Ken38] is

𝑐𝑜𝑟𝑟𝐾𝑒𝑛𝑑𝑎𝑙𝑙𝑇 𝑎𝑢 (𝑋, 𝑌 ) =214

2 · (|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋△𝑌 |)|𝑁 | · (|𝑁 | − 1)
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑒𝑛𝑑𝑎𝑙𝑙𝑇 𝑎𝑢 =2 · (𝑎 + 𝑑 − 𝑏 − 𝑐)𝑛 · (𝑛 − 1)

New in version 0.4.0.
Initialize KendallTau instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kendall's Tau correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kendall's Tau correlation
Return type float
Examples>>> cmp = KendallTau()>>> cmp.corr('cat', 'hat')
0.0025282143508744493>>> cmp.corr('Niall', 'Neil')
0.00250866630176975>>> cmp.corr('aluminum', 'Catalan')
0.0024535291823735866>>> cmp.corr('ATCG', 'TAGC')
0.0024891182526650506

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NotesThis correlation is not necessarily bounded to [-1.0, 1.0], but will typically be within these bounds for realdata.
New in version 0.4.0.
sim(src, tar)
Return the Kendall's Tau similarity of two strings.
The Tau correlation is first clamped to the range [-1.0, 1.0] before being converted to a similarity value toensure that the similarity is in the range [0.0, 1.0].
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kendall's Tau similarity
Return type float
Examples>>> cmp = KendallTau()>>> cmp.sim('cat', 'hat')
0.5012641071754372>>> cmp.sim('Niall', 'Neil')
0.5012543331508849>>> cmp.sim('aluminum', 'Catalan')
0.5012267645911868>>> cmp.sim('ATCG', 'TAGC')
0.5012445591263325

New in version 0.4.0.
class abydos.distance.KentFosterI(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Kent & Foster I similarity.
For two sets X and Y and a population N, Kent & Foster I similarity [KF77], 𝐾𝑜𝑐𝑐 , is𝑠𝑖𝑚𝐾𝑒𝑛𝑡𝐹 𝑜𝑠𝑡𝑒𝑟𝐼 (𝑋, 𝑌 ) =||𝑋 ∩ 𝑌 | − |𝑋|·|𝑌|𝑋∪𝑌 |||𝑋 ∩ 𝑌 | − |𝑋|·|𝑌|𝑋∪𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

Kent & Foster derived this from Cohen's 𝜅 by "subtracting appropriate chance agreement correction figures fromthe numerators and denominators" to arrive at an occurrence reliability measure.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑒𝑛𝑡𝐹 𝑜𝑠𝑡𝑒𝑟𝐼 =216

𝑎 − (𝑎+𝑏)(𝑎+𝑐)
𝑎+𝑏+𝑐
+𝑏+𝑐
𝑎 − (𝑎+𝑏)(𝑎+𝑐)𝑎+𝑏+𝑐

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New in version 0.4.0.
Initialize KentFosterI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Kent & Foster I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Kent & Foster I similarity
Return type float
Examples>>> cmp = KentFosterI()>>> cmp.sim('cat', 'hat')
0.8>>> cmp.sim('Niall', 'Neil')
0.7647058823529411>>> cmp.sim('aluminum', 'Catalan')0.6956521739130435>>> cmp.sim('ATCG', 'TAGC')
0.6666666666666667

New in version 0.4.0.
sim_score(src, tar)
Return the Kent & Foster I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kent & Foster I similarity
Return type float
Examples>>> cmp = KentFosterI()>>> cmp.sim_score('cat', 'hat')-0.19999999999999996>>> cmp.sim_score('Niall', 'Neil')-0.23529411764705888>>> cmp.sim_score('aluminum', 'Catalan')-0.30434782608695654>>> cmp.sim_score('ATCG', 'TAGC')-0.3333333333333333

New in version 0.4.0.
class abydos.distance.KentFosterII(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Kent & Foster II similarity.
For two sets X and Y and a population N, Kent & Foster II similarity [KF77], 𝐾𝑛𝑜𝑛𝑜𝑐𝑐 , is𝑠𝑖𝑚𝐾𝑒𝑛𝑡𝐹 𝑜𝑠𝑡𝑒𝑟𝐼𝐼 (𝑋, 𝑌 ) =|·|𝑌 ∖𝑋||(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋∖𝑌|𝑁 ∖(𝑋∩𝑌 )||·|𝑌 ∖𝑋||(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋∖𝑌|𝑁 ∖(𝑋∩𝑌 )| + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

Kent & Foster derived this from Cohen's 𝜅 by "subtracting appropriate chance agreement correction figures fromthe numerators and denominators" to arrive at an non-occurrence reliability measure.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑒𝑛𝑡𝐹 𝑜𝑠𝑡𝑒𝑟𝐼𝐼 =𝑑 − (𝑏+𝑑)(𝑐+𝑑)
𝑏+𝑐+𝑑
+𝑏+𝑐
𝑑 − (𝑏+𝑑)(𝑐+𝑑)𝑏+𝑐+𝑑New in version 0.4.0.
Initialize KentFosterII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters
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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Kent & Foster II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Kent & Foster II similarity
Return type float
Examples>>> cmp = KentFosterII()>>> cmp.sim('cat', 'hat')
0.998719590268876>>> cmp.sim('Niall', 'Neil')
0.9978030025631628>>> cmp.sim('aluminum', 'Catalan')
0.9952153110047858>>> cmp.sim('ATCG', 'TAGC')
0.9968010236724241

New in version 0.4.0.
sim_score(src, tar)
Return the Kent & Foster II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kent & Foster II similarity
Return type float

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Examples>>> cmp = KentFosterII()>>> cmp.sim_score('cat', 'hat')-0.0012804097311239404>>> cmp.sim_score('Niall', 'Neil')-0.002196997436837158>>> cmp.sim_score('aluminum', 'Catalan')-0.004784688995214218>>> cmp.sim_score('ATCG', 'TAGC')-0.0031989763275758767

New in version 0.4.0.
class abydos.distance.KoppenI(alphabet=None, tokenizer=None, intersection_type='crisp', normalizer='proportional', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Köppen I correlation.
For two sets X and Y and an alphabet N, provided that |𝑋| = |𝑌 |, Köppen I correlation [Koppen70][GK59] is

𝑐𝑜𝑟𝑟𝐾𝑜𝑝𝑝𝑒𝑛𝐼 (𝑋, 𝑌 ) =|𝑋| · |𝑁 ∖ 𝑋| − |𝑋 ∖ 𝑌 ||𝑋| · |𝑁 ∖ 𝑋|

To support cases where |𝑋| ̸= |𝑌 |, this class implements a slight variation, while still providing the expected
results when |𝑋| = |𝑌 |:𝑐𝑜𝑟𝑟𝐾𝑜𝑝𝑝𝑒𝑛𝐼 (𝑋, 𝑌 ) =|𝑋|+|𝑌 | |𝑁 ∖𝑋|+|𝑁 ∖𝑌 ||·− |𝑋△𝑌
2
2
2|𝑋|+|𝑌 | |𝑁 ∖𝑋|+|𝑁 ∖𝑌 |·2
2

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑜𝑝𝑝𝑒𝑛𝐼 =2𝑎+𝑏+𝑐 2𝑑+𝑏+𝑐
·
− 𝑏+𝑐
2
2
2
2𝑎+𝑏+𝑐 2𝑑+𝑏+𝑐
·
2
2

Notes
In the usual case all of the above values should be proportional to the total number of samples n. I.e., a, b, c, d,& n should all be divided by n prior to calculating the coefficient. This class's default normalizer is, accordingly,'proportional'.
New in version 0.4.0.
Initialize KoppenI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Köppen I correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Köppen I correlation
Return type float
Examples>>> cmp = KoppenI()>>> cmp.corr('cat', 'hat')
0.49615384615384617>>> cmp.corr('Niall', 'Neil')
0.3575056927658083>>> cmp.corr('aluminum', 'Catalan')
0.1068520131813188>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483896

New in version 0.4.0.
sim(src, tar)
Return the Köppen I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Köppen I similarity
Return type float

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Examples>>> cmp = KoppenI()>>> cmp.sim('cat', 'hat')
0.7480769230769231>>> cmp.sim('Niall', 'Neil')
0.6787528463829041>>> cmp.sim('aluminum', 'Catalan')
0.5534260065906594>>> cmp.sim('ATCG', 'TAGC')
0.49679075738125805

New in version 0.4.0.
class abydos.distance.KoppenII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Köppen II similarity.
For two sets X and Y, Köppen II similarity [Koppen70][GK59] is

𝑠𝑖𝑚𝐾𝑜𝑝𝑝𝑒𝑛𝐼𝐼 (𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | +|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|
2

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑜𝑝𝑝𝑒𝑛𝐼𝐼 = 𝑎 +𝑏+𝑐2

New in version 0.4.0.
Initialize KoppenII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.

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sim(src, tar)
Return the normalized Köppen II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Köppen II similarity
Return type float
Examples>>> cmp = KoppenII()>>> cmp.sim('cat', 'hat')
0.6666666666666666>>> cmp.sim('Niall', 'Neil')
0.6111111111111112>>> cmp.sim('aluminum', 'Catalan')
0.53125>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.4.0.
sim_score(src, tar)
Return the Köppen II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Köppen II similarity
Return type float
Examples>>> cmp = KoppenII()>>> cmp.sim_score('cat', 'hat')
4.0>>> cmp.sim_score('Niall', 'Neil')
5.5>>> cmp.sim_score('aluminum', 'Catalan')
8.5>>> cmp.sim_score('ATCG', 'TAGC')
5.0

New in version 0.4.0.
class abydos.distance.KuderRichardson(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Kuder & Richardson correlation.
For two sets X and Y and a population N, Kuder & Richardson similarity [KR37][Cro51] is
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𝑐𝑜𝑟𝑟𝐾𝑢𝑑𝑒𝑟𝑅𝑖𝑐ℎ𝑎𝑟𝑑𝑠𝑜𝑛 (𝑋, 𝑌 ) =4(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)|𝑋| · |𝑁 ∖ 𝑋| + |𝑌 | · |𝑁 ∖ 𝑌 | + 2(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢𝑑𝑒𝑟𝑅𝑖𝑐ℎ𝑎𝑟𝑑𝑠𝑜𝑛 =4(𝑎𝑑 − 𝑏𝑐)(𝑎 + 𝑏)(𝑐 + 𝑑) + (𝑎 + 𝑐)(𝑏 + 𝑑) + 2(𝑎𝑑 − 𝑏𝑐)

New in version 0.4.0.
Initialize KuderRichardson instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuder & Richardson correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuder & Richardson correlation
Return type float

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Examples>>> cmp = KuderRichardson()>>> cmp.corr('cat', 'hat')
0.6643835616438356>>> cmp.corr('Niall', 'Neil')
0.5285677463699631>>> cmp.corr('aluminum', 'Catalan')
0.19499521400246136>>> cmp.corr('ATCG', 'TAGC')-0.012919896640826873

New in version 0.4.0.
sim(src, tar)
Return the Kuder & Richardson similarity of two strings.
Since Kuder & Richardson correlation is unbounded in the negative, this measure is first clamped to [-1.0,
1.0].
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuder & Richardson similarity
Return type float
Examples>>> cmp = KuderRichardson()>>> cmp.sim('cat', 'hat')
0.8321917808219178>>> cmp.sim('Niall', 'Neil')
0.7642838731849815>>> cmp.sim('aluminum', 'Catalan')
0.5974976070012307>>> cmp.sim('ATCG', 'TAGC')
0.4935400516795866

New in version 0.4.0.
class abydos.distance.KuhnsI(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns I correlation.
For two sets X and Y and a population N, Kuhns I correlation [Kuh64], the excess of separation over its independence value (S), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼 (𝑋, 𝑌 ) =2𝛿(𝑋, 𝑌 )
|𝑁 |

where

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𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −|𝑋| · |𝑌 ||𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼 =2𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑛

where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns I correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns I correlation
Return type float

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Examples>>> cmp = KuhnsI()>>> cmp.corr('cat', 'hat')
0.005049979175343606>>> cmp.corr('Niall', 'Neil')
0.005004425239483548>>> cmp.corr('aluminum', 'Catalan')
0.0023140898210880765>>> cmp.corr('ATCG', 'TAGC')-8.134631403581842e-05

New in version 0.4.0.
sim(src, tar)
Return the Kuhns I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns I similarity
Return type float
Examples>>> cmp = KuhnsI()>>> cmp.sim('cat', 'hat')
0.5050499791753436>>> cmp.sim('Niall', 'Neil')
0.5050044252394835>>> cmp.sim('aluminum', 'Catalan')
0.502314089821088>>> cmp.sim('ATCG', 'TAGC')
0.49991865368596416

New in version 0.4.0.
class abydos.distance.KuhnsII(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns II correlation.
For two sets X and Y and a population N, Kuhns II correlation [Kuh64], the excess of rectangular distance overits independence value (R), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝐼 (𝑋, 𝑌 ) =𝛿(𝑋, 𝑌 )𝑚𝑎𝑥(|𝑋|, |𝑌 |)where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝐼 =𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑚𝑎𝑥(𝑎 + 𝑏, 𝑎 + 𝑐)where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns II correlation
Return type float

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Examples>>> cmp = KuhnsII()>>> cmp.corr('cat', 'hat')
0.49489795918367346>>> cmp.corr('Niall', 'Neil')
0.32695578231292516>>> cmp.corr('aluminum', 'Catalan')
0.10092002830856334>>> cmp.corr('ATCG', 'TAGC')-0.006377551020408163

New in version 0.4.0.
sim(src, tar)
Return the Kuhns II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns II similarity
Return type float
Examples>>> cmp = KuhnsII()>>> cmp.sim('cat', 'hat')
0.663265306122449>>> cmp.sim('Niall', 'Neil')
0.5513038548752834>>> cmp.sim('aluminum', 'Catalan')
0.40061335220570893>>> cmp.sim('ATCG', 'TAGC')
0.32908163265306123

New in version 0.4.0.
class abydos.distance.KuhnsIII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns III correlation.
For two sets X and Y and a population N, Kuhns III correlation [Kuh64], the excess of proportion of overlap
over its independence value (P), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝐼𝐼 (𝑋, 𝑌 ) = (︀𝛿(𝑋, 𝑌 )|𝑋∩𝑌 | )︀(︀|𝑋|·|𝑌 | )︀1 − |𝑋|+|𝑌| |𝑋| + |𝑌 | − |𝑁 |where

𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝐼𝐼 = (︀𝑎1 − 2𝑎+𝑏+𝑐

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐))︀(︀)︀
2𝑎 + 𝑏 + 𝑐 − (𝑎+𝑏)(𝑎+𝑐)
𝑛

where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛Notes
The coefficient presented in [Eid14][Mor12] as Kuhns' "Proportion of overlap above independence" is a significantly different coefficient, not evidenced in [Kuh64].
New in version 0.4.0.
Initialize KuhnsIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns III correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns III correlation
Return type float

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Examples>>> cmp = KuhnsIII()>>> cmp.corr('cat', 'hat')
0.3307757885763001>>> cmp.corr('Niall', 'Neil')
0.21873141468207793>>> cmp.corr('aluminum', 'Catalan')
0.05707545392902886>>> cmp.corr('ATCG', 'TAGC')-0.003198976327575176

New in version 0.4.0.
sim(src, tar)
Return the Kuhns III similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns III similarity
Return type float
Examples>>> cmp = KuhnsIII()>>> cmp.sim('cat', 'hat')
0.498081841432225>>> cmp.sim('Niall', 'Neil')
0.41404856101155846>>> cmp.sim('aluminum', 'Catalan')
0.29280659044677165>>> cmp.sim('ATCG', 'TAGC')
0.24760076775431863

New in version 0.4.0.
class abydos.distance.KuhnsIV(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns IV correlation.
For two sets X and Y and a population N, Kuhns IV correlation [Kuh64], the excess of conditional probabilities
over its independence value (W), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝑉 (𝑋, 𝑌 ) =𝛿(𝑋, 𝑌 )𝑚𝑖𝑛(|𝑋|, |𝑌 |)where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝑉 =𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)𝑚𝑖𝑛(𝑎 + 𝑏, 𝑎 + 𝑐)where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsIV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns IV correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns IV correlation
Return type float

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Examples>>> cmp = KuhnsIV()>>> cmp.corr('cat', 'hat')
0.49489795918367346>>> cmp.corr('Niall', 'Neil')
0.3923469387755102>>> cmp.corr('aluminum', 'Catalan')
0.11353503184713376>>> cmp.corr('ATCG', 'TAGC')-0.006377551020408163

New in version 0.4.0.
sim(src, tar)
Return the Kuhns IV similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns IV similarity
Return type float
Examples>>> cmp = KuhnsIV()>>> cmp.sim('cat', 'hat')
0.7474489795918368>>> cmp.sim('Niall', 'Neil')
0.696173469387755>>> cmp.sim('aluminum', 'Catalan')
0.5567675159235669>>> cmp.sim('ATCG', 'TAGC')
0.4968112244897959

New in version 0.4.0.
class abydos.distance.KuhnsV(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns V correlation.
For two sets X and Y and a population N, Kuhns V correlation [Kuh64], the excess of probability differences U
over its independence value (U), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 (𝑋, 𝑌 ) =𝛿(𝑋, 𝑌 )(︀
|𝑌 | )︀
𝑚𝑎𝑥 |𝑋| · (1 − |𝑋||𝑁 | ), |𝑌 | · (1 − |𝑁 | )where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 =𝛿(𝑎 + 𝑏, 𝑎 + 𝑐))︀𝑎+𝑐
𝑚𝑎𝑥 (𝑎 + 𝑏)(1 − 𝑎+𝑏𝑛 ), (𝑎 + 𝑐)(1 − 𝑛 )
(︀

where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns V correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns V correlation
Return type float

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Examples>>> cmp = KuhnsV()>>> cmp.corr('cat', 'hat')
0.497435897435897>>> cmp.corr('Niall', 'Neil')
0.329477292202228>>> cmp.corr('aluminum', 'Catalan')
0.10209049255441>>> cmp.corr('ATCG', 'TAGC')-0.006418485237484

New in version 0.4.0.
sim(src, tar)
Return the Kuhns V similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns V similarity
Return type float
Examples>>> cmp = KuhnsV()>>> cmp.sim('cat', 'hat')
0.7487179487179485>>> cmp.sim('Niall', 'Neil')
0.664738646101114>>> cmp.sim('aluminum', 'Catalan')
0.551045246277205>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.KuhnsVI(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns VI correlation.
For two sets X and Y and a population N, Kuhns VI correlation [Kuh64], the excess of probability differences
V over its independence value (V), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼 (𝑋, 𝑌 ) =𝛿(𝑋, 𝑌 )|𝑌 | )︀
𝑚𝑖𝑛 |𝑋| · (1 − |𝑋||𝑁 | ), |𝑌 |(1 − |𝑁 | )

(︀

where

𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼 =𝛿(𝑎 + 𝑏, 𝑎 + 𝑐))︀𝑎+𝑐𝑚𝑖𝑛 (𝑎 + 𝑏)(1 − 𝑎+𝑏𝑛 ), (𝑎 + 𝑐)(1 − 𝑛 )
(︀

where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsVI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns VI correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VI correlation
Return type float

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Examples>>> cmp = KuhnsVI()>>> cmp.corr('cat', 'hat')
0.497435897435897>>> cmp.corr('Niall', 'Neil')
0.394865211810013>>> cmp.corr('aluminum', 'Catalan')
0.11470398970399>>> cmp.corr('ATCG', 'TAGC')-0.006418485237484

New in version 0.4.0.
sim(src, tar)
Return the Kuhns VI similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VI similarity
Return type float
Examples>>> cmp = KuhnsVI()>>> cmp.sim('cat', 'hat')
0.7487179487179485>>> cmp.sim('Niall', 'Neil')
0.6974326059050064>>> cmp.sim('aluminum', 'Catalan')
0.557351994851995>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.KuhnsVII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns VII correlation.
For two sets X and Y and a population N, Kuhns VII correlation [Kuh64], the excess of angle between vector
over its independence value (G), is𝛿(𝑋, 𝑌 )𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼𝐼 (𝑋, 𝑌 ) = √︀
|𝑋| · |𝑌 |where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼𝐼 = √︀(𝑎 + 𝑏)(𝑎 + 𝑐)where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsVII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns VII correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VII correlation
Return type float

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Examples>>> cmp = KuhnsVII()>>> cmp.corr('cat', 'hat')
0.49489795918367346>>> cmp.corr('Niall', 'Neil')
0.3581621145590755>>> cmp.corr('aluminum', 'Catalan')
0.10704185456178524>>> cmp.corr('ATCG', 'TAGC')-0.006377551020408163

New in version 0.4.0.
sim(src, tar)
Return the Kuhns VII similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VII similarity
Return type float
Examples>>> cmp = KuhnsVII()>>> cmp.sim('cat', 'hat')
0.663265306122449>>> cmp.sim('Niall', 'Neil')
0.572108076372717>>> cmp.sim('aluminum', 'Catalan')
0.40469456970785683>>> cmp.sim('ATCG', 'TAGC')
0.32908163265306123

New in version 0.4.0.
class abydos.distance.KuhnsVIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceKuhns VIII correlation.
For two sets X and Y and a population N, Kuhns VIII correlation [Kuh64], the excess of coefficient by thearithmetic mean over its independence value (E), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼𝐼𝐼 (𝑋, 𝑌 ) =𝛿(𝑋, 𝑌 )|𝑋 ∩ 𝑌 | + 21 · |𝑋△𝑌 |where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑉 𝐼𝐼𝐼 =𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑎 + 21 (𝑏 + 𝑐)where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛Notes
The coefficient presented in [Eid14][Mor12] as Kuhns' "Coefficient of arithmetic means" is a significantly different coefficient, not evidenced in [Kuh64].
New in version 0.4.0.
Initialize KuhnsVIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns VIII correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VIII correlation
Return type float

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Examples>>> cmp = KuhnsVIII()>>> cmp.corr('cat', 'hat')
0.49489795918367346>>> cmp.corr('Niall', 'Neil')
0.35667903525046385>>> cmp.corr('aluminum', 'Catalan')
0.10685650056200824>>> cmp.corr('ATCG', 'TAGC')-0.006377551020408163

New in version 0.4.0.
sim(src, tar)
Return the Kuhns VIII similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns VIII similarity
Return type float
Examples>>> cmp = KuhnsVIII()>>> cmp.sim('cat', 'hat')
0.663265306122449>>> cmp.sim('Niall', 'Neil')
0.5711193568336426>>> cmp.sim('aluminum', 'Catalan')
0.40457100037467214>>> cmp.sim('ATCG', 'TAGC')
0.32908163265306123

New in version 0.4.0.
class abydos.distance.KuhnsIX(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns IX correlation.
For two sets X and Y and a population N, Kuhns IX correlation [Kuh64], the excess of coefficient of linear
correlation over its independence value (L), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝑋 (𝑋, 𝑌 ) = √︁𝛿(𝑋, 𝑌 )|𝑌 ||𝑋| · |𝑌 | · (1 − |𝑋||𝑁 | ) · (1 − |𝑁 | )where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝐼𝑋 = √︁𝑎+𝑐(𝑎 + 𝑏)(𝑎 + 𝑐)(1 − 𝑎+𝑏𝑛 )(1 − 𝑛 )where𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsIX instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns IX correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns IX correlation
Return type float

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Examples>>> cmp = KuhnsIX()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.36069255713421955>>> cmp.corr('aluminum', 'Catalan')
0.10821361655002706>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Kuhns IX similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns IX similarity
Return type float
Examples>>> cmp = KuhnsIX()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6803462785671097>>> cmp.sim('aluminum', 'Catalan')
0.5541068082750136>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.KuhnsX(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns X correlation.
For two sets X and Y and a population N, Kuhns X correlation [Kuh64], the excess of Yule's Q over its independence value (Q), is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑋 (𝑋, 𝑌 ) =|𝑁 | · 𝛿(𝑋, 𝑌 )|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|

where

𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑋 =𝑛 · 𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
𝑎𝑑 + 𝑏𝑐where𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsX instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns X correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns X correlation
Return type float

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Examples>>> cmp = KuhnsX()>>> cmp.corr('cat', 'hat')
0.994871794871795>>> cmp.corr('Niall', 'Neil')
0.984635083226633>>> cmp.corr('aluminum', 'Catalan')
0.864242424242424>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Kuhns X similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns X similarity
Return type float
Examples>>> cmp = KuhnsX()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9923175416133165>>> cmp.sim('aluminum', 'Catalan')
0.932121212121212>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.KuhnsXI(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns XI correlation.
For two sets X and Y and a population N, Kuhns XI correlation [Kuh64], the excess of Yule's Y over its
independence value (Y), is|𝑁 | · 𝛿(𝑋, 𝑌 )√︀
𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑋𝐼 (𝑋, 𝑌 ) = √︀
( |𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)2
where

𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝐾𝑢ℎ𝑛𝑠𝑋𝐼 =𝑛 · 𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)√√
( 𝑎𝑑 + 𝑏𝑐)2

where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsXI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Kuhns XI correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns XI correlation
Return type float

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Examples>>> cmp = KuhnsXI()>>> cmp.corr('cat', 'hat')
0.9034892632818761>>> cmp.corr('Niall', 'Neil')
0.8382551144735259>>> cmp.corr('aluminum', 'Catalan')
0.5749826820237787>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Kuhns XI similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns XI similarity
Return type float
Examples>>> cmp = KuhnsXI()>>> cmp.sim('cat', 'hat')
0.951744631640938>>> cmp.sim('Niall', 'Neil')
0.919127557236763>>> cmp.sim('aluminum', 'Catalan')
0.7874913410118893>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.KuhnsXII(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Kuhns XII similarity.
For two sets X and Y and a population N, Kuhns XII similarity [Kuh64], the excess of index of independence
over its independence value (I), is

𝑠𝑖𝑚𝐾𝑢ℎ𝑛𝑠𝑋𝐼𝐼 (𝑋, 𝑌 ) =|𝑁 | · 𝛿(𝑋, 𝑌 )|𝑋| · |𝑌 |where𝛿(𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | −3.1. abydos package|𝑋| · |𝑌 |
|𝑁 |
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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑢ℎ𝑛𝑠𝑋𝐼𝐼 =𝑛 · 𝛿(𝑎 + 𝑏, 𝑎 + 𝑐)
(𝑎 + 𝑏)(𝑎 + 𝑐)where

𝛿(𝑎 + 𝑏, 𝑎 + 𝑐) = 𝑎 −(𝑎 + 𝑏)(𝑎 + 𝑐)𝑛

New in version 0.4.0.
Initialize KuhnsXII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Kuhns XII similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Kuhns XII similarity
Return type float

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Examples>>> cmp = KuhnsXII()>>> cmp.sim('cat', 'hat')
0.2493573264781491>>> cmp.sim('Niall', 'Neil')
0.1323010752688172>>> cmp.sim('aluminum', 'Catalan')
0.012877474353417137>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Kuhns XII similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kuhns XII similarity
Return type float
Examples>>> cmp = KuhnsXII()>>> cmp.sim_score('cat', 'hat')
97.0>>> cmp.sim_score('Niall', 'Neil')
51.266666666666666>>> cmp.sim_score('aluminum', 'Catalan')
9.902777777777779>>> cmp.sim_score('ATCG', 'TAGC')-1.0

New in version 0.4.0.
class abydos.distance.KulczynskiI(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Kulczynski I similarity.
For two sets X and Y, Kulczynski I similarity [Kulczynski27] is

𝑠𝑖𝑚𝐾𝑢𝑙𝑐𝑧𝑦𝑛𝑠𝑘𝑖𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝐾𝑢𝑙𝑐𝑧𝑦𝑛𝑠𝑘𝑖𝐼 =3.1. abydos package

𝑎
𝑏+𝑐

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New in version 0.4.0.
Initialize KulczynskiI instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Kulczynski I similarity.
New in version 0.3.6.
sim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Kulczynski I similarity.
New in version 0.3.6.
sim_score(src, tar)
Return the Kulczynski I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kulczynski I similarity
Return type float

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Examples>>> cmp = KulczynskiI()>>> cmp.sim_score('cat', 'hat')
0.5>>> cmp.sim_score('Niall', 'Neil')
0.2857142857142857>>> cmp.sim_score('aluminum', 'Catalan')
0.06666666666666667>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.KulczynskiII(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Kulczynski II similarity.
For two sets X and Y, Kulczynski II similarity [Kulczynski27] or Driver & Kroeber similarity [DK32] is

1
𝑠𝑖𝑚𝐾𝑢𝑙𝑐𝑧𝑦𝑛𝑠𝑘𝑖𝐼𝐼 (𝑋, 𝑌 ) =2(︃|𝑋 ∩ 𝑌 | |𝑋 ∩ 𝑌 |
+
|𝑋|
|𝑌 |

)︃

In 2x2 confusion table terms, where a+b+c+d=n, this is1
𝑠𝑖𝑚𝐾𝑢𝑙𝑐𝑧𝑦𝑛𝑠𝑘𝑖𝐼𝐼 =
2

(︃

𝑎
𝑎
+
𝑎+𝑏 𝑎+𝑐)︃

New in version 0.4.0.
Initialize KulczynskiII instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Kulczynski II similarity of two strings.
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Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Kulczynski II similarity
Return type float
Examples>>> cmp = KulczynskiII()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3666666666666667>>> cmp.sim('aluminum', 'Catalan')
0.11805555555555555>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Lorentzian(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Lorentzian distance.
For two multisets X and Y drawn from an alphabet S, Lorentzian distance is

𝑑𝑖𝑠𝑡𝐿𝑜𝑟𝑒𝑛𝑡𝑧𝑖𝑎𝑛 (𝑋, 𝑌 ) =∑︁𝑙𝑜𝑔(1 + |𝐴𝑖 − 𝐵𝑖 |)

𝑖∈𝑆

Notes
No primary source for this measure could be located, but it is included in surveys and catalogues, such as [DD16]and [Cha08].
New in version 0.4.0.
Initialize Lorentzian instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized Lorentzian distance of two strings.
Parameters

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Abydos Documentation, Release 0.5.0• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Lorentzian distance
Return type float
Examples>>> cmp = Lorentzian()>>> cmp.dist('cat', 'hat')
0.6666666666666667>>> cmp.dist('Niall', 'Neil')
0.7777777777777778>>> cmp.dist('aluminum', 'Catalan')
0.9358355851062377>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Lorentzian distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Lorentzian distance
Return type float
Examples>>> cmp = Lorentzian()>>> cmp.dist_abs('cat', 'hat')
2.772588722239781>>> cmp.dist_abs('Niall', 'Neil')
4.852030263919617>>> cmp.dist_abs('aluminum', 'Catalan')
10.1095256359474>>> cmp.dist_abs('ATCG', 'TAGC')
6.931471805599453

New in version 0.4.0.
class abydos.distance.Maarel(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Maarel correlation.
For two sets X and Y and a population N, Maarel correlation [vandMaarel69] is𝑐𝑜𝑟𝑟𝑀 𝑎𝑎𝑟𝑒𝑙 (𝑋, 𝑌 ) =3.1. abydos package

2|𝑋 ∩ 𝑌 | − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
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In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑀 𝑎𝑎𝑟𝑒𝑙 =2𝑎 − 𝑏 − 𝑐
2𝑎 + 𝑏 + 𝑐

New in version 0.4.0.
Initialize Maarel instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Maarel correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Maarel correlation
Return type float
Examples>>> cmp = Maarel()>>> cmp.corr('cat', 'hat')
0.0>>> cmp.corr('Niall', 'Neil')-0.2727272727272727>>> cmp.corr('aluminum', 'Catalan')-0.7647058823529411>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.

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sim(src, tar)
Return the Maarel similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Maarel similarity
Return type float
Examples>>> cmp = Maarel()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352944>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Morisita(**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceMorisita index of overlap.
Morisita index of overlap [Mor59], following the description of [Hor66], given two populations X and Y drawn
from S species, is:∑︀𝑆
2 𝑖=1 𝑥𝑖 𝑦𝑖
𝑠𝑖𝑚𝑀 𝑜𝑟𝑖𝑠𝑖𝑡𝑎 (𝑋, 𝑌 ) = 𝐶𝜆 =(𝜆𝑥 + 𝜆𝑦 )𝑋𝑌
where
𝑋=

𝑆
∑︁

𝑥𝑖 ; 𝑌 =

𝑖=1

𝑆
∑︁

∑︀𝑆
𝜆𝑥 =

𝑖=1 𝑥𝑖 (𝑥𝑖 − 1)

𝑋(𝑋 − 1)

𝑦𝑖

𝑖=1

∑︀𝑆
; 𝜆𝑦 =

𝑖=1 𝑦𝑖 (𝑦𝑖 − 1)

𝑌 (𝑌 − 1)

New in version 0.4.1.
Initialize Morisita instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
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Raises NotImplementedError -- Method disabled for Morisita similarity.
New in version 0.3.6.
sim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Morisita similarity.
New in version 0.3.6.
sim_score(src, tar)
Return the Morisita similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Morisita similarity
Return type float
Examples>>> cmp = Morisita()>>> cmp.sim_score('cat', 'hat')
0.25>>> cmp.sim_score('Niall', 'Neil')
0.13333333333333333>>> cmp.sim_score('aluminum', 'Catalan')
1.0>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.Manhattan(alphabet=0,
tokenizer=None,**kwargs)
Bases: abydos.distance._minkowski.Minkowski

intersection_type='crisp',

Manhattan distance.
Manhattan distance is the city-block or taxi-cab distance, equivalent to Minkowski distance in 𝐿1 -space.
New in version 0.3.6.
Initialize Manhattan instance.
Parameters• alphabet (collection or int) -- The values or size of the alphabet• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized Manhattan distance between two strings.
The normalized Manhattan distance is a distance metric in 𝐿1 -space, normalized to [0, 1].
This is identical to Canberra distance.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns The normalized Manhattan distance
Return type float
Examples>>> cmp = Manhattan()>>> cmp.dist('cat', 'hat')
0.5>>> round(cmp.dist('Niall', 'Neil'), 12)
0.636363636364>>> round(cmp.dist('Colin', 'Cuilen'), 12)
0.692307692308>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, normalized=False)
Return the Manhattan distance between two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• normalized (bool) -- Normalizes to [0, 1] if True
Returns The Manhattan distance
Return type float

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Examples>>> cmp = Manhattan()>>> cmp.dist_abs('cat', 'hat')
4.0>>> cmp.dist_abs('Niall', 'Neil')
7.0>>> cmp.dist_abs('Colin', 'Cuilen')
9.0>>> cmp.dist_abs('ATCG', 'TAGC')
10.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.manhattan(src, tar, qval=2, normalized=False, alphabet=None)
Return the Manhattan distance between two strings.
This is a wrapper for Manhattan.dist_abs().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• normalized (bool) -- Normalizes to [0, 1] if True
• alphabet (collection or int) -- The values or size of the alphabet
Returns The Manhattan distance
Return type float
Examples>>> manhattan('cat', 'hat')
4.0>>> manhattan('Niall', 'Neil')
7.0>>> manhattan('Colin', 'Cuilen')
9.0>>> manhattan('ATCG', 'TAGC')
10.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Manhattan.dist_abs method instead.
abydos.distance.dist_manhattan(src, tar, qval=2, alphabet=0)
Return the normalized Manhattan distance between two strings.
This is a wrapper for Manhattan.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Manhattan distance
Return type float
Examples>>> dist_manhattan('cat', 'hat')
0.5>>> round(dist_manhattan('Niall', 'Neil'), 12)
0.636363636364>>> round(dist_manhattan('Colin', 'Cuilen'), 12)
0.692307692308>>> dist_manhattan('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Manhattan.dist method instead.
abydos.distance.sim_manhattan(src, tar, qval=2, alphabet=0)
Return the normalized Manhattan similarity of two strings.
This is a wrapper for Manhattan.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Manhattan similarity
Return type float
Examples>>> sim_manhattan('cat', 'hat')
0.5>>> round(sim_manhattan('Niall', 'Neil'), 12)0.363636363636>>> round(sim_manhattan('Colin', 'Cuilen'), 12)0.307692307692>>> sim_manhattan('ATCG', 'TAGC')
0.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Manhattan.sim method instead.
class abydos.distance.Michelet(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceMichelet similarity.

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For two sets X and Y and a population N, Michelet similarity [TCLM88] is𝑠𝑖𝑚𝑀 𝑖𝑐ℎ𝑒𝑙𝑒𝑡 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |2|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑀 𝑖𝑐ℎ𝑒𝑙𝑒𝑡 =𝑎2(𝑎 + 𝑏)(𝑎 + 𝑐)

Following [Seq18], this is termed "Michelet", though Turner is most often listed as the first author in papers
presenting this measure.
New in version 0.4.0.
Initialize Michelet instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Michelet similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Michelet similarity
Return type float

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Examples>>> cmp = Michelet()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.13333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.013888888888888888>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Millar(**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceMillar's binomial deviance dissimilarity.
For two sets X and Y drawn from a population S, Millar's binomial deviance dissimilarity [AM04] is:𝑑𝑖𝑠𝑡𝑀 𝑖𝑙𝑙𝑎𝑟 (𝑋, 𝑌 ) =

|𝑆|
∑︁

}︂
{︂
1
𝑥𝑖
𝑦𝑖
1
𝑥𝑖 𝑙𝑜𝑔() + 𝑦𝑖 𝑙𝑜𝑔(
) − (𝑥𝑖 + 𝑦𝑖 )𝑙𝑜𝑔( )𝑥 + 𝑦𝑖𝑥𝑖 + 𝑦𝑖𝑥𝑖 + 𝑦𝑖2𝑖=0 𝑖

New in version 0.4.1.
Initialize Millar instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Millar dissimilarity.
New in version 0.3.6.
dist_abs(src, tar)
Return Millar's binomial deviance dissimilarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Millar's binomial deviance dissimilarity
Return type float

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Examples>>> cmp = Millar()>>> cmp.dist_abs('cat', 'hat')
2.772588722239781>>> cmp.dist_abs('Niall', 'Neil')
4.852030263919617>>> cmp.dist_abs('aluminum', 'Catalan')
9.704060527839234>>> cmp.dist_abs('ATCG', 'TAGC')
6.931471805599453

New in version 0.4.1.
sim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Millar dissimilarity.
New in version 0.3.6.
class abydos.distance.Minkowski(pval=1,
alphabet=0,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Minkowski distance.
The Minkowski distance [Min10] is a distance metric in 𝐿𝑝 − 𝑠𝑝𝑎𝑐𝑒.
New in version 0.3.6.
Initialize Euclidean instance.
Parameters• pval (int) -- The 𝑝-value of the 𝐿𝑝 -space• alphabet (collection or int) -- The values or size of the alphabet• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.

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dist(src, tar)
Return normalized Minkowski distance of two strings.
The normalized Minkowski distance [Min10] is a distance metric in 𝐿𝑝 -space, normalized to [0, 1].
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns The normalized Minkowski distance
Return type float
Examples>>> cmp = Minkowski()>>> cmp.dist('cat', 'hat')
0.5>>> round(cmp.dist('Niall', 'Neil'), 12)
0.636363636364>>> round(cmp.dist('Colin', 'Cuilen'), 12)
0.692307692308>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, normalized=False)
Return the Minkowski distance (𝐿𝑝 -norm) of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• normalized (bool) -- Normalizes to [0, 1] if True
Returns The Minkowski distance
Return type float
Examples>>> cmp = Minkowski()>>> cmp.dist_abs('cat', 'hat')
4.0>>> cmp.dist_abs('Niall', 'Neil')
7.0>>> cmp.dist_abs('Colin', 'Cuilen')
9.0>>> cmp.dist_abs('ATCG', 'TAGC')
10.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
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abydos.distance.minkowski(src, tar, qval=2, pval=1, normalized=False, alphabet=0)
Return the Minkowski distance (𝐿𝑝 -norm) of two strings.
This is a wrapper for Minkowski.dist_abs().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• pval (int or float) -- The 𝑝-value of the 𝐿𝑝 -space• normalized (bool) -- Normalizes to [0, 1] if True
• alphabet (collection or int) -- The values or size of the alphabet
Returns The Minkowski distance
Return type float
Examples>>> minkowski('cat', 'hat')
4.0>>> minkowski('Niall', 'Neil')
7.0>>> minkowski('Colin', 'Cuilen')
9.0>>> minkowski('ATCG', 'TAGC')
10.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Minkowski.dist_abs method instead.
abydos.distance.dist_minkowski(src, tar, qval=2, pval=1, alphabet=0)
Return normalized Minkowski distance of two strings.
This is a wrapper for Minkowski.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• pval (int or float) -- The 𝑝-value of the 𝐿𝑝 -space• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Minkowski distance
Return type float

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Examples>>> dist_minkowski('cat', 'hat')
0.5>>> round(dist_minkowski('Niall', 'Neil'), 12)
0.636363636364>>> round(dist_minkowski('Colin', 'Cuilen'), 12)
0.692307692308>>> dist_minkowski('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Minkowski.dist method instead.
abydos.distance.sim_minkowski(src, tar, qval=2, pval=1, alphabet=0)
Return normalized Minkowski similarity of two strings.
This is a wrapper for Minkowski.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• pval (int or float) -- The 𝑝-value of the 𝐿𝑝 -space• alphabet (collection or int) -- The values or size of the alphabet
Returns The normalized Minkowski similarity
Return type float
Examples>>> sim_minkowski('cat', 'hat')
0.5>>> round(sim_minkowski('Niall', 'Neil'), 12)0.363636363636>>> round(sim_minkowski('Colin', 'Cuilen'), 12)0.307692307692>>> sim_minkowski('ATCG', 'TAGC')
0.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Minkowski.sim method instead.
class abydos.distance.MASI(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
MASI similarity.
Measuring Agreement on Set-valued Items (MASI) similarity [Pas06] for two sets X and Y is based on Jaccard
similarity:

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑 (𝑋, 𝑌 ) =3.1. abydos package|𝑋 ∩ 𝑌 ||𝑋 ∪ 𝑌 |265

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This Jaccard similarity is scaled by a value M, which is:• 1 if 𝑋 = 𝑌• 23 if 𝑋 ⊂ 𝑌 or 𝑌 ⊂ 𝑋• 13 if 𝑋 ∩ 𝑌 ̸= ∅, 𝑋 ∖ 𝑌 ̸= ∅, and 𝑌 ∖ 𝑋 ̸= ∅• 0 if 𝑋 ∩ 𝑌 = ∅New in version 0.4.0.
Initialize MASI instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the MASI similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns MASI similarity
Return type float
Examples>>> cmp = MASI()>>> cmp.sim('cat', 'hat')
0.1111111111111111>>> cmp.sim('Niall', 'Neil')
0.07407407407407407>>> cmp.sim('aluminum', 'Catalan')
0.020833333333333332>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.

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class abydos.distance.Matusita(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceMatusita distance.
For two multisets X and Y drawn from an alphabet S, Matusita distance [Mat55] is

⎯
√︃
(︃√︃
)︃2
⎸
⎸∑︁
|𝐴
|
|𝐵
|
𝑖
𝑖
−
𝑑𝑖𝑠𝑡𝑀 𝑎𝑡𝑢𝑠𝑖𝑡𝑎 (𝑋, 𝑌 ) = ⎷
|𝐴|
|𝐵|
𝑖∈𝑆

New in version 0.4.0.
Initialize Matusita instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized Matusita distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Matusita distance
Return type float
Examples>>> cmp = Matusita()>>> cmp.dist('cat', 'hat')
0.707106781186547>>> cmp.dist('Niall', 'Neil')
0.796775770420944>>> cmp.dist('aluminum', 'Catalan')
0.939227805062351>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Matusita distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Matusita distance
Return type float
Examples>>> cmp = Matusita()>>> cmp.dist_abs('cat', 'hat')
1.0>>> cmp.dist_abs('Niall', 'Neil')
1.126811100699571>>> cmp.dist_abs('aluminum', 'Catalan')
1.3282687000770907>>> cmp.dist_abs('ATCG', 'TAGC')
1.414213562373095

New in version 0.4.0.
class abydos.distance.MaxwellPilliner(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Maxwell & Pilliner correlation.
For two sets X and Y and a population N, Maxwell & Pilliner correlation [MP68] is𝑐𝑜𝑟𝑟𝑀 𝑎𝑥𝑤𝑒𝑙𝑙𝑃 𝑖𝑙𝑙𝑖𝑛𝑒𝑟 (𝑋, 𝑌 ) =2(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)|𝑋| · |𝑁 ∖ 𝑋| + |𝑌 | · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑀 𝑎𝑥𝑤𝑒𝑙𝑙𝑃 𝑖𝑙𝑙𝑖𝑛𝑒𝑟 =2(𝑎𝑑 − 𝑏𝑐)(𝑎 + 𝑏)(𝑐 + 𝑑) + (𝑎 + 𝑐)(𝑏 + 𝑐)

New in version 0.4.0.
Initialize MaxwellPilliner instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.

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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Maxwell & Pilliner correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Maxwell & Pilliner correlation
Return type float
Examples>>> cmp = MaxwellPilliner()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.35921989956790845>>> cmp.corr('aluminum', 'Catalan')
0.10803030303030303>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Maxwell & Pilliner similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Maxwell & Pilliner similarity
Return type float
Examples>>> cmp = MaxwellPilliner()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6796099497839543>>> cmp.sim('aluminum', 'Catalan')
0.5540151515151515>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

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New in version 0.4.0.
class abydos.distance.McConnaughey(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

McConnaughey correlation.
For two sets X and Y, McConnaughey correlation [McC64] is𝑐𝑜𝑟𝑟𝑀 𝑐𝐶𝑜𝑛𝑛𝑎𝑢𝑔ℎ𝑒𝑦 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |2 − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑀 𝑐𝐶𝑜𝑛𝑛𝑎𝑢𝑔ℎ𝑒𝑦 =𝑎2 − 𝑏𝑐(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize McConnaughey instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the McConnaughey correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns McConnaughey correlation
Return type float

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Examples>>> cmp = McConnaughey()>>> cmp.corr('cat', 'hat')
0.0>>> cmp.corr('Niall', 'Neil')-0.26666666666666666>>> cmp.corr('aluminum', 'Catalan')-0.7638888888888888>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the McConnaughey similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns McConnaughey similarity
Return type float
Examples>>> cmp = McConnaughey()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3666666666666667>>> cmp.sim('aluminum', 'Catalan')
0.11805555555555558>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.McEwenMichael(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

McEwen & Michael correlation.
For two sets X and Y and a population N, the McEwen & Michael correlation [Mic20] is𝑐𝑜𝑟𝑟𝑀 𝑐𝐸𝑤𝑒𝑛𝑀 𝑖𝑐ℎ𝑎𝑒𝑙 (𝑋, 𝑌 ) =4(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)(|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)2 + (|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|)2

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑀 𝑐𝐸𝑤𝑒𝑛𝑀 𝑖𝑐ℎ𝑎𝑒𝑙 =3.1. abydos package

4(𝑎𝑑 − 𝑏𝑐)(𝑎 + 𝑑)2 + (𝑏 + 𝑐)2271

Abydos Documentation, Release 0.5.0

New in version 0.4.0.
Initialize Michael instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the McEwen & Michael correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Michael correlation
Return type float
Examples>>> cmp = McEwenMichael()>>> cmp.corr('cat', 'hat')
0.010203544942933782>>> cmp.corr('Niall', 'Neil')
0.010189175491654217>>> cmp.corr('aluminum', 'Catalan')
0.0048084299262381456>>> cmp.corr('ATCG', 'TAGC')-0.00016689587032858459

New in version 0.4.0.
sim(src, tar)
Return the McEwen & Michael similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Michael similarity
Return type float
Examples>>> cmp = McEwenMichael()>>> cmp.sim('cat', 'hat')
0.5051017724714669>>> cmp.sim('Niall', 'Neil')
0.5050945877458272>>> cmp.sim('aluminum', 'Catalan')
0.502404214963119>>> cmp.sim('ATCG', 'TAGC')
0.4999165520648357

New in version 0.4.0.
class abydos.distance.Mountford(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Mountford similarity.
For two sets X and Y, the Mountford similarity [Mou62] is𝑠𝑖𝑚𝑀 𝑜𝑢𝑛𝑡𝑓 𝑜𝑟𝑑 (𝑋, 𝑌 ) =2|𝑋 ∩ 𝑌 |2|𝑋| · |𝑌 | − (|𝑋| + |𝑌 |) · |𝑋 ∩ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑀 𝑜𝑢𝑛𝑡𝑓 𝑜𝑟𝑑 =2𝑎2(𝑎 + 𝑏)(𝑎 + 𝑐) − (2𝑎 + 𝑏 + 𝑐)𝑎

New in version 0.4.0.
Initialize Mountford instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
sim(src, tar)
Return the Mountford similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Mountford similarity
Return type float
Examples>>> cmp = Mountford()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.10526315789473684>>> cmp.sim('aluminum', 'Catalan')
0.015748031496062992>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.MutualInformation(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Mutual Information similarity.
For two sets X and Y and a population N, Mutual Information similarity [CGHH91] is𝑠𝑖𝑚𝑀 𝐼 (𝑋, 𝑌 ) = 𝑙𝑜𝑔2 (|𝑋 ∩ 𝑌 | · |𝑁 |)
|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑀 𝐼 = 𝑙𝑜𝑔2 (𝑎𝑛)(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize MutualInformation instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Mutual Information similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Mutual Information similarity
Return type float
Examples>>> cmp = MutualInformation()>>> cmp.sim('cat', 'hat')
0.933609253088981>>> cmp.sim('Niall', 'Neil')
0.8911684881725231>>> cmp.sim('aluminum', 'Catalan')
0.7600321183863901>>> cmp.sim('ATCG', 'TAGC')
0.17522996523538537

New in version 0.4.0.
sim_score(src, tar)
Return the Mutual Information similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Mutual Information similarity
Return type float

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Examples>>> cmp = MutualInformation()>>> cmp.sim_score('cat', 'hat')
6.528166795717758>>> cmp.sim_score('Niall', 'Neil')
5.661433326581222>>> cmp.sim_score('aluminum', 'Catalan')
3.428560943378589>>> cmp.sim_score('ATCG', 'TAGC')-4.700439718141092

New in version 0.4.0.
class abydos.distance.MSContingency(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Mean squared contingency correlation.
For two sets X and Y and a population N, the mean squared contingency correlation [Col49] is√𝑐𝑜𝑟𝑟𝑀 𝑆𝐶𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 (𝑋, 𝑌 ) = √︀2(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)2 + |𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |[Hubalek08] and [CCT10] identify this as Cole similarity. Although Cole discusses this correlation, he does
not claim to have developed it. Rather, he presents his coefficient of interspecific association as being his own
development: Cole.
In 2x2 confusion table terms, where a+b+c+d=n, this is√𝑐𝑜𝑟𝑟𝑀 𝑆𝐶𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 = √︀2(𝑎𝑑 − 𝑏𝑐)(𝑎𝑑 − 𝑏𝑐)2 + (𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize MSContingency instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
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Abydos Documentation, Release 0.5.0• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the normalized mean squared contingency corr. of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Mean squared contingency correlation
Return type float
Examples>>> cmp = MSContingency()>>> cmp.corr('cat', 'hat')
0.6298568508557214>>> cmp.corr('Niall', 'Neil')
0.4798371954796814>>> cmp.corr('aluminum', 'Catalan')
0.15214891090821628>>> cmp.corr('ATCG', 'TAGC')-0.009076921903905553

New in version 0.4.0.
sim(src, tar)
Return the normalized ms contingency similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Mean squared contingency similarity
Return type float
Examples>>> cmp = MSContingency()>>> cmp.sim('cat', 'hat')
0.8149284254278607>>> cmp.sim('Niall', 'Neil')
0.7399185977398407>>> cmp.sim('aluminum', 'Catalan')
0.5760744554541082>>> cmp.sim('ATCG', 'TAGC')
0.49546153904804724

New in version 0.4.0.

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class abydos.distance.Overlap(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceOverlap coefficient.
For two sets X and Y, the overlap coefficient [Szy34][Sim49], also called the Szymkiewicz-Simpson coefficientand Simpson's ecological coexistence coefficient, is𝑠𝑖𝑚𝑜𝑣𝑒𝑟𝑙𝑎𝑝 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |𝑚𝑖𝑛(|𝑋|, |𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is𝑠𝑖𝑚𝑜𝑣𝑒𝑟𝑙𝑎𝑝 =𝑎𝑚𝑖𝑛(𝑎 + 𝑏, 𝑎 + 𝑐)

New in version 0.3.6.
Initialize Overlap instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the overlap coefficient of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Overlap similarity
Return type float

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Examples>>> cmp = Overlap()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.4>>> cmp.sim('aluminum', 'Catalan')
0.125>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_overlap(src, tar, qval=2)
Return the overlap distance between two strings.
This is a wrapper for Overlap.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Overlap distance
Return type float
Examples>>> dist_overlap('cat', 'hat')
0.5>>> dist_overlap('Niall', 'Neil')
0.6>>> dist_overlap('aluminum', 'Catalan')
0.875>>> dist_overlap('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Overlap.dist method instead.
abydos.distance.sim_overlap(src, tar, qval=2)
Return the overlap coefficient of two strings.
This is a wrapper for Overlap.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram
Returns Overlap similarity

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Return type float
Examples>>> sim_overlap('cat', 'hat')
0.5>>> sim_overlap('Niall', 'Neil')
0.4>>> sim_overlap('aluminum', 'Catalan')
0.125>>> sim_overlap('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Overlap.sim method instead.
class abydos.distance.Pattern(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Pattern difference.
For two sets X and Y and a population N, the pattern difference [BB95], Batagelj & Bren's −𝑏𝑐− is𝑑𝑖𝑠𝑡𝑝𝑎𝑡𝑡𝑒𝑟𝑛 (𝑋, 𝑌 ) =4 · |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑁 |2

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝑝𝑎𝑡𝑡𝑒𝑟𝑛 =4𝑏𝑐𝑛2

In [Cor17], the formula omits the 4 in the numerator: 𝑛𝑏𝑐2 .
New in version 0.4.0.
Initialize Pattern instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
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Abydos Documentation, Release 0.5.0• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Pattern difference of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pattern difference
Return type float
Examples>>> cmp = Pattern()>>> cmp.dist('cat', 'hat')
2.6030820491461892e-05>>> cmp.dist('Niall', 'Neil')
7.809246147438568e-05>>> cmp.dist('aluminum', 'Catalan')
0.0003635035904093472>>> cmp.dist('ATCG', 'TAGC')
0.0001626926280716368

New in version 0.4.0.
class abydos.distance.PearsonHeronII(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Pearson & Heron II correlation.
For two sets X and Y and a population N, Pearson & Heron II correlation [PH13] is√︀)︁|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
√︀𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐻𝑒𝑟𝑜𝑛𝐼𝐼 (𝑋, 𝑌 ) = cos √︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|(︁𝜋

In 2x2 confusion table terms, where a+b+c+d=n, this is√𝜋 𝑏𝑐 )︁
√𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐻𝑒𝑟𝑜𝑛𝐼𝐼 = cos √𝑎𝑑 + 𝑏𝑐(︁

New in version 0.4.0.
Initialize PearsonHeronII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.

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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Pearson & Heron II correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson & Heron II correlation
Return type float
Examples>>> cmp = PearsonHeronII()>>> cmp.corr('cat', 'hat')
0.9885309061036239>>> cmp.corr('Niall', 'Neil')
0.9678978997263907>>> cmp.corr('aluminum', 'Catalan')
0.7853000893691571>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Pearson & Heron II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson & Heron II similarity
Return type float

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Examples>>> cmp = PearsonHeronII()>>> cmp.sim('cat', 'hat')
0.994265453051812>>> cmp.sim('Niall', 'Neil')
0.9839489498631954>>> cmp.sim('aluminum', 'Catalan')
0.8926500446845785>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.PearsonII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._pearson_chi_squared.PearsonChiSquared
Pearson II similarity.
For two sets X and Y and a population N, the Pearson II similarity [PH13], Pearson's coefficient of mean squarecontingency, is√︃𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐼𝐼 =𝜒2|𝑁 | + 𝜒2where𝜒2 = 𝑠𝑖𝑚𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐶ℎ𝑖𝑆𝑞𝑢𝑎𝑟𝑒𝑑 (𝑋, 𝑌 ) =|𝑁 | · (|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)2
|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝜒2 = 𝑠𝑖𝑚𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐶ℎ𝑖𝑆𝑞𝑢𝑎𝑟𝑒𝑑 =𝑛 · (𝑎𝑑 − 𝑏𝑐)2
(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize PearsonII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters3.1. abydos package283

Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Pearson II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Pearson II similarity
Return type float
Examples>>> cmp = PearsonII()>>> cmp.sim('cat', 'hat')
0.6298568508557214>>> cmp.sim('Niall', 'Neil')
0.47983719547968123>>> cmp.sim('aluminum', 'Catalan')
0.15214891090821628>>> cmp.sim('ATCG', 'TAGC')
0.009076921903905551

New in version 0.4.0.
sim_score(src, tar)
Return the Pearson II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson II similarity
Return type float

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Examples>>> cmp = PearsonII()>>> cmp.sim_score('cat', 'hat')
0.44537605041688455>>> cmp.sim_score('Niall', 'Neil')
0.3392961347892176>>> cmp.sim_score('aluminum', 'Catalan')
0.10758552665334761>>> cmp.sim_score('ATCG', 'TAGC')
0.006418353030552324

New in version 0.4.0.
class abydos.distance.PearsonIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._pearson_phi.PearsonPhi
Pearson III correlation.
For two sets X and Y and a population N, the Pearson III correlation [PH13], Pearson's coefficient of racial
likeness, is√︃𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐼𝐼𝐼 =𝜑|𝑁 | + 𝜑

where

𝜑 = 𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝑃 ℎ𝑖 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
√︀|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝜑 = 𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝑃 ℎ𝑖 = √︀𝑎𝑑 − 𝑏𝑐(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑐)(𝑏 + 𝑑)

New in version 0.4.0.
Initialize PearsonIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters3.1. abydos package285

Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Pearson III correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson III correlation
Return type float
Examples>>> cmp = PearsonIII()>>> cmp.corr('cat', 'hat')
0.025180989806958435>>> cmp.corr('Niall', 'Neil')
0.021444241017487504>>> cmp.corr('aluminum', 'Catalan')
0.011740218922356615>>> cmp.corr('ATCG', 'TAGC')-0.0028612777635371113

New in version 0.4.0.
sim(src, tar)
Return the Pearson III similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson III similarity
Return type float

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Examples>>> cmp = PearsonIII()>>> cmp.sim('cat', 'hat')
0.5125904949034792>>> cmp.sim('Niall', 'Neil')
0.5107221205087438>>> cmp.sim('aluminum', 'Catalan')
0.5058701094611783>>> cmp.sim('ATCG', 'TAGC')
0.49856936111823147

New in version 0.4.0.
class abydos.distance.PearsonChiSquared(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Pearson's Chi-Squared similarity.
For two sets X and Y and a population N, the Pearson's 𝜒2 similarity [PH13] is

𝑠𝑖𝑚𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐶ℎ𝑖𝑆𝑞𝑢𝑎𝑟𝑒𝑑 (𝑋, 𝑌 ) =|𝑁 | · (|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)2
|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |This is also Pearson I similarity.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝐶ℎ𝑖𝑆𝑞𝑢𝑎𝑟𝑒𝑑 =𝑛(𝑎𝑑 − 𝑏𝑐)2
(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize PearsonChiSquared instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
corr(src, tar)
Return Pearson's Chi-Squared correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson's Chi-Squared correlation
Return type float
Examples>>> cmp = PearsonChiSquared()>>> cmp.corr('cat', 'hat')
0.2474424720578567>>> cmp.corr('Niall', 'Neil')
0.1300991207720222>>> cmp.corr('aluminum', 'Catalan')
0.011710186806836291>>> cmp.corr('ATCG', 'TAGC')-4.1196952743799446e-05

New in version 0.4.0.
sim(src, tar)
Return Pearson's normalized Chi-Squared similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Pearson's Chi-Squared similarity
Return type float
Examples>>> cmp = PearsonChiSquared()>>> cmp.corr('cat', 'hat')
0.2474424720578567>>> cmp.corr('Niall', 'Neil')
0.1300991207720222>>> cmp.corr('aluminum', 'Catalan')
0.011710186806836291>>> cmp.corr('ATCG', 'TAGC')-4.1196952743799446e-05

New in version 0.4.0.
sim_score(src, tar)
Return Pearson's Chi-Squared similarity of two strings.
Parameters

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Returns Pearson's Chi-Squared similarity
Return type float
Examples>>> cmp = PearsonChiSquared()>>> cmp.sim_score('cat', 'hat')
193.99489809335964>>> cmp.sim_score('Niall', 'Neil')
101.99771068526542>>> cmp.sim_score('aluminum', 'Catalan')
9.19249664336649>>> cmp.sim_score('ATCG', 'TAGC')
0.032298410951138765

New in version 0.4.0.
class abydos.distance.PearsonPhi(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Pearson's Phi correlation.
For two sets X and Y and a population N, the Pearson's 𝜑 correlation [Pea00][PH13][Guirk] is

𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝑃 ℎ𝑖 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
√︀|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |This is also Pearson & Heron I similarity.
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑎𝑑 − 𝑏𝑐
𝑐𝑜𝑟𝑟𝑃 𝑒𝑎𝑟𝑠𝑜𝑛𝑃 ℎ𝑖 = √︀(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)
Notes
In terms of a confusion matrix, this is equivalent to the Matthews correlation coefficient ConfusionTable.
mcc().
New in version 0.4.0.
Initialize PearsonPhi instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return Pearson's Phi correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson's Phi correlation
Return type float
Examples>>> cmp = PearsonPhi()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.36069255713421955>>> cmp.corr('aluminum', 'Catalan')
0.10821361655002706>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the normalized Pearson's Phi similarity of two strings.
This is normalized to [0, 1] by adding 1 and dividing by 2.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Pearson's Phi similarity
Return type float

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Examples>>> cmp = PearsonPhi()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6803462785671097>>> cmp.sim('aluminum', 'Catalan')
0.5541068082750136>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.Peirce(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Peirce correlation.
For two sets X and Y and a population N, the Peirce correlation [Pei84] is

𝑐𝑜𝑟𝑟𝑃 𝑒𝑖𝑟𝑐𝑒 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑌 ||𝑋| · |𝑁 ∖ 𝑋|

Both [CCT10] and [Hubalek08] present a different formula and incorrectly attribute it to Peirce. Likewise,[Doo84] presents a different formula and incorrectly attributes it to Peirce. This is distinct from the formula hepresents and attributes to himself.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝑃 𝑒𝑖𝑟𝑐𝑒 =𝑎𝑑 − 𝑏𝑐(𝑎 + 𝑏)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize Peirce instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.

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Abydos Documentation, Release 0.5.0• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Peirce correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Peirce correlation
Return type float
Examples>>> cmp = Peirce()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.32947729220222793>>> cmp.corr('aluminum', 'Catalan')
0.10209049255441008>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Peirce similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Peirce similarity
Return type float
Examples>>> cmp = Peirce()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.664738646101114>>> cmp.sim('aluminum', 'Catalan')
0.5510452462772051>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.

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class abydos.distance.QGram(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceq-gram distance.
For two multisets X and Y, q-gram distance [Ukk92] is

𝑠𝑖𝑚𝑄𝐺𝑟𝑎𝑚 (𝑋, 𝑌 ) = |𝑋△𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑄𝐺𝑟𝑎𝑚 = 𝑏 + 𝑐
Notes
This class uses bigrams without appended start or stop symbols, by default, as in [Ukk92]'s examples. It is
described as the 𝐿1 norm of the difference of two strings' q-gram profiles, which are the vectors of q-gramoccurrences. But this norm is simply the symmetric difference of the two multisets.
There aren't any limitations on which tokenizer is used with this class, but, as the name would imply, q-grams
are expected and the default.
The normalized form uses the union of X and Y, making it equivalent to the Jaccard distance Jaccard, but the
Jaccard class, by default uses bigrams with start & stop symbols.
New in version 0.4.0.
Initialize QGram instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized q-gram distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns q-gram distance
Return type float
Examples>>> cmp = QGram()>>> cmp.sim('cat', 'hat')
0.33333333333333337>>> cmp.sim('Niall', 'Neil')
0.0>>> cmp.sim('aluminum', 'Catalan')
0.08333333333333337>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
dist_abs(src, tar)
Return the q-gram distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns q-gram distance
Return type int
Examples>>> cmp = QGram()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')7>>> cmp.dist_abs('aluminum', 'Catalan')11>>> cmp.dist_abs('ATCG', 'TAGC')
6>>> cmp.dist_abs('01000', '001111')
5

New in version 0.4.0.
class abydos.distance.RaupCrick(**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceRaup-Crick similarity.
For two sets X and Y and a population N, Raup-Crick similarity [RC79] is:294

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Notes
Observe that Raup-Crick similarity is related to Henderson-Heron similarity in that the former is the sum of all
Henderson-Heron similarities for an intersection size ranging from 0 to the true intersection size.
New in version 0.4.1.
Initialize RaupCrick instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
sim(src, tar)
Return the Raup-Crick similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Raup-Crick similarity
Return type float
Examples>>> cmp = RaupCrick()>>> cmp.sim('cat', 'hat')
0.9999998002120004>>> cmp.sim('Niall', 'Neil')
0.9999975146378747>>> cmp.sim('aluminum', 'Catalan')
0.9968397599851411>>> cmp.sim('ATCG', 'TAGC')
0.9684367974410505

New in version 0.4.1.
class abydos.distance.ReesLevenshtein(block_limit=2, normalizer=<built-in function max>,**kwargs)
Bases: abydos.distance._distance._DistanceRees-Levenshtein distance.
Rees-Levenshtein distance [Ree14][RB13] is the "Modified Damerau-Levenshtein Distance Algorithm, createdby Tony Rees as part of Taxamatch.
New in version 0.4.0.
Initialize ReesLevenshtein instance.
Parameters• block_limit (int) -- The block length limit• normalizer (function) -- A function that takes an list and computes a normalization
term by which the edit distance is divided (max by default). Another good option is the sum
function.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
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dist(src, tar)
Return the normalized Rees-Levenshtein distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Rees-Levenshtein distance
Return type float
Examples>>> cmp = ReesLevenshtein()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.6>>> cmp.dist('aluminum', 'Catalan')
0.875>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.4.0.
dist_abs(src, tar)
Return the Rees-Levenshtein distance of two strings.
This is a straightforward port of the PL/SQL implementation at https://confluence.csiro.au/public/
taxamatch/the-mdld-modified-damerau-levenshtein-distance-algorithm
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Rees-Levenshtein distance
Return type float
Examples>>> cmp = ReesLevenshtein()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')3>>> cmp.dist_abs('aluminum', 'Catalan')7>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.4.0.
class abydos.distance.RogersTanimoto(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance296

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Rogers & Tanimoto similarity.
For two sets X and Y and a population N, the Rogers-Tanimoto similarity [RT60] is

𝑠𝑖𝑚𝑅𝑜𝑔𝑒𝑟𝑠𝑇 𝑎𝑛𝑖𝑚𝑜𝑡𝑜 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋| + |𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑅𝑜𝑔𝑒𝑟𝑠𝑇 𝑎𝑛𝑖𝑚𝑜𝑡𝑜 =

𝑎+𝑑
𝑏+𝑐+𝑛

New in version 0.4.0.
Initialize RogersTanimoto instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Rogers & Tanimoto similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Rogers & Tanimoto similarity
Return type float

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Examples>>> cmp = RogersTanimoto()>>> cmp.sim('cat', 'hat')
0.9898477157360406>>> cmp.sim('Niall', 'Neil')
0.9823008849557522>>> cmp.sim('aluminum', 'Catalan')
0.9625>>> cmp.sim('ATCG', 'TAGC')
0.9748110831234257

New in version 0.4.0.
class abydos.distance.RogotGoldberg(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Rogot & Goldberg similarity.
For two sets X and Y and a population N, Rogot & Goldberg's "second index adjusted agreement" 𝐴2 [RG66]
is1
𝑠𝑖𝑚𝑅𝑜𝑔𝑜𝑡𝐺𝑜𝑙𝑑𝑏𝑒𝑟𝑔 (𝑋, 𝑌 ) =2(︃

2|𝑋 ∩ 𝑌 |2|(𝑁 ∖ 𝑋) ∖ 𝑌 |+|𝑋| + |𝑌 | |𝑁 ∖ 𝑋| + |𝑁 ∖ 𝑌 |)︃

In 2x2 confusion table terms, where a+b+c+d=n, this is1
𝑠𝑖𝑚𝑅𝑜𝑔𝑜𝑡𝐺𝑜𝑙𝑑𝑏𝑒𝑟𝑔 =
2

(︃

2𝑎
2𝑑
+
2𝑎 + 𝑏 + 𝑐 2𝑑 + 𝑏 + 𝑐)︃New in version 0.4.0.
Initialize RogotGoldberg instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
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New in version 0.4.0.
sim(src, tar)
Return the Rogot & Goldberg similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Rogot & Goldberg similarity
Return type float
Examples>>> cmp = RogotGoldberg()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6795702691656449>>> cmp.sim('aluminum', 'Catalan')
0.5539941668876179>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.RussellRao(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceRussell & Rao similarity.
For two sets X and Y and a population N, the Russell & Rao similarity [RR40] is

𝑠𝑖𝑚𝑅𝑢𝑠𝑠𝑒𝑙𝑙𝑅𝑎𝑜 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑅𝑢𝑠𝑠𝑒𝑙𝑙𝑅𝑎𝑜 =𝑎𝑛

New in version 0.4.0.
Initialize RussellRao instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Russell & Rao similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Russell & Rao similarity
Return type float
Examples>>> cmp = RussellRao()>>> cmp.sim('cat', 'hat')
0.002551020408163265>>> cmp.sim('Niall', 'Neil')
0.002551020408163265>>> cmp.sim('aluminum', 'Catalan')
0.0012738853503184713>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.ScottPi(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Scott's Pi correlation.
For two sets X and Y and a population N, Scott's 𝜋 correlation [Sco55] is𝑐𝑜𝑟𝑟𝑆𝑐𝑜𝑡𝑡𝜋 (𝑋, 𝑌 ) = 𝜋 =𝑝𝑜 − 𝑝𝜋𝑒1 − 𝑝𝜋𝑒

where

𝑝𝑜
𝑝𝜋𝑒

300

∖𝑋)∖𝑌 |
= |𝑋∩𝑌 |+|(𝑁|𝑁 |(︁
)︁2 (︁
)︁2||𝑁 ∖𝑋|+|𝑁 ∖𝑌 |
= |𝑋|+|𝑌+
2·|𝑁 |
2·|𝑁 |

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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑝𝑜
𝑝𝜋𝑒

= 𝑎+𝑑
)︁2 (︁
)︁2(︁𝑛2𝑑+𝑏+𝑐= 2𝑎+𝑏+𝑐+2𝑛2𝑛

New in version 0.4.0.
Initialize ScottPi instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Scott's Pi correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Scott's Pi correlation
Return type float
Examples>>> cmp = ScottPi()>>> cmp.corr('cat', 'hat')
0.49743589743589733>>> cmp.corr('Niall', 'Neil')
0.35914053833129245>>> cmp.corr('aluminum', 'Catalan')
0.10798833377524023>>> cmp.corr('ATCG', 'TAGC')-0.006418485237489689

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New in version 0.4.0.
sim(src, tar)
Return the Scott's Pi similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Scott's Pi similarity
Return type float
Examples>>> cmp = ScottPi()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6795702691656462>>> cmp.sim('aluminum', 'Catalan')
0.5539941668876202>>> cmp.sim('ATCG', 'TAGC')
0.49679075738125517

New in version 0.4.0.
class abydos.distance.Shape(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Penrose's shape difference.
For two sets X and Y and a population N, the Penrose's shape difference [Pen52] is

𝑑𝑖𝑠𝑡𝑆ℎ𝑎𝑝𝑒 (𝑋, 𝑌 ) =

1 (︁
·
|𝑁 |

∑︁
𝑥∈(𝑋△𝑌 ))︁ (︁ |𝑋△𝑌 | )︁2
𝑥2 −
|𝑁 |

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆ℎ𝑎𝑝𝑒 =1 (︁ ∑︁ 2 ∑︁ 2 )︁ (︁ 𝑏 + 𝑐 )︁2
𝑥 +
𝑥 −
𝑛
𝑛
𝑥∈𝑐
𝑥∈𝑏

2

, but it is clear from [Pen52] that this should not be an assymIn [Cor17], the formula is instead 𝑛(𝑏+𝑐)−(𝑏−𝑐)𝑛2metric value with respect√︀∑︀ to the ordering of the two sets, among other errors in this formula. Meanwhile, [DD16]
gives the formula((𝑥𝑖 − 𝑥¯) − (𝑦𝑖 − 𝑦¯))2 .
New in version 0.4.0.
Initialize Shape instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Penrose's shape difference of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Shape ifference
Return type float
Examples>>> cmp = Shape()>>> cmp.sim('cat', 'hat')
0.994923990004165>>> cmp.sim('Niall', 'Neil')
0.9911511479591837>>> cmp.sim('aluminum', 'Catalan')
0.9787090754188811>>> cmp.sim('ATCG', 'TAGC')
0.9874075905872554

New in version 0.4.0.
class abydos.distance.Size(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Penrose's size difference.
For two sets X and Y and a population N, the Penrose's size difference [Pen52] is

𝑠𝑖𝑚𝑆𝑖𝑧𝑒 (𝑋, 𝑌 ) =(|𝑋△𝑌 |)2
|𝑁 |2

In 2x2 confusion table terms, where a+b+c+d=n, this is3.1. abydos package

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𝑠𝑖𝑚𝑆𝑖𝑧𝑒 =(𝑏 + 𝑐)2𝑛2

2

In [Cor17], the formula is instead (𝑏−𝑐)𝑛2 , but it is clear from [Pen52] that this should not be an assymmetric
value
with
respecttwo
theorderingofthe two sets. Meanwhile, [DD16] gives a formula that is equivalent to√𝑛 · (𝑏 + 𝑐).
New in version 0.4.0.
Initialize Size instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the Penrose's size difference of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Size difference
Return type float
Examples>>> cmp = Size()>>> cmp.sim('cat', 'hat')
0.9999739691795085>>> cmp.sim('Niall', 'Neil')
0.9999202806122449>>> cmp.sim('aluminum', 'Catalan')
0.9996348736257049>>> cmp.sim('ATCG', 'TAGC')
0.9998373073719283

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New in version 0.4.0.
class abydos.distance.SokalMichener(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Sokal & Michener similarity.
For two sets X and Y and a population N, the Sokal & Michener's simple matching coefficient [SM58], equivalent to the Rand index [Ran71] is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑀 𝑖𝑐ℎ𝑒𝑛𝑒𝑟 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑀 𝑖𝑐ℎ𝑒𝑛𝑒𝑟 =

𝑎+𝑑
𝑛

Notes
The associated distance metric is the mean Manhattan distance and 4 times the value of the variance dissimilarity
of [Cor17].
In terms of a confusion matrix, this is equivalent to accuracy ConfusionTable.accuracy().
New in version 0.4.0.
Initialize SokalMichener instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sokal & Michener similarity of two strings.
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Abydos Documentation, Release 0.5.0• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Michener similarity
Return type float
Examples>>> cmp = SokalMichener()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.9910714285714286>>> cmp.sim('aluminum', 'Catalan')
0.9808917197452229>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837

New in version 0.4.0.
class abydos.distance.SokalSneathI(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Sokal & Sneath I similarity.
For two sets X and Y and a population N, Sokal & Sneath I similarity [SS63] is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼 (𝑋, 𝑌 ) =2(|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑁 |This is the first of five "Unnamed coefficients" presented in [SS63]. It corresponds to the "Matched pairs carry
twice the weight of unmatched pairs in the Denominator" with "Negative Matches in Numerator Included".
"Negative Matches in Numerator Excluded" corresponds to the Sørensen–Dice coefficient, Dice.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼 =2(𝑎 + 𝑑)𝑎+𝑑+𝑛

New in version 0.4.0.
Initialize SokalSneathI instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sokal & Sneath I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Sneath I similarity
Return type float
Examples>>> cmp = SokalSneathI()>>> cmp.sim('cat', 'hat')
0.9974424552429667>>> cmp.sim('Niall', 'Neil')
0.9955156950672646>>> cmp.sim('aluminum', 'Catalan')
0.9903536977491961>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.
class abydos.distance.SokalSneathII(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Sokal & Sneath II similarity.
For two sets X and Y, Sokal & Sneath II similarity [SS63] is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝐼 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑋 ∩ 𝑌 | + 2|𝑋△𝑌 |This is the second of five "Unnamed coefficients" presented in [SS63]. It corresponds to the "Unmatchedpairs carry twice the weight of matched pairs in the Denominator" with "Negative Matches in Numerator
Excluded". "Negative Matches in Numerator Included" corresponds to the Rogers & Tanimoto similarity,
RogersTanimoto.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝐼 =3.1. abydos package𝑎𝑎 + 2(𝑏 + 𝑐)
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New in version 0.4.0.
Initialize SokalSneathII instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sokal & Sneath II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Sneath II similarity
Return type float
Examples>>> cmp = SokalSneathII()>>> cmp.sim('cat', 'hat')
0.2>>> cmp.sim('Niall', 'Neil')
0.125>>> cmp.sim('aluminum', 'Catalan')
0.03225806451612903>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.SokalSneathIII(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Sokal & Sneath III similarity.
For two sets X and Y and a population N, Sokal & Sneath III similarity [SS63] is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝐼𝐼 (𝑋, 𝑌 ) =308|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋△𝑌 |
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This is the third of five "Unnamed coefficients" presented in [SS63]. It corresponds to the "Unmatched pairs
only in the Denominator" with "Negative Matches in Numerator Excluded". "Negative Matches in Numerator
Included" corresponds to the Kulczynski I coefficient, KulczynskiI.
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝐼𝐼 =

𝑎+𝑑
𝑏+𝑐

New in version 0.4.0.
Initialize SokalSneathIII instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Sokal & Sneath III similarity.
New in version 0.3.6.
sim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Sokal & Sneath III similarity.
New in version 0.3.6.
sim_score(src, tar)
Return the Sokal & Sneath III similarity of two strings.

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Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Sneath III similarity
Return type float
Examples>>> cmp = SokalSneathIII()>>> cmp.sim_score('cat', 'hat')
195.0>>> cmp.sim_score('Niall', 'Neil')
111.0>>> cmp.sim_score('aluminum', 'Catalan')
51.333333333333336>>> cmp.sim_score('ATCG', 'TAGC')
77.4

New in version 0.4.0.
class abydos.distance.SokalSneathIV(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Sokal & Sneath IV similarity.
For two sets X and Y and a population N, Sokal & Sneath IV similarity [SS63] is

1
𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝑉 (𝑋, 𝑌 ) =4(︃|𝑋 ∩ 𝑌 | |𝑋 ∩ 𝑌 | |(𝑁 ∖ 𝑋) ∖ 𝑌 | |(𝑁 ∖ 𝑋) ∖ 𝑌 |
+
+
+
|𝑋|
|𝑌 |
|𝑁 ∖ 𝑌 ||𝑁 ∖ 𝑋|)︃This is the fourth of five "Unnamed coefficients" presented in [SS63]. It corresponds to the first "Marginal
totals in the Denominator" with "Negative Matches in Numerator Included". "Negative Matches in Numerator
Excluded" corresponds to the Kulczynski II similarity, KulczynskiII. This is also Rogot & Goldberg's"adjusted agreement" 𝐴1 [RG66].
In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝐼𝑉 =1 (︁ 𝑎
𝑎
𝑑
𝑑 )︁
+
+
+
4 𝑎+𝑏 𝑎+𝑐 𝑏+𝑑 𝑐+𝑑

New in version 0.4.0.
Initialize SokalSneathIV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sokal & Sneath IV similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Sneath IV similarity
Return type float
Examples>>> cmp = SokalSneathIV()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6810856260030602>>> cmp.sim('aluminum', 'Catalan')
0.5541986205645999>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.SokalSneathV(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Sokal & Sneath V similarity.
For two sets X and Y and a population N, Sokal & Sneath V similarity [SS63] is

𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝑉 (𝑋, 𝑌 ) = √︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 | · |𝑁 ∖ 𝑋|

This is the fifth of five "Unnamed coefficients" presented in [SS63]. It corresponds to the second "Marginal totals
in the Denominator" with "Negative Matches in Numerator Included", also sometimes referred to as Ochiai II
similarity. "Negative Matches in Numerator Excluded" corresponds to the Cosine similarity, Cosine.
In 2x2 confusion table terms, where a+b+c+d=n, this is3.1. abydos package

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𝑎𝑑
𝑠𝑖𝑚𝑆𝑜𝑘𝑎𝑙𝑆𝑛𝑒𝑎𝑡ℎ𝑉 = √︀(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)
New in version 0.4.0.
Initialize SokalSneathV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sokal & Sneath V similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sokal & Sneath V similarity
Return type float
Examples>>> cmp = SokalSneathV()>>> cmp.sim('cat', 'hat')
0.4987179487179487>>> cmp.sim('Niall', 'Neil')
0.3635068033537323>>> cmp.sim('aluminum', 'Catalan')
0.11671286273067434>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.

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class abydos.distance.Sorgenfrei(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Sorgenfrei similarity.
For two sets X and Y, Sorgenfrei similarity [Sor58] is

𝑠𝑖𝑚𝑆𝑜𝑟𝑔𝑒𝑛𝑓 𝑟𝑒𝑖 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 |2|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑜𝑟𝑔𝑒𝑛𝑓 𝑟𝑒𝑖 =𝑎2(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize Sorgenfrei instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Sorgenfrei similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Sorgenfrei similarity
Return type float

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Examples>>> cmp = Sorgenfrei()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.13333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.013888888888888888>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Steffensen(alphabet=None, tokenizer=None, intersection_type='crisp',
normalizer='proportional', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Steffensen similarity.
For two sets X and Y and a population N, Steffensen similarity 𝜓 2 [Ste34] is

𝑠𝑖𝑚𝑆𝑡𝑒𝑓 𝑓 𝑒𝑛𝑠𝑒𝑛𝜓 (𝑋, 𝑌 ) = 𝜓 2

=

𝜑2𝑖𝑗

𝑖𝑗
𝑖* *𝑖
= 𝑝𝑖* (1−𝑝𝑖* )𝑝*𝑗 (1−𝑝*𝑗 )

∑︀

𝑖∈𝑋

2
𝑗∈𝑌 𝑝𝑖𝑗 𝜑𝑖𝑗∑︀(𝑝 −𝑝 𝑝 )2

Where each value 𝑝𝑖𝑗 is drawn from the 2x2 contingency table:𝑥 ∈ src𝑥∈/ src

𝑥 ∈ tar
𝑝11 = 𝑎𝑝01 = 𝑐𝑝*1 = 𝑎 + 𝑐

𝑥∈
/ tar
𝑝10 = 𝑏𝑝00 = 𝑑𝑝*0 = 𝑏 + 𝑑

𝑝1* = 𝑎 + 𝑏
𝑝0* = 𝑐 + 𝑑
1

New in version 0.4.0.
Initialize Steffensen instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• normalizer (str) -- Specifies the normalization type. See normalizer description in_TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.

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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Steffensen similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Steffensen similarity
Return type float
Examples>>> cmp = Steffensen()>>> cmp.sim('cat', 'hat')
0.24744247205786737>>> cmp.sim('Niall', 'Neil')
0.1300991207720166>>> cmp.sim('aluminum', 'Catalan')
0.011710186806836031>>> cmp.sim('ATCG', 'TAGC')
4.1196952743871653e-05

New in version 0.4.0.
class abydos.distance.Stiles(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Stiles similarity.
For two sets X and Y and a population N, Stiles similarity [Sti61] is

𝑠𝑖𝑚𝑆𝑡𝑖𝑙𝑒𝑠 (𝑋, 𝑌 ) = 𝑙𝑜𝑔10(︁)︁2|𝑁 | ||𝑋 ∩ 𝑌 | · |𝑁 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|| − |𝑁2 ||𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋| · (|𝑁 | − |𝑋 ∖ 𝑌 |) · (|𝑁 | − |𝑌 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑆𝑡𝑖𝑙𝑒𝑠 = 𝑙𝑜𝑔10𝑛(|𝑎𝑛 − 𝑏𝑐| − 21 𝑛)2
𝑏𝑐(𝑛 − 𝑏)(𝑛 − 𝑐)

New in version 0.4.0.
Initialize Stiles instance.
Parameters3.1. abydos package

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Abydos Documentation, Release 0.5.0• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Stiles correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Stiles correlation
Return type float
Examples>>> cmp = Stiles()>>> cmp.corr('cat', 'hat')
0.14701542182970487>>> cmp.corr('Niall', 'Neil')
0.11767566062554877>>> cmp.corr('aluminum', 'Catalan')
0.022355640924908403>>> cmp.corr('ATCG', 'TAGC')-0.046296656196428934

New in version 0.4.0.
sim(src, tar)
Return the normalized Stiles similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Stiles similarity
Return type float

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Examples>>> cmp = Stiles()>>> cmp.sim('cat', 'hat')
0.5735077109148524>>> cmp.sim('Niall', 'Neil')
0.5588378303127743>>> cmp.sim('aluminum', 'Catalan')
0.5111778204624542>>> cmp.sim('ATCG', 'TAGC')
0.4768516719017855

New in version 0.4.0.
sim_score(src, tar)
Return the Stiles similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Stiles similarity
Return type float
Examples>>> cmp = Stiles()>>> cmp.sim_score('cat', 'hat')
2.6436977886009236>>> cmp.sim_score('Niall', 'Neil')
2.1622951406967723>>> cmp.sim_score('aluminum', 'Catalan')
0.41925115106844024>>> cmp.sim_score('ATCG', 'TAGC')-0.8426334527850912

New in version 0.4.0.
class abydos.distance.StuartTau(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceStuart's Tau correlation.
For two sets X and Y and a population N, Stuart's Tau-C correlation [Stu53] is𝑐𝑜𝑟𝑟𝑆𝑡𝑢𝑎𝑟𝑡𝜏𝑐 (𝑋, 𝑌 ) =4 · (|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋△𝑌 |)|𝑁 |2

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑆𝑡𝑢𝑎𝑟𝑡𝜏𝑐 =3.1. abydos package

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New in version 0.4.0.
Initialize StuartTau instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Stuart's Tau correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Stuart's Tau correlation
Return type float
Examples>>> cmp = StuartTau()>>> cmp.corr('cat', 'hat')
0.005049979175343606>>> cmp.corr('Niall', 'Neil')
0.005010932944606414>>> cmp.corr('aluminum', 'Catalan')
0.004900807334983164>>> cmp.corr('ATCG', 'TAGC')
0.0049718867138692216

New in version 0.4.0.
sim(src, tar)
Return the Stuart's Tau similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Stuart's Tau similarity
Return type float
Examples>>> cmp = StuartTau()>>> cmp.sim('cat', 'hat')
0.5025249895876718>>> cmp.sim('Niall', 'Neil')
0.5025054664723032>>> cmp.sim('aluminum', 'Catalan')
0.5024504036674916>>> cmp.sim('ATCG', 'TAGC')
0.5024859433569346

New in version 0.4.0.
class abydos.distance.Tarantula(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tarantula similarity.
For two sets X and Y and a population N, Tarantula similarity [JH05] is𝑠𝑖𝑚𝑇 𝑎𝑟𝑎𝑛𝑡𝑢𝑙𝑎 (𝑋, 𝑌 ) =|𝑋∩𝑌 ||𝑋∩𝑌 |+|𝑋∖𝑌 ||𝑋∩𝑌 ||𝑌 ∖𝑋||𝑋∩𝑌 |+|𝑋∖𝑌 | + |𝑌 ∖𝑋|+|(𝑁 ∖𝑋)∖𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑇 𝑎𝑟𝑎𝑛𝑡𝑢𝑙𝑎 =

𝑎
𝑎+𝑏
𝑎
𝑐
𝑎+𝑏 + 𝑐+𝑑

New in version 0.4.0.
Initialize Tarantula instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Tarantula similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tarantula similarity
Return type float
Examples>>> cmp = Tarantula()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.98856416772554>>> cmp.sim('aluminum', 'Catalan')
0.9249106078665077>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Tarwid(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Tarwid correlation.
For two sets X and Y and a population N, the Tarwid correlation [Tar60] is

𝑐𝑜𝑟𝑟𝑇 𝑎𝑟𝑤𝑖𝑑 (𝑋, 𝑌 ) =|𝑁 | · |𝑋 ∩ 𝑌 | − |𝑋| · |𝑌 |
|𝑁 | · |𝑋 ∩ 𝑌 | + |𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑇 𝑎𝑟𝑤𝑖𝑑 =𝑛𝑎 − (𝑎 + 𝑏)(𝑎 + 𝑐)
𝑛𝑎 + (𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize Tarwid instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
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Abydos Documentation, Release 0.5.0• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Tarwid correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tarwid correlation
Return type float
Examples>>> cmp = Tarwid()>>> cmp.corr('cat', 'hat')
0.9797979797979798>>> cmp.corr('Niall', 'Neil')
0.9624530663329162>>> cmp.corr('aluminum', 'Catalan')
0.8319719953325554>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Tarwid similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tarwid similarity
Return type float

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Examples>>> cmp = Tarwid()>>> cmp.sim('cat', 'hat')
0.9898989898989898>>> cmp.sim('Niall', 'Neil')
0.981226533166458>>> cmp.sim('aluminum', 'Catalan')
0.9159859976662776>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Tetrachoric(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tetrachoric correlation coefficient.
For two sets X and Y and a population N, the Tetrachoric correlation coefficient [Pea00] is(︁

𝑐𝑜𝑟𝑟𝑇 𝑒𝑡𝑟𝑎𝑐ℎ𝑜𝑟𝑖𝑐 (𝑋, 𝑌 ) = cos √︀√︀)︁|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|√︀
|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|𝜋

In 2x2 confusion table terms, where a+b+c+d=n, this is√
𝜋 𝑏𝑐√
𝑐𝑜𝑟𝑟𝑇 𝑒𝑡𝑟𝑎𝑐ℎ𝑜𝑟𝑖𝑐 = cos √𝑎𝑑 + 𝑏𝑐
New in version 0.4.0.
Initialize Tetrachoric instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
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corr(src, tar)
Return the Tetrachoric correlation coefficient of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tetrachoric correlation coefficient
Return type float
Examples>>> cmp = Tetrachoric()>>> cmp.corr('cat', 'hat')
0.9885309061036239>>> cmp.corr('Niall', 'Neil')
0.9678978997263907>>> cmp.corr('aluminum', 'Catalan')
0.7853000893691571>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Tetrachoric correlation coefficient of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tetrachoric correlation coefficient
Return type float
Examples>>> cmp = Tetrachoric()>>> cmp.sim('cat', 'hat')
0.994265453051812>>> cmp.sim('Niall', 'Neil')
0.9839489498631954>>> cmp.sim('aluminum', 'Catalan')
0.8926500446845785>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.TullossR(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tulloss' R similarity.
For two sets X and Y and a population N, Tulloss' R similarity [Tul97] is

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𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑅 (𝑋, 𝑌 ) =||𝑋∩𝑌 |
𝑙𝑜𝑔(1 + |𝑋∩𝑌
|𝑋| ) · 𝑙𝑜𝑔(1 + |𝑌 | )𝑙𝑜𝑔 2 (2)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑅 =𝑎𝑎) · 𝑙𝑜𝑔(1 + 𝑎+𝑐)𝑙𝑜𝑔(1 + 𝑎+𝑏𝑙𝑜𝑔 2 (2)New in version 0.4.0.
Initialize TullossR instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Tulloss' R similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tulloss' R similarity
Return type float
Examples>>> cmp = TullossR()>>> cmp.sim('cat', 'hat')
0.34218112724994865>>> cmp.sim('Niall', 'Neil')
0.2014703364316006>>> cmp.sim('aluminum', 'Catalan')
0.025829125872886074>>> cmp.sim('ATCG', 'TAGC')
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New in version 0.4.0.
class abydos.distance.TullossS(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tulloss' S similarity.
For two sets X and Y and a population N, Tulloss' S similarity [Tul97] is𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑆 (𝑋, 𝑌 ) = √︁1|,|𝑌 ∖𝑋|)
𝑙𝑜𝑔2 (2 + 𝑚𝑖𝑛(|𝑋∖𝑌)|𝑋∩𝑌 |+1

In 2x2 confusion table terms, where a+b+c+d=n, this is1
𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑆 = √︁
𝑙𝑜𝑔2 (2 + 𝑚𝑖𝑛(𝑏,𝑐)𝑎+1 )New in version 0.4.0.
Initialize TullossS instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Tulloss' S similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tulloss' S similarity
Return type float

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Examples>>> cmp = TullossS()>>> cmp.sim('cat', 'hat')
0.8406515643305636>>> cmp.sim('Niall', 'Neil')
0.7943108670863427>>> cmp.sim('aluminum', 'Catalan')
0.6376503816669968>>> cmp.sim('ATCG', 'TAGC')
0.5968309535438173

New in version 0.4.0.
class abydos.distance.TullossT(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tulloss' T similarity.
For two sets X and Y and a population N, Tulloss' T similarity [Tul97] is𝑠𝑖𝑚√︃𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑇 (𝑋, 𝑌 ) ==√︀𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑈 (𝑋, 𝑌 ) · 𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑆 (𝑋, 𝑌 ) · 𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑅 (𝑋, 𝑌 )𝑚𝑖𝑛(|𝑋∖𝑌 |,|𝑌 ∖𝑋|)+|𝑋∩𝑌 |√︁
𝑙𝑜𝑔2 (1 + 𝑚𝑎𝑥(|𝑋∖𝑌|,|𝑌 ∖𝑋|)+|𝑋∩𝑌 | ) ·1
𝑙𝑜𝑔2 (2+𝑚𝑖𝑛(|𝑋∖𝑌 |,|𝑌 ∖𝑋|))
|𝑋∩𝑌 |+1·𝑙𝑜𝑔(1+|𝑋∩𝑌 ||𝑋∩𝑌 |)·𝑙𝑜𝑔(1+ |𝑌 | )|𝑋|
2
𝑙𝑜𝑔 (2)In 2x2 confusion table terms, where a+b+c+d=n, this is

⎯
⎸
𝑎
𝑎
(︁
𝑙𝑜𝑔(1 + 𝑎+𝑏) · 𝑙𝑜𝑔(1 + 𝑎+𝑐)
1
𝑚𝑖𝑛(𝑏, 𝑐) + 𝑎 )︁⎸· √︁·𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑇 = ⎷𝑙𝑜𝑔2 1 +2𝑚𝑎𝑥(𝑏, 𝑐) + 𝑎𝑙𝑜𝑔 (2)𝑙𝑜𝑔 (2 + 𝑚𝑖𝑛(𝑏,𝑐) )2

𝑎+1

New in version 0.4.0.
Initialize TullossT instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.

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sim(src, tar)
Return Tulloss' T similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tulloss' T similarity
Return type float
Examples>>> cmp = TullossT()>>> cmp.sim('cat', 'hat')
0.5363348766461724>>> cmp.sim('Niall', 'Neil')
0.3740873705689327>>> cmp.sim('aluminum', 'Catalan')
0.1229300783095269>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.TullossU(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Tulloss' U similarity.
For two sets X and Y, Tulloss' U similarity [Tul97] is(︁𝑚𝑖𝑛(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|) + |𝑋 ∩ 𝑌 | )︁𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑈 (𝑋, 𝑌 ) = 𝑙𝑜𝑔2 1 +𝑚𝑎𝑥(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|) + |𝑋 ∩ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is(︁𝑚𝑖𝑛(𝑏, 𝑐) + 𝑎 )︁𝑠𝑖𝑚𝑇 𝑢𝑙𝑙𝑜𝑠𝑠𝑈 = 𝑙𝑜𝑔2 1 +𝑚𝑎𝑥(𝑏, 𝑐) + 𝑎
New in version 0.4.0.
Initialize TullossU instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters3.1. abydos package

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Tulloss' U similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tulloss' U similarity
Return type float
Examples>>> cmp = TullossU()>>> cmp.sim('cat', 'hat')
1.0>>> cmp.sim('Niall', 'Neil')
0.8744691179161412>>> cmp.sim('aluminum', 'Catalan')
0.917537839808027>>> cmp.sim('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.Tversky(alpha=1.0, beta=1.0, bias=None, tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceTversky index.
The Tversky index [Tve77] is defined as: For two sets X and Y:

𝑠𝑖𝑚𝑇 𝑣𝑒𝑟𝑠𝑘𝑦 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 ||𝑋 ∩ 𝑌 | + 𝛼|𝑋 − 𝑌 | + 𝛽|𝑌 − 𝑋|

𝛼 = 𝛽 = 1 is equivalent to the Jaccard & Tanimoto similarity coefficients.
𝛼 = 𝛽 = 0.5 is equivalent to the Sørensen-Dice similarity coefficient [Dic45][Sorensen48].
Unequal and will tend to emphasize one or the other set's contributions:• 𝛼 > 𝛽 emphasizes the contributions of X over Y• 𝛼 < 𝛽 emphasizes the contributions of Y over X)
Parameter values' relation to 1 emphasizes different types of contributions:• 𝛼 and 𝛽 > 1 emphsize unique contributions over the intersection
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Abydos Documentation, Release 0.5.0• 𝛼 and 𝛽 < 1 emphsize the intersection over unique contributions
The symmetric variant is defined in [JBG13]. This is activated by specifying a bias parameter.
New in version 0.3.6.
Initialize Tversky instance.
Parameters• alpha (float) -- Tversky index parameter as described above• beta (float) -- Tversky index parameter as described above• bias (float) -- The symmetric Tversky index bias parameter• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Tversky index of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Tversky similarity
Return type float
Raises ValueError -- Unsupported weight assignment; alpha and beta must be greater thanor equal to 0.
Examples>>> cmp = Tversky()>>> cmp.sim('cat', 'hat')
0.3333333333333333>>> cmp.sim('Niall', 'Neil')
0.2222222222222222>>> cmp.sim('aluminum', 'Catalan')
0.0625>>> cmp.sim('ATCG', 'TAGC')
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New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_tversky(src, tar, qval=2, alpha=1.0, beta=1.0, bias=None)
Return the Tversky distance between two strings.
This is a wrapper for Tversky.dist().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alpha (float) -- Tversky index parameter as described above• beta (float) -- Tversky index parameter as described above• bias (float) -- The symmetric Tversky index bias parameter
Returns Tversky distance
Return type float
Examples>>> dist_tversky('cat', 'hat')
0.6666666666666667>>> dist_tversky('Niall', 'Neil')
0.7777777777777778>>> dist_tversky('aluminum', 'Catalan')
0.9375>>> dist_tversky('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Tversky.dist method instead.
abydos.distance.sim_tversky(src, tar, qval=2, alpha=1.0, beta=1.0, bias=None)
Return the Tversky index of two strings.
This is a wrapper for Tversky.sim().
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• qval (int) -- The length of each q-gram• alpha (float) -- Tversky index parameter as described above• beta (float) -- Tversky index parameter as described above• bias (float) -- The symmetric Tversky index bias parameter
Returns Tversky similarity
Return type float

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Examples>>> sim_tversky('cat', 'hat')
0.3333333333333333>>> sim_tversky('Niall', 'Neil')
0.2222222222222222>>> sim_tversky('aluminum', 'Catalan')
0.0625>>> sim_tversky('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Tversky.sim method instead.
class abydos.distance.UnigramSubtuple(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Unigram subtuple similarity.
For two sets X and Y and a population N, unigram subtuple similarity [Pec10] is|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |) − 3.29 ·𝑠𝑖𝑚𝑢𝑛𝑖𝑔𝑟𝑎𝑚 𝑠𝑢𝑏𝑡𝑢𝑝𝑙𝑒 (𝑋, 𝑌 ) = 𝑙𝑜𝑔(
|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑌 |

√︃

1
1
1
1
+
+
+
|𝑋 ∩ 𝑌 | |𝑋 ∖ 𝑌 | |𝑌 ∖ 𝑋| |(𝑁 ∖ 𝑋) ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑎𝑑𝑠𝑖𝑚𝑢𝑛𝑖𝑔𝑟𝑎𝑚 𝑠𝑢𝑏𝑡𝑢𝑝𝑙𝑒 = 𝑙𝑜𝑔( ) − 3.29 ·𝑏𝑐√︂

1 1 1 1
+ + +
𝑎 𝑏
𝑐 𝑑

New in version 0.4.0.
Initialize UnigramSubtuple instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.

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New in version 0.4.0.
sim(src, tar)
Return the unigram subtuple similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unigram subtuple similarity
Return type float
Examples>>> cmp = UnigramSubtuple()>>> cmp.sim('cat', 'hat')
0.6215275850074894>>> cmp.sim('Niall', 'Neil')
0.39805896767519555>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the unigram subtuple similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unigram subtuple similarity
Return type float
Examples>>> cmp = UnigramSubtuple()>>> cmp.sim_score('cat', 'hat')
1.9324426894059226>>> cmp.sim_score('Niall', 'Neil')
1.4347242883606355>>> cmp.sim_score('aluminum', 'Catalan')-1.0866724701675263>>> cmp.sim_score('ATCG', 'TAGC')-0.461880260111438

New in version 0.4.0.
class abydos.distance.UnknownA(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown A correlation.
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For two sets X and Y and a population N, Unknown A correlation is sometimes attributed to [Pei84]. It differsfrom Peirce in that the numerator is the product of the opposite pair of marginals:

𝑐𝑜𝑟𝑟𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐴 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑌 ||𝑌 | · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐴 =𝑎𝑑 − 𝑏𝑐(𝑎 + 𝑐)(𝑏 + 𝑑)

New in version 0.4.0.
Initialize UnknownA instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Unknown A correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown A correlation
Return type float

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Examples>>> cmp = UnknownA()>>> cmp.corr('cat', 'hat')
0.49743589743589745>>> cmp.corr('Niall', 'Neil')
0.39486521181001283>>> cmp.corr('aluminum', 'Catalan')
0.1147039897039897>>> cmp.corr('ATCG', 'TAGC')-0.006418485237483954

New in version 0.4.0.
sim(src, tar)
Return the Unknown A similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown A similarity
Return type float
Examples>>> cmp = UnknownA()>>> cmp.sim('cat', 'hat')
0.7487179487179487>>> cmp.sim('Niall', 'Neil')
0.6974326059050064>>> cmp.sim('aluminum', 'Catalan')
0.5573519948519948>>> cmp.sim('ATCG', 'TAGC')
0.496790757381258

New in version 0.4.0.
class abydos.distance.UnknownB(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown B similarity.
For two sets X and Y and a population N, Unknown B similarity, which [Mor12] attributes to [Doo84] but could
not be located in that source, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐵 (𝑋, 𝑌 ) =(|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|)2
|𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐵 =334(𝑎𝑑 − 𝑏𝑐)2
(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)
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New in version 0.4.0.
Initialize UnknownB instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Unknown B similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown B similarity
Return type float
Examples>>> cmp = UnknownB()>>> cmp.sim('cat', 'hat')
0.24744247205785666>>> cmp.sim('Niall', 'Neil')
0.13009912077202224>>> cmp.sim('aluminum', 'Catalan')
0.011710186806836291>>> cmp.sim('ATCG', 'TAGC')
4.1196952743799446e-05

New in version 0.4.0.
class abydos.distance.UnknownC(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown C similarity.

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For two sets X and Y and a population N, Unknown C similarity, which [Mor12] attributes to [Gow71] but could
not be located in that source, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐶 (𝑋, 𝑌 ) = √︀|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 ||𝑋| · |𝑌 | · |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐶 = √︀𝑎+𝑑(𝑎 + 𝑏)(𝑎 + 𝑐)(𝑏 + 𝑑)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize UnknownC instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Unknown C similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown C similarity
Return type float

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Examples>>> cmp = UnknownC()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.18222244271345164>>> cmp.sim('aluminum', 'Catalan')
0.11686463498390019>>> cmp.sim('ATCG', 'TAGC')
0.1987163029525032

New in version 0.4.0.
class abydos.distance.UnknownD(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown D similarity.
For two sets X and Y and a population N, Unknown D similarity, which [Mor12] attributes to [Pei84] but could
not be located in that source, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐷 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |𝑋 ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | · |𝑋 ∖ 𝑌 | + 2 · |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋| + |𝑌 ∖ 𝑋| + |(𝑁 ∖ 𝑋) ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐷 =𝑎𝑏 + 𝑏𝑐
𝑎𝑏 + 2𝑏𝑐 + 𝑐𝑑

New in version 0.4.0.
Initialize UnknownD instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.

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New in version 0.4.0.
sim(src, tar)
Return the Unknown D similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown D similarity
Return type float
Examples>>> cmp = UnknownD()>>> cmp.sim('cat', 'hat')
0.00510204081632653>>> cmp.sim('Niall', 'Neil')
0.00848536274925753>>> cmp.sim('aluminum', 'Catalan')
0.011630019989096857>>> cmp.sim('ATCG', 'TAGC')
0.006377551020408163

New in version 0.4.0.
class abydos.distance.UnknownE(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown E correlation.
For two sets X and Y and a population N, Unknown E correlation, which [Mor12] attributes to [GK54] but could
not be located in that source, is𝑐𝑜𝑟𝑟𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐸 (𝑋, 𝑌 ) =2 · 𝑚𝑖𝑛(|𝑋 ∩ 𝑌 |, |(𝑁 ∖ 𝑋) ∖ 𝑌 |) − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
2 · 𝑚𝑖𝑛(|𝑋 ∩ 𝑌 |, |(𝑁 ∖ 𝑋) ∖ 𝑌 |) + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑐𝑜𝑟𝑟𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐸 =2 · 𝑚𝑖𝑛(𝑎, 𝑑) − 𝑏 − 𝑐
2 · 𝑚𝑖𝑛(𝑎, 𝑑) + 𝑏 + 𝑐

New in version 0.4.0.
Initialize UnknownE instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Unknown E correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown E correlation
Return type float
Examples>>> cmp = UnknownE()>>> cmp.corr('cat', 'hat')
0.0>>> cmp.corr('Niall', 'Neil')-0.2727272727272727>>> cmp.corr('aluminum', 'Catalan')-0.7647058823529411>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Unknown E similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown E similarity
Return type float

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Examples>>> cmp = UnknownE()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352944>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.UnknownF(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown F similarity.
For two sets X and Y and a population N, Unknown F similarity, which [CCT10] attributes to [GW66] but could
not be located in that source, is given as𝑠𝑖𝑚(𝑋, 𝑌 ) = 𝑙𝑜𝑔(|𝑋 ∩ 𝑌 |) − 𝑙𝑜𝑔(|𝑁 |) − 𝑙𝑜𝑔(︁ |𝑋| )︁
|𝑁 |

− 𝑙𝑜𝑔

(︁ |𝑌 | )︁|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚 = 𝑙𝑜𝑔(𝑎) − 𝑙𝑜𝑔(𝑛) − 𝑙𝑜𝑔(︁ 𝑎 + 𝑏 )︁
𝑛

− 𝑙𝑜𝑔

(︁ 𝑎 + 𝑐 )︁𝑛This formula is not very normalizable, so the following formula is used instead:(︃𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐹 (𝑋, 𝑌 ) = 𝑚𝑖𝑛 1, 1 + 𝑙𝑜𝑔(︁ |𝑋 ∩ 𝑌 | )︁
|𝑁 |

(︃
)︃)︃
(︁ |𝑋| )︁
(︁ |𝑌 | )︁
1
𝑙𝑜𝑔
−
+ 𝑙𝑜𝑔
2
|𝑁 |
|𝑁 |

In 2x2 confusion table terms, where a+b+c+d=n, this is(︃)︃)︃
(︁ 𝑎 + 𝑏 )︁(︁ 𝑎 + 𝑐 )︁1
𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐹 = 𝑚𝑖𝑛 1, 1 + 𝑙𝑜𝑔
−
𝑙𝑜𝑔
+ 𝑙𝑜𝑔
𝑛
2
𝑛
𝑛
(︃

(︁ 𝑎 )︁

New in version 0.4.0.
Initialize UnknownF instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package
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Abydos Documentation, Release 0.5.0• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Unknown F similarity
New in version 0.4.0.
sim(*args, **kwargs)
Raise exception when called.
Parameters• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Raises NotImplementedError -- Method disabled for Unknown F similarity
New in version 0.4.0.
sim_score(src, tar)
Return the Unknown F similarity between two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown F similarity
Return type float

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Examples>>> cmp = UnknownF()>>> cmp.sim_score('cat', 'hat')
0.3068528194400555>>> cmp.sim_score('Niall', 'Neil')-0.007451510271132555>>> cmp.sim_score('aluminum', 'Catalan')-1.1383330595080272>>> cmp.sim_score('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.UnknownG(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceUnknown G similarity.
For two sets X and Y and a population N, Unknown G similarity, which [CCT10] attributes to [Kulczynski27]
but could not be located in that source, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐺 (𝑋, 𝑌 ) =|𝑋∩𝑌 |· (|𝑋| + |𝑌 |)2|𝑋| · |𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑎· (2𝑎 + 𝑏 + 𝑐)
𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐺 = 2(𝑎 + 𝑏)(𝑎 + 𝑐)

New in version 0.4.0.
Initialize UnknownG instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Unknown G similarity of two strings.
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Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown G similarity
Return type float
Examples>>> cmp = UnknownG()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36666666666666664>>> cmp.sim('aluminum', 'Catalan')
0.11805555555555555>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.UnknownH(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown H similarity.
For two sets X and Y and a population N, Unknown H similarity is a variant of Fager-McGowan index ofaffinity [Fag57][FM63]. It uses minimum rather than maximum in the denominator of the second term, and issometimes misidentified as the Fager-McGown index of affinity (cf. [Whi82], for example).

|𝑋 ∩ 𝑌 |1
𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐻 (𝑋, 𝑌 ) = √︀− √︀
|𝑋| · |𝑌 | 2 𝑚𝑖𝑛(|𝑋|, |𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐻 = √︀

𝑎

1
− √︀
(𝑎 + 𝑏)(𝑎 + 𝑐) 2 𝑚𝑖𝑛(𝑎 + 𝑏, 𝑎 + 𝑐)

New in version 0.4.0.
Initialize UnknownH instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Unknown H similarity of two strings.
As this similarity ranges from (− inf, 1.0), this normalization simply clamps the value to the range (0.0,
1.0).
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Unknown H similarity
Return type float
Examples>>> cmp = UnknownH()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.14154157392013175>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Unknown H similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown H similarity
Return type float

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Examples>>> cmp = UnknownH()>>> cmp.sim('cat', 'hat')
0.25>>> cmp.sim('Niall', 'Neil')
0.14154157392013175>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.UnknownI(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceUnknown I similarity.
For two sets X and Y, the Unknown I similarity is based on Mountford similarity [Mou62] Mountford.

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐼 (𝑋, 𝑌 ) =2(|𝑋 ∩ 𝑌 | + 1)
2((|𝑋| + 2) · (|𝑌 | + 2)) − (|𝑋| + |𝑌 | + 4) · (|𝑋 ∩ 𝑌 | + 1)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐼 =2(𝑎 + 1)2(𝑎 + 𝑏 + 2)(𝑎 + 𝑐 + 2) − (2𝑎 + 𝑏 + 𝑐 + 4)(𝑎 + 1)

New in version 0.4.0.
Initialize UnknownI instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Unknown I similarity of two strings.
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Returns Unknown I similarity
Return type float
Examples>>> cmp = UnknownI()>>> cmp.sim('cat', 'hat')
0.16666666666666666>>> cmp.sim('Niall', 'Neil')
0.08955223880597014>>> cmp.sim('aluminum', 'Catalan')
0.02247191011235955>>> cmp.sim('ATCG', 'TAGC')
0.023809523809523808

New in version 0.4.0.
class abydos.distance.UnknownJ(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown J similarity.
For two sets X and Y and a population N, Unknown J similarity, which [Seq18] attributes to "Kocher & Wang"
but could not be located, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐽 (𝑋, 𝑌 ) = |𝑋 ∩ 𝑌 | ·|𝑁 ||𝑋| · |𝑁 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐽 = 𝑎 ·𝑛(𝑎 + 𝑏)(𝑐 + 𝑑)

New in version 0.4.0.
Initialize UnknownJ instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters

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Abydos Documentation, Release 0.5.0• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Unknown J similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Unknown J similarity
Return type float
Examples>>> cmp = UnknownJ()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.33333333333333337>>> cmp.sim('aluminum', 'Catalan')
0.11111111111111112>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Unknown J similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown J similarity
Return type float

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Examples>>> cmp = UnknownJ()>>> cmp.sim_score('cat', 'hat')
0.5025641025641026>>> cmp.sim_score('Niall', 'Neil')
0.33590402742073694>>> cmp.sim_score('aluminum', 'Catalan')
0.11239977090492555>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.UnknownK(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown K distance.
For two sets X and Y and a population N, Unknown K distance, which [Seq18] attributes to "Excoffier" butcould not be located, is

𝑑𝑖𝑠𝑡𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐾 (𝑋, 𝑌 ) = |𝑁 | · (1 −|𝑋 ∩ 𝑌 |)|𝑁 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑑𝑖𝑠𝑡𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐾 = 𝑛 · (1 −𝑎)𝑛

New in version 0.4.0.
Initialize UnknownK instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
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dist(src, tar)
Return the normalized Unknown K distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Unknown K distance
Return type float
Examples>>> cmp = UnknownK()>>> cmp.dist('cat', 'hat')
0.9974489795918368>>> cmp.dist('Niall', 'Neil')
0.9974489795918368>>> cmp.dist('aluminum', 'Catalan')
0.9987261146496815>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Unknown K distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown K distance
Return type float
Examples>>> cmp = UnknownK()>>> cmp.dist_abs('cat', 'hat')
782.0>>> cmp.dist_abs('Niall', 'Neil')
782.0>>> cmp.dist_abs('aluminum', 'Catalan')
784.0>>> cmp.dist_abs('ATCG', 'TAGC')
784.0

New in version 0.4.0.
class abydos.distance.UnknownL(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown L similarity.

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For two sets X and Y and a population N, Unknown L similarity, which [Seq18] attributes to "Roux" but could
not be located, is𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐿 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |𝑚𝑖𝑛(|𝑋 ∖ 𝑌 |, |𝑌 ∖ 𝑋|) + 𝑚𝑖𝑛(|𝑁 | − |𝑋 ∖ 𝑌 |, |𝑁 | − |𝑌 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝐿 =𝑎+𝑑𝑚𝑖𝑛(𝑏, 𝑐) + 𝑚𝑖𝑛(𝑛 − 𝑏, 𝑛 − 𝑐)

New in version 0.4.0.
Initialize UnknownL instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Unknown L similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown L similarity
Return type float

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Examples>>> cmp = UnknownL()>>> cmp.sim('cat', 'hat')
0.9948979591836735>>> cmp.sim('Niall', 'Neil')
0.9923371647509579>>> cmp.sim('aluminum', 'Catalan')
0.9821428571428571>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837

New in version 0.4.0.
class abydos.distance.UnknownM(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Unknown M similarity.
For two sets X and Y and a population N, Unknown < similarity, which [Seq18] attributes to "Roux" but could
not be located, is|𝑁 | − |𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝑀 (𝑋, 𝑌 ) = √︀
|𝑋| · |𝑁 ∖ 𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is𝑛 − 𝑎𝑑𝑠𝑖𝑚𝑈 𝑛𝑘𝑛𝑜𝑤𝑛𝑀 = √︀(𝑎 + 𝑏)(𝑐 + 𝑑)(𝑎 + 𝑐)(𝑏 + 𝑑)
New in version 0.4.0.
Initialize UnknownM instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.

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New in version 0.4.0.
sim(src, tar)
Return the normalized Unknown M similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Unknown M similarity
Return type float
Examples>>> cmp = UnknownM()>>> cmp.sim('cat', 'hat')
0.6237179487179487>>> cmp.sim('Niall', 'Neil')
0.5898213585061158>>> cmp.sim('aluminum', 'Catalan')0.49878582197419324>>> cmp.sim('ATCG', 'TAGC')
0.3993581514762516

New in version 0.4.0.
sim_score(src, tar)
Return the Unknown M similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Unknown M similarity
Return type float
Examples>>> cmp = UnknownM()>>> cmp.sim_score('cat', 'hat')-0.24743589743589745>>> cmp.sim_score('Niall', 'Neil')-0.17964271701223158>>> cmp.sim_score('aluminum', 'Catalan')
0.0024283560516135103>>> cmp.sim_score('ATCG', 'TAGC')
0.2012836970474968

New in version 0.4.0.
class abydos.distance.Upholt(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Upholt similarity.
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For two sets X and Y and a population N, Upholt similarity, Upholt's S, [Uph77] is

1
𝑠𝑖𝑚𝑈 𝑝ℎ𝑜𝑙𝑡 (𝑋, 𝑌 ) =2(︃

2 · |𝑋 ∩ 𝑌 |−+|𝑋| + |𝑌 |√︃(︁ 2 · |𝑋 ∩ 𝑌 | )︁2|𝑋| + |𝑌 |

2 · |𝑋 ∩ 𝑌 |+8|𝑋| + |𝑌 |)︃In 2x2 confusion table terms, where a+b+c+d=n, this is1
𝑠𝑖𝑚𝑈 𝑝ℎ𝑜𝑙𝑡 (𝑋, 𝑌 ) =
2

(︃

2𝑎
−
+
2𝑎 + 𝑏 + 𝑐

√︂(︁

)︁2
2𝑎
2𝑎
+8
2𝑎 + 𝑏 + 𝑐
2𝑎 + 𝑏 + 𝑐)︃New in version 0.4.0.
Initialize Upholt instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Upholt similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Upholt similarity
Return type float

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Examples>>> cmp = Upholt()>>> cmp.sim('cat', 'hat')
0.7807764064044151>>> cmp.sim('Niall', 'Neil')
0.6901511860568581>>> cmp.sim('aluminum', 'Catalan')
0.42980140370106323>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.WarrensI(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceWarrens I correlation.
For two sets X and Y, Warrens I correlation 𝑆𝑁 𝑆1 [War08] is

𝑐𝑜𝑟𝑟𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼 (𝑋, 𝑌 ) =2|𝑋 ∩ 𝑌 | − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
2|𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼 =2𝑎 − 𝑏 − 𝑐
2𝑎 + 𝑏 + 𝑐

New in version 0.4.0.
Initialize WarrensI instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Warrens I correlation of two strings.
Parameters
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Returns Warrens I correlation
Return type float
Examples>>> cmp = WarrensI()>>> cmp.corr('cat', 'hat')
0.0>>> cmp.corr('Niall', 'Neil')-0.2727272727272727>>> cmp.corr('aluminum', 'Catalan')-0.7647058823529411>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return the Warrens I similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens I similarity
Return type float
Examples>>> cmp = WarrensI()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352944>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.WarrensII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceWarrens II similarity.
For two sets X and Y and a population N, Warrens II similarity 𝑆𝑁 𝑆2 [War08] is

𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝐼 (𝑋, 𝑌 ) =3.1. abydos package

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In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝐼 =2𝑑
𝑏 + 𝑐 + 2𝑑

New in version 0.4.0.
Initialize WarrensII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Warrens II similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens II similarity
Return type float
Examples>>> cmp = WarrensII()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9955041746949261>>> cmp.sim('aluminum', 'Catalan')
0.9903412749517064>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.

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class abydos.distance.WarrensIII(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceWarrens III correlation.
For two sets X and Y and a population N, Warrens III correlation 𝑆𝑁 𝑆3 [War08] is

𝑐𝑜𝑟𝑟𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝐼𝐼 (𝑋, 𝑌 ) =2|(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | − |𝑌 ∖ 𝑋|
|𝑁 ∖ 𝑋| + |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑐𝑜𝑟𝑟𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝐼𝐼 =2𝑑 − 𝑏 − 𝑐
2𝑑 + 𝑏 + 𝑐

New in version 0.4.0.
Initialize WarrensIII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return the Warrens III correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens III correlation
Return type float

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Examples>>> cmp = WarrensIII()>>> cmp.corr('cat', 'hat')
0.9948717948717949>>> cmp.corr('Niall', 'Neil')
0.9910083493898523>>> cmp.corr('aluminum', 'Catalan')
0.9806825499034127>>> cmp.corr('ATCG', 'TAGC')
0.9871630295250321

New in version 0.4.0.
sim(src, tar)
Return the Warrens III similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens III similarity
Return type float
Examples>>> cmp = WarrensIII()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9955041746949261>>> cmp.sim('aluminum', 'Catalan')
0.9903412749517064>>> cmp.sim('ATCG', 'TAGC')
0.993581514762516

New in version 0.4.0.
class abydos.distance.WarrensIV(alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceWarrens IV similarity.
For two sets X and Y and a population N, Warrens IV similarity [War08] is

𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝑉 (𝑋, 𝑌 ) =4|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 |4|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + (|𝑋 ∩ 𝑌 | + |(𝑁 ∖ 𝑋) ∖ 𝑌 |)(|𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|)

In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝐼𝑉 =

358

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New in version 0.4.0.
Initialize WarrensIV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Warrens IV similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens IV similarity
Return type float
Examples>>> cmp = WarrensIV()>>> cmp.sim('cat', 'hat')
0.666095890410959>>> cmp.sim('Niall', 'Neil')
0.5326918120113412>>> cmp.sim('aluminum', 'Catalan')
0.21031040612607685>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.WarrensV(alphabet=None, tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Warrens V similarity.
For two sets X and Y and a population N, Warrens V similarity [War08] is
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𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝑉 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
𝑚𝑖𝑛(|𝑋| · |𝑌 |, |𝑁 ∖ 𝑋| · |𝑁 ∖ 𝑌 |)In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑊 𝑎𝑟𝑟𝑒𝑛𝑠𝑉 =𝑎𝑑 − 𝑏𝑐𝑚𝑖𝑛((𝑎 + 𝑏)(𝑎 + 𝑐), (𝑏 + 𝑑)(𝑐 + 𝑑))

New in version 0.4.0.
Initialize WarrensV instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Warrens V similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Warrens V similarity
Return type float

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Examples>>> cmp = WarrensV()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3333333333333333>>> cmp.sim('aluminum', 'Catalan')
0.11125283446712018>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Warrens V similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Warrens V similarity
Return type float
Examples>>> cmp = WarrensV()>>> cmp.sim_score('cat', 'hat')
97.0>>> cmp.sim_score('Niall', 'Neil')
51.266666666666666>>> cmp.sim_score('aluminum', 'Catalan')
9.902777777777779>>> cmp.sim_score('ATCG', 'TAGC')-1.0

New in version 0.4.0.
class abydos.distance.WeightedJaccard(tokenizer=None, intersection_type='crisp', weight=3,**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Weighted Jaccard similarity.
For two sets X and Y and a weight w, the Weighted Jaccard similarity [LL98] is

𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑤 (𝑋, 𝑌 ) =𝑤 · |𝑋 ∩ 𝑌 |𝑤 · |𝑋 ∩ 𝑌 | + |𝑋 ∖ 𝑌 | + |𝑌 ∖ 𝑋|

Here, the intersection between the two sets is weighted by w. Compare to Jaccard similarity (𝑤 = 1), and to
Dice similarity (𝑤 = 2). In the default case, the weight of the intersection is 3, following [LL98].
In 2x2 confusion table terms, where a+b+c+d=n, this is3.1. abydos package

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𝑠𝑖𝑚𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑤 =𝑤·𝑎
𝑤·𝑎+𝑏+𝑐

New in version 0.4.0.
Initialize TripleWeightedJaccard instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• weight (int) -- The weight to apply to the intersection cardinality. (3, by default.)• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the Triple Weighted Jaccard similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Weighted Jaccard similarity
Return type float
Examples>>> cmp = WeightedJaccard()>>> cmp.sim('cat', 'hat')
0.6>>> cmp.sim('Niall', 'Neil')
0.46153846153846156>>> cmp.sim('aluminum', 'Catalan')
0.16666666666666666>>> cmp.sim('ATCG', 'TAGC')
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class abydos.distance.Whittaker(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Whittaker distance.
For two multisets X and Y drawn from an alphabet S, Whittaker distance [Whi52] is⃒⃒
1 ∑︁ ⃒⃒ |𝑋𝑖 | |𝑌𝑖 | ⃒⃒𝑠𝑖𝑚𝑊 ℎ𝑖𝑡𝑡𝑎𝑘𝑒𝑟 (𝑋, 𝑌 ) = 1 −
−
⃒
⃒
⃒ |𝑋|
2
|𝑌 | ⃒𝑖∈𝑆

New in version 0.4.0.
Initialize Whittaker instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Whittaker distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Whittaker distance
Return type float
Examples>>> cmp = Whittaker()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.33333333333333>>> cmp.sim('aluminum', 'Catalan')
0.11111111111111>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.YatesChiSquared(alphabet=None,
tokenizer=None,tion_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersec-

Yates's Chi-Squared similarity.
For two sets X and Y and a population N, Yates's 𝜒2 similarity [Yat34] is

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𝑠𝑖𝑚𝑌 𝑎𝑡𝑒𝑠𝜒2 (𝑋, 𝑌 ) =|𝑁 | · (||𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|| − |𝑁2 | )2
|𝑋| · |𝑁 ∖ 𝑋| · |𝑌 | · |𝑁 ∖ 𝑌 |In 2x2 confusion table terms, where a+b+c+d=n, this is

𝑠𝑖𝑚𝑌 𝑎𝑡𝑒𝑠𝜒2 =𝑛 · (|𝑎𝑑 − 𝑏𝑐| − 𝑛2 )2(𝑎 + 𝑏)(𝑐 + 𝑑)(𝑎 + 𝑐)(𝑏 + 𝑑)

New in version 0.4.0.
Initialize YatesChiSquared instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return Yates's normalized Chi-Squared similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Yates's Chi-Squared similarity
Return type float

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Examples>>> cmp = YatesChiSquared()>>> cmp.sim('cat', 'hat')
0.18081199852082455>>> cmp.sim('Niall', 'Neil')
0.08608296705052738>>> cmp.sim('aluminum', 'Catalan')
0.0026563223707532654>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar, signed=False)
Return Yates's Chi-Squared similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison• signed (bool) -- If True, negative correlations will carry a negative sign
Returns Yates's Chi-Squared similarity
Return type float
Examples>>> cmp = YatesChiSquared()>>> cmp.sim_score('cat', 'hat')
108.37343852728468>>> cmp.sim_score('Niall', 'Neil')
56.630055670871954>>> cmp.sim_score('aluminum', 'Catalan')
1.8574215841854373>>> cmp.sim_score('ATCG', 'TAGC')
6.960385076156687

New in version 0.4.0.
class abydos.distance.YuleQ(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Yule's Q correlation.
For two sets X and Y and a population N, Yule's Q correlation [Yul12] is𝑐𝑜𝑟𝑟𝑌 𝑢𝑙𝑒𝑄 (𝑋, 𝑌 ) =|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | − |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|

Yule himself terms this the coefficient of association.
In 2x2 confusion table terms, where a+b+c+d=n, this is3.1. abydos package365Abydos Documentation, Release 0.5.0

𝑐𝑜𝑟𝑟𝑌 𝑢𝑙𝑒𝑄 =𝑎𝑑 − 𝑏𝑐𝑎𝑑 + 𝑏𝑐

New in version 0.4.0.
Initialize YuleQ instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return Yule's Q correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Yule's Q correlation
Return type float
Examples>>> cmp = YuleQ()>>> cmp.corr('cat', 'hat')
0.9948717948717949>>> cmp.corr('Niall', 'Neil')
0.9846350832266325>>> cmp.corr('aluminum', 'Catalan')
0.8642424242424243>>> cmp.corr('ATCG', 'TAGC')-1.0

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sim(src, tar)
Return Yule's Q similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Yule's Q similarity
Return type float
Examples>>> cmp = YuleQ()>>> cmp.sim('cat', 'hat')
0.9974358974358974>>> cmp.sim('Niall', 'Neil')
0.9923175416133163>>> cmp.sim('aluminum', 'Catalan')
0.9321212121212121>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.YuleQII(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Yule's Q dissimilarity.
For two sets X and Y and a population N, Yule's Q dissimilarity [YK68] is

𝑑𝑖𝑠𝑡𝑌 𝑢𝑙𝑒𝑄 𝐼𝐼 (𝑋, 𝑌 ) =2 · |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|
|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | + |𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|

In 2x2 confusion table terms, where a+b+c+d=n, this is𝑑𝑖𝑠𝑡𝑌 𝑢𝑙𝑒𝑄 𝐼𝐼 =2𝑏𝑐
𝑎𝑑 + 𝑏𝑐

New in version 0.4.0.
Initialize YuleQII instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
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Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return normalized Yule's Q dissimilarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Yule's Q II distance
Return type float
Examples>>> cmp = YuleQII()>>> cmp.dist('cat', 'hat')
0.002564102564102564>>> cmp.dist('Niall', 'Neil')
0.0076824583866837385>>> cmp.dist('aluminum', 'Catalan')
0.06787878787878789>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return Yule's Q dissimilarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Yule's Q II distance
Return type float

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Examples>>> cmp = YuleQII()>>> cmp.dist_abs('cat', 'hat')
0.005128205128205128>>> cmp.dist_abs('Niall', 'Neil')
0.015364916773367477>>> cmp.dist_abs('aluminum', 'Catalan')
0.13575757575757577>>> cmp.dist_abs('ATCG', 'TAGC')
2.0

New in version 0.4.0.
class abydos.distance.YuleY(alphabet=None,
tokenizer=None,**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceintersection_type='crisp',

Yule's Y correlation.
For two sets X and Y and a population N, Yule's Y correlation [Yul12] is√︀𝑐𝑜𝑟𝑟𝑌 𝑢𝑙𝑒𝑌 (𝑋, 𝑌 ) = √︀|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | −√︀|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|

|𝑋 ∩ 𝑌 | · |(𝑁 ∖ 𝑋) ∖ 𝑌 | +√︀|𝑋 ∖ 𝑌 | · |𝑌 ∖ 𝑋|

In [Yul12], this is labeled 𝜔, so it is sometimes referred to as Yule's 𝜔. Yule himself terms this the coefficient ofcolligation.
In 2x2 confusion table terms, where a+b+c+d=n, this is√√𝑎𝑑 − 𝑏𝑐√
𝑐𝑜𝑟𝑟𝑌 𝑢𝑙𝑒𝑌 = √𝑎𝑑 + 𝑏𝑐
New in version 0.4.0.
Initialize YuleY instance.
Parameters• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.

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the intersection for the fuzzy variant.
New in version 0.4.0.
corr(src, tar)
Return Yule's Y correlation of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Yule's Y correlation
Return type float
Examples>>> cmp = YuleY()>>> cmp.corr('cat', 'hat')
0.9034892632818762>>> cmp.corr('Niall', 'Neil')
0.8382551144735259>>> cmp.corr('aluminum', 'Catalan')
0.5749826820237787>>> cmp.corr('ATCG', 'TAGC')-1.0

New in version 0.4.0.
sim(src, tar)
Return Yule's Y similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Yule's Y similarity
Return type float
Examples>>> cmp = YuleY()>>> cmp.sim('cat', 'hat')
0.9517446316409381>>> cmp.sim('Niall', 'Neil')
0.919127557236763>>> cmp.sim('aluminum', 'Catalan')
0.7874913410118893>>> cmp.sim('ATCG', 'TAGC')
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class abydos.distance.YJHHR(pval=1, alphabet=None, tokenizer=None, intersection_type='crisp',**kwargs)
Bases: abydos.distance._token_distance._TokenDistanceYJHHR distance.
For two sets X and Y and a parameter p, YJHHR distance [YJH+16] is√︀𝑑𝑖𝑠𝑡𝑌 𝐽𝐻𝐻𝑅𝑝 (𝑋, 𝑌 ) = 𝑝 |𝑋 ∖ 𝑌 |𝑝 + |𝑌 ∖ 𝑋|𝑝
In 2x2 confusion table terms, where a+b+c+d=n, this is𝑑𝑖𝑠𝑡𝑌 𝐽𝐻𝐻𝑅 =√𝑝

𝑏𝑝 + 𝑐 𝑝

New in version 0.4.0.
Initialize YJHHR instance.
Parameters• pval (int) -- The 𝑝-value of the 𝐿𝑝 -space• alphabet (Counter, collection, int, or None) -- This represents the alphabet of possible tokens. See alphabet description in _TokenDistance for details.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized YJHHR distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns normalized YJHHR distance
Return type float

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Examples>>> cmp = YJHHR()>>> cmp.dist('cat', 'hat')
0.6666666666666666>>> cmp.dist('Niall', 'Neil')
0.7777777777777778>>> cmp.dist('aluminum', 'Catalan')
0.9375>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the YJHHR distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns YJHHR distance
Return type float
Examples>>> cmp = YJHHR()>>> cmp.dist_abs('cat', 'hat')
4.0>>> cmp.dist_abs('Niall', 'Neil')
7.0>>> cmp.dist_abs('aluminum', 'Catalan')
15.0>>> cmp.dist_abs('ATCG', 'TAGC')
10.0

New in version 0.4.0.
class abydos.distance.Bhattacharyya(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Bhattacharyya distance.
For two multisets X and Y drawn from an alphabet S, Bhattacharyya distance [Bha46] is∑︁ √︀𝑑𝑖𝑠𝑡𝐵ℎ𝑎𝑡𝑡𝑎𝑐ℎ𝑎𝑟𝑦𝑦𝑎 (𝑋, 𝑌 ) = −𝑙𝑜𝑔(
𝑋𝑖 𝑌𝑖 )𝑖∈𝑆

New in version 0.4.0.
Initialize Bhattacharyya instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package
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Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the Bhattacharyya coefficient of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Bhattacharyya distance
Return type float
Examples>>> cmp = Bhattacharyya()>>> cmp.dist('cat', 'hat')
0.5>>> cmp.dist('Niall', 'Neil')
0.3651483716701107>>> cmp.dist('aluminum', 'Catalan')
0.11785113019775792>>> cmp.dist('ATCG', 'TAGC')
0.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Bhattacharyya distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Bhattacharyya distance
Return type float
Examples>>> cmp = Bhattacharyya()>>> cmp.dist_abs('cat', 'hat')
0.6931471805599453>>> cmp.dist_abs('Niall', 'Neil')
1.0074515102711326>>> cmp.dist_abs('aluminum', 'Catalan')
2.1383330595080277>>> cmp.dist_abs('ATCG', 'TAGC')-inf

New in version 0.4.0.

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class abydos.distance.BrainerdRobinson(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceBrainerd-Robinson similarity.
For two multisets X and Y drawn from an alphabet S, Brainerd-Robinson similarity [Rob51][Bra51] is

𝑠𝑖𝑚𝐵𝑟𝑎𝑖𝑛𝑒𝑟𝑑𝑅𝑜𝑏𝑖𝑛𝑠𝑜𝑛 (𝑋, 𝑌 ) = 200 − 100 ·

∑︁
𝑖∈𝑆

𝑌𝑖
𝑋𝑖
− ∑︀
|
|𝑋
|
𝑖
𝑖∈𝑆
𝑖∈𝑆 |𝑌𝑖 || ∑︀New in version 0.4.0.
Initialize BrainerdRobinson instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the normalized Brainerd-Robinson similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Brainerd-Robinson similarity
Return type float
Examples>>> cmp = BrainerdRobinson()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3333333333333334>>> cmp.sim('aluminum', 'Catalan')
0.111111111111111>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Brainerd-Robinson similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison

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Returns Brainerd-Robinson similarity
Return type float
Examples>>> cmp = BrainerdRobinson()>>> cmp.sim_score('cat', 'hat')
100.0>>> cmp.sim_score('Niall', 'Neil')
66.66666666666669>>> cmp.sim_score('aluminum', 'Catalan')
22.2222222222222>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.QuantitativeCosine(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Quantitative Cosine similarity.
For two multisets X and Y drawn from an alphabet S, Quantitative Cosine similarity is∑︀𝑠𝑖𝑚𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑎𝑡𝑖𝑣𝑒𝐶𝑜𝑠𝑖𝑛𝑒 (𝑋, 𝑌 ) = √︁∑︀𝑖∈𝑆 𝑋𝑖 𝑌𝑖

2
𝑖∈𝑆 𝑋𝑖√︁∑︀

2
𝑖∈𝑆 𝑌𝑖

New in version 0.4.0.
Initialize QuantitativeCosine instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Quantitative Cosine similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Quantitative Cosine similarity
Return type float

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Examples>>> cmp = QuantitativeCosine()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3651483716701107>>> cmp.sim('aluminum', 'Catalan')
0.10660035817780521>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.QuantitativeDice(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Quantitative Dice similarity.
For two multisets X and Y drawn from an alphabet S, Quantitative Dice similarity is2·∑︀𝑖∈𝑆 𝑋𝑖 𝑌𝑖∑︀22𝑖∈𝑆 𝑋𝑖 +𝑖∈𝑆 𝑌𝑖𝑠𝑖𝑚𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑎𝑡𝑖𝑣𝑒𝐷𝑖𝑐𝑒 (𝑋, 𝑌 ) = ∑︀New in version 0.4.0.
Initialize QuantitativeDice instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Quantitative Dice similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Quantitative Dice similarity
Return type float

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Examples>>> cmp = QuantitativeDice()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.10526315789473684>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.QuantitativeJaccard(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Quantitative Jaccard similarity.
For two multisets X and Y drawn from an alphabet S, Quantitative Jaccard similarity is

∑︀
𝑋𝑖 𝑌𝑖
∑︀𝑖∈𝑆 2 ∑︀
𝑠𝑖𝑚𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑎𝑡𝑖𝑣𝑒𝐽𝑎𝑐𝑐𝑎𝑟𝑑 (𝑋, 𝑌 ) = ∑︀2𝑖∈𝑆 𝑋𝑖 +𝑖∈𝑆 𝑌𝑖 −𝑖∈𝑆 𝑋𝑖 𝑌𝑖
New in version 0.4.0.
Initialize QuantitativeJaccard instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Quantitative Jaccard similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Quantitative Jaccard similarity
Return type float

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Examples>>> cmp = QuantitativeJaccard()>>> cmp.sim('cat', 'hat')
0.3333333333333333>>> cmp.sim('Niall', 'Neil')
0.2222222222222222>>> cmp.sim('aluminum', 'Catalan')
0.05555555555555555>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.Roberts(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceRoberts similarity.
For two multisets X and Y drawn from an alphabet S, Roberts similarity [Rob86] is[︁ ∑︀
𝑠𝑖𝑚𝑅𝑜𝑏𝑒𝑟𝑡𝑠 (𝑋, 𝑌 ) =𝑚𝑖𝑛(𝑋𝑖 ,𝑌𝑖 )𝑖∈𝑆 (𝑋𝑖 + 𝑌𝑖 ) · 𝑚𝑎𝑥(𝑋𝑖 ,𝑌𝑖 )∑︀𝑖∈𝑆 (𝑋𝑖 + 𝑌𝑖 )]︁New in version 0.4.0.
Initialize Roberts instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Roberts similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Roberts similarity
Return type float

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Examples>>> cmp = Roberts()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.11764705882352941>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.AverageLinkage(tokenizer=None, metric=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Average linkage distance.
For two lists of tokens X and Y, average linkage distance [DD16] is∑︀𝑑𝑖𝑠𝑡𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐿𝑖𝑛𝑘𝑎𝑔𝑒 (𝑋, 𝑌 ) =

𝑖∈𝑋

∑︀

𝑗∈𝑌 𝑑𝑖𝑠𝑡(𝑋𝑖 , 𝑌𝑗 )|𝑋| · |𝑌 |New in version 0.4.0.
Initialize AverageLinkage instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants. (Defaults to Levenshtein distance)• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the average linkage distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns average linkage distance
Return type float

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Examples>>> cmp = AverageLinkage()>>> cmp.dist('cat', 'hat')
0.8125>>> cmp.dist('Niall', 'Neil')
0.8333333333333334>>> cmp.dist('aluminum', 'Catalan')
0.9166666666666666>>> cmp.dist('ATCG', 'TAGC')
0.8

New in version 0.4.0.
class abydos.distance.SingleLinkage(tokenizer=None, metric=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceSingle linkage distance.
For two multisets X and Y, single linkage distance [DD16] is

𝑑𝑖𝑠𝑡𝑆𝑖𝑛𝑔𝑙𝑒𝐿𝑖𝑛𝑘𝑎𝑔𝑒 (𝑋, 𝑌 ) = 𝑚𝑖𝑛𝑖∈𝑋,𝑗∈𝑌 𝑑𝑖𝑠𝑡(𝑋𝑖 , 𝑌𝑗 )New in version 0.4.0.
Initialize SingleLinkage instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants. (Defaults to Levenshtein distance)• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized single linkage distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns normalized single linkage distance
Return type float

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Examples>>> cmp = SingleLinkage()>>> cmp.dist('cat', 'hat')
0.0>>> cmp.dist('Niall', 'Neil')
0.0>>> cmp.dist('aluminum', 'Catalan')
0.0>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.4.0.
dist_abs(src, tar)
Return the single linkage distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns single linkage distance
Return type float
Examples>>> cmp = SingleLinkage()>>> cmp.dist_abs('cat', 'hat')
0.0>>> cmp.dist_abs('Niall', 'Neil')
0.0>>> cmp.dist_abs('aluminum', 'Catalan')
0.0>>> cmp.dist_abs('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.CompleteLinkage(tokenizer=None, metric=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceComplete linkage distance.
For two multisets X and Y, complete linkage distance [DD16] is

𝑠𝑖𝑚𝐶𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝐿𝑖𝑛𝑘𝑎𝑔𝑒 (𝑋, 𝑌 ) = 𝑚𝑎𝑥𝑖∈𝑋,𝑗∈𝑌 𝑑𝑖𝑠𝑡(𝑋𝑖 , 𝑌𝑗 )New in version 0.4.0.
Initialize CompleteLinkage instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package

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Abydos Documentation, Release 0.5.0• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants. (Defaults to Levenshtein distance)• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
dist(src, tar)
Return the normalized complete linkage distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns normalized complete linkage distance
Return type float
Examples>>> cmp = CompleteLinkage()>>> cmp.dist('cat', 'hat')
1.0>>> cmp.dist('Niall', 'Neil')
1.0>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the complete linkage distance of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns complete linkage distance
Return type float
Examples>>> cmp = CompleteLinkage()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('aluminum', 'Catalan')2>>> cmp.dist_abs('ATCG', 'TAGC')
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New in version 0.4.0.
class abydos.distance.Bag(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceBag distance.
Bag distance is proposed in [BCP02]. It is defined as

𝑑𝑖𝑠𝑡𝑏𝑎𝑔 (𝑠𝑟𝑐, 𝑡𝑎𝑟) = 𝑚𝑎𝑥(|𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑡(𝑠𝑟𝑐) − 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑡(𝑡𝑎𝑟)|, |𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑡(𝑡𝑎𝑟) − 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑡(𝑠𝑟𝑐)|)
New in version 0.3.6.
Initialize Bag instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
dist(src, tar)
Return the normalized bag distance between two strings.
Bag distance is normalized by dividing by 𝑚𝑎𝑥(|𝑠𝑟𝑐|, |𝑡𝑎𝑟|).
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized bag distance
Return type float

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Examples>>> cmp = Bag()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.4>>> cmp.dist('aluminum', 'Catalan')
0.625>>> cmp.dist('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, normalized=False)
Return the bag distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• normalized (bool) -- Normalizes to [0, 1] if True
Returns Bag distance
Return type int or float
Examples>>> cmp = Bag()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('aluminum', 'Catalan')5>>> cmp.dist_abs('ATCG', 'TAGC')0>>> cmp.dist_abs('abcdefg', 'hijklm')
7>>> cmp.dist_abs('abcdefg', 'hijklmno')
8

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.bag(src, tar)
Return the bag distance between two strings.
This is a wrapper for Bag.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns Bag distance
Return type int
Examples>>> bag('cat', 'hat')1>>> bag('Niall', 'Neil')2>>> bag('aluminum', 'Catalan')5>>> bag('ATCG', 'TAGC')0>>> bag('abcdefg', 'hijklm')
7>>> bag('abcdefg', 'hijklmno')
8

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Bag.dist_abs method instead.
abydos.distance.dist_bag(src, tar)
Return the normalized bag distance between two strings.
This is a wrapper for Bag.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized bag distance
Return type float
Examples>>> dist_bag('cat', 'hat')
0.3333333333333333>>> dist_bag('Niall', 'Neil')
0.4>>> dist_bag('aluminum', 'Catalan')
0.625>>> dist_bag('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Bag.dist method instead.
abydos.distance.sim_bag(src, tar)
Return the normalized bag similarity of two strings.
This is a wrapper for Bag.sim().
Parameters• src (str) -- Source string for comparison
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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison
Returns Normalized bag similarity
Return type float
Examples>>> round(sim_bag('cat', 'hat'), 12)
0.666666666667>>> sim_bag('Niall', 'Neil')
0.6>>> sim_bag('aluminum', 'Catalan')
0.375>>> sim_bag('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Bag.sim method instead.
class abydos.distance.SoftCosine(tokenizer=None, metric=None, sim_method='a', **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceSoft Cosine similarity.
As described in [SGGomezAP14], soft cosine similarity of two multi-sets X and Y, drawn from an alphabet S,is∑︀
𝑠𝑖𝑚𝑠𝑜𝑓 𝑡𝑐𝑜𝑠𝑖𝑛𝑒 (𝑋, 𝑌 ) = √︁∑︀𝑖∈𝑆

where 𝑠𝑖𝑗

is the similarity of two tokens,

∑︀

𝑖∈𝑆

∑︀

𝑗∈𝑆 𝑠𝑖𝑗 𝑋𝑖 𝑌𝑗𝑗∈𝑆 𝑠𝑖𝑗 𝑋𝑖 𝑋𝑗

√︁∑︀

𝑖∈𝑆

∑︀

𝑗∈𝑆 𝑠𝑖𝑗 𝑌𝑖 𝑌𝑗

by default a function of Levenshtein distance:

1
1+𝐿𝑒𝑣𝑒𝑛𝑠ℎ𝑡𝑒𝑖𝑛_𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑖,𝑗) .

Notes
This class implements soft cosine similarity, as defined by [SGGomezAP14]. An alternative formulation of softcosine similarity using soft (multi-)sets is provided by the Cosine class using intersection_type=``soft``, basedon the soft intersection defined in [RHJF14].
New in version 0.4.0.
Initialize SoftCosine instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package, defaulting to the QGrams tokenizer with q=4• threshold (float) -- The minimum similarity for a pair of tokens to contribute to similarity• metric (_Distance) -- A distance instance from the abydos.distance package, defaulting to Levenshtein distance

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Abydos Documentation, Release 0.5.0• sim_method (str) -- Selects the similarity method from the four given in[SGGomezAP14]:
1– a : 1+𝑑𝑑
– b: 1− 𝑚√︁𝑑– c: 1− 𝑚)︁2(︁𝑑– d: 1− 𝑚
Where 𝑑 is the distance (Levenshtein by default) and 𝑚 is the maximum length of the two
tokens. Option a is default, as suggested by the paper.
• **kwargs -- Arbitrary keyword arguments
Raises ValueError -- sim_method must be one of 'a', 'b', 'c', or 'd'
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Soft Cosine similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fuzzy Cosine similarity
Return type float
Examples>>> cmp = SoftCosine()>>> cmp.sim('cat', 'hat')
0.8750000000000001>>> cmp.sim('Niall', 'Neil')
0.8844691709074513>>> cmp.sim('aluminum', 'Catalan')
0.831348688760277>>> cmp.sim('ATCG', 'TAGC')
0.8571428571428572

New in version 0.4.0.
class abydos.distance.MongeElkan(sim_func=None, symmetric=False, **kwargs)
Bases: abydos.distance._distance._Distance
Monge-Elkan similarity.
Monge-Elkan is defined in [ME96].
Note: Monge-Elkan is NOT a symmetric similarity algorithm. Thus, the similarity of src to tar is not necessarilyequal to the similarity of tar to src. If the symmetric argument is True, a symmetric value is calculated, at thecost of doubling the computation time (since 𝑠𝑖𝑚𝑀 𝑜𝑛𝑔𝑒−𝐸𝑙𝑘𝑎𝑛 (𝑠𝑟𝑐, 𝑡𝑎𝑟) and 𝑠𝑖𝑚𝑀 𝑜𝑛𝑔𝑒−𝐸𝑙𝑘𝑎𝑛 (𝑡𝑎𝑟, 𝑠𝑟𝑐) areboth calculated and then averaged).

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New in version 0.3.6.
Initialize MongeElkan instance.
Parameters• sim_func (function) -- The internal similarity metric to employ• symmetric (bool) -- Return a symmetric similarity measure• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the Monge-Elkan similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Monge-Elkan similarity
Return type float
Examples>>> cmp = MongeElkan()>>> cmp.sim('cat', 'hat')
0.75>>> round(cmp.sim('Niall', 'Neil'), 12)
0.666666666667>>> round(cmp.sim('aluminum', 'Catalan'), 12)0.388888888889>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_monge_elkan(src, tar, sim_func=<function sim_levenshtein>, symmetric=False)
Return the Monge-Elkan distance between two strings.
This is a wrapper for MongeElkan.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• sim_func (function) -- The internal similarity metric to employ• symmetric (bool) -- Return a symmetric similarity measure
Returns Monge-Elkan distance
Return type float

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Examples>>> dist_monge_elkan('cat', 'hat')
0.25>>> round(dist_monge_elkan('Niall', 'Neil'), 12)0.333333333333>>> round(dist_monge_elkan('aluminum', 'Catalan'), 12)0.611111111111>>> dist_monge_elkan('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MongeElkan.dist method instead.
abydos.distance.sim_monge_elkan(src, tar, sim_func=<function sim_levenshtein>, symmetric=False)
Return the Monge-Elkan similarity of two strings.
This is a wrapper for MongeElkan.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• sim_func (function) -- Rhe internal similarity metric to employ• symmetric (bool) -- Return a symmetric similarity measure
Returns Monge-Elkan similarity
Return type float
Examples>>> sim_monge_elkan('cat', 'hat')
0.75>>> round(sim_monge_elkan('Niall', 'Neil'), 12)
0.666666666667>>> round(sim_monge_elkan('aluminum', 'Catalan'), 12)0.388888888889>>> sim_monge_elkan('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MongeElkan.sim method instead.
class abydos.distance.TFIDF(tokenizer=None, corpus=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
TF-IDF similarity.
For two sets X and Y and a population N, TF-IDF similarity [CRF03] is

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𝑠𝑖𝑚𝑇 𝐹 −𝐼𝐷𝐹 (𝑋, 𝑌 ) =∑︁

𝑉 (𝑤, 𝑋) · 𝑉 (𝑤, 𝑌 )𝑤∈𝑋∩𝑌𝑉 ′ (𝑤, 𝑆)′
2𝑤∈𝑆 𝑉 (𝑤, 𝑆)

𝑉 (𝑤, 𝑆) = √︀∑︀𝑉 ′ (𝑤, 𝑆) = 𝑙𝑜𝑔(1 + 𝑇 𝐹𝑤,𝑆 ) · 𝑙𝑜𝑔(1 + 𝐼𝐷𝐹𝑤 )Notes
One is added to both the TF & IDF values before taking the logarithm to ensure the logarithms do not fall to 0,
which will tend to result in 0.0 similarities even when there is a degree of matching.
New in version 0.4.0.
Initialize TFIDF instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• corpus (UnigramCorpus) -- A unigram corpus UnigramCorpus. If None, a corpus
will be created from the two words when a similarity function is called.
• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the TF-IDF similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns TF-IDF similarity
Return type float
Examples>>> cmp = TFIDF()>>> cmp.sim('cat', 'hat')
0.30404449697373>>> cmp.sim('Niall', 'Neil')
0.20108911303601>>> cmp.sim('aluminum', 'Catalan')
0.05355175631194>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.

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class abydos.distance.SoftTFIDF(tokenizer=None, corpus=None, metric=None, threshold=0.9,**kwargs)
Bases: abydos.distance._token_distance._TokenDistance
SoftTF-IDF similarity.
For two sets X and Y and a population N, SoftTF-IDF similarity [CRF03] is∑︀𝑤∈{𝑠𝑖𝑚𝑚𝑒𝑡𝑟𝑖𝑐 (𝑥,𝑦)≥𝜃|𝑥∈𝑋,𝑦∈𝑌 } 𝑉 (𝑤, 𝑆) · 𝑉 (𝑤, 𝑋) · 𝑉 (𝑤, 𝑌 )𝑠𝑖𝑚𝑆𝑜𝑓 𝑡𝑇 𝐹 −𝐼𝐷𝐹 (𝑋, 𝑌 )=𝑉 (𝑤, 𝑆)= √∑︀ 𝑉 (𝑤,𝑆)′

𝑉 ′ (𝑤, 𝑆)= 𝑙𝑜𝑔(1 + 𝑇 𝐹𝑤,𝑆 ) · 𝑙𝑜𝑔(1 + 𝐼𝐷𝐹𝑤 )

′

𝑤∈𝑆 𝑉

(𝑤,𝑆)2Notes
One is added to both the TF & IDF values before taking the logarithm to ensure the logarithms do not fall to 0,
which will tend to result in 0.0 similarities even when there is a degree of matching.
Rather than needing to exceed the threshold value, as in [CRF03] the similarity must be greater than or equal to
the threshold.
New in version 0.4.0.
Initialize SoftTFIDF instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• corpus (UnigramCorpus) -- A unigram corpus UnigramCorpus. If None, a corpus
will be created from the two words when a similarity function is called.
• metric (_Distance) -- A string distance measure class for making soft matches, by
default Jaro-Winkler.
• threshold (float) -- A threshold value, similarities above which are counted as soft
matches, by default 0.9.
• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the SoftTF-IDF similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns SoftTF-IDF similarity
Return type float

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Examples>>> cmp = SoftTFIDF()>>> cmp.sim('cat', 'hat')
0.30404449697373>>> cmp.sim('Niall', 'Neil')
0.20108911303601>>> cmp.sim('aluminum', 'Catalan')
0.05355175631194>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.JensenShannon(tokenizer=None, intersection_type='crisp', **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
Jensen-Shannon divergence.
Jensen-Shannon divergence [DLP99] of two multi-sets X and Y is

𝑑𝑖𝑠𝑡𝐽𝑆 (𝑋, 𝑌 ) = 𝑙𝑜𝑔2 + 12ℎ(𝑥) = −𝑥𝑙𝑜𝑔𝑥𝑖|𝑝(𝑋𝑖 ∈ 𝑋) = |𝑋|𝑋|

∑︀𝑖∈𝑋∩𝑌 ℎ(𝑝(𝑋𝑖 ) + 𝑝(𝑌𝑖 )) − ℎ(𝑝(𝑋𝑖 )) − ℎ(𝑝(𝑌𝑖 ))New in version 0.4.0.
Initialize JensenShannon instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Jensen-Shannon distance of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Jensen-Shannon distance
Return type float

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Examples>>> cmp = JensenShannon()>>> cmp.dist('cat', 'hat')
0.49999999999999994>>> cmp.dist('Niall', 'Neil')
0.6355222557917826>>> cmp.dist('aluminum', 'Catalan')
0.8822392827203127>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.0.
dist_abs(src, tar)
Return the Jensen-Shannon divergence of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Jensen-Shannon divergence
Return type float
Examples>>> cmp = JensenShannon()>>> cmp.dist_abs('cat', 'hat')
0.3465735902799726>>> cmp.dist_abs('Niall', 'Neil')
0.44051045978517045>>> cmp.dist_abs('aluminum', 'Catalan')
0.6115216713968132>>> cmp.dist_abs('ATCG', 'TAGC')
0.6931471805599453

New in version 0.4.0.
class abydos.distance.FellegiSunter(tokenizer=None,intersection_type='crisp',
fied=False, mismatch_factor=0.5, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance

simpli-Fellegi-Sunter similarity.
Fellegi-Sunter similarity is based on the description in [CRF03] and implementation in [CRFR03].
New in version 0.4.0.
Initialize FellegiSunter instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• intersection_type (str) -- Specifies the intersection type, and set type as a result:See intersection_type description in _TokenDistance for details.
• simplified (bool) -- Specifies to use the simplified scoring variant
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Abydos Documentation, Release 0.5.0• mismatch_factor (float) -- Specifies the penalty factor for mismatches• **kwargs -- Arbitrary keyword arguments
Other Parameters• qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause
the instance to use the QGram tokenizer with this q value.
• metric (_Distance) -- A string distance measure class for use in the soft and fuzzy
variants.
• threshold (float) -- A threshold value, similarities above which are counted as members of
the intersection for the fuzzy variant.
New in version 0.4.0.
sim(src, tar)
Return the normalized Fellegi-Sunter similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized Fellegi-Sunter similarity
Return type float
Examples>>> cmp = FellegiSunter()>>> cmp.sim('cat', 'hat')
0.2934477792670495>>> cmp.sim('Niall', 'Neil')
0.13917536933271363>>> cmp.sim('aluminum', 'Catalan')
0.056763632331436484>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
sim_score(src, tar)
Return the Fellegi-Sunter similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Fellegi-Sunter similarity
Return type float

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Examples>>> cmp = FellegiSunter()>>> cmp.sim_score('cat', 'hat')
0.8803433378011485>>> cmp.sim_score('Niall', 'Neil')
0.6958768466635681>>> cmp.sim_score('aluminum', 'Catalan')
0.45410905865149187>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.MinHash(tokenizer=None, k=0, seed=10, **kwargs)
Bases: abydos.distance._distance._Distance
MinHash similarity.
MinHash similarity [Bro97] is a method of approximating the intersection over the union of two sets. This
implementation is based on [Kul15].
New in version 0.4.0.
Initialize MinHash instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• k (int) -- The number of hash functions to use for similarity estimation• seed (int) -- A seed value for the random functions• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the MinHash similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns MinHash similarity
Return type float

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Examples>>> cmp = MinHash()>>> cmp.sim('cat', 'hat')
0.75>>> cmp.sim('Niall', 'Neil')
1.0>>> cmp.sim('aluminum', 'Catalan')
0.5>>> cmp.sim('ATCG', 'TAGC')
0.6

New in version 0.4.0.
class abydos.distance.BLEU(n_min=1, n_max=4, tokenizers=None, weights=None, **kwargs)
Bases: abydos.distance._distance._Distance
BLEU similarity.
BLEU similarity [PRWZ02] compares two strings for similarity using a set of tokenizers and a brevity penalty:

{︂
𝐵𝑃 =

1
𝑟
𝑒(1− 𝑐 )if 𝑐 > 𝑟if 𝑐 ≤ 𝑟

The BLEU score is then:∑︀𝑁

BLEU = 𝐵𝑃 · 𝑒

𝑛=1 𝑤𝑛 𝑙𝑜𝑔𝑝𝑛

For tokenizers 1 to N, by default q-gram tokenizers for q=1 to N in Abydos, weights 𝑤𝑛 , which are uniformly
1
𝑁 , and 𝑝𝑛 :∑︀
𝑝𝑛 =𝑡𝑜𝑘𝑒𝑛∈𝑡𝑎𝑟 𝑚𝑖𝑛(𝐶𝑜𝑢𝑛𝑡(𝑡𝑜𝑘𝑒𝑛 ∈ 𝑡𝑎𝑟), 𝐶𝑜𝑢𝑛𝑡(𝑡𝑜𝑘𝑒𝑛 ∈ 𝑠𝑟𝑐))|𝑡𝑎𝑟|

New in version 0.4.0.
Initialize BLEU instance.
Parameters• n_min (int) -- The minimum q-gram value for BLEU score calculation (1 by default)• n_max (int) -- The maximum q-gram value for BLEU score calculation (4 by default)• tokenizers (list(_Tokenizer)) -- A list of initialized tokenizers• weights (list(float)) -- A list of floats representing the weights of the tokenizers.
If tokenizers is set, this must have the same length. If n_min and n_max are used to set
tokenizers, this must have length equal to n_max-n_min-1. Otherwise, uniform weights will
be used.
• **kwargs -- Arbitrary keyword arguments
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sim(src, tar)
Return the BLEU similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns BLEU similarity
Return type float
Examples>>> cmp = BLEU()>>> cmp.sim('cat', 'hat')
0.7598356856515925>>> cmp.sim('Niall', 'Neil')
0.7247557929987696>>> cmp.sim('aluminum', 'Catalan')
0.44815260192961937>>> cmp.sim('ATCG', 'TAGC')
1.0

New in version 0.4.0.
class abydos.distance.RougeL(**kwargs)
Bases: abydos.distance._distance._DistanceRouge-L similarity.
Rouge-L similarity [Lin04]
New in version 0.4.0.
Initialize RougeL instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar, beta=8)
Return the Rouge-L similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• beta (int or float) -- A weighting factor to prejudice similarity towards src
Returns Rouge-L similarity
Return type float

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Examples>>> cmp = RougeL()>>> cmp.sim('cat', 'hat')
0.6666666666666666>>> cmp.sim('Niall', 'Neil')
0.6018518518518519>>> cmp.sim('aluminum', 'Catalan')
0.3757225433526012>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.4.0.
class abydos.distance.RougeW(f_func=None, f_inv=None, **kwargs)
Bases: abydos.distance._distance._DistanceRouge-W similarity.
Rouge-W similarity [Lin04]
New in version 0.4.0.
Initialize RougeW instance.
Parameters• f_func (function) -- A weighting function based on the value supplied to this function,such that f(x+y) > f(x) + f(y)• f_inv (function) -- The close form inverse of f_func• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar, beta=8)
Return the Rouge-W similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• beta (int or float) -- A weighting factor to prejudice similarity towards src
Returns Rouge-W similarity
Return type float
Examples>>> cmp = RougeW()>>> cmp.sim('cat', 'hat')
0.6666666666666666>>> cmp.sim('Niall', 'Neil')
0.34747932867894143>>> cmp.sim('aluminum', 'Catalan')
0.280047049205176>>> cmp.sim('ATCG', 'TAGC')
0.43301270189221935

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New in version 0.4.0.
wlcs(src, tar)
Return the Rouge-W weighted longest common sub-sequence length.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Levenshtein distance between src & tar
Return type int (may return a float if cost has float values)
Examples>>> cmp = RougeW()>>> cmp.wlcs('cat', 'hat')4>>> cmp.wlcs('Niall', 'Neil')3>>> cmp.wlcs('aluminum', 'Catalan')5>>> cmp.wlcs('ATCG', 'TAGC')3

New in version 0.4.0.
class abydos.distance.RougeS(qval=2, **kwargs)
Bases: abydos.distance._distance._DistanceRouge-S similarity.
Rouge-S similarity [Lin04], operating on character-level skipgrams
New in version 0.4.0.
Initialize RougeS instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar, beta=8)
Return the Rouge-S similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• beta (int or float) -- A weighting factor to prejudice similarity towards src
Returns Rouge-S similarity
Return type float

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Examples>>> cmp = RougeS()>>> cmp.sim('cat', 'hat')
0.3333333333333333>>> cmp.sim('Niall', 'Neil')
0.30185758513931893>>> cmp.sim('aluminum', 'Catalan')
0.10755653612796467>>> cmp.sim('ATCG', 'TAGC')
0.6666666666666666

New in version 0.4.0.
class abydos.distance.RougeSU(qval=2, **kwargs)
Bases: abydos.distance._rouge_s.RougeS
Rouge-SU similarity.
Rouge-SU similarity [Lin04], operating on character-level skipgrams
New in version 0.4.0.
Initialize RougeSU instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar, beta=8)
Return the Rouge-SU similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• beta (int or float) -- A weighting factor to prejudice similarity towards src
Returns Rouge-SU similarity
Return type float
Examples>>> cmp = RougeSU()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.4020618556701031>>> cmp.sim('aluminum', 'Catalan')
0.1672384219554031>>> cmp.sim('ATCG', 'TAGC')
0.8

New in version 0.4.0.
class abydos.distance.PositionalQGramDice(max_dist=1, tokenizer=None, **kwargs)
Bases: abydos.distance._distance._Distance
Positional Q-Gram Dice coefficient.

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Positional Q-Gram Dice coefficient [GIJ+01][Chr06]
New in version 0.4.0.
Initialize PositionalQGramDice instance.
Parameters• max_dist (int) -- The maximum positional distance between to q-grams to count as amatch.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Positional Q-Gram Dice coefficient of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Positional Q-Gram Dice coefficient
Return type float
Examples>>> cmp = PositionalQGramDice()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.36363636363636365>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.PositionalQGramJaccard(max_dist=1, tokenizer=None, **kwargs)
Bases: abydos.distance._distance._Distance
Positional Q-Gram Jaccard coefficient.
Positional Q-Gram Jaccard coefficient [GIJ+01][Chr06]
New in version 0.4.0.
Initialize PositionalQGramJaccard instance.
Parameters• max_dist (int) -- The maximum positional distance between to q-grams to count as amatch.

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package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the Positional Q-Gram Jaccard coefficient of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Positional Q-Gram Jaccard coefficient
Return type float
Examples>>> cmp = PositionalQGramJaccard()>>> cmp.sim('cat', 'hat')
0.3333333333333333>>> cmp.sim('Niall', 'Neil')
0.2222222222222222>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.PositionalQGramOverlap(max_dist=1, tokenizer=None, **kwargs)
Bases: abydos.distance._distance._Distance
Positional Q-Gram Overlap coefficient.
Positional Q-Gram Overlap coefficient [GIJ+01][Chr06]
New in version 0.4.0.
Initialize PositionalQGramOverlap instance.
Parameters• max_dist (int) -- The maximum positional distance between to q-grams to count as amatch.
• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.

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sim(src, tar)
Return the Positional Q-Gram Overlap coefficient of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Positional Q-Gram Overlap coefficient
Return type float
Examples>>> cmp = PositionalQGramOverlap()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.4>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.NeedlemanWunsch(gap_cost=1, sim_func=None, **kwargs)
Bases: abydos.distance._distance._Distance
Needleman-Wunsch score.
The Needleman-Wunsch score [NW70] is a standard edit distance measure.
New in version 0.3.6.
Initialize NeedlemanWunsch instance.
Parameters• gap_cost (float) -- The cost of an alignment gap (1 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the normalized Needleman-Wunsch score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Needleman-Wunsch score
Return type float

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Examples>>> cmp = NeedlemanWunsch()>>> cmp.sim('cat', 'hat')
0.6666666666666667>>> cmp.sim('Niall', 'Neil')
0.22360679774997896>>> round(cmp.sim('aluminum', 'Catalan'), 12)0.0>>> cmp.sim('cat', 'hat')
0.6666666666666667

New in version 0.4.1.
static sim_matrix(src, tar, mat=None, mismatch_cost=0, match_cost=1, symmetric=True, alphabet=None)
Return the matrix similarity of two strings.
With the default parameters, this is identical to sim_ident. It is possible for sim_matrix to return values
outside of the range [0, 1], if values outside that range are present in mat, mismatch_cost, or match_cost.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• mat (dict) -- A dict mapping tuples to costs; the tuples are (src, tar) pairs of symbols
from the alphabet parameter• mismatch_cost (float) -- The value returned if (src, tar) is absent from mat whensrc does not equal tar• match_cost (float) -- The value returned if (src, tar) is absent from mat when src
equals tar• symmetric (bool) -- True if the cost of src not matching tar is identical to the cost of
tar not matching src; in this case, the values in mat need only contain (src, tar) or (tar, src),
not both• alphabet (str) -- A collection of tokens from which src and tar are drawn; if this isdefined a ValueError is raised if either tar or src is not found in alphabet
Returns Matrix similarity
Return type float
Raises• ValueError -- src value not in alphabet• ValueError -- tar value not in alphabet

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Examples>>> NeedlemanWunsch.sim_matrix('cat', 'hat')0>>> NeedlemanWunsch.sim_matrix('hat', 'hat')1

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classsim_score(src, tar)
Return the Needleman-Wunsch score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Needleman-Wunsch score
Return type float
Examples>>> cmp = NeedlemanWunsch()>>> cmp.sim_score('cat', 'hat')
2.0>>> cmp.sim_score('Niall', 'Neil')
1.0>>> cmp.sim_score('aluminum', 'Catalan')-1.0>>> cmp.sim_score('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.needleman_wunsch(src, tar, gap_cost=1, sim_func=<function sim_ident>)
Return the Needleman-Wunsch score of two strings.
This is a wrapper for NeedlemanWunsch.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• gap_cost (float) -- The cost of an alignment gap (1 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)
Returns Needleman-Wunsch score
Return type float

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Examples>>> needleman_wunsch('cat', 'hat')
2.0>>> needleman_wunsch('Niall', 'Neil')
1.0>>> needleman_wunsch('aluminum', 'Catalan')-1.0>>> needleman_wunsch('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NeedlemanWunsch.dist_abs method
instead.
class abydos.distance.SmithWaterman(gap_cost=1, sim_func=None, **kwargs)
Bases: abydos.distance._needleman_wunsch.NeedlemanWunsch
Smith-Waterman score.
The Smith-Waterman score [SW81] is a standard edit distance measure, differing from Needleman-Wunsch inthat it focuses on local alignment and disallows negative scores.
New in version 0.3.6.
Initialize SmithWaterman instance.
Parameters• gap_cost (float) -- The cost of an alignment gap (1 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the normalized Smith-Waterman score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Smith-Waterman score
Return type float
Examples>>> cmp = SmithWaterman()>>> cmp.sim('cat', 'hat')
0.6666666666666667>>> cmp.sim('Niall', 'Neil')
0.22360679774997896>>> round(cmp.sim('aluminum', 'Catalan'), 12)
0.0(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> cmp.sim('cat', 'hat')
0.6666666666666667

New in version 0.4.1.
sim_score(src, tar)
Return the Smith-Waterman score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Smith-Waterman score
Return type float
Examples>>> cmp = SmithWaterman()>>> cmp.sim_score('cat', 'hat')
2.0>>> cmp.sim_score('Niall', 'Neil')
1.0>>> cmp.sim_score('aluminum', 'Catalan')
0.0>>> cmp.sim_score('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.smith_waterman(src, tar, gap_cost=1, sim_func=<function sim_ident>)
Return the Smith-Waterman score of two strings.
This is a wrapper for SmithWaterman.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• gap_cost (float) -- The cost of an alignment gap (1 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)
Returns Smith-Waterman score
Return type float

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Examples>>> smith_waterman('cat', 'hat')
2.0>>> smith_waterman('Niall', 'Neil')
1.0>>> smith_waterman('aluminum', 'Catalan')
0.0>>> smith_waterman('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SmithWaterman.dist_abs method instead.
class abydos.distance.Gotoh(gap_open=1, gap_ext=0.4, sim_func=None, **kwargs)
Bases: abydos.distance._needleman_wunsch.NeedlemanWunsch
Gotoh score.
The Gotoh score [Got82] is essentially Needleman-Wunsch with affine gap penalties.
New in version 0.3.6.
Initialize Gotoh instance.
Parameters• gap_open (float) -- The cost of an open alignment gap (1 by default)• gap_ext (float) -- The cost of an alignment gap extension (0.4 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the normalized Gotoh score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Gotoh score
Return type float
Examples>>> cmp = Gotoh()>>> cmp.sim('cat', 'hat')
0.6666666666666667>>> cmp.sim('Niall', 'Neil')
0.22360679774997896>>> round(cmp.sim('aluminum', 'Catalan'), 12)0.0>>> cmp.sim('cat', 'hat')
0.6666666666666667

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New in version 0.4.1.
sim_score(src, tar)
Return the Gotoh score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Gotoh score
Return type float
Examples>>> cmp = Gotoh()>>> cmp.sim_score('cat', 'hat')
2.0>>> cmp.sim_score('Niall', 'Neil')
1.0>>> round(cmp.sim_score('aluminum', 'Catalan'), 12)-0.4>>> cmp.sim_score('cat', 'hat')
2.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.gotoh(src, tar, gap_open=1, gap_ext=0.4, sim_func=<function sim_ident>)
Return the Gotoh score of two strings.
This is a wrapper for Gotoh.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• gap_open (float) -- The cost of an open alignment gap (1 by default)• gap_ext (float) -- The cost of an alignment gap extension (0.4 by default)• sim_func (function) -- A function that returns the similarity of two characters (identity
similarity by default)
Returns Gotoh score
Return type float

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Examples>>> gotoh('cat', 'hat')
2.0>>> gotoh('Niall', 'Neil')
1.0>>> round(gotoh('aluminum', 'Catalan'), 12)-0.4>>> gotoh('cat', 'hat')
2.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Gotoh.dist_abs method instead.
class abydos.distance.LCSseq(normalizer=<built-in function max>, **kwargs)
Bases: abydos.distance._distance._DistanceLongest common subsequence.
Longest common subsequence (LCSseq) is the longest subsequence of characters that two strings have in common.
New in version 0.3.6.
Initialize LCSseq.
Parameters• normalizer (function) -- A normalization function for the normalized similarity &distance. By default, the max of the lengths of the input strings. If lambda x: sum(x)/2.0 is˙supplied, the normalization proposed in [RTS+01] is used, i.e. 2|𝐿𝐶𝑆(𝑠𝑟𝑐,𝑡𝑎𝑟)|.
|𝑠𝑟𝑐|+|𝑡𝑎𝑟|• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
lcsseq(src, tar)
Return the longest common subsequence of two strings.
Based on the dynamic programming algorithm from http://rosettacode.org/wiki/Longest_common_
subsequence [Cod18a]. This is licensed GFDL 1.2.
Modifications include: conversion to a numpy array in place of a list of lists
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The longest common subsequence
Return type str

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Examples>>> sseq = LCSseq()>>> sseq.lcsseq('cat', 'hat')'at'>>> sseq.lcsseq('Niall', 'Neil')'Nil'>>> sseq.lcsseq('aluminum', 'Catalan')'aln'>>> sseq.lcsseq('ATCG', 'TAGC')'AC'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classsim(src, tar)
Return the longest common subsequence similarity of two strings.
Longest common subsequence similarity (𝑠𝑖𝑚𝐿𝐶𝑆𝑠𝑒𝑞 ).
This employs the LCSseq function to derive a similarity metric: 𝑠𝑖𝑚𝐿𝐶𝑆𝑠𝑒𝑞 (𝑠, 𝑡) = |𝐿𝐶𝑆𝑠𝑒𝑞(𝑠,𝑡)|𝑚𝑎𝑥(|𝑠|,|𝑡|)Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSseq similarity
Return type float
Examples>>> sseq = LCSseq()>>> sseq.sim('cat', 'hat')
0.6666666666666666>>> sseq.sim('Niall', 'Neil')
0.6>>> sseq.sim('aluminum', 'Catalan')
0.375>>> sseq.sim('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
Changed in version 0.4.0: Added normalization option
abydos.distance.lcsseq(src, tar)
Return the longest common subsequence of two strings.
This is a wrapper for LCSseq.lcsseq().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns The longest common subsequence
Return type str
Examples>>> lcsseq('cat', 'hat')'at'>>> lcsseq('Niall', 'Neil')'Nil'>>> lcsseq('aluminum', 'Catalan')'aln'>>> lcsseq('ATCG', 'TAGC')'AC'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSseq.lcsseq method instead.
abydos.distance.dist_lcsseq(src, tar)
Return the longest common subsequence distance between two strings.
This is a wrapper for LCSseq.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSseq distance
Return type float
Examples>>> dist_lcsseq('cat', 'hat')
0.33333333333333337>>> dist_lcsseq('Niall', 'Neil')
0.4>>> dist_lcsseq('aluminum', 'Catalan')
0.625>>> dist_lcsseq('ATCG', 'TAGC')
0.5

Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSseq.dist method instead.
abydos.distance.sim_lcsseq(src, tar)
Return the longest common subsequence similarity of two strings.
This is a wrapper for LCSseq.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSseq similarity
Return type float

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Examples>>> sim_lcsseq('cat', 'hat')
0.6666666666666666>>> sim_lcsseq('Niall', 'Neil')
0.6>>> sim_lcsseq('aluminum', 'Catalan')
0.375>>> sim_lcsseq('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSseq.sim method instead.
class abydos.distance.LCSstr(normalizer=<built-in function max>, **kwargs)
Bases: abydos.distance._distance._DistanceLongest common substring.
New in version 0.3.6.
Initialize LCSseq.
Parameters• normalizer (function) -- A normalization function for the normalized similarity &distance. By default, the max of the lengths of the input strings. If lambda x: sum(x)/2.0 is˙supplied, the normalization proposed in [RTS+01] is used, i.e. 2|𝐿𝐶𝑆(𝑠𝑟𝑐,𝑡𝑎𝑟)|.
|𝑠𝑟𝑐|+|𝑡𝑎𝑟|• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
lcsstr(src, tar)
Return the longest common substring of two strings.
Longest common substring (LCSstr).
Based on the code from https://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Longest_
common_substring [Wik18]. This is licensed Creative Commons: Attribution-ShareAlike 3.0.
Modifications include:• conversion to a numpy array in place of a list of lists
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The longest common substring
Return type str

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Examples>>> sstr = LCSstr()>>> sstr.lcsstr('cat', 'hat')'at'>>> sstr.lcsstr('Niall', 'Neil')'N'>>> sstr.lcsstr('aluminum', 'Catalan')'al'>>> sstr.lcsstr('ATCG', 'TAGC')'A'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classsim(src, tar)
Return the longest common substring similarity of two strings.
Longest common substring similarity (𝑠𝑖𝑚𝐿𝐶𝑆𝑠𝑡𝑟 ).
This employs the LCS function to derive a similarity metric: 𝑠𝑖𝑚𝐿𝐶𝑆𝑠𝑡𝑟 (𝑠, 𝑡) = |𝐿𝐶𝑆𝑠𝑡𝑟(𝑠,𝑡)|𝑚𝑎𝑥(|𝑠|,|𝑡|)Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSstr similarity
Return type float
Examples>>> sstr = LCSstr()>>> sstr.sim('cat', 'hat')
0.6666666666666666>>> sstr.sim('Niall', 'Neil')
0.2>>> sstr.sim('aluminum', 'Catalan')
0.25>>> sstr.sim('ATCG', 'TAGC')
0.25

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
Changed in version 0.4.0: Added normalization option
abydos.distance.lcsstr(src, tar)
Return the longest common substring of two strings.
This is a wrapper for LCSstr.lcsstr().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Returns The longest common substring
Return type str
Examples>>> lcsstr('cat', 'hat')'at'>>> lcsstr('Niall', 'Neil')'N'>>> lcsstr('aluminum', 'Catalan')'al'>>> lcsstr('ATCG', 'TAGC')'A'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSstr.lcsstr method instead.
abydos.distance.dist_lcsstr(src, tar)
Return the longest common substring distance between two strings.
This is a wrapper for LCSstr.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSstr distance
Return type float
Examples>>> dist_lcsstr('cat', 'hat')
0.33333333333333337>>> dist_lcsstr('Niall', 'Neil')
0.8>>> dist_lcsstr('aluminum', 'Catalan')
0.75>>> dist_lcsstr('ATCG', 'TAGC')
0.75

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSstr.dist method instead.
abydos.distance.sim_lcsstr(src, tar)
Return the longest common substring similarity of two strings.
This is a wrapper for LCSstr.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns LCSstr similarity

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Return type float
Examples>>> sim_lcsstr('cat', 'hat')
0.6666666666666666>>> sim_lcsstr('Niall', 'Neil')
0.2>>> sim_lcsstr('aluminum', 'Catalan')
0.25>>> sim_lcsstr('ATCG', 'TAGC')
0.25

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LCSstr.sim method instead.
class abydos.distance.LCPrefix(**kwargs)
Bases: abydos.distance._distance._DistanceLongest common prefix.
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist_abs(src, tar, *args)
Return the length of the longest common prefix of the strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• *args (strs) -- Additional strings for comparison
Raises ValueError -- All arguments must be of type str
Returns The length of the longest common prefix
Return type int
Examples>>> pfx = LCPrefix()>>> pfx.dist_abs('cat', 'hat')0>>> pfx.dist_abs('Niall', 'Neil')1>>> pfx.dist_abs('aluminum', 'Catalan')0>>> pfx.dist_abs('ATCG', 'TAGC')0

New in version 0.4.0.

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lcprefix(strings)
Return the longest common prefix of a list of strings.
Longest common prefix (LCPrefix).
Parameters strings (list of strings) -- Strings for comparison
Returns The longest common prefix
Return type str
Examples>>> pfx = LCPrefix()>>> pfx.lcprefix(['cat', 'hat'])''>>> pfx.lcprefix(['Niall', 'Neil'])'N'>>> pfx.lcprefix(['aluminum', 'Catalan'])''>>> pfx.lcprefix(['ATCG', 'TAGC'])''

New in version 0.4.0.
sim(src, tar, *args)
Return the longest common prefix similarity of two or more strings.
Longest common prefix similarity (𝑠𝑖𝑚𝐿𝐶𝑃 𝑟𝑒𝑓 𝑖𝑥 ).
𝑟𝑒𝑓 𝑖𝑥(𝑠,𝑡)|This employs the LCPrefix function to derive a similarity metric: 𝑠𝑖𝑚𝐿𝐶𝑃 𝑟𝑒𝑓 𝑖𝑥 (𝑠, 𝑡) = |𝐿𝐶𝑃
𝑚𝑎𝑥(|𝑠|,|𝑡|)Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• *args (strs) -- Additional strings for comparison
Returns LCPrefix similarity
Return type float
Examples>>> pfx = LCPrefix()>>> pfx.sim('cat', 'hat')
0.0>>> pfx.sim('Niall', 'Neil')
0.2>>> pfx.sim('aluminum', 'Catalan')
0.0>>> pfx.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.

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class abydos.distance.LCSuffix(**kwargs)
Bases: abydos.distance._lcprefix.LCPrefix
Longest common suffix.
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist_abs(src, tar, *args)
Return the length of the longest common suffix of the strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• *args (strs) -- Additional strings for comparison
Raises ValueError -- All arguments must be of type str
Returns The length of the longest common suffix
Return type int
Examples>>> sfx = LCSuffix()>>> sfx.dist_abs('cat', 'hat')2>>> sfx.dist_abs('Niall', 'Neil')1>>> sfx.dist_abs('aluminum', 'Catalan')0>>> sfx.dist_abs('ATCG', 'TAGC')0

New in version 0.4.0.
lcsuffix(strings)
Return the longest common suffix of a list of strings.
Longest common suffix (LCSuffix).
Parameters strings (list of strings) -- Strings for comparison
Returns The longest common suffix
Return type str

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Examples>>> sfx = LCSuffix()>>> sfx.lcsuffix(['cat', 'hat'])'at'>>> sfx.lcsuffix(['Niall', 'Neil'])'l'>>> sfx.lcsuffix(['aluminum', 'Catalan'])''>>> sfx.lcsuffix(['ATCG', 'TAGC'])''

New in version 0.4.0.
sim(src, tar, *args)
Return the longest common suffix similarity of two or more strings.
Longest common prefix similarity (𝑠𝑖𝑚𝐿𝐶𝑃 𝑟𝑒𝑓 𝑖𝑥 ).
𝑓 𝑖𝑥(𝑠,𝑡)|This employs the LCSuffix function to derive a similarity metric: 𝑠𝑖𝑚𝐿𝐶𝑆𝑢𝑓 𝑓 𝑖𝑥 (𝑠, 𝑡) = |𝐿𝐶𝑆𝑢𝑓𝑚𝑎𝑥(|𝑠|,|𝑡|)Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• *args (strs) -- Additional strings for comparison
Returns LCSuffix similarity
Return type float
Examples>>> pfx = LCPrefix()>>> pfx.sim('cat', 'hat')
0.0>>> pfx.sim('Niall', 'Neil')
0.2>>> pfx.sim('aluminum', 'Catalan')
0.0>>> pfx.sim('ATCG', 'TAGC')
0.0

New in version 0.4.0.
class abydos.distance.RatcliffObershelp(**kwargs)
Bases: abydos.distance._distance._DistanceRatcliff-Obershelp similarity.
This follows the Ratcliff-Obershelp algorithm [RM88] to derive a similarity measure:
1. Find the length of the longest common substring in src & tar.
2. Recurse on the strings to the left & right of each this substring in src & tar. The base case is a 0 lengthcommon substring, in which case, return 0. Otherwise, return the sum of the current longest common
substring and the left & right recursed sums.
3. Multiply this length by 2 and divide by the sum of the lengths of src & tar.

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Cf. http://www.drdobbs.com/database/pattern-matching-the-gestalt-approach/184407970
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the Ratcliff-Obershelp similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Ratcliff-Obershelp similarity
Return type float
Examples>>> cmp = RatcliffObershelp()>>> round(cmp.sim('cat', 'hat'), 12)
0.666666666667>>> round(cmp.sim('Niall', 'Neil'), 12)
0.666666666667>>> round(cmp.sim('aluminum', 'Catalan'), 12)0.4>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ratcliff_obershelp(src, tar)
Return the Ratcliff-Obershelp distance between two strings.
This is a wrapper for RatcliffObershelp.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Ratcliff-Obershelp distance
Return type float

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Examples>>> round(dist_ratcliff_obershelp('cat', 'hat'), 12)0.333333333333>>> round(dist_ratcliff_obershelp('Niall', 'Neil'), 12)0.333333333333>>> round(dist_ratcliff_obershelp('aluminum', 'Catalan'), 12)0.6>>> dist_ratcliff_obershelp('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RatcliffObershelp.dist method instead.
abydos.distance.sim_ratcliff_obershelp(src, tar)
Return the Ratcliff-Obershelp similarity of two strings.
This is a wrapper for RatcliffObershelp.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Ratcliff-Obershelp similarity
Return type float
Examples>>> round(sim_ratcliff_obershelp('cat', 'hat'), 12)
0.666666666667>>> round(sim_ratcliff_obershelp('Niall', 'Neil'), 12)
0.666666666667>>> round(sim_ratcliff_obershelp('aluminum', 'Catalan'), 12)
0.4>>> sim_ratcliff_obershelp('ATCG', 'TAGC')
0.5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RatcliffObershelp.sim method instead.
class abydos.distance.Ident(**kwargs)
Bases: abydos.distance._distance._Distance
Identity distance and similarity.
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the identity similarity of two strings.
Identity similarity is 1.0 if the two strings are identical, otherwise 0.0

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Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Identity similarity
Return type float
Examples>>> cmp = Ident()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('cat', 'cat')1.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ident(src, tar)
Return the identity distance between two strings.
This is a wrapper for Ident.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Identity distance
Return type float
Examples>>> dist_ident('cat', 'hat')
1.0>>> dist_ident('cat', 'cat')0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Ident.dist method instead.
abydos.distance.sim_ident(src, tar)
Return the identity similarity of two strings.
This is a wrapper for Ident.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Identity similarity
Return type float

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Examples>>> sim_ident('cat', 'hat')
0.0>>> sim_ident('cat', 'cat')1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Ident.sim method instead.
class abydos.distance.Length(**kwargs)
Bases: abydos.distance._distance._Distance
Length similarity and distance.
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the length similarity of two strings.
Length similarity is the ratio of the length of the shorter string to the longer.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Length similarity
Return type float
Examples>>> cmp = Length()>>> cmp.sim('cat', 'hat')
1.0>>> cmp.sim('Niall', 'Neil')
0.8>>> cmp.sim('aluminum', 'Catalan')
0.875>>> cmp.sim('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_length(src, tar)
Return the length distance between two strings.
This is a wrapper for Length.dist().
Parameters• src (str) -- Source string for comparison

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Returns Length distance
Return type float
Examples>>> dist_length('cat', 'hat')
0.0>>> dist_length('Niall', 'Neil')
0.19999999999999996>>> dist_length('aluminum', 'Catalan')
0.125>>> dist_length('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Length.dist method instead.
abydos.distance.sim_length(src, tar)
Return the length similarity of two strings.
This is a wrapper for Length.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Length similarity
Return type float
Examples>>> sim_length('cat', 'hat')
1.0>>> sim_length('Niall', 'Neil')
0.8>>> sim_length('aluminum', 'Catalan')
0.875>>> sim_length('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Length.sim method instead.
class abydos.distance.Prefix(**kwargs)
Bases: abydos.distance._distance._DistancePrefix similiarity and distance.
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments

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New in version 0.4.0.
sim(src, tar)
Return the prefix similarity of two strings.
Prefix similarity is the ratio of the length of the shorter term that exactly matches the longer term to thelength of the shorter term, beginning at the start of both terms.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Prefix similarity
Return type float
Examples>>> cmp = Prefix()>>> cmp.sim('cat', 'hat')
0.0>>> cmp.sim('Niall', 'Neil')
0.25>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_prefix(src, tar)
Return the prefix distance between two strings.
This is a wrapper for Prefix.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Prefix distance
Return type float
Examples>>> dist_prefix('cat', 'hat')
1.0>>> dist_prefix('Niall', 'Neil')
0.75>>> dist_prefix('aluminum', 'Catalan')
1.0>>> dist_prefix('ATCG', 'TAGC')
1.0

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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Prefix.dist method instead.
abydos.distance.sim_prefix(src, tar)
Return the prefix similarity of two strings.
This is a wrapper for Prefix.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Prefix similarity
Return type float
Examples>>> sim_prefix('cat', 'hat')
0.0>>> sim_prefix('Niall', 'Neil')
0.25>>> sim_prefix('aluminum', 'Catalan')
0.0>>> sim_prefix('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Prefix.sim method instead.
class abydos.distance.Suffix(**kwargs)
Bases: abydos.distance._distance._Distance
Suffix similarity and distance.
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the suffix similarity of two strings.
Suffix similarity is the ratio of the length of the shorter term that exactly matches the longer term to thelength of the shorter term, beginning at the end of both terms.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Suffix similarity
Return type float

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Examples>>> cmp = Suffix()>>> cmp.sim('cat', 'hat')
0.6666666666666666>>> cmp.sim('Niall', 'Neil')
0.25>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_suffix(src, tar)
Return the suffix distance between two strings.
This is a wrapper for Suffix.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Suffix distance
Return type float
Examples>>> dist_suffix('cat', 'hat')
0.33333333333333337>>> dist_suffix('Niall', 'Neil')
0.75>>> dist_suffix('aluminum', 'Catalan')
1.0>>> dist_suffix('ATCG', 'TAGC')
1.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Suffix.dist method instead.
abydos.distance.sim_suffix(src, tar)
Return the suffix similarity of two strings.
This is a wrapper for Suffix.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Suffix similarity
Return type float

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Examples>>> sim_suffix('cat', 'hat')
0.6666666666666666>>> sim_suffix('Niall', 'Neil')
0.25>>> sim_suffix('aluminum', 'Catalan')
0.0>>> sim_suffix('ATCG', 'TAGC')
0.0

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Suffix.sim method instead.
class abydos.distance.NCDzlib(level=-1, **kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using zlib compression.
Cf. https://zlib.net/
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize zlib compressor.
Parameters level (int) -- The compression level (0 to 9)
New in version 0.3.6.
dist(src, tar)
Return the NCD between two strings using zlib compression.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> cmp = NCDzlib()>>> cmp.dist('cat', 'hat')
0.3333333333333333>>> cmp.dist('Niall', 'Neil')
0.45454545454545453>>> cmp.dist('aluminum', 'Catalan')
0.5714285714285714>>> cmp.dist('ATCG', 'TAGC')
0.4

New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in class

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abydos.distance.dist_ncd_zlib(src, tar)
Return the NCD between two strings using zlib compression.
This is a wrapper for NCDzlib.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> dist_ncd_zlib('cat', 'hat')
0.3333333333333333>>> dist_ncd_zlib('Niall', 'Neil')
0.45454545454545453>>> dist_ncd_zlib('aluminum', 'Catalan')
0.5714285714285714>>> dist_ncd_zlib('ATCG', 'TAGC')
0.4

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDzlib.dist method instead.
abydos.distance.sim_ncd_zlib(src, tar)
Return the NCD similarity between two strings using zlib compression.
This is a wrapper for NCDzlib.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns float
Return type Compression similarity
Examples>>> sim_ncd_zlib('cat', 'hat')
0.6666666666666667>>> sim_ncd_zlib('Niall', 'Neil')
0.5454545454545454>>> sim_ncd_zlib('aluminum', 'Catalan')
0.4285714285714286>>> sim_ncd_zlib('ATCG', 'TAGC')
0.6

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDzlib.sim method instead.

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class abydos.distance.NCDbz2(level=9, **kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using bzip2 compression.
Cf. https://en.wikipedia.org/wiki/Bzip2
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize bzip2 compressor.
Parameters level (int) -- The compression level (0 to 9)
New in version 0.3.6.
Changed in version 0.3.6: Encapsulated in classdist(src, tar)
Return the NCD between two strings using bzip2 compression.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> cmp = NCDbz2()>>> cmp.dist('cat', 'hat')
0.06666666666666667>>> cmp.dist('Niall', 'Neil')
0.03125>>> cmp.dist('aluminum', 'Catalan')
0.17647058823529413>>> cmp.dist('ATCG', 'TAGC')
0.03125

New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ncd_bz2(src, tar)
Return the NCD between two strings using bzip2 compression.
This is a wrapper for NCDbz2.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float

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Examples>>> dist_ncd_bz2('cat', 'hat')
0.06666666666666667>>> dist_ncd_bz2('Niall', 'Neil')
0.03125>>> dist_ncd_bz2('aluminum', 'Catalan')
0.17647058823529413>>> dist_ncd_bz2('ATCG', 'TAGC')
0.03125

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDbz2.dist method instead.
abydos.distance.sim_ncd_bz2(src, tar)
Return the NCD similarity between two strings using bzip2 compression.
This is a wrapper for NCDbz2.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression similarity
Return type float
Examples>>> sim_ncd_bz2('cat', 'hat')
0.9333333333333333>>> sim_ncd_bz2('Niall', 'Neil')
0.96875>>> sim_ncd_bz2('aluminum', 'Catalan')
0.8235294117647058>>> sim_ncd_bz2('ATCG', 'TAGC')
0.96875

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDbz2.sim method instead.
class abydos.distance.NCDlzma(level=6, **kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using LZMA compression.
Cf. https://en.wikipedia.org/wiki/Lempel-Ziv-Markov_chain_algorithm
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize LZMA compressor.
Parameters level (int) -- The compression level (0 to 9)
New in version 0.5.0.

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dist(src, tar)
Return the NCD between two strings using LZMA compression.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> cmp = NCDlzma()>>> cmp.dist('cat', 'hat')
0.08695652173913043>>> cmp.dist('Niall', 'Neil')
0.16>>> cmp.dist('aluminum', 'Catalan')
0.16>>> cmp.dist('ATCG', 'TAGC')
0.08695652173913043

New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ncd_lzma(src, tar)
Return the NCD between two strings using LZMA compression.
This is a wrapper for NCDlzma.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> dist_ncd_lzma('cat', 'hat')
0.08695652173913043>>> dist_ncd_lzma('Niall', 'Neil')
0.16>>> dist_ncd_lzma('aluminum', 'Catalan')
0.16>>> dist_ncd_lzma('ATCG', 'TAGC')
0.08695652173913043

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDlzma.dist method instead.

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abydos.distance.sim_ncd_lzma(src, tar)
Return the NCD similarity between two strings using LZMA compression.
This is a wrapper for NCDlzma.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression similarity
Return type float
Examples>>> sim_ncd_lzma('cat', 'hat')
0.9130434782608696>>> sim_ncd_lzma('Niall', 'Neil')
0.84>>> sim_ncd_lzma('aluminum', 'Catalan')
0.84>>> sim_ncd_lzma('ATCG', 'TAGC')
0.9130434782608696

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDlzma.sim method instead.
class abydos.distance.NCDarith(probs=None, **kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using arithmetic coding.
Cf. https://en.wikipedia.org/wiki/Arithmetic_coding
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize the arithmetic coder object.
Parameters probs (dict) -- A dictionary trained with Arithmetic.train()
New in version 0.3.6.
Changed in version 0.3.6: Encapsulated in classdist(src, tar)
Return the NCD between two strings using arithmetic coding.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float

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Examples>>> cmp = NCDarith()>>> cmp.dist('cat', 'hat')
0.5454545454545454>>> cmp.dist('Niall', 'Neil')
0.6875>>> cmp.dist('aluminum', 'Catalan')
0.8275862068965517>>> cmp.dist('ATCG', 'TAGC')
0.6923076923076923

New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ncd_arith(src, tar, probs=None)
Return the NCD between two strings using arithmetic coding.
This is a wrapper for NCDarith.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• probs (dict) -- A dictionary trained with Arithmetic.train()
Returns Compression distance
Return type float
Examples>>> dist_ncd_arith('cat', 'hat')
0.5454545454545454>>> dist_ncd_arith('Niall', 'Neil')
0.6875>>> dist_ncd_arith('aluminum', 'Catalan')
0.8275862068965517>>> dist_ncd_arith('ATCG', 'TAGC')
0.6923076923076923

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDarith.dist method instead.
abydos.distance.sim_ncd_arith(src, tar, probs=None)
Return the NCD similarity between two strings using arithmetic coding.
This is a wrapper for NCDarith.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• probs (dict) -- A dictionary trained with Arithmetic.train()
Returns Compression similarity

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Return type float
Examples>>> sim_ncd_arith('cat', 'hat')
0.4545454545454546>>> sim_ncd_arith('Niall', 'Neil')
0.3125>>> sim_ncd_arith('aluminum', 'Catalan')
0.1724137931034483>>> sim_ncd_arith('ATCG', 'TAGC')
0.3076923076923077

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDarith.sim method instead.
class abydos.distance.NCDbwtrle(**kwargs)
Bases: abydos.distance._ncd_rle.NCDrle
Normalized Compression Distance using BWT plus RLE.
Cf. https://en.wikipedia.org/wiki/Burrows-Wheeler_transform
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the NCD between two strings using BWT plus RLE.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> cmp = NCDbwtrle()>>> cmp.dist('cat', 'hat')
0.75>>> cmp.dist('Niall', 'Neil')
0.8333333333333334>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
0.8

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New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ncd_bwtrle(src, tar)
Return the NCD between two strings using BWT plus RLE.
This is a wrapper for NCDbwtrle.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> dist_ncd_bwtrle('cat', 'hat')
0.75>>> dist_ncd_bwtrle('Niall', 'Neil')
0.8333333333333334>>> dist_ncd_bwtrle('aluminum', 'Catalan')
1.0>>> dist_ncd_bwtrle('ATCG', 'TAGC')
0.8

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDbwtrle.dist method instead.
abydos.distance.sim_ncd_bwtrle(src, tar)
Return the NCD similarity between two strings using BWT plus RLE.
This is a wrapper for NCDbwtrle.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression similarity
Return type float
Examples>>> sim_ncd_bwtrle('cat', 'hat')
0.25>>> sim_ncd_bwtrle('Niall', 'Neil')
0.16666666666666663>>> sim_ncd_bwtrle('aluminum', 'Catalan')
0.0>>> sim_ncd_bwtrle('ATCG', 'TAGC')
0.19999999999999996

New in version 0.3.5.

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class abydos.distance.NCDrle(**kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using RLE.
Cf. https://en.wikipedia.org/wiki/Run-length_encoding
Normalized compression distance (NCD) [CV05].
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the NCD between two strings using RLE.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Examples>>> cmp = NCDrle()>>> cmp.dist('cat', 'hat')
1.0>>> cmp.dist('Niall', 'Neil')
1.0>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.3.5.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_ncd_rle(src, tar)
Return the NCD between two strings using RLE.
This is a wrapper for NCDrle.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float

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Examples>>> dist_ncd_rle('cat', 'hat')
1.0>>> dist_ncd_rle('Niall', 'Neil')
1.0>>> dist_ncd_rle('aluminum', 'Catalan')
1.0>>> dist_ncd_rle('ATCG', 'TAGC')
1.0

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDrle.dist method instead.
abydos.distance.sim_ncd_rle(src, tar)
Return the NCD similarity between two strings using RLE.
This is a wrapper for NCDrle.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression similarity
Return type float
Examples>>> sim_ncd_rle('cat', 'hat')
0.0>>> sim_ncd_rle('Niall', 'Neil')
0.0>>> sim_ncd_rle('aluminum', 'Catalan')
0.0>>> sim_ncd_rle('ATCG', 'TAGC')
0.0

New in version 0.3.5.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NCDrle.sim method instead.
class abydos.distance.NCDpaq9a(**kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using PAQ9A compression.
Cf. http://mattmahoney.net/dc/#paq9a
Normalized compression distance (NCD) [CV05].
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.

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dist(src, tar)
Return the NCD between two strings using PAQ9A compression.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Raises ValueError -- Install the paq module in order to use PAQ9A
Examples>>> cmp = NCDpaq9a()>>> cmp.dist('cat', 'hat')
0.42857142857142855>>> cmp.dist('Niall', 'Neil')
0.5555555555555556>>> cmp.dist('aluminum', 'Catalan')
0.5833333333333334>>> cmp.dist('ATCG', 'TAGC')
0.5

New in version 0.4.0.
class abydos.distance.NCDlzss(**kwargs)
Bases: abydos.distance._distance._Distance
Normalized Compression Distance using LZSS compression.
Cf. https://en.wikipedia.org/wiki/Lempel-Ziv-Storer-Szymanski
Normalized compression distance (NCD) [CV05].
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the NCD between two strings using LZSS compression.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Compression distance
Return type float
Raises ValueError -- Install the PyLZSS module in order to use LZSS

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Examples>>> cmp = NCDlzss()>>> cmp.dist('cat', 'hat')
0.75>>> cmp.dist('Niall', 'Neil')
1.0>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
0.8

New in version 0.4.0.
class abydos.distance.FuzzyWuzzyPartialString(**kwargs)
Bases: abydos.distance._distance._Distance
FuzzyWuzzy Partial String similarity.
This follows the FuzzyWuzzy Partial String similarity algorithm [Coh11]. Rather than returning an integer inthe range [0, 100], as demonstrated in the blog post, this implementation returns a float in the range [0.0, 1.0].
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the FuzzyWuzzy Partial String similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns FuzzyWuzzy Partial String similarity
Return type float
Examples>>> cmp = FuzzyWuzzyPartialString()>>> round(cmp.sim('cat', 'hat'), 12)
0.666666666667>>> round(cmp.sim('Niall', 'Neil'), 12)0.75>>> round(cmp.sim('aluminum', 'Catalan'), 12)0.428571428571>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.4.0.
class abydos.distance.FuzzyWuzzyTokenSort(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
FuzzyWuzzy Token Sort similarity.

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This follows the FuzzyWuzzy Token Sort similarity algorithm [Coh11]. Rather than returning an integer in therange [0, 100], as demonstrated in the blog post, this implementation returns a float in the range [0.0, 1.0].
New in version 0.4.0.
Initialize FuzzyWuzzyTokenSort instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package. By default, the regexp tokenizer is employed, matching only letters.
• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the FuzzyWuzzy Token Sort similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns FuzzyWuzzy Token Sort similarity
Return type float
Examples>>> cmp = FuzzyWuzzyTokenSort()>>> cmp.sim('cat', 'hat')
0.6666666666666666>>> cmp.sim('Niall', 'Neil')
0.6666666666666666>>> cmp.sim('aluminum', 'Catalan')
0.4>>> cmp.sim('ATCG', 'TAGC')
0.5

New in version 0.4.0.
class abydos.distance.FuzzyWuzzyTokenSet(tokenizer=None, **kwargs)
Bases: abydos.distance._token_distance._TokenDistance
FuzzyWuzzy Token Set similarity.
This follows the FuzzyWuzzy Token Set similarity algorithm [Coh11]. Rather than returning an integer in therange [0, 100], as demonstrated in the blog post, this implementation returns a float in the range [0.0, 1.0].
Distinct from the
New in version 0.4.0.
Initialize FuzzyWuzzyTokenSet instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package. By default, the regexp tokenizer is employed, matching only letters.

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Abydos Documentation, Release 0.5.0• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.0.
sim(src, tar)
Return the FuzzyWuzzy Token Set similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns FuzzyWuzzy Token Set similarity
Return type float
Examples>>> cmp = FuzzyWuzzyTokenSet()>>> cmp.sim('cat', 'hat')
0.75>>> cmp.sim('Niall', 'Neil')
0.7272727272727273>>> cmp.sim('aluminum', 'Catalan')
0.47058823529411764>>> cmp.sim('ATCG', 'TAGC')
0.6

New in version 0.4.0.
class abydos.distance.PhoneticDistance(transforms=None,
metric=None,
code_alpha=False, **kwargs)
Bases: abydos.distance._distance._Distanceen-Phonetic distance.
Phonetic distance applies one or more supplied string transformations to words and compares the resulting
transformed strings using a supplied distance measure.
A simple example would be to create a 'Soundex distance':>>> from abydos.phonetic import Soundex>>> soundex = PhoneticDistance(transforms=Soundex())>>> soundex.dist('Ashcraft', 'Ashcroft')
0.0>>> soundex.dist('Robert', 'Ashcraft')
1.0

New in version 0.4.1.
Initialize PhoneticDistance instance.
Parameters• transforms (list or _Phonetic or _Stemmer or _Fingerprint ortype) -- An instance of a subclass of _Phonetic, _Stemmer, or _Fingerprint, or a list (orother iterable) of such instances to apply to each input word before computing their distanceor similarity. If omitted, no transformations will be performed.

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Abydos Documentation, Release 0.5.0• metric (_Distance or type) -- An instance of a subclass of _Distance, used forcomputing the inputs' distance or similarity after being transformed. If omitted, the strings
will be compared for identify (returning 0.0 if identical, otherwise 1.0, when distance iscomputed).
• encode_alpha (bool) -- Set to true to use the encode_alpha method of phonetic algoritms whenever possible.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
dist(src, tar)
Return the normalized Phonetic distance.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Phonetic distance
Return type float
Examples>>> from abydos.phonetic import Soundex>>> cmp = PhoneticDistance(Soundex())>>> cmp.dist('cat', 'hat')
1.0>>> cmp.dist('Niall', 'Neil')
0.0>>> cmp.dist('Colin', 'Cuilen')
0.0>>> cmp.dist('ATCG', 'TAGC')
1.0>>> from abydos.distance import Levenshtein>>> cmp = PhoneticDistance(transforms=[Soundex], metric=Levenshtein)>>> cmp.dist('cat', 'hat')
0.25>>> cmp.dist('Niall', 'Neil')
0.0>>> cmp.dist('Colin', 'Cuilen')
0.0>>> cmp.dist('ATCG', 'TAGC')
0.75

New in version 0.4.1.
dist_abs(src, tar)
Return the Phonetic distance.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
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Return type float or int
Examples>>> from abydos.phonetic import Soundex>>> cmp = PhoneticDistance(Soundex())>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')0>>> cmp.dist_abs('Colin', 'Cuilen')0>>> cmp.dist_abs('ATCG', 'TAGC')
1>>> from abydos.distance import Levenshtein>>> cmp = PhoneticDistance(transforms=[Soundex], metric=Levenshtein)>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')0>>> cmp.dist_abs('Colin', 'Cuilen')0>>> cmp.dist_abs('ATCG', 'TAGC')3

New in version 0.4.1.
class abydos.distance.MRA(**kwargs)
Bases: abydos.distance._distance._DistanceMatch Rating Algorithm comparison rating.
The Western Airlines Surname Match Rating Algorithm comparison rating, as presented on page 18 of[MKTM77].
New in version 0.3.6.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist_abs(src, tar)
Return the MRA comparison rating of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns MRA comparison rating
Return type int

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Examples>>> cmp = MRA()>>> cmp.dist_abs('cat', 'hat')5>>> cmp.dist_abs('Niall', 'Neil')6>>> cmp.dist_abs('aluminum', 'Catalan')0>>> cmp.dist_abs('ATCG', 'TAGC')5

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classsim(src, tar)
Return the normalized MRA similarity of two strings.
This is the MRA normalized to [0, 1], given that MRA itself is constrained to the range [0, 6].
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized MRA similarity
Return type float
Examples>>> cmp = MRA()>>> cmp.sim('cat', 'hat')
0.8333333333333334>>> cmp.sim('Niall', 'Neil')
1.0>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.8333333333333334

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.mra_compare(src, tar)
Return the MRA comparison rating of two strings.
This is a wrapper for MRA.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns MRA comparison rating
Return type int

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Examples>>> mra_compare('cat', 'hat')5>>> mra_compare('Niall', 'Neil')
6>>> mra_compare('aluminum', 'Catalan')0>>> mra_compare('ATCG', 'TAGC')5

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MRA.dist_abs method instead.
abydos.distance.dist_mra(src, tar)
Return the normalized MRA distance between two strings.
This is a wrapper for MRA.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized MRA distance
Return type float
Examples>>> dist_mra('cat', 'hat')
0.16666666666666663>>> dist_mra('Niall', 'Neil')
0.0>>> dist_mra('aluminum', 'Catalan')
1.0>>> dist_mra('ATCG', 'TAGC')
0.16666666666666663

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MRA.dist method instead.
abydos.distance.sim_mra(src, tar)
Return the normalized MRA similarity of two strings.
This is a wrapper for MRA.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized MRA similarity
Return type float

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Examples>>> sim_mra('cat', 'hat')
0.8333333333333334>>> sim_mra('Niall', 'Neil')
1.0>>> sim_mra('aluminum', 'Catalan')
0.0>>> sim_mra('ATCG', 'TAGC')
0.8333333333333334

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MRA.sim method instead.
class abydos.distance.Editex(cost=(0, 1, 2), local=False, taper=False, **kwargs)
Bases: abydos.distance._distance._Distance
Editex.
As described on pages 3 & 4 of [ZD96].
The local variant is based on [RU09].
New in version 0.3.6.
Changed in version 0.4.0: Added taper option
Initialize Editex instance.
Parameters• cost (tuple) -- A 3-tuple representing the cost of the four possible edits: match, samegroup, and mismatch respectively (by default: (0, 1, 2))• local (bool) -- If True, the local variant of Editex is used
• taper (bool) -- Enables cost tapering. Following [ZD96], it causes edits at the start of thestring to "just [exceed] twice the minimum penalty for replacement or deletion at the end of
the string".
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Editex distance between two strings.
The Editex distance is normalized by dividing the Editex distance (calculated by any of the three supported
methods) by the greater of the number of characters in src times the cost of a delete and the number ofcharacters in tar times the cost of an insert. For the case in which all operations have 𝑐𝑜𝑠𝑡 = 1, this isequivalent to the greater of the length of the two strings src & tar.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Editex distance
Return type int

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Examples>>> cmp = Editex()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> round(cmp.dist('Niall', 'Neil'), 12)0.2>>> cmp.dist('aluminum', 'Catalan')
0.75>>> cmp.dist('ATCG', 'TAGC')
0.75

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the Editex distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Editex distance
Return type int
Examples>>> cmp = Editex()>>> cmp.dist_abs('cat', 'hat')2>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('aluminum', 'Catalan')12>>> cmp.dist_abs('ATCG', 'TAGC')6

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.editex(src, tar, cost=(0, 1, 2), local=False)
Return the Editex distance between two strings.
This is a wrapper for Editex.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• cost (tuple) -- A 3-tuple representing the cost of the four possible edits: match, samegroup, and mismatch respectively (by default: (0, 1, 2))• local (bool) -- If True, the local variant of Editex is used
Returns Editex distance

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Return type int
Examples>>> editex('cat', 'hat')2>>> editex('Niall', 'Neil')2>>> editex('aluminum', 'Catalan')12>>> editex('ATCG', 'TAGC')6

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Editex.dist_abs method instead.
abydos.distance.dist_editex(src, tar, cost=(0, 1, 2), local=False)
Return the normalized Editex distance between two strings.
This is a wrapper for Editex.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• cost (tuple) -- A 3-tuple representing the cost of the four possible edits: match, samegroup, and mismatch respectively (by default: (0, 1, 2))• local (bool) -- If True, the local variant of Editex is used
Returns Normalized Editex distance
Return type int
Examples>>> round(dist_editex('cat', 'hat'), 12)0.333333333333>>> round(dist_editex('Niall', 'Neil'), 12)0.2>>> dist_editex('aluminum', 'Catalan')
0.75>>> dist_editex('ATCG', 'TAGC')
0.75

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Editex.dist method instead.
abydos.distance.sim_editex(src, tar, cost=(0, 1, 2), local=False)
Return the normalized Editex similarity of two strings.
This is a wrapper for Editex.sim().
Parameters• src (str) -- Source string for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison• cost (tuple) -- A 3-tuple representing the cost of the four possible edits: match, samegroup, and mismatch respectively (by default: (0, 1, 2))• local (bool) -- If True, the local variant of Editex is used
Returns Normalized Editex similarity
Return type int
Examples>>> round(sim_editex('cat', 'hat'), 12)
0.666666666667>>> round(sim_editex('Niall', 'Neil'), 12)0.8>>> sim_editex('aluminum', 'Catalan')
0.25>>> sim_editex('ATCG', 'TAGC')
0.25

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Editex.sim method instead.
class abydos.distance.Baystat(min_ss_len=None, left_ext=None, right_ext=None, **kwargs)
Bases: abydos.distance._distance._Distance
Baystat similarity and distance.
Good results for shorter words are reported when setting min_ss_len to 1 and either left_ext OR right_ext to 1.
The Baystat similarity is defined in [FurnrohrRvR02].
This is ostensibly a port of the R module PPRL's implementation: https://github.com/cran/PPRL/blob/master/src/MTB_Baystat.cpp [Ruk18]. As such, this could be made more pythonic.
New in version 0.3.6.
Initialize Levenshtein instance.
Parameters• min_ss_len (int) -- Minimum substring length to be considered• left_ext (int) -- Left-side extension length• right_ext (int) -- Right-side extension length• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the Baystat similarity.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Baystat similarity
Return type float
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Examples>>> cmp = Baystat()>>> round(cmp.sim('cat', 'hat'), 12)
0.666666666667>>> cmp.sim('Niall', 'Neil')
0.4>>> round(cmp.sim('Colin', 'Cuilen'), 12)0.166666666667>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.dist_baystat(src, tar, min_ss_len=None, left_ext=None, right_ext=None)
Return the Baystat distance.
This is a wrapper for Baystat.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• min_ss_len (int) -- Minimum substring length to be considered• left_ext (int) -- Left-side extension length• right_ext (int) -- Right-side extension length
Returns The Baystat distance
Return type float
Examples>>> round(dist_baystat('cat', 'hat'), 12)0.333333333333>>> dist_baystat('Niall', 'Neil')
0.6>>> round(dist_baystat('Colin', 'Cuilen'), 12)0.833333333333>>> dist_baystat('ATCG', 'TAGC')
1.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Baystat.dist method instead.
abydos.distance.sim_baystat(src, tar, min_ss_len=None, left_ext=None, right_ext=None)
Return the Baystat similarity.
This is a wrapper for Baystat.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison

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Abydos Documentation, Release 0.5.0• min_ss_len (int) -- Minimum substring length to be considered• left_ext (int) -- Left-side extension length• right_ext (int) -- Right-side extension length
Returns The Baystat similarity
Return type float
Examples>>> round(sim_baystat('cat', 'hat'), 12)
0.666666666667>>> sim_baystat('Niall', 'Neil')
0.4>>> round(sim_baystat('Colin', 'Cuilen'), 12)0.166666666667>>> sim_baystat('ATCG', 'TAGC')
0.0

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Baystat.sim method instead.
class abydos.distance.Eudex(weights='exponential', max_length=8, **kwargs)
Bases: abydos.distance._distance._Distance
Distance between the Eudex hashes of two terms.
Cf. [Tic].
New in version 0.3.6.
Initialize Eudex instance.
Parameters• weights (str, iterable, or generator function) -- The weights or
weights generator function– If set to None, a simple Hamming distance is calculated.
– If set to exponential, weight decays by powers of 2, as proposed in the eudex specification: https://github.com/ticki/eudex.
– If set to fibonacci, weight decays through the Fibonacci series, as in the eudex reference implementation.
– If set to a callable function, this assumes it creates a generator and the generator is used
to populate a series of weights.
– If set to an iterable, the iterable's values should be integers and will be used as the weights.
In all cases, the weights should be ordered or generated from least significant to most significant, so larger values should generally come first.
• max_length (int) -- The number of characters to encode as a eudex hash
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.

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dist(src, tar)
Return normalized distance between the Eudex hashes of two terms.
This is Eudex distance normalized to [0, 1].
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Eudex Hamming distance
Return type int
Examples>>> cmp = Eudex()>>> round(cmp.dist('cat', 'hat'), 12)
0.062745098039>>> round(cmp.dist('Niall', 'Neil'), 12)0.000980392157>>> round(cmp.dist('Colin', 'Cuilen'), 12)0.004901960784>>> round(cmp.dist('ATCG', 'TAGC'), 12)0.197549019608

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, normalized=False)
Calculate the distance between the Eudex hashes of two terms.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• normalized (bool) -- Normalizes to [0, 1] if True
Returns The Eudex Hamming distance
Return type int
Examples>>> cmp = Eudex()>>> cmp.dist_abs('cat', 'hat')128>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('Colin', 'Cuilen')10>>> cmp.dist_abs('ATCG', 'TAGC')
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Abydos Documentation, Release 0.5.0>>> cmp = Eudex(weights='fibonacci')>>> cmp.dist_abs('cat', 'hat')34>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('Colin', 'Cuilen')7>>> cmp.dist_abs('ATCG', 'TAGC')
117>>> cmp = Eudex(weights=None)>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')1>>> cmp.dist_abs('Colin', 'Cuilen')2>>> cmp.dist_abs('ATCG', 'TAGC')
9>>> # Using the OEIS A000142:>>> cmp = Eudex(weights=[1, 1, 2, 6, 24, 120, 720, 5040])>>> cmp.dist_abs('cat', 'hat')
5040>>> cmp.dist_abs('Niall', 'Neil')1>>> cmp.dist_abs('Colin', 'Cuilen')7>>> cmp.dist_abs('ATCG', 'TAGC')
15130

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classstatic gen_exponential(base=2)
Yield the next value in an exponential series of the base.
Starts at base**0
Parameters base (int) -- The base to exponentiate
Yields int -- The next power of baseNew in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classstatic gen_fibonacci()
Yield the next Fibonacci number.
Based on https://www.python-course.eu/generators.php Starts at Fibonacci number 3 (the second 1)
Yields int -- The next Fibonacci number
New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.eudex_hamming(src, tar, weights='exponential',
ized=False)
Calculate the Hamming distance between the Eudex hashes of two terms.
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This is a wrapper for Eudex.eudex_hamming().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• weights (str, iterable, or generator function) -- The weights or
weights generator function• max_length (int) -- The number of characters to encode as a eudex hash• normalized (bool) -- Normalizes to [0, 1] if True
Returns The Eudex Hamming distance
Return type int
Examples>>> eudex_hamming('cat', 'hat')128>>> eudex_hamming('Niall', 'Neil')2>>> eudex_hamming('Colin', 'Cuilen')10>>> eudex_hamming('ATCG', 'TAGC')
403>>> eudex_hamming('cat', 'hat', weights='fibonacci')
34>>> eudex_hamming('Niall', 'Neil', weights='fibonacci')
2>>> eudex_hamming('Colin', 'Cuilen', weights='fibonacci')
7>>> eudex_hamming('ATCG', 'TAGC', weights='fibonacci')
117>>> eudex_hamming('cat', 'hat', weights=None)1>>> eudex_hamming('Niall', 'Neil', weights=None)1>>> eudex_hamming('Colin', 'Cuilen', weights=None)2>>> eudex_hamming('ATCG', 'TAGC', weights=None)9>>> # Using the OEIS A000142:>>> eudex_hamming('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
5040>>> eudex_hamming('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
1>>> eudex_hamming('Colin', 'Cuilen', [1, 1, 2, 6, 24, 120, 720, 5040])
7>>> eudex_hamming('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
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New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Eudex.dist_abs method instead.
abydos.distance.dist_eudex(src, tar, weights='exponential', max_length=8)
Return normalized Hamming distance between Eudex hashes of two terms.
This is a wrapper for Eudex.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• weights (str, iterable, or generator function) -- The weights or
weights generator function• max_length (int) -- The number of characters to encode as a eudex hash
Returns The normalized Eudex Hamming distance
Return type int
Examples>>> round(dist_eudex('cat', 'hat'), 12)
0.062745098039>>> round(dist_eudex('Niall', 'Neil'), 12)0.000980392157>>> round(dist_eudex('Colin', 'Cuilen'), 12)0.004901960784>>> round(dist_eudex('ATCG', 'TAGC'), 12)0.197549019608

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Eudex.dist method instead.
abydos.distance.sim_eudex(src, tar, weights='exponential', max_length=8)
Return normalized Hamming similarity between Eudex hashes of two terms.
This is a wrapper for Eudex.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• weights (str, iterable, or generator function) -- The weights or
weights generator function• max_length (int) -- The number of characters to encode as a eudex hash
Returns The normalized Eudex Hamming similarity
Return type int

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Examples>>> round(sim_eudex('cat', 'hat'), 12)0.937254901961>>> round(sim_eudex('Niall', 'Neil'), 12)
0.999019607843>>> round(sim_eudex('Colin', 'Cuilen'), 12)0.995098039216>>> round(sim_eudex('ATCG', 'TAGC'), 12)0.802450980392

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Eudex.sim method instead.
class abydos.distance.Sift4(max_offset=5, max_distance=0, **kwargs)
Bases: abydos.distance._distance._Distance
Sift4 Common version.
This is an approximation of edit distance, described in [Zac14].
New in version 0.3.6.
Initialize Sift4 instance.
Parameters• max_offset (int) -- The number of characters to search for matching letters• max_distance (int) -- The distance at which to stop and exit
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized "common" Sift4 distance between two terms.
This is Sift4 distance, normalized to [0, 1].
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The normalized Sift4 distance
Return type float
Examples>>> cmp = Sift4()>>> round(cmp.dist('cat', 'hat'), 12)0.333333333333>>> cmp.dist('Niall', 'Neil')
0.4>>> cmp.dist('Colin', 'Cuilen')
0.5>>> cmp.dist('ATCG', 'TAGC')
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New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the "common" Sift4 distance between two terms.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Sift4 distance according to the common formula
Return type int
Examples>>> cmp = Sift4()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('Colin', 'Cuilen')3>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classclass abydos.distance.Sift4Simplest(max_offset=5, **kwargs)
Bases: abydos.distance._sift4.Sift4
Sift4 Simplest version.
This is an approximation of edit distance, described in [Zac14].
New in version 0.3.6.
Initialize Sift4Simplest instance.
Parameters• max_offset (int) -- The number of characters to search for matching letters• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist_abs(src, tar)
Return the "simplest" Sift4 distance between two terms.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Sift4 distance according to the simplest formula
Return type int

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Examples>>> cmp = Sift4Simplest()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('Colin', 'Cuilen')3>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classclass abydos.distance.Sift4Extended(max_offset=5,
max_distance=0,
tokenizer=None,
token_matcher=None,
matching_evaluator=None,
local_length_evaluator=None,transposition_cost_evaluator=None,transpositions_evaluator=None, **kwargs)
Bases: abydos.distance._distance._Distance
Sift4 Extended version.
This is an approximation of edit distance, described in [Zac14].
New in version 0.4.0.
Initialize Sift4Extended instance.
Parameters• max_offset (int) -- The number of characters to search for matching letters• max_distance (int) -- The distance at which to stop and exit
• tokenizer (_Tokenizer) -- A tokenizer instance (character tokenization by default)• token_matcher (function) -- A token matcher function of two parameters (equality
by default). 𝑆𝑖𝑓 𝑡4𝐸𝑥𝑡𝑒𝑛𝑑𝑒𝑑.𝑠𝑖𝑓 𝑡4𝑡 𝑜𝑘𝑒𝑛𝑚 𝑎𝑡𝑐ℎ𝑒𝑟 is also supplied.
• matching_evaluator (function) -- A token match quality function of two parameters (1 by default). 𝑆𝑖𝑓 𝑡4𝐸𝑥𝑡𝑒𝑛𝑑𝑒𝑑.𝑠𝑖𝑓 𝑡4𝑚 𝑎𝑡𝑐ℎ𝑖𝑛𝑔𝑒 𝑣𝑎𝑙𝑢𝑎𝑡𝑜𝑟 is also supplied.
• local_length_evaluator (function) -- A local length evaluator function(its single parameter by default).
𝑆𝑖𝑓 𝑡4𝐸𝑥𝑡𝑒𝑛𝑑𝑒𝑑.𝑟𝑒𝑤𝑎𝑟𝑑𝑙 𝑒𝑛𝑔𝑡ℎ𝑒 𝑣𝑎𝑙𝑢𝑎𝑡𝑜𝑟 and
𝑆𝑖𝑓 𝑡4𝐸𝑥𝑡𝑒𝑛𝑑𝑒𝑑.𝑟𝑒𝑤𝑎𝑟𝑑𝑙 𝑒𝑛𝑔𝑡ℎ𝑒 𝑣𝑎𝑙𝑢𝑎𝑡𝑜𝑟𝑒 𝑥𝑝 are also supplied.
• transposition_cost_evaluator(function)-Asitioncost
evaluator
functionoftwoparameters(1
by
𝑆𝑖𝑓 𝑡4𝐸𝑥𝑡𝑒𝑛𝑑𝑒𝑑.𝑙𝑜𝑛𝑔𝑒𝑟𝑡 𝑟𝑎𝑛𝑠𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑠𝑎 𝑟𝑒𝑚 𝑜𝑟𝑒𝑐 𝑜𝑠𝑡𝑙𝑦 is also supplied.

transpodefault).

• transpositions_evaluator (function) -- A transpositions evaluator function of
two parameters (the second parameter subtracted from the first, by default).
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist_abs(src, tar)
Return the Sift4 Extended distance between two strings.
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Abydos Documentation, Release 0.5.0• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The Sift4 distance according to the extended formula
Return type int
Examples>>> cmp = Sift4Extended()>>> cmp.dist_abs('cat', 'hat')1>>> cmp.dist_abs('Niall', 'Neil')2>>> cmp.dist_abs('aluminum', 'Catalan')5>>> cmp.dist_abs('ATCG', 'TAGC')2

New in version 0.4.0.
static longer_transpositions_are_more_costly(pos1, pos2)
Longer Transpositions Are More Costly.
Parameters• pos1 (int) -- The position of the first transposition• pos2 (int) -- The position of the second transposition
Returns• float -- A cost that grows as difference in the positions increases• .. versionadded:: 0.4.0
static reward_length_evaluator(length)
Reward Length Evaluator.
Parameters length (int) -- The length of a local match
Returns• float -- A reward value that grows sub-linearly• .. versionadded:: 0.4.0
static reward_length_evaluator_exp(length)
Reward Length Evaluator.
Parameters length (int) -- The length of a local match
Returns• float -- A reward value that grows exponentially• .. versionadded:: 0.4.0
static sift4_matching_evaluator(src, tar)
Sift4 Matching Evaluator.
Parameters• src (str) -- Source string for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison
Returns• float -- The Sift4 similarity of the two tokens• .. versionadded:: 0.4.0
static sift4_token_matcher(src, tar)
Sift4 Token Matcher.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns• bool -- Whether the Sift4 similarity of the two tokens is over 0.7• .. versionadded:: 0.4.0
abydos.distance.sift4_common(src, tar, max_offset=5, max_distance=0)
Return the "common" Sift4 distance between two terms.
This is a wrapper for Sift4.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• max_offset (int) -- The number of characters to search for matching letters• max_distance (int) -- The distance at which to stop and exit
Returns The Sift4 distance according to the common formula
Return type int
Examples>>> sift4_common('cat', 'hat')1>>> sift4_common('Niall', 'Neil')2>>> sift4_common('Colin', 'Cuilen')3>>> sift4_common('ATCG', 'TAGC')2

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Sift4.dist_abs method instead.
abydos.distance.sift4_simplest(src, tar, max_offset=5)
Return the "simplest" Sift4 distance between two terms.
This is a wrapper for Sift4Simplest.dist_abs().
Parameters• src (str) -- Source string for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison• max_offset (int) -- The number of characters to search for matching letters
Returns The Sift4 distance according to the simplest formula
Return type int
Examples>>> sift4_simplest('cat', 'hat')1>>> sift4_simplest('Niall', 'Neil')2>>> sift4_simplest('Colin', 'Cuilen')3>>> sift4_simplest('ATCG', 'TAGC')2

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Sift4Simplest.dist_abs method instead.
abydos.distance.dist_sift4(src, tar, max_offset=5, max_distance=0)
Return the normalized "common" Sift4 distance between two terms.
This is a wrapper for Sift4.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• max_offset (int) -- The number of characters to search for matching letters• max_distance (int) -- The distance at which to stop and exit
Returns The normalized Sift4 distance
Return type float
Examples>>> round(dist_sift4('cat', 'hat'), 12)0.333333333333>>> dist_sift4('Niall', 'Neil')
0.4>>> dist_sift4('Colin', 'Cuilen')
0.5>>> dist_sift4('ATCG', 'TAGC')
0.5

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Sift4.dist method instead.
abydos.distance.sim_sift4(src, tar, max_offset=5, max_distance=0)
Return the normalized "common" Sift4 similarity of two terms.
This is a wrapper for Sift4.sim().

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Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• max_offset (int) -- The number of characters to search for matching letters• max_distance (int) -- The distance at which to stop and exit
Returns The normalized Sift4 similarity
Return type float
Examples>>> round(sim_sift4('cat', 'hat'), 12)
0.666666666667>>> sim_sift4('Niall', 'Neil')
0.6>>> sim_sift4('Colin', 'Cuilen')
0.5>>> sim_sift4('ATCG', 'TAGC')
0.5

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Sift4.sim method instead.
class abydos.distance.Typo(metric='euclidean', cost=(1, 1, 0.5, 0.5), layout='QWERTY', failsafe=False, **kwargs)
Bases: abydos.distance._distance._Distance
Typo distance.
This is inspired by Typo-Distance [Son11], and a fair bit of this was copied from that module. Compared to theoriginal, this supports different metrics for substitution.
New in version 0.3.6.
Initialize Typo instance.
Parameters• metric (str) -- Supported values include:
log-euclidean, and log-manhattan

euclidean,manhattan,• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and shift, respectively (by default: (1, 1, 0.5, 0.5)) The substitution & shift
costs should be significantly less than the cost of an insertion & deletion unless a log metricis used.
• layout (str) -- Name of the keyboard layout to use (Currently supported: QWERTY,
Dvorak, AZERTY, QWERTZ, auto). If auto is selected, the class will attempt to determine an appropriate keyboard based on the supplied words.
• failsafe (bool) -- If True, substitution of an unknown character (one not present on theselected keyboard) will incur a cost equal to an insertion plus a deletion.
• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.

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dist(src, tar)
Return the normalized typo distance between two strings.
This is typo distance, normalized to [0, 1].
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized typo distance
Return type float
Examples>>> cmp = Typo()>>> round(cmp.dist('cat', 'hat'), 12)
0.527046283086>>> round(cmp.dist('Niall', 'Neil'), 12)0.565028142929>>> round(cmp.dist('Colin', 'Cuilen'), 12)
0.569035609563>>> cmp.dist('ATCG', 'TAGC')
0.625

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar)
Return the typo distance between two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Typo distance
Return type float
Raises ValueError -- char not found in any keyboard layouts
Examples>>> cmp = Typo()>>> cmp.dist_abs('cat', 'hat')
1.5811388>>> cmp.dist_abs('Niall', 'Neil')
2.8251407>>> cmp.dist_abs('Colin', 'Cuilen')
3.4142137>>> cmp.dist_abs('ATCG', 'TAGC')
2.5

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Abydos Documentation, Release 0.5.0>>> cmp = Typo(metric='manhattan')>>> cmp.dist_abs('cat', 'hat')
2.0>>> cmp.dist_abs('Niall', 'Neil')
3.0>>> cmp.dist_abs('Colin', 'Cuilen')
3.5>>> cmp.dist_abs('ATCG', 'TAGC')
2.5>>> cmp = Typo(metric='log-manhattan')>>> cmp.dist_abs('cat', 'hat')
0.804719>>> cmp.dist_abs('Niall', 'Neil')
2.2424533>>> cmp.dist_abs('Colin', 'Cuilen')
2.2424533>>> cmp.dist_abs('ATCG', 'TAGC')
2.3465736

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5), layout='QWERTY')
Return the typo distance between two strings.
This is a wrapper for Typo.typo().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• metric (str) -- Supported values include:
log-euclidean, and log-manhattan

euclidean,manhattan,• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and shift, respectively (by default: (1, 1, 0.5, 0.5)) The substitution & shift
costs should be significantly less than the cost of an insertion & deletion unless a log metricis used.
• layout (str) -- Name of the keyboard layout to use (Currently supported: QWERTY,
Dvorak, AZERTY, QWERTZ)
Returns Typo distance
Return type float

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Examples>>> typo('cat', 'hat')
1.5811388>>> typo('Niall', 'Neil')
2.8251407>>> typo('Colin', 'Cuilen')
3.4142137>>> typo('ATCG', 'TAGC')
2.5>>> typo('cat', 'hat', metric='manhattan')
2.0>>> typo('Niall', 'Neil', metric='manhattan')
3.0>>> typo('Colin', 'Cuilen', metric='manhattan')
3.5>>> typo('ATCG', 'TAGC', metric='manhattan')
2.5>>> typo('cat', 'hat', metric='log-manhattan')
0.804719>>> typo('Niall', 'Neil', metric='log-manhattan')
2.2424533>>> typo('Colin', 'Cuilen', metric='log-manhattan')
2.2424533>>> typo('ATCG', 'TAGC', metric='log-manhattan')
2.3465736

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Typo.dist_abs method instead.
abydos.distance.dist_typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5), layout='QWERTY')
Return the normalized typo distance between two strings.
This is a wrapper for Typo.dist().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• metric (str) -- Supported values include:
log-euclidean, and log-manhattan

euclidean,manhattan,• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and shift, respectively (by default: (1, 1, 0.5, 0.5)) The substitution & shift
costs should be significantly less than the cost of an insertion & deletion unless a log metricis used.
• layout (str) -- Name of the keyboard layout to use (Currently supported: QWERTY,
Dvorak, AZERTY, QWERTZ)
Returns Normalized typo distance
Return type float

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Examples>>> round(dist_typo('cat', 'hat'), 12)
0.527046283086>>> round(dist_typo('Niall', 'Neil'), 12)0.565028142929>>> round(dist_typo('Colin', 'Cuilen'), 12)
0.569035609563>>> dist_typo('ATCG', 'TAGC')
0.625

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Typo.dist method instead.
abydos.distance.sim_typo(src, tar, metric='euclidean', cost=(1, 1, 0.5, 0.5), layout='QWERTY')
Return the normalized typo similarity between two strings.
This is a wrapper for Typo.sim().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• metric (str) -- Supported values include:
log-euclidean, and log-manhattan

euclidean,manhattan,• cost (tuple) -- A 4-tuple representing the cost of the four possible edits: inserts, deletes,
substitutions, and shift, respectively (by default: (1, 1, 0.5, 0.5)) The substitution & shift
costs should be significantly less than the cost of an insertion & deletion unless a log metricis used.
• layout (str) -- Name of the keyboard layout to use (Currently supported: QWERTY,
Dvorak, AZERTY, QWERTZ)
Returns Normalized typo similarity
Return type float
Examples>>> round(sim_typo('cat', 'hat'), 12)0.472953716914>>> round(sim_typo('Niall', 'Neil'), 12)0.434971857071>>> round(sim_typo('Colin', 'Cuilen'), 12)0.430964390437>>> sim_typo('ATCG', 'TAGC')
0.375

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Typo.sim method instead.
class abydos.distance.Synoname(word_approx_min=0.3, char_approx_min=0.73, tests=4095,
ret_name=False, **kwargs)
Bases: abydos.distance._distance._Distance
Synoname.

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Cf. [JPGTrust91][Gro91]
New in version 0.3.6.
Initialize Synoname instance.
Parameters• word_approx_min (float) -- The minimum word approximation value to signal a'word_approx' match• char_approx_min (float) -- The minimum character approximation value to signal a'char_approx' match• tests (int or Iterable) -- Either an integer indicating tests to perform or a list of
test names to perform (defaults to performing all tests)• ret_name (bool) -- If True, returns the match name rather than its integer equivalent• **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Synoname distance between two words.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Synoname distance
Return type float
New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class
dist_abs(src, tar, force_numeric=False)
Return the Synoname similarity type of two words.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• force_numeric (bool) -- Overrides the instance's ret_name setting
Returns Synoname value
Return type int (or str if ret_name is True)
Examples>>> cmp = Synoname()>>> cmp.dist_abs(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''))
2>>> cmp = Synoname(ret_name=True)>>> cmp.dist_abs(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''))
'omission'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> cmp.dist_abs(('Dore', 'Gustave', ''),... ('Dore', 'Paul Gustave Louis Christophe', ''))
'inclusion'>>> cmp.dist_abs(('Pereira', 'I. R.', ''), ('Pereira', 'I. Smith', ''))
'word_approx'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.distance.synoname(src, tar, word_approx_min=0.3, char_approx_min=0.73, tests=4095,
ret_name=False)
Return the Synoname similarity type of two words.
This is a wrapper for Synoname.dist_abs().
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison• word_approx_min (float) -- The minimum word approximation value to signal a'word_approx' match• char_approx_min (float) -- The minimum character approximation value to signal a'char_approx' match• tests (int or Iterable) -- Either an integer indicating tests to perform or a list of
test names to perform (defaults to performing all tests)• ret_name (bool) -- If True, returns the match name rather than its integer equivalent
Returns Synoname value
Return type int (or str if ret_name is True)
Examples>>> synoname(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''))
2>>> synoname(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''),... ret_name=True)'omission'>>> synoname(('Dore', 'Gustave', ''),... ('Dore', 'Paul Gustave Louis Christophe', ''), ret_name=True)'inclusion'>>> synoname(('Pereira', 'I. R.', ''), ('Pereira', 'I. Smith', ''),... ret_name=True)'word_approx'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Synoname.dist_abs method instead.
class abydos.distance.Ozbay(**kwargs)
Bases: abydos.distance._distance._DistanceOzbay metric.

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The Ozbay metric [Ozb15] is a string distance measure developed by Hakan Ozbay, which combines Jaccard
distance, Levenshtein distance, and longest common substring distance.
The normalized variant should be considered experimental.
New in version 0.4.0.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the normalized Ozbay distance.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Normalized Ozbay distance
Return type float
Examples>>> cmp = Ozbay()>>> round(cmp.dist('cat', 'hat'), 12)
0.027777777778>>> round(cmp.dist('Niall', 'Neil'), 12)
0.24>>> round(cmp.dist('Colin', 'Cuilen'), 12)0.214285714286>>> cmp.dist('ATCG', 'TAGC')
0.140625

New in version 0.4.0.
dist_abs(src, tar)
Return the Ozbay metric.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Ozbay metric
Return type float

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Examples>>> cmp = Ozbay()>>> round(cmp.dist_abs('cat', 'hat'), 12)0.75>>> round(cmp.dist_abs('Niall', 'Neil'), 12)
6.0>>> round(cmp.dist_abs('Colin', 'Cuilen'), 12)7.714285714286>>> cmp.dist_abs('ATCG', 'TAGC')
3.0

New in version 0.4.0.
class abydos.distance.ISG(full_guth=False, symmetric=True, **kwargs)
Bases: abydos.distance._distance._Distance
Indice de Similitude-Guth (ISG) similarity.
This is an implementation of Bouchard & Pouyez's Indice de Similitude-Guth (ISG) [BP80]. At its heart, ISG is
Jaccard similarity, but limits on token matching are added according to part of Guth's matching criteria [Gut76].
[BP80] is limited in its implementation details. Based on the examples given in the paper, it appears that only the
first 4 of Guth's rules are considered (a letter in the first string must match a letter in the second string appearing
in the same position, an adjacent position, or two positions ahead). It also appears that the distance in the paper
is the greater of the distance from string 1 to string 2 and the distance from string 2 to string 1.
These qualities can be specified as parameters. At initialization, specify full_guth=True to apply all ofGuth's rules and symmetric=False to calculate only the distance from string 1 to string 2.
New in version 0.4.1.
Initialize ISG instance.
Parameters• full_guth (bool) -- Whether to apply all of Guth's matching rules• symmetric (bool) -- Whether to calculate the symmetric distance• **kwargs -- Arbitrary keyword arguments
New in version 0.4.1.
sim(src, tar)
Return the Indice de Similitude-Guth (ISG) similarity of two words.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The ISG similarity
Return type float

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Examples>>> cmp = ISG()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.5>>> cmp.sim('aluminum', 'Catalan')
0.15384615384615385>>> cmp.sim('ATCG', 'TAGC')
1.0

New in version 0.4.1.
class abydos.distance.Inclusion(**kwargs)
Bases: abydos.distance._distance._Distance
Inclusion distance.
The INC Programme, developed by [BP80] designates two terms as being "included" when:• One name is shorter than the other• There are at least 3 common characters• There is at most one difference, disregarding unmatching prefixes and suffixes
In addition to these rules, this implementation considers two terms as being "included" if they are identical.
The return value, though a float, can only take one of two values: 0.0, indicating inclusion, or 1.0, indication
non-inclusion.
New in version 0.4.1.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
dist(src, tar)
Return the INClusion Programme value of two words.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The INC Programme distance
Return type float

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Examples>>> cmp = Inclusion()>>> cmp.dist('cat', 'hat')
1.0>>> cmp.dist('Niall', 'Neil')
1.0>>> cmp.dist('aluminum', 'Catalan')
1.0>>> cmp.dist('ATCG', 'TAGC')
1.0

New in version 0.4.1.
class abydos.distance.Guth(tokenizer=None, **kwargs)
Bases: abydos.distance._distance._Distance
Guth matching.
Guth matching [Gut76] uses a simple positional matching rule list to determine whether two names match.
Following the original, the sim_score() method returns only 1.0 for matching or 0.0 for non-matching.
The .𝑠𝑖𝑚 mathod instead penalizes more distant matches and never outrightly declares two names a nonmatching unless no matches can be made in the two strings.
Tokens other than single characters can be matched by specifying a tokenizer during initialization or setting theqval parameter.
New in version 0.4.1.
Initialize Guth instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value.
New in version 0.4.1.
sim(src, tar)
Return the relative Guth similarity of two strings.
This deviates from the algorithm described in [Gut76] in that more distant matches are penalized, so thatless similar terms score lower that more similar terms.
If no match is found for a particular token in the source string, this does not result in an automatic 0.0
score. Rather, the score is further penalized towards 0.0.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Relative Guth matching score
Return type float

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Examples>>> cmp = Guth()>>> cmp.sim('cat', 'hat')
0.8666666666666667>>> cmp.sim('Niall', 'Neil')
0.8800000000000001>>> cmp.sim('aluminum', 'Catalan')
0.4>>> cmp.sim('ATCG', 'TAGC')
0.8

New in version 0.4.1.
sim_score(src, tar)
Return the Guth matching score of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns Guth matching score (1.0 if matching, otherwise 0.0)
Return type float
Examples>>> cmp = Guth()>>> cmp.sim_score('cat', 'hat')
1.0>>> cmp.sim_score('Niall', 'Neil')
1.0>>> cmp.sim_score('aluminum', 'Catalan')
0.0>>> cmp.sim_score('ATCG', 'TAGC')
1.0

New in version 0.4.1.
class abydos.distance.VPS(**kwargs)
Bases: abydos.distance._distance._Distance
Victorian Panel Study (VPS) score.
VPS score is presented in [Schurer07].
New in version 0.4.1.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the Victorian Panel Study score of two words.
Parameters• src (str) -- Source string for comparison

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Abydos Documentation, Release 0.5.0• tar (str) -- Target string for comparison
Returns The VPS score
Return type float
Examples>>> cmp = VPS()>>> cmp.sim('cat', 'hat')
0.5>>> cmp.sim('Niall', 'Neil')
0.3>>> cmp.sim('aluminum', 'Catalan')
0.14285714285714285>>> cmp.sim('ATCG', 'TAGC')
0.3333333333333333

New in version 0.4.1.
class abydos.distance.LIG3(**kwargs)
Bases: abydos.distance._distance._Distance
LIG3 similarity.
[SD02] proposes three Levenshtein-ISG-Guth hybrid similarity measures: LIG1, LIG2, and LIG3. Of these,
LIG1 is identical to ISG and LIG2 is identical to normalized Levenshtein similarity. Only LIG3 is a novel
measure, defined as:

𝑠𝑖𝑚𝐿𝐼𝐺3 (𝑋, 𝑌 ) =2𝐼
2𝐼 + 𝐶

Here, I is the number of exact matches between the two words, truncated to the length of the shorter word, andC is the Levenshtein distance between the two words.
New in version 0.4.1.
Initialize _Distance instance.
Parameters **kwargs -- Arbitrary keyword arguments
New in version 0.4.0.
sim(src, tar)
Return the LIG3 similarity of two words.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns The LIG3 similarity
Return type float

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Examples>>> cmp = LIG3()>>> cmp.sim('cat', 'hat')
0.8>>> cmp.sim('Niall', 'Neil')
0.5714285714285714>>> cmp.sim('aluminum', 'Catalan')
0.0>>> cmp.sim('ATCG', 'TAGC')
0.0

New in version 0.4.1.
class abydos.distance.SSK(tokenizer=None, ssk_lambda=0.9, **kwargs)
Bases: abydos.distance._token_distance._TokenDistanceString subsequence kernel (SSK) similarity.
This is based on [LSShaweTaylor+02].
New in version 0.4.1.
Initialize SSK instance.
Parameters• tokenizer (_Tokenizer) -- A tokenizer instance from the abydos.tokenizer
package• ssk_lambda (float or Iterable) -- A value in the range (0.0, 1.0) used
for discouting gaps between characters according to the method described in[LSShaweTaylor+02]. To supply multiple values of lambda, provide an Iterable of numericvalues, such as (0.5, 0.05) or np.arange(0.05, 0.5, 0.05)• **kwargs -- Arbitrary keyword arguments
Other Parameters qval (int) -- The length of each q-skipgram. Using this parameter and tokenizer=None will cause the instance to use the QGramskipgrams tokenizer with this q value.
New in version 0.4.1.
sim(src, tar)
Return the normalized SSK similarity of two strings.
Parameters• src (str) -- Source string (or QGrams/Counter objects) for comparison• tar (str) -- Target string (or QGrams/Counter objects) for comparison
Returns Normalized string subsequence kernel similarity
Return type float

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Examples>>> cmp = SSK()>>> cmp.sim('cat', 'hat')
0.3558718861209964>>> cmp.sim('Niall', 'Neil')
0.4709007822130597>>> cmp.sim('aluminum', 'Catalan')
0.13760157193822603>>> cmp.sim('ATCG', 'TAGC')
0.6140899528060498

New in version 0.4.1.
sim_score(src, tar)
Return the SSK similarity of two strings.
Parameters• src (str) -- Source string for comparison• tar (str) -- Target string for comparison
Returns String subsequence kernel similarity
Return type float
Examples>>> cmp = SSK()>>> cmp.dist_abs('cat', 'hat')
0.6441281138790036>>> cmp.dist_abs('Niall', 'Neil')
0.5290992177869402>>> cmp.dist_abs('aluminum', 'Catalan')
0.862398428061774>>> cmp.dist_abs('ATCG', 'TAGC')
0.38591004719395017

New in version 0.4.1.
3.1.1.4 abydos.fingerprint package
abydos.fingerprint.
The fingerprint package implements string fingerprints such as:• Basic fingerprinters originating in OpenRefine <http://openrefine.org>:– String (String)– Phonetic, which applies a phonetic algorithm and returns the string fingerprint of the result (Phonetic)– QGram, which applies Q-gram tokenization and returns the string fingerprint of the result (QGram)• Fingerprints developed by Pollock & Zomora:– Skeleton key (SkeletonKey)– Omission key (OmissionKey)

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Abydos Documentation, Release 0.5.0• Fingerprints developed by Cisłak & Grabowski:– Occurrence (Occurrence)– Occurrence halved (OccurrenceHalved)– Count (Count)– Position (Position)• The Synoname toolcode (SynonameToolcode)• Taft's codings:– Consonant coding (Consonant)– Extract - letter list (Extract)– Extract - position & frequency (ExtractPositionFrequency)• L.A. County Sheriff's System (LACSS)• Library of Congress Cutter table encoding (LCCutter)• Burrows-Wheeler transform (BWTF) and run-length encoded Burrows-Wheeler transform (BWTRLEF)
Each fingerprint class has a fingerprint method that takes a string and returns the string's fingerprint:>>> sk = SkeletonKey()>>> sk.fingerprint('orange')'ORNGAE'>>> sk.fingerprint('strange')'STRNGAE'

class abydos.fingerprint.String(joiner=' ')Bases: abydos.fingerprint._fingerprint._Fingerprint
String Fingerprint.
The fingerprint of a string is a string consisting of all of the unique words in a string, alphabetized & concatenatedwith intervening joiners. This fingerprint is described at [Ope12].
New in version 0.3.6.
Initialize String instance.
Parameters joiner (str) -- The string that will be placed between each word
New in version 0.4.0.
fingerprint(phrase)
Return string fingerprint.
Parameters phrase (str) -- The string from which to calculate the fingerprint
Returns The fingerprint of the phrase
Return type str

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Example>>> sf = String()>>> sf.fingerprint('The quick brown fox jumped over the lazy dog.')'brown dog fox jumped lazy over quick the'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.str_fingerprint(phrase, joiner=' ')Return string fingerprint.
This is a wrapper for String.fingerprint().
Parameters• phrase (str) -- The string from which to calculate the fingerprint• joiner (str) -- The string that will be placed between each word
Returns The fingerprint of the phrase
Return type str
Example>>> str_fingerprint('The quick brown fox jumped over the lazy dog.')'brown dog fox jumped lazy over quick the'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the String.fingerprint method instead.
class abydos.fingerprint.QGram(qval=2, start_stop='', joiner='', skip=0)
Bases: abydos.fingerprint._fingerprint._Fingerprint
Q-Gram Fingerprint.
A q-gram fingerprint is a string consisting of all of the unique q-grams in a string, alphabetized & concatenated.
This fingerprint is described at [Ope12].
New in version 0.3.6.
Initialize Q-Gram fingerprinter.
qval [int] The length of each q-gram (by default 2)
start_stop [str] The start & stop symbol(s) to concatenate on either end of the phrase, as defined intokenizer.QGrams
joiner [str] The string that will be placed between each word
skip [int or Iterable] The number of characters to skip, can be an integer, range object, or list
New in version 0.4.0.
fingerprint(phrase)
Return Q-Gram fingerprint.
Parameters phrase (str) -- The string from which to calculate the q-gram fingerprint
Returns The q-gram fingerprint of the phrase

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Return type str
Examples>>> qf = QGram()>>> qf.fingerprint('The quick brown fox jumped over the lazy dog.')'azbrckdoedeleqerfoheicjukblampnfogovowoxpequrortthuiumvewnxjydzy'>>> qf.fingerprint('Christopher')'cherhehrisopphristto'>>> qf.fingerprint('Niall')'aliallni'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.qgram_fingerprint(phrase, qval=2, start_stop='', joiner='')Return Q-Gram fingerprint.
This is a wrapper for QGram.fingerprint().
Parameters• phrase (str) -- The string from which to calculate the q-gram fingerprint• qval (int) -- The length of each q-gram (by default 2)• start_stop (str) -- The start & stop symbol(s) to concatenate on either end of thephrase, as defined in tokenizer.QGrams• joiner (str) -- The string that will be placed between each word
Returns The q-gram fingerprint of the phrase
Return type str
Examples>>> qgram_fingerprint('The quick brown fox jumped over the lazy dog.')'azbrckdoedeleqerfoheicjukblampnfogovowoxpequrortthuiumvewnxjydzy'>>> qgram_fingerprint('Christopher')'cherhehrisopphristto'>>> qgram_fingerprint('Niall')'aliallni'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the QGram.fingerprint method instead.
class abydos.fingerprint.Phonetic(phonetic_algorithm=None, joiner=' ')Bases: abydos.fingerprint._string.String
Phonetic Fingerprint.
A phonetic fingerprint is identical to a standard string fingerprint, as implemented in String, but performs
the fingerprinting function after converting the string to its phonetic form, as determined by some phonetic
algorithm. This fingerprint is described at [Ope12].
New in version 0.3.6.
Initialize Phonetic instance.

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phonetic_algorithm [function] A phonetic algorithm that takes a string and returns a string (presumably aphonetic representation of the original string). By default, this function uses double_metaphone().
joiner [str] The string that will be placed between each word
New in version 0.4.0.
fingerprint(phrase)
Return the phonetic fingerprint of a phrase.
Parameters phrase (str) -- The string from which to calculate the phonetic fingerprint
Returns The phonetic fingerprint of the phrase
Return type str
Examples>>> pf = Phonetic()>>> pf.fingerprint('The quick brown fox jumped over the lazy dog.')'0 afr fks jmpt kk ls prn tk'>>> from abydos.phonetic import Soundex>>> pf = Phonetic(Soundex())>>> pf.fingerprint('The quick brown fox jumped over the lazy dog.')'b650 d200 f200 j513 l200 o160 q200 t000'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.phonetic_fingerprint(phrase,phonetic_algorithm=<function double_metaphone>, joiner=' ', *args, **kwargs)
Return the phonetic fingerprint of a phrase.
This is a wrapper for Phonetic.fingerprint().
Parameters• phrase (str) -- The string from which to calculate the phonetic fingerprint• phonetic_algorithm (function) -- A phonetic algorithm that takes a string andreturns a string (presumably a phonetic representation of the original string). By default,
this function uses double_metaphone().
• joiner (str) -- The string that will be placed between each word
• *args -- Variable length argument list
• **kwargs -- Arbitrary keyword arguments
Returns The phonetic fingerprint of the phrase
Return type str

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Examples>>> phonetic_fingerprint('The quick brown fox jumped over the lazy dog.')'0 afr fks jmpt kk ls prn tk'>>> from abydos.phonetic import soundex>>> phonetic_fingerprint('The quick brown fox jumped over the lazy dog.',... phonetic_algorithm=soundex)'b650 d200 f200 j513 l200 o160 q200 t000'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Phonetic.fingerprint method instead.
class abydos.fingerprint.OmissionKey
Bases: abydos.fingerprint._fingerprint._Fingerprint
Omission Key.
The omission key of a word is defined in [PZ84].
New in version 0.3.6.
fingerprint(word)
Return the omission key.
Parameters word (str) -- The word to transform into its omission key
Returns The omission key
Return type str
Examples>>> ok = OmissionKey()>>> ok.fingerprint('The quick brown fox jumped over the lazy dog.')'JKQXZVWYBFMGPDHCLNTREUIOA'>>> ok.fingerprint('Christopher')'PHCTSRIOE'>>> ok.fingerprint('Niall')'LNIA'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.omission_key(word)
Return the omission key.
This is a wrapper for OmissionKey.fingerprint().
Parameters word (str) -- The word to transform into its omission key
Returns The omission key
Return type str

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Examples>>> omission_key('The quick brown fox jumped over the lazy dog.')'JKQXZVWYBFMGPDHCLNTREUIOA'>>> omission_key('Christopher')'PHCTSRIOE'>>> omission_key('Niall')'LNIA'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the OmissionKey.fingerprint method instead.
class abydos.fingerprint.SkeletonKey
Bases: abydos.fingerprint._fingerprint._Fingerprint
Skeleton Key.
The skeleton key of a word is defined in [PZ84].
New in version 0.3.6.
fingerprint(word)
Return the skeleton key.
Parameters word (str) -- The word to transform into its skeleton key
Returns The skeleton key
Return type str
Examples>>> sk = SkeletonKey()>>> sk.fingerprint('The quick brown fox jumped over the lazy dog.')'THQCKBRWNFXJMPDVLZYGEUIOA'>>> sk.fingerprint('Christopher')'CHRSTPIOE'>>> sk.fingerprint('Niall')'NLIA'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.skeleton_key(word)
Return the skeleton key.
This is a wrapper for SkeletonKey.fingerprint().
Parameters word (str) -- The word to transform into its skeleton key
Returns The skeleton key
Return type str

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Examples>>> skeleton_key('The quick brown fox jumped over the lazy dog.')'THQCKBRWNFXJMPDVLZYGEUIOA'>>> skeleton_key('Christopher')'CHRSTPIOE'>>> skeleton_key('Niall')'NLIA'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SkeletonKey.fingerprint method instead.
class abydos.fingerprint.Occurrence(n_bits=16, most_common=('e', 't', 'a', 'o', 'i', 'n', 's', 'h','r', 'd', 'l', 'c', 'u', 'm', 'w', 'f'))Bases: abydos.fingerprint._fingerprint._Fingerprint
Occurrence Fingerprint.
Based on the occurrence fingerprint from [CislakG17].
New in version 0.3.6.
Initialize Count instance.
Parameters• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
New in version 0.4.0.
fingerprint(word)
Return the occurrence fingerprint.
Parameters word (str) -- The word to fingerprint
Returns The occurrence fingerprint
Return type int
Examples>>> of = Occurrence()>>> bin(of.fingerprint('hat'))'0b110000100000000'>>> bin(of.fingerprint('niall'))'0b10110000100000'>>> bin(of.fingerprint('colin'))'0b1110000110000'>>> bin(of.fingerprint('atcg'))'0b110000000010000'>>> bin(of.fingerprint('entreatment'))'0b1110010010000100'

New in version 0.3.0.
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abydos.fingerprint.occurrence_fingerprint(word, n_bits=16, most_common=('e', 't', 'a', 'o','i', 'n', 's', 'h', 'r', 'd', 'l', 'c', 'u', 'm', 'w', 'f'))
Return the occurrence fingerprint.
This is a wrapper for Occurrence.fingerprint().
Parameters• word (str) -- The word to fingerprint• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
Returns The occurrence fingerprint
Return type int
Examples>>> bin(occurrence_fingerprint('hat'))'0b110000100000000'>>> bin(occurrence_fingerprint('niall'))'0b10110000100000'>>> bin(occurrence_fingerprint('colin'))'0b1110000110000'>>> bin(occurrence_fingerprint('atcg'))'0b110000000010000'>>> bin(occurrence_fingerprint('entreatment'))'0b1110010010000100'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Occurrence.fingerprint method instead.
class abydos.fingerprint.OccurrenceHalved(n_bits=16, most_common=('e', 't', 'a', 'o', 'i', 'n','s', 'h', 'r', 'd', 'l', 'c', 'u', 'm', 'w', 'f'))Bases: abydos.fingerprint._fingerprint._Fingerprint
Occurrence Halved Fingerprint.
Based on the occurrence halved fingerprint from [CislakG17].
New in version 0.3.6.
Initialize Count instance.
Parameters• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
New in version 0.4.0.
fingerprint(word)
Return the occurrence halved fingerprint.
Based on the occurrence halved fingerprint from [CislakG17].
Parameters• word (str) -- The word to fingerprint
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Returns The occurrence halved fingerprint
Return type int
Examples>>> ohf = OccurrenceHalved()>>> bin(ohf.fingerprint('hat'))'0b1010000000010'>>> bin(ohf.fingerprint('niall'))'0b10010100000'>>> bin(ohf.fingerprint('colin'))'0b1001010000'>>> bin(ohf.fingerprint('atcg'))'0b10100000000000'>>> bin(ohf.fingerprint('entreatment'))'0b1111010000110000'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.occurrence_halved_fingerprint(word, n_bits=16, most_common=('e','t', 'a', 'o', 'i', 'n', 's', 'h', 'r', 'd', 'l', 'c','u', 'm', 'w', 'f'))
Return the occurrence halved fingerprint.
This is a wrapper for OccurrenceHalved.fingerprint().
Parameters• word (str) -- The word to fingerprint• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
Returns The occurrence halved fingerprint
Return type int
Examples>>> bin(occurrence_halved_fingerprint('hat'))'0b1010000000010'>>> bin(occurrence_halved_fingerprint('niall'))'0b10010100000'>>> bin(occurrence_halved_fingerprint('colin'))'0b1001010000'>>> bin(occurrence_halved_fingerprint('atcg'))'0b10100000000000'>>> bin(occurrence_halved_fingerprint('entreatment'))'0b1111010000110000'

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New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the OccurrenceHalved.fingerprint method
instead.
class abydos.fingerprint.Count(n_bits=16, most_common=('e', 't', 'a', 'o', 'i', 'n', 's', 'h', 'r', 'd', 'l','c', 'u', 'm', 'w', 'f'))Bases: abydos.fingerprint._fingerprint._Fingerprint
Count Fingerprint.
Based on the count fingerprint from [CislakG17].
New in version 0.3.6.
Initialize Count instance.
Parameters• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
New in version 0.4.0.
fingerprint(word)
Return the count fingerprint.
Parameters word (str) -- The word to fingerprint
Returns The count fingerprint
Return type int
Examples>>> cf = Count()>>> bin(cf.fingerprint('hat'))'0b1010000000001'>>> bin(cf.fingerprint('niall'))'0b10001010000'>>> bin(cf.fingerprint('colin'))'0b101010000'>>> bin(cf.fingerprint('atcg'))'0b1010000000000'>>> bin(cf.fingerprint('entreatment'))'0b1111010000100000'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.count_fingerprint(word, n_bits=16, most_common=('e', 't', 'a', 'o', 'i', 'n','s', 'h', 'r', 'd', 'l', 'c', 'u', 'm', 'w', 'f'))
Return the count fingerprint.
This is a wrapper for Count.fingerprint().
Parameters• word (str) -- The word to fingerprint• n_bits (int) -- Number of bits in the fingerprint returned

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Abydos Documentation, Release 0.5.0• most_common (list) -- The most common tokens in the target language, ordered byfrequency
Returns The count fingerprint
Return type int
Examples>>> bin(count_fingerprint('hat'))'0b1010000000001'>>> bin(count_fingerprint('niall'))'0b10001010000'>>> bin(count_fingerprint('colin'))'0b101010000'>>> bin(count_fingerprint('atcg'))'0b1010000000000'>>> bin(count_fingerprint('entreatment'))'0b1111010000100000'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Count.fingerprint method instead.
class abydos.fingerprint.Position(n_bits=16, most_common=('e', 't', 'a', 'o', 'i', 'n', 's', 'h', 'r','d', 'l', 'c', 'u', 'm', 'w', 'f'), bits_per_letter=3)
Bases: abydos.fingerprint._fingerprint._Fingerprint
Position Fingerprint.
Based on the position fingerprint from [CislakG17].
New in version 0.3.6.
Initialize Count instance.
Parameters• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency
New in version 0.4.0.
fingerprint(word)
Return the position fingerprint.
Parameters word (str) -- The word to fingerprint
Returns The position fingerprint
Return type int

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Examples>>> bin(position_fingerprint('hat'))'0b1110100011111111'>>> bin(position_fingerprint('niall'))'0b1111110101110010'>>> bin(position_fingerprint('colin'))'0b1111111110010111'>>> bin(position_fingerprint('atcg'))'0b1110010001111111'>>> bin(position_fingerprint('entreatment'))'0b101011111111'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.position_fingerprint(word, n_bits=16, most_common=('e', 't', 'a', 'o','i', 'n', 's', 'h', 'r', 'd', 'l', 'c', 'u', 'm', 'w', 'f'),bits_per_letter=3)
Return the position fingerprint.
This is a wrapper for Position.fingerprint().
Parameters• word (str) -- The word to fingerprint• n_bits (int) -- Number of bits in the fingerprint returned• most_common (list) -- The most common tokens in the target language, ordered byfrequency• bits_per_letter (int) -- The bits to assign for letter position
Returns The position fingerprint
Return type int
Examples>>> bin(position_fingerprint('hat'))'0b1110100011111111'>>> bin(position_fingerprint('niall'))'0b1111110101110010'>>> bin(position_fingerprint('colin'))'0b1111111110010111'>>> bin(position_fingerprint('atcg'))'0b1110010001111111'>>> bin(position_fingerprint('entreatment'))'0b101011111111'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Position.fingerprint method instead.
class abydos.fingerprint.SynonameToolcode
Bases: abydos.fingerprint._fingerprint._Fingerprint
Synoname Toolcode.

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Cf. [JPGTrust91][Gro91].
New in version 0.3.6.
fingerprint(lname, fname='', qual='', normalize=0)
Build the Synoname toolcode.
Parameters• lname (str) -- Last name
• fname (str) -- First name (can be blank)• qual (str) -- Qualifier• normalize (int) -- Normalization mode (0, 1, or 2)
Returns The transformed names and the synoname toolcode
Return type tuple
Examples>>> st = SynonameToolcode()>>> st.fingerprint('hat')('hat', '', '0000000003$$h')>>> st.fingerprint('niall')('niall', '', '0000000005$$n')>>> st.fingerprint('colin')('colin', '', '0000000005$$c')>>> st.fingerprint('atcg')('atcg', '', '0000000004$$a')>>> st.fingerprint('entreatment')('entreatment', '', '0000000011$$e')>>> st.fingerprint('Ste.-Marie', 'Count John II', normalize=2)('ste.-marie ii', 'count john', '0200491310$015b049a127c$smcji')>>> st.fingerprint('Michelangelo IV', '', 'Workshop of')('michelangelo iv', '', '3000550015$055b$mi')

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.fingerprint.synoname_toolcode(lname, fname='', qual='', normalize=0)
Build the Synoname toolcode.
This is a wrapper for SynonameToolcode.fingerprint().
Parameters• lname (str) -- Last name
• fname (str) -- First name (can be blank)• qual (str) -- Qualifier• normalize (int) -- Normalization mode (0, 1, or 2)
Returns The transformed names and the synoname toolcode
Return type tuple

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Examples>>> synoname_toolcode('hat')('hat', '', '0000000003$$h')>>> synoname_toolcode('niall')('niall', '', '0000000005$$n')>>> synoname_toolcode('colin')('colin', '', '0000000005$$c')>>> synoname_toolcode('atcg')('atcg', '', '0000000004$$a')>>> synoname_toolcode('entreatment')('entreatment', '', '0000000011$$e')>>> synoname_toolcode('Ste.-Marie', 'Count John II', normalize=2)('ste.-marie ii', 'count john', '0200491310$015b049a127c$smcji')>>> synoname_toolcode('Michelangelo IV', '', 'Workshop of')('michelangelo iv', '', '3000550015$055b$mi')

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SynonameToolcode.fingerprint method
instead.
class abydos.fingerprint.Consonant(variant=1, doubles=True, vowels=None)
Bases: abydos.fingerprint._fingerprint._Fingerprint
Consonant Coding Fingerprint.
Based on the consonant coding from [Taf70], variants 1, 2, 3, 1-D, 2-D, and 3-D.
New in version 0.4.1.
Initialize Consonant instance.
Parameters• variant (int) -- Selects between Taft's 3 variants, which assign to the vowel set one of:
1. A, E, I, O, & U
2. A, E, I, O, U, W, & Y
3. A, E, I, O, U, W, H, & Y• doubles (bool) -- If set to False, multiple consonants in a row are conflated to a single
instance.
• vowels (list, set, or str) -- Setting vowels to a non-None value overrides thevariant setting and defines the set of letters to be removed from the input.
New in version 0.4.1.
fingerprint(word)
Return the consonant coding.
Parameters word (str) -- The word to fingerprint
Returns The consonant coding
Return type int

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Examples>>> cf = Consonant()>>> cf.fingerprint('hat')'HT'>>> cf.fingerprint('niall')'NLL'>>> cf.fingerprint('colin')'CLN'>>> cf.fingerprint('atcg')'ATCG'>>> cf.fingerprint('entreatment')'ENTRTMNT'

New in version 0.4.1.
class abydos.fingerprint.Extract(letter_list=1)
Bases: abydos.fingerprint._fingerprint._Fingerprint
Extract Letter List fingerprint.
Based on the extract letter list coding from [Taf70], for lists 1, 2, 3, & 4.
New in version 0.4.1.
Initialize Extract instance.
Parameters letter_list (int or iterable) -- If an integer (1-4) is supplied, Taft's specified letter lists are used. If an iterable is supplied, its values will be used as the list of letters toremove (in order).
New in version 0.4.1.
fingerprint(word)
Return the extract letter list coding.
Parameters word (str) -- The word to fingerprint
Returns The extract letter list coding
Return type int
Examples>>> fp = Extract()>>> fp.fingerprint('hat')'HAT'>>> fp.fingerprint('niall')'NILL'>>> fp.fingerprint('colin')'CLIN'>>> fp.fingerprint('atcg')'ATCG'>>> fp.fingerprint('entreatment')'NRMN'New in version 0.4.1.
class abydos.fingerprint.ExtractPositionFrequency
Bases: abydos.fingerprint._fingerprint._Fingerprint

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Extract - Position & Frequency fingerprint.
Based on the extract - position & frequency coding from [Taf70].
New in version 0.4.1.
fingerprint(word)
Return the extract - position & frequency coding.
Parameters word (str) -- The word to fingerprint
Returns The extract - position & frequency coding
Return type int
Examples>>> fp = ExtractPositionFrequency()>>> fp.fingerprint('hat')'HAT'>>> fp.fingerprint('niall')'NILL'>>> fp.fingerprint('colin')'COLN'>>> fp.fingerprint('atcg')'ATCG'>>> fp.fingerprint('entreatment')'NMNT'New in version 0.4.1.
class abydos.fingerprint.LACSS
Bases: abydos.fingerprint._fingerprint._Fingerprint
L.A. County Sheriff's System fingerprint.
Based on the description from [Taf70].
New in version 0.4.1.
fingerprint(word)
Return the LACSS coding.
Parameters word (str) -- The word to fingerprint
Returns The L.A. County Sheriff's System fingerprint
Return type int
Examples>>> cf = LACSS()>>> cf.fingerprint('hat')'4911211'>>> cf.fingerprint('niall')'6488374'>>> cf.fingerprint('colin')'3015957'>>> cf.fingerprint('atcg')'1772371'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> cf.fingerprint('entreatment')'3882324'

New in version 0.4.1.
class abydos.fingerprint.LCCutter(max_length=64)
Bases: abydos.fingerprint._fingerprint._Fingerprint
Library of Congress Cutter table encoding.
This is based on the Library of Congress Cutter table encoding scheme, as described at https://www.loc.gov/
aba/pcc/053/table.html [oC13]. Handling for numerals is not included.
New in version 0.4.1.
Initialize LCCutter instance.
Parameters max_length (int) -- The length of the code returned (defaults to 64)
New in version 0.4.1.
fingerprint(word)
Return the Library of Congress Cutter table encoding of a word.
Parameters word (str) -- The word to fingerprint
Returns The Library of Congress Cutter table encoding
Return type str
Examples>>> cf = LCCutter()>>> cf.fingerprint('hat')'H38'>>> cf.fingerprint('niall')'N5355'>>> cf.fingerprint('colin')'C6556'>>> cf.fingerprint('atcg')'A834'>>> cf.fingerprint('entreatment')'E5874386468'

New in version 0.4.1.
class abydos.fingerprint.BWTF(terminator='x00')
Bases: abydos.fingerprint._fingerprint._Fingerprint
Burrows-Wheeler transform fingerprint.
This is a wrapper of the BWT class in abydos.compression, which provides the same interface as other descendants of _Fingerprint.
New in version 0.4.1.
Initialize BWTF instance.
Parameters terminator (str) -- A character added to signal the end of the string
New in version 0.4.1.

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fingerprint(word)
Return the Burrows-Wheeler transform of a word.
Parameters word (str) -- The word to fingerprint
Returns The Burrows-Wheeler transform of a word
Return type str
Examples>>> fp = BWTF()>>> fp.fingerprint('hat')'th\x00a'>>> fp.fingerprint('niall')'linla\x00'>>> fp.fingerprint('colin')'n\x00loic'>>> fp.fingerprint('atcg')'g\x00tca'>>> fp.fingerprint('entreatment')'term\x00teetnan'

New in version 0.4.1.
class abydos.fingerprint.BWTRLEF(terminator='x00')
Bases: abydos.fingerprint._fingerprint._Fingerprint
Burrows-Wheeler transform plus run-length encoding fingerprint.
This is a wrapper of the BWT and RLE classes in abydos.compression, which provides the same interface asother descendants of _Fingerprint.
New in version 0.4.1.
Initialize BWTRLEF instance.
Parameters terminator (str) -- A character added to signal the end of the string
New in version 0.4.1.
fingerprint(word)
Return the run-length encoded Burrows-Wheeler transform of a word.
Parameters word (str) -- The word to fingerprint
Returns The run-length encoded Burrows-Wheeler transform of a word
Return type str
Examples>>> fp = BWTRLEF()>>> fp.fingerprint('hat')'th\x00a'>>> fp.fingerprint('niall')'linla\x00'>>> fp.fingerprint('colin')'n\x00loic'>>> fp.fingerprint('atcg')(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'g\x00tca'>>> fp.fingerprint('entreatment')'term\x00teetnan'

New in version 0.4.1.
3.1.1.5 abydos.phones package
abydos.phones.
The phones module implements phonetic feature coding, decoding, and comparison functions. It has three functions:• ipa_to_features() takes a string of IPA symbols and returns list of integers that represent the phonetic
features bundled in the phone that the symbols represents.
• ipa_to_feature_dicts() takes a string of IPA symbols and returns list of human-readable dicts that
represent the phonetic features bundled in the phone that the symbols represents.
• get_feature() takes a list of feature bundles produced by ipa_to_features() and a feature name andreturns a list representing whether that feature is present in each component of the list.
• cmp_features() takes two phonetic feature bundles, such as the components of the lists returned by
ipa_to_features(), and returns a measure of their similarity.
An example using these functions on two different pronunciations of the word 'international':>>> int1 = 'ntnæn'>>> int2 = 'nnæn'>>> feat1 = ipa_to_features(int1)>>> feat1[1826957413067434410,
2711173160463936106,
2783230754502126250,
1828083331160779178,
2711173160463936106,
1826957425885227434,
2783231556184615322,
1828083331160779178,
2711173160463936106,
1828083331160779178,
2693158721554917798]>>> feat2 = ipa_to_features(int2)>>> feat2[1826957413067434410,
2711173160463936106,
2711173160463935914,
1828083331160779178,
2711173160463936106,
1826957425885227434,
2783231556184615322,
1826957414069873066,
2711173160463936106,
1828083331160779178,
2693158721554917798]>>> ipa_to_feature_dicts('n')[{'syllabic': '-',
'consonantal': '+',
(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'sonorant': '-',
'approximant': '-',
'labial': '-',
'round': '0',
'protruded': '0','compressed': '0',
'labiodental': '0','coronal': '+',
'anterior': '-',
'distributed': '+',
'dorsal': '+',
'high': '-',
'low': '-',
'front': '-',
'back': '-',
'tense': '-',
'pharyngeal': '-',
'atr': '0','rtr': '0',
'voice': '+',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
'velaric_suction': '-',
'continuant': '+/-','nasal': '-',
'strident': '+',
'lateral': '-',
'delayed_release': '+'},{'syllabic': '+',
'consonantal': '-',
'sonorant': '+',
'approximant': '+',
'labial': '+',
'round': '-',
'protruded': '-',
'compressed': '-',
'labiodental': '-',
'coronal': '-',
'anterior': '0',
'distributed': '0',
'dorsal': '+',
'high': '+',
'low': '-',
'front': '+',
'back': '-',
'tense': '-',
'pharyngeal': '+',
'atr': '-',
'rtr': '-',
'voice': '+',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
'velaric_suction': '-',
'continuant': '+',
'nasal': '-',
(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'strident': '-',
'lateral': '-',
'delayed_release': '-'},{'syllabic': '-',
'consonantal': '+',
'sonorant': '+',
'approximant': '-',
'labial': '-',
'round': '0',
'protruded': '0','compressed': '0',
'labiodental': '0','coronal': '+',
'anterior': '+',
'distributed': '-',
'dorsal': '-',
'high': '0','low': '0','front': '0','back': '0','tense': '0','pharyngeal': '-',
'atr': '0','rtr': '0',
'voice': '+',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
'velaric_suction': '-',
'continuant': '-',
'nasal': '+',
'strident': '-',
'lateral': '-',
'delayed_release': '-'}]>>> get_feature(feat1, 'consonantal')[-1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1]>>> get_feature(feat1, 'nasal')[-1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1]>>> [cmp_features(f1, f2) for f1, f2 in zip(feat1, feat2)]
[1.0,
1.0,
0.9032258064516129,
1.0,
1.0,
1.0,
1.0,
0.9193548387096774,
1.0,
1.0,
1.0]
>>> sum(cmp_features(f1, f2) for f1, f2 in zip(feat1, feat2))/len(feat1)
0.9838709677419355

abydos.phones.ipa_to_features(ipa)
Convert IPA to features.
This translates an IPA string of one or more phones to a list of ints representing the features of the string.
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Parameters ipa (str) -- The IPA representation of a phone or series of phones
Returns A representation of the features of the input string
Return type list of ints
Examples>>> ipa_to_features('mut')[2709662981243185770, 1825831513894594986, 2783230754502126250]>>> ipa_to_features('fon')[2781702983095331242, 1825831531074464170, 2711173160463936106]>>> ipa_to_features('telz')[2783230754502126250, 1826957430176000426, 2693158761954453926,
2783230754501863834]

New in version 0.1.0.
abydos.phones.ipa_to_feature_dicts(ipa)
Convert IPA to a feature dict list.
This translates an IPA string of one or more phones to a list of dicts representing the features of the string.
Parameters ipa (str) -- The IPA representation of a phone or series of phones
Returns A representation of the features of the input string
Return type list of dicts
Examples>>> ipa_to_feature_dicts('mut')[{'syllabic': '-',
'consonantal': '+',
'sonorant': '+',
'approximant': '-',
'labial': '+',
'round': '-',
'protruded': '-',
'compressed': '-',
'labiodental': '-',
'coronal': '-',
'anterior': '0',
'distributed': '0',
'dorsal': '-',
'high': '0','low': '0','front': '0','back': '0','tense': '0','pharyngeal': '-',
'atr': '0','rtr': '0',
'voice': '+',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'velaric_suction': '-',
'continuant': '-',
'nasal': '+',
'strident': '-',
'lateral': '-',
'delayed_release': '-'},{'syllabic': '+',
'consonantal': '-',
'sonorant': '+',
'approximant': '+',
'labial': '+',
'round': '+',
'protruded': '-',
'compressed': '-',
'labiodental': '-',
'coronal': '-',
'anterior': '0',
'distributed': '0',
'dorsal': '+',
'high': '+',
'low': '-',
'front': '-',
'back': '+',
'tense': '+',
'pharyngeal': '+',
'atr': '+',
'rtr': '-',
'voice': '+',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
'velaric_suction': '-',
'continuant': '+',
'nasal': '-',
'strident': '-',
'lateral': '-',
'delayed_release': '-'},{'syllabic': '-',
'consonantal': '+',
'sonorant': '-',
'approximant': '-',
'labial': '-',
'round': '0',
'protruded': '0','compressed': '0',
'labiodental': '0','coronal': '+',
'anterior': '+',
'distributed': '-',
'dorsal': '-',
'high': '0','low': '0','front': '0','back': '0','tense': '0','pharyngeal': '-',
'atr': '0',(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'rtr': '0',
'voice': '-',
'spread_glottis': '-',
'constricted_glottis': '-',
'glottalic_suction': '-',
'velaric_suction': '-',
'continuant': '-',
'nasal': '-',
'strident': '-',
'lateral': '-',
'delayed_release': '-'}]New in version 0.4.1.
abydos.phones.get_feature(vector, feature)
Get a feature vector.
This returns a list of ints, equal in length to the vector input, representing presence/absence/neutrality withrespect to a particular phonetic feature.
Parameters• vector (list) -- A tuple or list of ints representing the phonetic features of a phone orseries of phones (such as is returned by the ipa_to_features function)• feature (str) -- A feature name from the set:
– syllabic– consonantal– sonorant– approximant– labial– round– protruded– compressed– labiodental– coronal– anterior– distributed– dorsal– high– low– front– back– tense– pharyngeal– atr
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Abydos Documentation, Release 0.5.0– rtr– voice– spread_glottis– constricted_glottis– glottalic_suction– velaric_suction– continuant– nasal– strident– lateral– delayed_release
Returns A list indicating presence/absence/neutrality with respect to the feature
Return type list of ints
Raises AttributeError -- feature must be one of ...
Examples>>> tails = ipa_to_features('telz')>>> get_feature(tails, 'consonantal')[1, -1, 1, 1]>>> get_feature(tails, 'sonorant')[-1, 1, 1, -1]>>> get_feature(tails, 'nasal')[-1, -1, -1, -1]>>> get_feature(tails, 'coronal')[1, -1, 1, 1]

New in version 0.1.0.
abydos.phones.cmp_features(feat1, feat2, weights=None)
Compare features.
This returns a number in the range [0, 1] representing a comparison of two feature bundles.
If one of the bundles is negative, -1 is returned (for unknown values)
If the bundles are identical, 1 is returned.
If they are inverses of one another, 0 is returned.
Otherwise, a float representing their similarity is returned.
Parameters• feat1 (int) -- A feature bundle• feat2 (int) -- A feature bundle• weights (None or list or tuple or dict) -- If None, all features are of equalsignificance and a simple normalized hamming distance of the features is calculated. If alist or tuple of numeric values is supplied, the values are inferred as the weights for eachfeature, in order of the features listed in _FEATURE_MASK. If a dict is supplied, its key

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values should match keys in _FEATURE_MASK to which each weight (value) should be
assigned. Missing values in all cases are assigned a weight of 0 and will be omitted fromthe comparison.
Returns A comparison of the feature bundles
Return type float
Examples>>> cmp_features(ipa_to_features('l')[0], ipa_to_features('l')[0])1.0>>> cmp_features(ipa_to_features('l')[0], ipa_to_features('n')[0])0.8709677419354839>>> cmp_features(ipa_to_features('l')[0], ipa_to_features('z')[0])0.8709677419354839>>> cmp_features(ipa_to_features('l')[0], ipa_to_features('i')[0])0.564516129032258

New in version 0.1.0.
Changed in version 0.4.1: Added weights parameter for modifiable feature weighting
3.1.1.6 abydos.phonetic package
abydos.phonetic.
The phonetic package includes classes for phonetic algorithms, including:• Robert C. Russell's Index (RussellIndex)• American Soundex (Soundex)• Refined Soundex (RefinedSoundex)• Daitch-Mokotoff Soundex (DaitchMokotoff)• NYSIIS (NYSIIS)• Match Rating Algorithm (phonetic.MRA)• Metaphone (Metaphone)• Double Metaphone (DoubleMetaphone)• Caverphone (Caverphone)• Alpha Search Inquiry System (AlphaSIS)• Fuzzy Soundex (FuzzySoundex)• Phonex (Phonex)• Phonem (Phonem)• Phonix (Phonix)• PHONIC (PHONIC)• Standardized Phonetic Frequency Code (SPFC)• Statistics Canada (StatisticsCanada)• LEIN (LEIN )3.1. abydos package503

Abydos Documentation, Release 0.5.0• Roger Root (RogerRoot)• Eudex phonetic hash (phonetic.Eudex)• Parmar-Kumbharana (ParmarKumbharana)• Davidson's Consonant Code (Davidson)• SoundD (SoundD)• PSHP Soundex/Viewex Coding (PSHPSoundexFirst and PSHPSoundexLast)• Dolby Code (Dolby)• NRL English-to-phoneme (NRL)• Ainsworth grapheme to phoneme (Ainsworth)• Beider-Morse Phonetic Matching (BeiderMorse)
There are also language-specific phonetic algorithms for German:• Kölner Phonetik (Koelner)• phonet (Phonet)• Haase Phonetik (Haase)• Reth-Schek Phonetik (RethSchek)
For French:• FONEM (FONEM )• an early version of Henry Code (HenryEarly)
For Spanish:• Phonetic Spanish (PhoneticSpanish)• Spanish Metaphone (SpanishMetaphone)
For Swedish:• SfinxBis (SfinxBis)• Wåhlin (Waahlin)
For Norwegian:• Norphone (Norphone)
For Brazilian Portuguese:• SoundexBR (SoundexBR)
And there are some hybrid phonetic algorithms that employ multiple underlying phonetic algorithms:• Oxford Name Compression Algorithm (ONCA) (ONCA)• MetaSoundex (MetaSoundex)
Each class has an encode method to return the phonetically encoded string. Classes for which encode returns anumeric value generally have an encode_alpha method that returns an alphabetic version of the phonetic encoding,as demonstrated below:

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Abydos Documentation, Release 0.5.0>>> rus = RussellIndex()>>> rus.encode('Abramson')
128637>>> rus.encode_alpha('Abramson')'ABRMCN'class abydos.phonetic.RussellIndex
Bases: abydos.phonetic._phonetic._Phonetic
Russell Index.
This follows Robert C. Russell's Index algorithm, as described in [Rus18].
New in version 0.3.6.
encode(word)
Return the Russell Index (integer output) of a word.
Parameters word (str) -- The word to transform
Returns The Russell Index value
Return type int
Examples>>> pe = RussellIndex()>>> pe.encode('Christopher')
3813428>>> pe.encode('Niall')715>>> pe.encode('Smith')
3614>>> pe.encode('Schmidt')
3614

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the Russell Index (alphabetic output) for the word.
This follows Robert C. Russell's Index algorithm, as described in [Rus18].
Parameters word (str) -- The word to transform
Returns The Russell Index value as an alphabetic string
Return type str

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Examples>>> pe = RussellIndex()>>> pe.encode_alpha('Christopher')'CRACDBR'>>> pe.encode_alpha('Niall')'NAL'>>> pe.encode_alpha('Smith')'CMAD'>>> pe.encode_alpha('Schmidt')'CMAD'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.russell_index(word)
Return the Russell Index (integer output) of a word.
This is a wrapper for RussellIndex.encode().
Parameters word (str) -- The word to transform
Returns The Russell Index value
Return type int
Examples>>> russell_index('Christopher')
3813428>>> russell_index('Niall')715>>> russell_index('Smith')3614>>> russell_index('Schmidt')
3614

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RussellIndex.encode method instead.
abydos.phonetic.russell_index_num_to_alpha(num)Convert the Russell Index integer to an alphabetic string.
This is a wrapper for RussellIndex._to_alpha().
Parameters num (int) -- A Russell Index integer value
Returns The Russell Index as an alphabetic string
Return type str

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Examples>>> russell_index_num_to_alpha(3813428)'CRACDBR'>>> russell_index_num_to_alpha(715)'NAL'>>> russell_index_num_to_alpha(3614)'CMAD'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RussellIndex._to_alpha method instead.
abydos.phonetic.russell_index_alpha(word)
Return the Russell Index (alphabetic output) for the word.
This is a wrapper for RussellIndex.encode_alpha().
Parameters word (str) -- The word to transform
Returns The Russell Index value as an alphabetic string
Return type str
Examples>>> russell_index_alpha('Christopher')'CRACDBR'>>> russell_index_alpha('Niall')'NAL'>>> russell_index_alpha('Smith')'CMAD'>>> russell_index_alpha('Schmidt')'CMAD'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RussellIndex.encode_alpha method
instead.
class abydos.phonetic.Soundex(max_length=4, var='American', reverse=False, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Soundex.
Three variants of Soundex are implemented:• 'American' follows the American Soundex algorithm, as described at [Sta07] and in [Knu98]; this is also
called Miracode• 'special' follows the rules from the 1880-1910 US Census retrospective re-analysis, in which h & w are nottreated as blocking consonants but as vowels. Cf. [Rep13].
• 'Census' follows the rules laid out in GIL 55 [Sta97] by the US Census, including coding prefixed andunprefixed versions of some names
New in version 0.3.6.
Initialize Soundex instance.
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Abydos Documentation, Release 0.5.0• max_length (int) -- The length of the code returned (defaults to 4)• var (str) -- The variant of the algorithm to employ (defaults to American):– American follows the American Soundex algorithm, as described at [Sta07] and in[Knu98]; this is also called Miracode– special follows the rules from the 1880-1910 US Census retrospective re-analysis, inwhich h & w are not treated as blocking consonants but as vowels. Cf. [Rep13].
– Census follows the rules laid out in GIL 55 [Sta97] by the US Census, including coding
prefixed and unprefixed versions of some names• reverse (bool) -- Reverse the word before computing the selected Soundex (defaults toFalse); This results in "Reverse Soundex", which is useful for blocking in cases where theinitial elements may be in error.
• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The Soundex value
Return type str
Examples>>> pe = Soundex()>>> pe.encode("Christopher")'C623'>>> pe.encode("Niall")'N400'>>> pe.encode('Smith')'S530'>>> pe.encode('Schmidt')'S530'>>> Soundex(max_length=-1).encode('Christopher')'C623160000000000000000000000000000000000000000000000000000000000'>>> Soundex(max_length=-1, zero_pad=False).encode('Christopher')'C62316'>>> Soundex(reverse=True).encode('Christopher')'R132'>>> pe.encode('Ashcroft')'A261'>>> pe.encode('Asicroft')'A226'>>> pe_special = Soundex(var='special')>>> pe_special.encode('Ashcroft')'A226'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> pe_special.encode('Asicroft')'A226'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Soundex value
Return type str
Examples>>> pe = Soundex()>>> pe.encode_alpha("Christopher")'CRKT'>>> pe.encode_alpha("Niall")'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SNT'

New in version 0.4.0.
abydos.phonetic.soundex(word, max_length=4, var='American', reverse=False, zero_pad=True)
Return the Soundex code for a word.
This is a wrapper for Soundex.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)• var (str) -- The variant of the algorithm to employ (defaults to American):– American follows the American Soundex algorithm, as described at [Sta07] and in[Knu98]; this is also called Miracode– special follows the rules from the 1880-1910 US Census retrospective re-analysis, inwhich h & w are not treated as blocking consonants but as vowels. Cf. [Rep13].
– Census follows the rules laid out in GIL 55 [Sta97] by the US Census, including coding
prefixed and unprefixed versions of some names• reverse (bool) -- Reverse the word before computing the selected Soundex (defaults toFalse); This results in "Reverse Soundex", which is useful for blocking in cases where theinitial elements may be in error.
• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Soundex value
Return type str

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Examples>>> soundex("Christopher")'C623'>>> soundex("Niall")'N400'>>> soundex('Smith')'S530'>>> soundex('Schmidt')'S530'>>> soundex('Christopher', max_length=-1)'C623160000000000000000000000000000000000000000000000000000000000'>>> soundex('Christopher', max_length=-1, zero_pad=False)'C62316'>>> soundex('Christopher', reverse=True)'R132'>>> soundex('Ashcroft')'A261'>>> soundex('Asicroft')'A226'>>> soundex('Ashcroft', var='special')'A226'>>> soundex('Asicroft', var='special')'A226'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Soundex.encode method instead.
class abydos.phonetic.RefinedSoundex(max_length=-1,
tain_vowels=False)
Bases: abydos.phonetic._phonetic._Phonetic

zero_pad=False,re-Refined Soundex.
This is Soundex, but with more character classes. It was defined at [Boy98].
New in version 0.3.6.
Initialize RefinedSoundex instance.
Parameters• max_length (int) -- The length of the code returned (defaults to unlimited)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string• retain_vowels (bool) -- Retain vowels (as 0) in the resulting code
New in version 0.4.0.
encode(word)
Return the Refined Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The Refined Soundex value
Return type str

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Examples>>> pe = RefinedSoundex()>>> pe.encode('Christopher')'C93619'>>> pe.encode('Niall')'N7'>>> pe.encode('Smith')'S86'>>> pe.encode('Schmidt')'S386'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Refined Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Refined Soundex value
Return type str
Examples>>> pe = RefinedSoundex()>>> pe.encode_alpha('Christopher')'CRKTPR'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SKNT'New in version 0.4.0.
abydos.phonetic.refined_soundex(word, max_length=-1, zero_pad=False, retain_vowels=False)
Return the Refined Soundex code for a word.
This is a wrapper for RefinedSoundex.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to unlimited)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string• retain_vowels (bool) -- Retain vowels (as 0) in the resulting code
Returns The Refined Soundex value
Return type str

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Examples>>> refined_soundex('Christopher')'C93619'>>> refined_soundex('Niall')'N7'>>> refined_soundex('Smith')'S86'>>> refined_soundex('Schmidt')'S386'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RefinedSoundex.encode method instead.
class abydos.phonetic.DaitchMokotoff(max_length=6, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Daitch-Mokotoff Soundex.
Based on Daitch-Mokotoff Soundex [Mok97], this returns values of a word as a set. A collection is necessary
since there can be multiple values for a single word.
New in version 0.3.6.
Initialize DaitchMokotoff instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 6; must be between 6
and 64)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Daitch-Mokotoff Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The Daitch-Mokotoff Soundex value
Return type str
Examples>>> pe = DaitchMokotoff()>>> sorted(pe.encode('Christopher'))['494379', '594379']>>> pe.encode('Niall'){'680000'}>>> pe.encode('Smith'){'463000'}>>> pe.encode('Schmidt'){'463000'}>>> sorted(DaitchMokotoff(max_length=20,... zero_pad=False).encode('The quick brown fox'))['35457976754', '3557976754']

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New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Daitch-Mokotoff Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Daitch-Mokotoff Soundex value
Return type str
Examples>>> pe = DaitchMokotoff()>>> sorted(pe.encode_alpha('Christopher'))['KRSTPR', 'SRSTPR']>>> pe.encode_alpha('Niall'){'NL'}>>> pe.encode_alpha('Smith'){'SNT'}>>> pe.encode_alpha('Schmidt'){'SNT'}>>> sorted(DaitchMokotoff(max_length=20,... zero_pad=False).encode_alpha('The quick brown fox'))['TKKPRPNPKS', 'TKSKPRPNPKS']

New in version 0.4.0.
abydos.phonetic.dm_soundex(word, max_length=6, zero_pad=True)
Return the Daitch-Mokotoff Soundex code for a word.
This is a wrapper for DaitchMokotoff.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 6; must be between 6
and 64)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Daitch-Mokotoff Soundex value
Return type str
Examples>>> sorted(dm_soundex('Christopher'))['494379', '594379']>>> dm_soundex('Niall'){'680000'}>>> dm_soundex('Smith'){'463000'}>>> dm_soundex('Schmidt'){'463000'}

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Abydos Documentation, Release 0.5.0>>> sorted(dm_soundex('The quick brown fox', max_length=20,... zero_pad=False))['35457976754', '3557976754']

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the DaitchMokotoff.encode method instead.
class abydos.phonetic.FuzzySoundex(max_length=5, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Fuzzy Soundex.
Fuzzy Soundex is an algorithm derived from Soundex, defined in [HM02].
New in version 0.3.6.
Initialize FuzzySoundex instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Fuzzy Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The Fuzzy Soundex value
Return type str
Examples>>> pe = FuzzySoundex()>>> pe.encode('Christopher')'K6931'>>> pe.encode('Niall')'N4000'>>> pe.encode('Smith')'S5300'>>> pe.encode('Smith')'S5300'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Fuzzy Soundex code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Fuzzy Soundex value
Return type str

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Examples>>> pe = FuzzySoundex()>>> pe.encode_alpha('Christopher')'KRSTP'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SNT'

New in version 0.4.0.
abydos.phonetic.fuzzy_soundex(word, max_length=5, zero_pad=True)
Return the Fuzzy Soundex code for a word.
This is a wrapper for FuzzySoundex.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Fuzzy Soundex value
Return type str
Examples>>> fuzzy_soundex('Christopher')'K6931'>>> fuzzy_soundex('Niall')'N4000'>>> fuzzy_soundex('Smith')'S5300'>>> fuzzy_soundex('Smith')'S5300'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the FuzzySoundex.encode method instead.
class abydos.phonetic.LEIN(max_length=4, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
LEIN code.
This is Michigan LEIN (Law Enforcement Information Network) name coding, described in [MKTM77].
New in version 0.3.6.
Initialize LEIN instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string

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New in version 0.4.0.
encode(word)
Return the LEIN code for a word.
Parameters word (str) -- The word to transform
Returns The LEIN code
Return type str
Examples>>> pe = LEIN()>>> pe.encode('Christopher')'C351'>>> pe.encode('Niall')'N300'>>> pe.encode('Smith')'S210'>>> pe.encode('Schmidt')'S521'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic LEIN code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic LEIN code
Return type str
Examples>>> pe = LEIN()>>> pe.encode_alpha('Christopher')'CLKT'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SKNT'New in version 0.4.0.
abydos.phonetic.lein(word, max_length=4, zero_pad=True)
Return the LEIN code for a word.
This is a wrapper for LEIN.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)

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Abydos Documentation, Release 0.5.0• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The LEIN code
Return type str
Examples>>> lein('Christopher')'C351'>>> lein('Niall')'N300'>>> lein('Smith')'S210'>>> lein('Schmidt')'S521'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the LEIN.encode method instead.
class abydos.phonetic.Phonex(max_length=4, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Phonex code.
Phonex is an algorithm derived from Soundex, defined in [LR96].
New in version 0.3.6.
Initialize Phonex instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Phonex code for a word.
Parameters word (str) -- The word to transform
Returns The Phonex value
Return type str
Examples>>> pe = Phonex()>>> pe.encode('Christopher')'C623'>>> pe.encode('Niall')'N400'>>> pe.encode('Schmidt')'S253'>>> pe.encode('Smith')'S530'

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New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Phonex code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Phonex value
Return type str
Examples>>> pe = Phonex()>>> pe.encode_alpha('Christopher')'CRST'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SSNT'New in version 0.4.0.
abydos.phonetic.phonex(word, max_length=4, zero_pad=True)
Return the Phonex code for a word.
This is a wrapper for Phonex.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Phonex value
Return type str
Examples>>> phonex('Christopher')'C623'>>> phonex('Niall')'N400'>>> phonex('Schmidt')'S253'>>> phonex('Smith')'S530'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Phonex.encode method instead.

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class abydos.phonetic.PHONIC(max_length=5, zero_pad=True, extended=False)
Bases: abydos.phonetic._phonetic._Phonetic
PHONIC code.
PHONIC is a Soundex-like algorithm defined in [Taf70].
New in version 0.4.1.
Initialize PHONIC instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 5)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string• extended (bool) -- If True, this uses Taft's 'Extended PHONIC coding' mode, whichsimply omits the first character of the code.
New in version 0.4.1.
encode(word)
Return the PHONIC code for a word.
Parameters word (str) -- The word to transform
Returns The PHONIC code
Return type str
Examples>>> pe = PHONIC()>>> pe.encode('Christopher')'C6401'>>> pe.encode('Niall')'N2500'>>> pe.encode('Smith')'S0310'>>> pe.encode('Schmidt')'S0631'

New in version 0.4.1.
encode_alpha(word)
Return the alphabetic PHONIC code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic PHONIC value
Return type str

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Examples>>> pe = PHONIC()>>> pe.encode_alpha('Christopher')'JRSTF'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SMT'>>> pe.encode_alpha('Schmidt')'SJMT'New in version 0.4.1.
class abydos.phonetic.Phonix(max_length=4, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Phonix code.
Phonix is a Soundex-like algorithm defined in [Gad90].
This implementation is based on: - [Pfe00] - [Chr11] - [Kollar]
New in version 0.3.6.
Initialize Phonix instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.3.6.
encode(word)
Return the Phonix code for a word.
Parameters word (str) -- The word to transform
Returns The Phonix value
Return type str
Examples>>> pe = Phonix()>>> pe.encode('Christopher')'K683'>>> pe.encode('Niall')'N400'>>> pe.encode('Smith')'S530'>>> pe.encode('Schmidt')'S530'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Phonix code for a word.

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Parameters word (str) -- The word to transform
Returns The alphabetic Phonix value
Return type str
Examples>>> pe = Phonix()>>> pe.encode_alpha('Christopher')'KRST'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SNT'

New in version 0.4.0.
abydos.phonetic.phonix(word, max_length=4, zero_pad=True)
Return the Phonix code for a word.
This is a wrapper for Phonix.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Phonix value
Return type str
Examples>>> phonix('Christopher')'K683'>>> phonix('Niall')'N400'>>> phonix('Smith')'S530'>>> phonix('Schmidt')'S530'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Phonix.encode method instead.
class abydos.phonetic.PSHPSoundexFirst(max_length=4, german=False)
Bases: abydos.phonetic._phonetic._Phonetic
PSHP Soundex/Viewex Coding of a first name.
This coding is based on [HBD76].
Reference was also made to the German version of the same: [HBD79].

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A separate class, PSHPSoundexLast is used for last names.
New in version 0.3.6.
Initialize PSHPSoundexFirst instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• german (bool) -- Set to True if the name is German (different rules apply)
New in version 0.4.0.
encode(fname)Calculate the PSHP Soundex/Viewex Coding of a first name.
Parameters fname (str) -- The first name to encode
Returns The PSHP Soundex/Viewex Coding
Return type str
Examples>>> pe = PSHPSoundexFirst()>>> pe.encode('Smith')'S530'>>> pe.encode('Waters')'W352'>>> pe.encode('James')'J700'>>> pe.encode('Schmidt')'S500'>>> pe.encode('Ashcroft')'A220'>>> pe.encode('John')'J500'>>> pe.encode('Colin')'K400'>>> pe.encode('Niall')'N400'>>> pe.encode('Sally')'S400'>>> pe.encode('Jane')'J500'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(fname)Calculate the alphabetic PSHP Soundex/Viewex Coding of a first name.
Parameters fname (str) -- The first name to encode
Returns The alphabetic PSHP Soundex/Viewex Coding
Return type str

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Examples>>> pe = PSHPSoundexFirst()>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Waters')'WTNK'>>> pe.encode_alpha('James')'JN'>>> pe.encode_alpha('Schmidt')'SN'>>> pe.encode_alpha('Ashcroft')'AKK'>>> pe.encode_alpha('John')'JN'>>> pe.encode_alpha('Colin')'KL'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Sally')'SL'>>> pe.encode_alpha('Jane')'JN'

New in version 0.4.0.
abydos.phonetic.pshp_soundex_first(fname, max_length=4, german=False)
Calculate the PSHP Soundex/Viewex Coding of a first name.
This is a wrapper for PSHPSoundexFirst.encode().
Parameters• fname (str) -- The first name to encode• max_length (int) -- The length of the code returned (defaults to 4)• german (bool) -- Set to True if the name is German (different rules apply)
Returns The PSHP Soundex/Viewex Coding
Return type str
Examples>>> pshp_soundex_first('Smith')'S530'>>> pshp_soundex_first('Waters')'W352'>>> pshp_soundex_first('James')'J700'>>> pshp_soundex_first('Schmidt')'S500'>>> pshp_soundex_first('Ashcroft')'A220'>>> pshp_soundex_first('John')'J500'>>> pshp_soundex_first('Colin')'K400'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> pshp_soundex_first('Niall')'N400'>>> pshp_soundex_first('Sally')'S400'>>> pshp_soundex_first('Jane')'J500'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the PSHPSoundexFirst.encode method
instead.
class abydos.phonetic.PSHPSoundexLast(max_length=4, german=False)
Bases: abydos.phonetic._phonetic._Phonetic
PSHP Soundex/Viewex Coding of a last name.
This coding is based on [HBD76].
Reference was also made to the German version of the same: [HBD79].
A separate function, PSHPSoundexFirst is used for first names.
New in version 0.3.6.
Initialize PSHPSoundexLast instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• german (bool) -- Set to True if the name is German (different rules apply)
New in version 0.4.0.
encode(lname)Calculate the PSHP Soundex/Viewex Coding of a last name.
Parameters lname (str) -- The last name to encode
Returns The PSHP Soundex/Viewex Coding
Return type str
Examples>>> pe = PSHPSoundexLast()>>> pe.encode('Smith')'S530'>>> pe.encode('Waters')'W350'>>> pe.encode('James')'J500'>>> pe.encode('Schmidt')'S530'>>> pe.encode('Ashcroft')'A225'New in version 0.3.0.
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encode_alpha(lname)Calculate the alphabetic PSHP Soundex/Viewex Coding of a last name.
Parameters lname (str) -- The last name to encode
Returns The PSHP alphabetic Soundex/Viewex Coding
Return type str
Examples>>> pe = PSHPSoundexLast()>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Waters')'WTN'>>> pe.encode_alpha('James')'JN'>>> pe.encode_alpha('Schmidt')'SNT'>>> pe.encode_alpha('Ashcroft')'AKKN'New in version 0.4.0.
abydos.phonetic.pshp_soundex_last(lname, max_length=4, german=False)
Calculate the PSHP Soundex/Viewex Coding of a last name.
This is a wrapper for PSHPSoundexLast.encode().
Parameters• lname (str) -- The last name to encode• max_length (int) -- The length of the code returned (defaults to 4)• german (bool) -- Set to True if the name is German (different rules apply)
Returns The PSHP Soundex/Viewex Coding
Return type str
Examples>>> pshp_soundex_last('Smith')'S530'>>> pshp_soundex_last('Waters')'W350'>>> pshp_soundex_last('James')'J500'>>> pshp_soundex_last('Schmidt')'S530'>>> pshp_soundex_last('Ashcroft')'A225'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the PSHPSoundexLast.encode method
instead.

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class abydos.phonetic.NYSIIS(max_length=6, modified=False)
Bases: abydos.phonetic._phonetic._Phonetic
NYSIIS Code.
The New York State Identification and Intelligence System algorithm is defined in [Taf70].
The modified version of this algorithm is described in Appendix B of [LA77].
New in version 0.3.6.
Initialize AlphaSIS instance.
Parameters• max_length (int) -- The maximum length (default 6) of the code to return• modified (bool) -- Indicates whether to use USDA modified NYSIISNew in version 0.4.0.
encode(word)
Return the NYSIIS code for a word.
Parameters word (str) -- The word to transform
Returns The NYSIIS value
Return type str
Examples>>> pe = NYSIIS()>>> pe.encode('Christopher')'CRASTA'>>> pe.encode('Niall')'NAL'>>> pe.encode('Smith')'SNAT'>>> pe.encode('Schmidt')'SNAD'>>> NYSIIS(max_length=-1).encode('Christopher')'CRASTAFAR'>>> pe_8m = NYSIIS(max_length=8, modified=True)>>> pe_8m.encode('Christopher')'CRASTAFA'>>> pe_8m.encode('Niall')'NAL'>>> pe_8m.encode('Smith')'SNAT'>>> pe_8m.encode('Schmidt')'SNAD'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.nysiis(word, max_length=6, modified=False)
Return the NYSIIS code for a word.

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This is a wrapper for NYSIIS.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length (default 6) of the code to return• modified (bool) -- Indicates whether to use USDA modified NYSIISReturns The NYSIIS value
Return type str
Examples>>> nysiis('Christopher')'CRASTA'>>> nysiis('Niall')'NAL'>>> nysiis('Smith')'SNAT'>>> nysiis('Schmidt')'SNAD'>>> nysiis('Christopher', max_length=-1)'CRASTAFAR'>>> nysiis('Christopher', max_length=8, modified=True)'CRASTAFA'>>> nysiis('Niall', max_length=8, modified=True)'NAL'>>> nysiis('Smith', max_length=8, modified=True)'SNAT'>>> nysiis('Schmidt', max_length=8, modified=True)'SNAD'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NYSIIS.encode method instead.
class abydos.phonetic.MRA
Bases: abydos.phonetic._phonetic._Phonetic
Western Airlines Surname Match Rating Algorithm.
A description of the Western Airlines Surname Match Rating Algorithm can be found on page 18 of [MKTM77].
New in version 0.3.6.
encode(word)
Return the MRA personal numeric identifier (PNI) for a word.
Parameters word (str) -- The word to transform
Returns The MRA PNIReturn type str

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Examples>>> pe = MRA()>>> pe.encode('Christopher')'CHRPHR'>>> pe.encode('Niall')'NL'>>> pe.encode('Smith')'SMTH'>>> pe.encode('Schmidt')'SCHMDT'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.mra(word)
Return the MRA personal numeric identifier (PNI) for a word.
This is a wrapper for MRA.encode().
Parameters word (str) -- The word to transform
Returns The MRA PNIReturn type str
Examples>>> mra('Christopher')'CHRPHR'>>> mra('Niall')'NL'>>> mra('Smith')'SMTH'>>> mra('Schmidt')'SCHMDT'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MRA.encode method instead.
class abydos.phonetic.Caverphone(version=2)
Bases: abydos.phonetic._phonetic._Phonetic
Caverphone.
A description of version 1 of the algorithm can be found in [Hoo02].
A description of version 2 of the algorithm can be found in [Hoo04].
New in version 0.3.6.
Initialize Caverphone instance.
Parameters version (int) -- The version of Caverphone to employ for encoding (defaults to 2)
New in version 0.4.0.
encode(word)
Return the Caverphone code for a word.
Parameters word (str) -- The word to transform
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Returns The Caverphone value
Return type str
Examples>>> pe = Caverphone()>>> pe.encode('Christopher')'KRSTFA1111'>>> pe.encode('Niall')'NA11111111'>>> pe.encode('Smith')'SMT1111111'>>> pe.encode('Schmidt')'SKMT111111'>>> pe_1 = Caverphone(version=1)>>> pe_1.encode('Christopher')'KRSTF1'>>> pe_1.encode('Niall')'N11111'>>> pe_1.encode('Smith')'SMT111'>>> pe_1.encode('Schmidt')'SKMT11'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Caverphone code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Caverphone value
Return type str
Examples>>> pe = Caverphone()>>> pe.encode_alpha('Christopher')'KRSTFA'>>> pe.encode_alpha('Niall')'NA'>>> pe.encode_alpha('Smith')'SMT'>>> pe.encode_alpha('Schmidt')'SKMT'>>> pe_1 = Caverphone(version=1)>>> pe_1.encode_alpha('Christopher')'KRSTF'>>> pe_1.encode_alpha('Niall')'N'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> pe_1.encode_alpha('Smith')'SMT'>>> pe_1.encode_alpha('Schmidt')'SKMT'New in version 0.4.0.
abydos.phonetic.caverphone(word, version=2)
Return the Caverphone code for a word.
This is a wrapper for Caverphone.encode().
Parameters• word (str) -- The word to transform• version (int) -- The version of Caverphone to employ for encoding (defaults to 2)
Returns The Caverphone value
Return type str
Examples>>> caverphone('Christopher')'KRSTFA1111'>>> caverphone('Niall')'NA11111111'>>> caverphone('Smith')'SMT1111111'>>> caverphone('Schmidt')'SKMT111111'>>> caverphone('Christopher', 1)'KRSTF1'>>> caverphone('Niall', 1)'N11111'>>> caverphone('Smith', 1)'SMT111'>>> caverphone('Schmidt', 1)'SKMT11'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Caverphone.encode method instead.
class abydos.phonetic.AlphaSIS(max_length=14)
Bases: abydos.phonetic._phonetic._Phonetic
Alpha-SIS.
The Alpha Search Inquiry System code is defined in [Cor73]. This implementation is based on the description
in [MKTM77].
New in version 0.3.6.
Initialize AlphaSIS instance.
Parameters max_length (int) -- The length of the code returned (defaults to 14)
New in version 0.4.0.
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encode(word)
Return the IBM Alpha Search Inquiry System code for a word.
A collection is necessary as the return type since there can be multiple values for a single word. But thecollection must be ordered since the first value is the primary coding.
Parameters word (str) -- The word to transform
Returns The Alpha-SIS value
Return type tuple
Examples>>> pe = AlphaSIS()>>> pe.encode('Christopher')('06401840000000', '07040184000000', '04018400000000')>>> pe.encode('Niall')('02500000000000',)>>> pe.encode('Smith')('03100000000000',)>>> pe.encode('Schmidt')('06310000000000',)

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Alpha-SIS code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Alpha-SIS value
Return type tuple
Examples>>> pe = AlphaSIS()>>> pe.encode_alpha('Christopher')('JRSTFR', 'KSRSTFR', 'RSTFR')>>> pe.encode_alpha('Niall')('NL',)>>> pe.encode_alpha('Smith')('MT',)>>> pe.encode_alpha('Schmidt')('JMT',)New in version 0.4.0.
abydos.phonetic.alpha_sis(word, max_length=14)
Return the IBM Alpha Search Inquiry System code for a word.
This is a wrapper for AlphaSIS.encode().
Parameters• word (str) -- The word to transform

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Returns The Alpha-SIS value
Return type tuple
Examples>>> alpha_sis('Christopher')('06401840000000', '07040184000000', '04018400000000')>>> alpha_sis('Niall')('02500000000000',)>>> alpha_sis('Smith')('03100000000000',)>>> alpha_sis('Schmidt')('06310000000000',)

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the AlphaSIS.encode method instead.
class abydos.phonetic.Davidson(omit_fname=False)
Bases: abydos.phonetic._phonetic._Phonetic
Davidson Consonant Code.
This is based on the name compression system described in [Dav62].
[Dol70] identifies this as having been the name compression algorithm used by SABRE.
New in version 0.3.6.
Initialize Davidson instance.
Parameters omit_fname (bool) -- Set to True to completely omit the first character of the firstname
New in version 0.4.0.
encode(lname, fname='.')
Return Davidson's Consonant Code.
Parameters• lname (str) -- Last name (or word) to be encoded• fname (str) -- First name (optional), of which the first character is included in the code.
Returns Davidson's Consonant Code
Return type str

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Example>>> pe = Davidson()>>> pe.encode('Gough')'G.'>>> pe.encode('pneuma')'PNM .'>>> pe.encode('knight')'KNGT.'>>> pe.encode('trice')'TRC .'>>> pe.encode('judge')'JDG .'>>> pe.encode('Smith', 'James')'SMT J'>>> pe.encode('Wasserman', 'Tabitha')'WSRMT'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.davidson(lname, fname='.', omit_fname=False)
Return Davidson's Consonant Code.
This is a wrapper for Davidson.encode().
Parameters• lname (str) -- Last name (or word) to be encoded• fname (str) -- First name (optional), of which the first character is included in the code.
• omit_fname (bool) -- Set to True to completely omit the first character of the first nameReturns Davidson's Consonant Code
Return type str
Example>>> davidson('Gough')'G.'>>> davidson('pneuma')'PNM .'>>> davidson('knight')'KNGT.'>>> davidson('trice')'TRC .'>>> davidson('judge')'JDG .'>>> davidson('Smith', 'James')'SMT J'>>> davidson('Wasserman', 'Tabitha')'WSRMT'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Davidson.encode method instead.

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class abydos.phonetic.Dolby(max_length=-1, keep_vowels=False, vowel_char='*')
Bases: abydos.phonetic._phonetic._Phonetic
Dolby Code.
This follows "A Spelling Equivalent Abbreviation Algorithm For Personal Names" from [Dol70] and [C+69].
New in version 0.3.6.
Initialize Dolby instance.
Parameters• max_length (int) -- Maximum length of the returned Dolby code -- this also activates
the fixed-length code mode if it is greater than 0• keep_vowels (bool) -- If True, retains all vowel markers• vowel_char (str) -- The vowel marker character (default to *)New in version 0.4.0.
encode(word)
Return the Dolby Code of a name.
Parameters word (str) -- The word to transform
Returns The Dolby Code
Return type str
Examples>>> pe = Dolby()>>> pe.encode('Hansen')'H*NSN'>>> pe.encode('Larsen')'L*RSN'>>> pe.encode('Aagaard')'*GR'>>> pe.encode('Braaten')'BR*DN'>>> pe.encode('Sandvik')'S*NVK'>>> pe_6 = Dolby(max_length=6)>>> pe_6.encode('Hansen')'H*NS*N'>>> pe_6.encode('Larsen')'L*RS*N'>>> pe_6.encode('Aagaard')'*G*R '>>> pe_6.encode('Braaten')'BR*D*N'>>> pe_6.encode('Sandvik')'S*NF*K'>>> pe.encode('Smith')'SM*D'>>> pe.encode('Waters')(continues on next page)534

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Abydos Documentation, Release 0.5.0(continued from previous page)'W*DRS'>>> pe.encode('James')'J*MS'>>> pe.encode('Schmidt')'SM*D'>>> pe.encode('Ashcroft')'*SKRFD'>>> pe_6.encode('Smith')'SM*D '>>> pe_6.encode('Waters')'W*D*RS'>>> pe_6.encode('James')'J*M*S '>>> pe_6.encode('Schmidt')'SM*D '>>> pe_6.encode('Ashcroft')'*SKRFD'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Dolby Code of a name.
Parameters word (str) -- The word to transform
Returns The alphabetic Dolby Code
Return type str
Examples>>> pe = Dolby()>>> pe.encode_alpha('Hansen')'HANSN'>>> pe.encode_alpha('Larsen')'LARSN'>>> pe.encode_alpha('Aagaard')'AGR'>>> pe.encode_alpha('Braaten')'BRADN'>>> pe.encode_alpha('Sandvik')'SANVK'

New in version 0.4.0.
abydos.phonetic.dolby(word, max_length=-1, keep_vowels=False, vowel_char='*')
Return the Dolby Code of a name.
This is a wrapper for Dolby.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- Maximum length of the returned Dolby code -- this also activates
the fixed-length code mode if it is greater than 0
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Abydos Documentation, Release 0.5.0• keep_vowels (bool) -- If True, retains all vowel markers• vowel_char (str) -- The vowel marker character (default to *)Returns The Dolby Code
Return type str
Examples>>> dolby('Hansen')'H*NSN'>>> dolby('Larsen')'L*RSN'>>> dolby('Aagaard')'*GR'>>> dolby('Braaten')'BR*DN'>>> dolby('Sandvik')'S*NVK'>>> dolby('Hansen', max_length=6)'H*NS*N'>>> dolby('Larsen', max_length=6)'L*RS*N'>>> dolby('Aagaard', max_length=6)'*G*R '>>> dolby('Braaten', max_length=6)'BR*D*N'>>> dolby('Sandvik', max_length=6)'S*NF*K'>>> dolby('Smith')'SM*D'>>> dolby('Waters')'W*DRS'>>> dolby('James')'J*MS'>>> dolby('Schmidt')'SM*D'>>> dolby('Ashcroft')'*SKRFD'>>> dolby('Smith', max_length=6)'SM*D '>>> dolby('Waters', max_length=6)'W*D*RS'>>> dolby('James', max_length=6)'J*M*S '>>> dolby('Schmidt', max_length=6)'SM*D '>>> dolby('Ashcroft', max_length=6)'*SKRFD'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Dolby.encode method instead.
class abydos.phonetic.SPFC
Bases: abydos.phonetic._phonetic._Phonetic

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Standardized Phonetic Frequency Code (SPFC).
Standardized Phonetic Frequency Code is roughly Soundex-like. This implementation is based on page 19-21
of [MKTM77].
New in version 0.3.6.
encode(word)
Return the Standardized Phonetic Frequency Code (SPFC) of a word.
Parameters word (str) -- The word to transform
Returns The SPFC value
Return type str
Raises AttributeError -- Word attribute must be a string with a space or period dividing
the first and last names or a tuple/list consisting of the first and last names
Examples>>> pe = SPFC()>>> pe.encode('Christopher Smith')'01160'>>> pe.encode('Christopher Schmidt')'01160'>>> pe.encode('Niall Smith')'01660'>>> pe.encode('Niall Schmidt')'01660'>>> pe.encode('L.Smith')'01960'>>> pe.encode('R.Miller')'65490'>>> pe.encode(('L', 'Smith'))'01960'>>> pe.encode(('R', 'Miller'))'65490'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic SPFC of a word.
Parameters word (str) -- The word to transform
Returns The alphabetic SPFC value
Return type str

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Examples>>> pe = SPFC()>>> pe.encode_alpha('Christopher Smith')'SDCMS'>>> pe.encode_alpha('Christopher Schmidt')'SDCMS'>>> pe.encode_alpha('Niall Smith')'SDMMS'>>> pe.encode_alpha('Niall Schmidt')'SDMMS'>>> pe.encode_alpha('L.Smith')'SDEMS'>>> pe.encode_alpha('R.Miller')'EROES'>>> pe.encode_alpha(('L', 'Smith'))'SDEMS'>>> pe.encode_alpha(('R', 'Miller'))'EROES'

New in version 0.4.0.
abydos.phonetic.spfc(word)
Return the Standardized Phonetic Frequency Code (SPFC) of a word.
This is a wrapper for SPFC.encode().
Parameters word (str) -- The word to transform
Returns The SPFC value
Return type str
Examples>>> spfc('Christopher Smith')'01160'>>> spfc('Christopher Schmidt')'01160'>>> spfc('Niall Smith')'01660'>>> spfc('Niall Schmidt')'01660'>>> spfc('L.Smith')'01960'>>> spfc('R.Miller')'65490'>>> spfc(('L', 'Smith'))'01960'>>> spfc(('R', 'Miller'))'65490'538

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New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SPFC.encode method instead.
class abydos.phonetic.RogerRoot(max_length=5, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Roger Root code.
This is Roger Root name coding, described in [MKTM77].
New in version 0.3.6.
Initialize RogerRoot instance.
Parameters• max_length (int) -- The maximum length (default 5) of the code to return• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Roger Root code for a word.
Parameters word (str) -- The word to transform
Returns The Roger Root code
Return type str
Examples>>> pe = RogerRoot()>>> pe.encode('Christopher')'06401'>>> pe.encode('Niall')'02500'>>> pe.encode('Smith')'00310'>>> pe.encode('Schmidt')'06310'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Roger Root code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Roger Root code
Return type str

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Examples>>> pe = RogerRoot()>>> pe.encode_alpha('Christopher')'JRST'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SMT'>>> pe.encode_alpha('Schmidt')'JMT'

New in version 0.4.0.
abydos.phonetic.roger_root(word, max_length=5, zero_pad=True)
Return the Roger Root code for a word.
This is a wrapper for RogerRoot.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length (default 5) of the code to return• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The Roger Root code
Return type str
Examples>>> roger_root('Christopher')'06401'>>> roger_root('Niall')'02500'>>> roger_root('Smith')'00310'>>> roger_root('Schmidt')'06310'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RogerRoot.encode method instead.
class abydos.phonetic.StatisticsCanada(max_length=4)
Bases: abydos.phonetic._phonetic._Phonetic
Statistics Canada code.
The original description of this algorithm could not be located, and may only have been specified in an unpublished TR. The coding does not appear to be in use by Statistics Canada any longer. In its place, this is an
implementation of the "Census modified Statistics Canada name coding procedure".
The modified version of this algorithm is described in Appendix B of [MKTM77].
New in version 0.3.6.
Initialize StatisticsCanada instance.
Parameters max_length (int) -- The length of the code returned (defaults to 4)

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New in version 0.4.0.
encode(word)
Return the Statistics Canada code for a word.
Parameters word (str) -- The word to transform
Returns The Statistics Canada name code value
Return type str
Examples>>> pe = StatisticsCanada()>>> pe.encode('Christopher')'CHRS'>>> pe.encode('Niall')'NL'>>> pe.encode('Smith')'SMTH'>>> pe.encode('Schmidt')'SCHM'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.statistics_canada(word, max_length=4)
Return the Statistics Canada code for a word.
This is a wrapper for StatisticsCanada.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length (default 4) of the code to return
Returns The Statistics Canada name code value
Return type str
Examples>>> statistics_canada('Christopher')'CHRS'>>> statistics_canada('Niall')'NL'>>> statistics_canada('Smith')'SMTH'>>> statistics_canada('Schmidt')'SCHM'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the StatisticsCanada.encode method instead.
class abydos.phonetic.SoundD(max_length=4)
Bases: abydos.phonetic._phonetic._Phonetic
SoundD code.
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SoundD is defined in [VB12].
New in version 0.3.6.
Initialize SoundD instance.
Parameters max_length (int) -- The length of the code returned (defaults to 4)
New in version 0.4.0.
encode(word)
Return the SoundD code.
Parameters word (str) -- The word to transform
Returns The SoundD code
Return type str
Examples>>> pe = SoundD()>>> pe.encode('Gough')'2000'>>> pe.encode('pneuma')'5500'>>> pe.encode('knight')'5300'>>> pe.encode('trice')'3620'>>> pe.encode('judge')'2200'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic SoundD code.
Parameters word (str) -- The word to transform
Returns The alphabetic SoundD code
Return type str
Examples>>> pe = SoundD()>>> pe.encode_alpha('Gough')'K'>>> pe.encode_alpha('pneuma')'NN'>>> pe.encode_alpha('knight')'NT'>>> pe.encode_alpha('trice')'TRK'>>> pe.encode_alpha('judge')
'KK'

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New in version 0.4.0.
abydos.phonetic.sound_d(word, max_length=4)
Return the SoundD code.
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)
Returns The SoundD code
Return type str
Examples>>> sound_d('Gough')'2000'>>> sound_d('pneuma')'5500'>>> sound_d('knight')'5300'>>> sound_d('trice')'3620'>>> sound_d('judge')'2200'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SoundD.encode method instead.
class abydos.phonetic.ParmarKumbharana
Bases: abydos.phonetic._phonetic._Phonetic
Parmar-Kumbharana code.
This is based on the phonetic algorithm proposed in [PK14].
New in version 0.3.6.
encode(word)
Return the Parmar-Kumbharana encoding of a word.
Parameters word (str) -- The word to transform
Returns The Parmar-Kumbharana encoding
Return type str
Examples>>> pe = ParmarKumbharana()>>> pe.encode('Gough')'GF'>>> pe.encode('pneuma')'NM'>>> pe.encode('knight')'NT'>>> pe.encode('trice')(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)'TRS'>>> pe.encode('judge')'JJ'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.parmar_kumbharana(word)
Return the Parmar-Kumbharana encoding of a word.
This is a wrapper for ParmarKumbharana.encode().
Parameters word (str) -- The word to transform
Returns The Parmar-Kumbharana encoding
Return type str
Examples>>> parmar_kumbharana('Gough')'GF'>>> parmar_kumbharana('pneuma')'NM'>>> parmar_kumbharana('knight')'NT'>>> parmar_kumbharana('trice')'TRS'>>> parmar_kumbharana('judge')'JJ'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the ParmarKumbharana.encode method
instead.
class abydos.phonetic.Metaphone(max_length=-1)
Bases: abydos.phonetic._phonetic._Phonetic
Metaphone.
Based on Lawrence Philips' Pick BASIC code from 1990 [Phi90b], as described in [Phi90a]. This incorporates
some corrections to the above code, particularly some of those suggested by Michael Kuhn in [Kuh95].
New in version 0.3.6.
Initialize AlphaSIS instance.
Parameters max_length (int) -- The maximum length of the returned Metaphone code (defaults
to 64, but in Philips' original implementation this was 4)
New in version 0.4.0.
encode(word)
Return the Metaphone code for a word.
Based on Lawrence Philips' Pick BASIC code from 1990 [Phi90b], as described in [Phi90a]. This incorporates some corrections to the above code, particularly some of those suggested by Michael Kuhn in[Kuh95].

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Parameters word (str) -- The word to transform
Returns The Metaphone value
Return type str
Examples>>> pe = Metaphone()>>> pe.encode('Christopher')'KRSTFR'>>> pe.encode('Niall')'NL'>>> pe.encode('Smith')'SM0'>>> pe.encode('Schmidt')'SKMTT'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.metaphone(word, max_length=-1)
Return the Metaphone code for a word.
This is a wrapper for Metaphone.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length of the returned Metaphone code (defaults to64, but in Philips' original implementation this was 4)
Returns The Metaphone value
Return type str
Examples>>> metaphone('Christopher')'KRSTFR'>>> metaphone('Niall')'NL'>>> metaphone('Smith')'SM0'>>> metaphone('Schmidt')'SKMTT'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Metaphone.encode method instead.
class abydos.phonetic.DoubleMetaphone(max_length=-1)
Bases: abydos.phonetic._phonetic._Phonetic
Double Metaphone.
Based on Lawrence Philips' (Visual) C++ code from 1999 [Phi00].
New in version 0.3.6.
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Initialize DoubleMetaphone instance.
Parameters max_length (int) -- Maximum length of the returned Dolby code -- this also activates the fixed-length code mode if it is greater than 0
New in version 0.4.0.
encode(word)
Return the Double Metaphone code for a word.
Parameters word (str) -- The word to transform
Returns The Double Metaphone value(s)Return type tuple
Examples>>> pe = DoubleMetaphone()>>> pe.encode('Christopher')('KRSTFR', '')>>> pe.encode('Niall')('NL', '')>>> pe.encode('Smith')('SM0', 'XMT')>>> pe.encode('Schmidt')('XMT', 'SMT')

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic Double Metaphone code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Double Metaphone value(s)Return type tuple
Examples>>> pe = DoubleMetaphone()>>> pe.encode_alpha('Christopher')('KRSTFR', '')>>> pe.encode_alpha('Niall')('NL', '')>>> pe.encode_alpha('Smith')('SMÞ', 'XMT')>>> pe.encode_alpha('Schmidt')('XMT', 'SMT')

New in version 0.4.0.
abydos.phonetic.double_metaphone(word, max_length=-1)
Return the Double Metaphone code for a word.
This is a wrapper for DoubleMetaphone.encode().

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Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length of the returned Double Metaphone codes(defaults to unlimited, but in Philips' original implementation this was 4)
Returns The Double Metaphone value(s)Return type tuple
Examples>>> double_metaphone('Christopher')('KRSTFR', '')>>> double_metaphone('Niall')('NL', '')>>> double_metaphone('Smith')('SM0', 'XMT')>>> double_metaphone('Schmidt')('XMT', 'SMT')

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the DoubleMetaphone.encode method
instead.
class abydos.phonetic.Eudex(max_length=8)
Bases: abydos.phonetic._phonetic._Phonetic
Eudex hash.
This implementation of eudex phonetic hashing is based on the specification (not the reference implementation)
at [Tic].
Further details can be found at [Tic16].
New in version 0.3.6.
Initialize Eudex instance.
Parameters max_length (int) -- The length in bits of the code returned (default 8)
New in version 0.4.0.
encode(word)
Return the eudex phonetic hash of a word.
Parameters word (str) -- The word to transform
Returns The eudex hash
Return type int

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Examples>>> pe = Eudex()>>> pe.encode('Colin')
432345564238053650>>> pe.encode('Christopher')
433648490138894409>>> pe.encode('Niall')648518346341351840>>> pe.encode('Smith')
720575940412906756>>> pe.encode('Schmidt')
720589151732307997

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.eudex(word, max_length=8)
Return the eudex phonetic hash of a word.
This is a wrapper for Eudex.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length in bits of the code returned (default 8)Returns The eudex hash
Return type int
Examples>>> eudex('Colin')
432345564238053650>>> eudex('Christopher')
433648490138894409>>> eudex('Niall')648518346341351840>>> eudex('Smith')
720575940412906756>>> eudex('Schmidt')
720589151732307997

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Eudex.encode method instead.
class abydos.phonetic.BeiderMorse(language_arg=0,
name_mode='gen',match_mode='approx', concat=False, filter_langs=False)
Bases: abydos.phonetic._phonetic._Phonetic
Beider-Morse Phonetic Matching.
The Beider-Morse Phonetic Matching algorithm is described in [BM08]. The reference implementation is licensed under GPLv3.
New in version 0.3.6.
Initialize BeiderMorse instance.
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Parameters• language_arg (str or int) -- The language of the term; supported values include:– any– arabic– cyrillic– czech– dutch– english– french– german– greek– greeklatin– hebrew– hungarian– italian– latvian– polish– portuguese– romanian– russian– spanish– turkish• name_mode (str) -- The name mode of the algorithm:– gen -- general (default)– ash -- Ashkenazi– sep -- Sephardic• match_mode (str) -- Matching mode: approx or exact• concat (bool) -- Concatenation mode
• filter_langs (bool) -- Filter out incompatible languages
New in version 0.4.0.
encode(word)
Return the Beider-Morse Phonetic Matching encoding(s) of a term.
Parameters word (str) -- The word to transform
Returns The Beider-Morse phonetic value(s)Return type tuple
Raises ValueError -- Unknown language

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Examples>>> pe = BeiderMorse()>>> pe.encode('Christopher')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir
xristofir xristYfir xristopi xritopir xritopi xristofi xritofir
xritofi tzristopir tzristofir zristopir zristopi zritopir zritopi
zristofir zristofi zritofir zritofi'>>> pe.encode('Niall')'nial niol'>>> pe.encode('Smith')'zmit'>>> pe.encode('Schmidt')'zmit stzmit'>>> BeiderMorse(language_arg='German').encode('Christopher')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir
xristofir xristYfir'>>> BeiderMorse(language_arg='English').encode('Christopher')'tzristofir tzrQstofir tzristafir tzrQstafir xristofir xrQstofir
xristafir xrQstafir'>>> BeiderMorse(language_arg='German',... name_mode='ash').encode('Christopher')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir
xristofir xristYfir'>>> BeiderMorse(language_arg='German',... match_mode='exact').encode('Christopher')'xriStopher xriStofer xristopher xristofer'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.bmpm(word, language_arg=0, name_mode='gen', match_mode='approx', concat=False, filter_langs=False)
Return the Beider-Morse Phonetic Matching encoding(s) of a term.
This is a wrapper for BeiderMorse.encode().
Parameters• word (str) -- The word to transform• language_arg (str) -- The language of the term; supported values include:– any– arabic– cyrillic– czech– dutch– english– french– german– greek
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Abydos Documentation, Release 0.5.0– greeklatin– hebrew– hungarian– italian– latvian– polish– portuguese– romanian– russian– spanish– turkish• name_mode (str) -- The name mode of the algorithm:– gen -- general (default)– ash -- Ashkenazi– sep -- Sephardic• match_mode (str) -- Matching mode: approx or exact• concat (bool) -- Concatenation mode
• filter_langs (bool) -- Filter out incompatible languages
Returns The Beider-Morse phonetic value(s)Return type tuple
Examples>>> bmpm('Christopher')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir xristofir
xristYfir xristopi xritopir xritopi xristofi xritofir xritofi
tzristopir tzristofir zristopir zristopi zritopir zritopi zristofir
zristofi zritofir zritofi'>>> bmpm('Niall')'nial niol'>>> bmpm('Smith')'zmit'>>> bmpm('Schmidt')'zmit stzmit'>>> bmpm('Christopher', language_arg='German')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir xristofir
xristYfir'>>> bmpm('Christopher', language_arg='English')'tzristofir tzrQstofir tzristafir tzrQstafir xristofir xrQstofir
xristafir xrQstafir'>>> bmpm('Christopher', language_arg='German', name_mode='ash')'xrQstopir xrQstYpir xristopir xristYpir xrQstofir xrQstYfir xristofir
xristYfir'

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Abydos Documentation, Release 0.5.0>>> bmpm('Christopher', language_arg='German', match_mode='exact')'xriStopher xriStofer xristopher xristofer'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the BeiderMorse.encode method instead.
class abydos.phonetic.NRL
Bases: abydos.phonetic._phonetic._Phonetic
Naval Research Laboratory English-to-phoneme encoder.
This is defined by [EJMS76].
New in version 0.3.6.
encode(word)
Return the Naval Research Laboratory phonetic encoding of a word.
Parameters word (str) -- The word to transform
Returns The NRL phonetic encoding
Return type str
Examples>>> pe = NRL()>>> pe.encode('the')'DHAX'>>> pe.encode('round')'rAWnd'>>> pe.encode('quick')'kwIHk'>>> pe.encode('eaten')'IYtEHn'>>> pe.encode('Smith')'smIHTH'>>> pe.encode('Larsen')'lAArsEHn'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.nrl(word)
Return the Naval Research Laboratory phonetic encoding of a word.
This is a wrapper for NRL.encode().
Parameters word (str) -- The word to transform
Returns The NRL phonetic encoding
Return type str

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Examples>>> nrl('the')'DHAX'>>> nrl('round')'rAWnd'>>> nrl('quick')'kwIHk'>>> nrl('eaten')'IYtEHn'>>> nrl('Smith')'smIHTH'>>> nrl('Larsen')'lAArsEHn'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the NRL.encode method instead.
class abydos.phonetic.MetaSoundex(lang='en')
Bases: abydos.phonetic._phonetic._Phonetic
MetaSoundex.
This is based on [KV17]. Only English ('en') and Spanish ('es') languages are supported, as in the original.
New in version 0.3.6.
Initialize MetaSoundex instance.
Parameters lang (str) -- Either en for English or es for Spanish
New in version 0.4.0.
encode(word)
Return the MetaSoundex code for a word.
Parameters word (str) -- The word to transform
Returns The MetaSoundex code
Return type str
Examples>>> pe = MetaSoundex()>>> pe.encode('Smith')'4500'>>> pe.encode('Waters')'7362'>>> pe.encode('James')'1520'>>> pe.encode('Schmidt')'4530'>>> pe.encode('Ashcroft')'0261'>>> pe = MetaSoundex(lang='es')>>> pe.encode('Perez')'094'(continues on next page)

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Abydos Documentation, Release 0.5.0(continued from previous page)>>> pe.encode('Martinez')'69364'>>> pe.encode('Gutierrez')'83994'>>> pe.encode('Santiago')'4638'>>> pe.encode('Nicolás')'6754'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the MetaSoundex code for a word.
Parameters word (str) -- The word to transform
Returns The MetaSoundex code
Return type str
Examples>>> pe = MetaSoundex()>>> pe.encode_alpha('Smith')'SN'>>> pe.encode_alpha('Waters')'WTRK'>>> pe.encode_alpha('James')'JNK'>>> pe.encode_alpha('Schmidt')'SNT'>>> pe.encode_alpha('Ashcroft')'AKRP'>>> pe = MetaSoundex(lang='es')>>> pe.encode_alpha('Perez')'PRS'>>> pe.encode_alpha('Martinez')'NRTNS'>>> pe.encode_alpha('Gutierrez')'GTRRS'>>> pe.encode_alpha('Santiago')'SNTG'>>> pe.encode_alpha('Nicolás')'NKLS'New in version 0.4.0.
abydos.phonetic.metasoundex(word, lang='en')
Return the MetaSoundex code for a word.
This is a wrapper for MetaSoundex.encode().
Parameters• word (str) -- The word to transform

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Abydos Documentation, Release 0.5.0• lang (str) -- Either en for English or es for Spanish
Returns The MetaSoundex code
Return type str
Examples>>> metasoundex('Smith')'4500'>>> metasoundex('Waters')'7362'>>> metasoundex('James')'1520'>>> metasoundex('Schmidt')'4530'>>> metasoundex('Ashcroft')'0261'>>> metasoundex('Perez', lang='es')'094'>>> metasoundex('Martinez', lang='es')'69364'>>> metasoundex('Gutierrez', lang='es')'83994'>>> metasoundex('Santiago', lang='es')'4638'>>> metasoundex('Nicolás', lang='es')'6754'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the MetaSoundex.encode method instead.
class abydos.phonetic.ONCA(max_length=4, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
Oxford Name Compression Algorithm (ONCA).
This is the Oxford Name Compression Algorithm, based on [Gil97].
I can find no complete description of the "anglicised version of the NYSIIS method" identified as the first stepin this algorithm, so this is likely not a precisely correct implementation, in that it employs the standard NYSIIS
algorithm.
New in version 0.3.6.
Initialize ONCA instance.
Parameters• max_length (int) -- The maximum length (default 5) of the code to return• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the Oxford Name Compression Algorithm (ONCA) code for a word.
Parameters word (str) -- The word to transform
Returns The ONCA code

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Return type str
Examples>>> pe = ONCA()>>> pe.encode('Christopher')'C623'>>> pe.encode('Niall')'N400'>>> pe.encode('Smith')'S530'>>> pe.encode('Schmidt')'S530'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic ONCA code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic ONCA code
Return type str
Examples>>> pe = ONCA()>>> pe.encode_alpha('Christopher')'CRKT'>>> pe.encode_alpha('Niall')'NL'>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SNT'

New in version 0.4.0.
abydos.phonetic.onca(word, max_length=4, zero_pad=True)
Return the Oxford Name Compression Algorithm (ONCA) code for a word.
This is a wrapper for ONCA.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The maximum length (default 5) of the code to return• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The ONCA code
Return type str

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Examples>>> onca('Christopher')'C623'>>> onca('Niall')'N400'>>> onca('Smith')'S530'>>> onca('Schmidt')'S530'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the ONCA.encode method instead.
class abydos.phonetic.FONEM
Bases: abydos.phonetic._phonetic._Phonetic
FONEM.
FONEM is a phonetic algorithm designed for French (particularly surnames in Saguenay, Canada), defined in[BBL81].
Guillaume Plique's Javascript implementation [Pli18] at https://github.com/Yomguithereal/talisman/blob/master/src/phonetics/french/fonem.js was also consulted for this implementation.
New in version 0.3.6.
encode(word)
Return the FONEM code of a word.
Parameters word (str) -- The word to transform
Returns The FONEM code
Return type str
Examples>>> pe = FONEM()>>> pe.encode('Marchand')'MARCHEN'>>> pe.encode('Beaulieu')'BOLIEU'>>> pe.encode('Beaumont')'BOMON'>>> pe.encode('Legrand')'LEGREN'>>> pe.encode('Pelletier')'PELETIER'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.fonem(word)
Return the FONEM code of a word.
This is a wrapper for FONEM.encode().
Parameters word (str) -- The word to transform

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Returns The FONEM code
Return type str
Examples>>> fonem('Marchand')'MARCHEN'>>> fonem('Beaulieu')'BOLIEU'>>> fonem('Beaumont')'BOMON'>>> fonem('Legrand')'LEGREN'>>> fonem('Pelletier')'PELETIER'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the FONEM.encode method instead.
class abydos.phonetic.HenryEarly(max_length=3)
Bases: abydos.phonetic._phonetic._Phonetic
Henry code, early version.
The early version of Henry coding is given in [LegareLC72]. This is different from the later version defined in[Hen76].
New in version 0.3.6.
Initialize HenryEarly instance.
Parameters max_length (int) -- The length of the code returned (defaults to 3)
New in version 0.4.0.
encode(word)
Calculate the early version of the Henry code for a word.
Parameters word (str) -- The word to transform
Returns The early Henry code
Return type str
Examples>>> pe = HenryEarly()>>> pe.encode('Marchand')'MRC'>>> pe.encode('Beaulieu')'BL'>>> pe.encode('Beaumont')'BM'>>> pe.encode('Legrand')'LGR'>>> pe.encode('Pelletier')
'PLT'

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New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.henry_early(word, max_length=3)
Calculate the early version of the Henry code for a word.
This is a wrapper for HenryEarly.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 3)
Returns The early Henry code
Return type str
Examples>>> henry_early('Marchand')'MRC'>>> henry_early('Beaulieu')'BL'>>> henry_early('Beaumont')'BM'>>> henry_early('Legrand')'LGR'>>> henry_early('Pelletier')'PLT'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the HenryEarly.encode method instead.
class abydos.phonetic.KoelnerBases: abydos.phonetic._phonetic._Phonetic
Kölner Phonetik.
Based on the algorithm defined by [Pos69].
New in version 0.3.6.
encode(word)
Return the Kölner Phonetik (numeric output) code for a word.
While the output code is numeric, it is still a str because 0s can lead the code.
Parameters word (str) -- The word to transform
Returns The Kölner Phonetik value as a numeric string
Return type str

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Example>>> pe = Koelner()>>> pe.encode('Christopher')'478237'>>> pe.encode('Niall')'65'>>> pe.encode('Smith')'862'>>> pe.encode('Schmidt')'862'>>> pe.encode('Müller')'657'>>> pe.encode('Zimmermann')'86766'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the Kölner Phonetik (alphabetic output) code for a word.
Parameters word (str) -- The word to transform
Returns The Kölner Phonetik value as an alphabetic string
Return type str
Examples>>> pe = Koelner()>>> pe.encode_alpha('Smith')'SNT'>>> pe.encode_alpha('Schmidt')'SNT'>>> pe.encode_alpha('Müller')'NLR'>>> pe.encode_alpha('Zimmermann')'SNRNN'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.koelner_phonetik(word)
Return the Kölner Phonetik (numeric output) code for a word.
This is a wrapper for Koelner.encode().
Parameters word (str) -- The word to transform
Returns The Kölner Phonetik value as a numeric string
Return type str

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Example>>> koelner_phonetik('Christopher')'478237'>>> koelner_phonetik('Niall')'65'>>> koelner_phonetik('Smith')'862'>>> koelner_phonetik('Schmidt')'862'>>> koelner_phonetik('Müller')'657'>>> koelner_phonetik('Zimmermann')'86766'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Koelner.encode method instead.
abydos.phonetic.koelner_phonetik_num_to_alpha(num)Convert a Kölner Phonetik code from numeric to alphabetic.
This is a wrapper for Koelner._to_alpha().
Parameters num (str or int) -- A numeric Kölner Phonetik representation
Returns An alphabetic representation of the same word
Return type str
Examples>>> koelner_phonetik_num_to_alpha('862')'SNT'>>> koelner_phonetik_num_to_alpha('657')'NLR'>>> koelner_phonetik_num_to_alpha('86766')'SNRNN'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Koelner._to_alpha method instead.
abydos.phonetic.koelner_phonetik_alpha(word)
Return the Kölner Phonetik (alphabetic output) code for a word.
This is a wrapper for Koelner.encode_alpha().
Parameters word (str) -- The word to transform
Returns The Kölner Phonetik value as an alphabetic string
Return type str

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Examples>>> koelner_phonetik_alpha('Smith')'SNT'>>> koelner_phonetik_alpha('Schmidt')'SNT'>>> koelner_phonetik_alpha('Müller')'NLR'>>> koelner_phonetik_alpha('Zimmermann')'SNRNN'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Koelner.encode_alpha method instead.
class abydos.phonetic.Haase(primary_only=False)
Bases: abydos.phonetic._phonetic._Phonetic
Haase Phonetik.
Based on the algorithm described at [Pra15].
Based on the original [HH00].
New in version 0.3.6.
Initialize Haase instance.
Parameters primary_only (bool) -- If True, only the primary code is returned
New in version 0.4.0.
encode(word)
Return the Haase Phonetik (numeric output) code for a word.
While the output code is numeric, it is nevertheless a str.
Parameters word (str) -- The word to transform
Returns The Haase Phonetik value as a numeric string
Return type tuple
Examples>>> pe = Haase()>>> pe.encode('Joachim')('9496',)>>> pe.encode('Christoph')('4798293', '8798293')>>> pe.encode('Jörg')('974',)>>> pe.encode('Smith')('8692',)>>> pe.encode('Schmidt')('8692', '4692')

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in class

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encode_alpha(word)
Return the alphabetic Haase Phonetik code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Haase Phonetik value
Return type tuple
Examples>>> pe = Haase()>>> pe.encode_alpha('Joachim')('AKAN',)>>> pe.encode_alpha('Christoph')('KRASTAF', 'SRASTAF')>>> pe.encode_alpha('Jörg')('ARK',)>>> pe.encode_alpha('Smith')('SNAT',)>>> pe.encode_alpha('Schmidt')('SNAT', 'KNAT')

New in version 0.4.0.
abydos.phonetic.haase_phonetik(word, primary_only=False)
Return the Haase Phonetik code for a word.
This is a wrapper for Haase.encode().
Parameters• word (str) -- The word to transform• primary_only (bool) -- If True, only the primary code is returned
Returns The Haase Phonetik value as a numeric string
Return type tuple
Examples>>> haase_phonetik('Joachim')('9496',)>>> haase_phonetik('Christoph')('4798293', '8798293')>>> haase_phonetik('Jörg')('974',)>>> haase_phonetik('Smith')('8692',)>>> haase_phonetik('Schmidt')('8692', '4692')

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Haase.encode method instead.
class abydos.phonetic.RethSchek
Bases: abydos.phonetic._phonetic._Phonetic

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Reth-Schek Phonetik.
This algorithm is proposed in [vonRethS77].
Since I couldn't secure a copy of that document (maybe I'll look for it next time I'm in Germany), this implementation is based on what I could glean from the implementations published by German Record Linkage Center(www.record-linkage.de):• Privacy-preserving Record Linkage (PPRL) (in R) [Ruk18]• Merge ToolBox (in Java) [SBB04]
Rules that are unclear:• Should 'C' become 'G' or 'Z'? (PPRL has both, 'Z' rule blocked)• Should 'CC' become 'G'? (PPRL has blocked 'CK' that may be typo)• Should 'TUI' -> 'ZUI' rule exist? (PPRL has rule, but I can't think of a German word with '-tui-' in it.)• Should we really change 'SCH' -> 'CH' and then 'CH' -> 'SCH'?
New in version 0.3.6.
encode(word)
Return Reth-Schek Phonetik code for a word.
Parameters word (str) -- The word to transform
Returns The Reth-Schek Phonetik code
Return type str
Examples>>> pe = RethSchek()>>> pe.encode('Joachim')'JOAGHIM'>>> pe.encode('Christoph')'GHRISDOF'>>> pe.encode('Jörg')'JOERG'>>> pe.encode('Smith')'SMID'>>> pe.encode('Schmidt')'SCHMID'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.reth_schek_phonetik(word)
Return Reth-Schek Phonetik code for a word.
This is a wrapper for RethSchek.encode().
Parameters word (str) -- The word to transform
Returns The Reth-Schek Phonetik code
Return type str

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Examples>>> reth_schek_phonetik('Joachim')'JOAGHIM'>>> reth_schek_phonetik('Christoph')'GHRISDOF'>>> reth_schek_phonetik('Jörg')'JOERG'>>> reth_schek_phonetik('Smith')'SMID'>>> reth_schek_phonetik('Schmidt')'SCHMID'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the RethSchek.encode method instead.
class abydos.phonetic.Phonem
Bases: abydos.phonetic._phonetic._Phonetic
Phonem.
Phonem is defined in [GM88].
This version is based on the Perl implementation documented at [Wil05]. It includes some enhancements presented in the Java port at [dcm4che].
Phonem is intended chiefly for German names/words.
New in version 0.3.6.
encode(word)
Return the Phonem code for a word.
Parameters• word (str) -• word to transform (The) -Returns The Phonem value
Return type str
Examples>>> pe = Phonem()>>> pe.encode('Christopher')'CRYSDOVR'>>> pe.encode('Niall')'NYAL'>>> pe.encode('Smith')'SMYD'>>> pe.encode('Schmidt')'CMYD'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class

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abydos.phonetic.phonem(word)
Return the Phonem code for a word.
This is a wrapper for Phonem.encode().
Parameters word (str) -- The word to transform
Returns The Phonem value
Return type str
Examples>>> phonem('Christopher')'CRYSDOVR'>>> phonem('Niall')'NYAL'>>> phonem('Smith')'SMYD'>>> phonem('Schmidt')'CMYD'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Phonem.encode method instead.
class abydos.phonetic.Phonet(mode=1, lang='de')Bases: abydos.phonetic._phonetic._Phonetic
Phonet code.
phonet ("Hannoveraner Phonetik") was developed by Jörg Michael and documented in [Mic99].
This is a port of Jesper Zedlitz's code, which is licensed LGPL [Zed15].
That is, in turn, based on Michael's C code, which is also licensed LGPL [Mic07].
New in version 0.3.6.
Initialize AlphaSIS instance.
Parameters• mode (int) -- The ponet variant to employ (1 or 2)• lang (str) -- de (default) for German, none for no language
New in version 0.4.0.
encode(word)
Return the phonet code for a word.
Parameters word (str) -- The word to transform
Returns The phonet value
Return type str

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Examples>>> pe = Phonet()>>> pe.encode('Christopher')'KRISTOFA'>>> pe.encode('Niall')'NIAL'>>> pe.encode('Smith')'SMIT'>>> pe.encode('Schmidt')'SHMIT'>>> pe2 = Phonet(mode=2)>>> pe2.encode('Christopher')'KRIZTUFA'>>> pe2.encode('Niall')'NIAL'>>> pe2.encode('Smith')'ZNIT'>>> pe2.encode('Schmidt')'ZNIT'>>> pe_none = Phonet(lang='none')>>> pe_none.encode('Christopher')'CHRISTOPHER'>>> pe_none.encode('Niall')'NIAL'>>> pe_none.encode('Smith')'SMITH'>>> pe_none.encode('Schmidt')'SCHMIDT'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.phonet(word, mode=1, lang='de')Return the phonet code for a word.
This is a wrapper for Phonet.encode().
Parameters• word (str) -- The word to transform• mode (int) -- The ponet variant to employ (1 or 2)• lang (str) -- de (default) for German, none for no language
Returns The phonet value
Return type str

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Examples>>> phonet('Christopher')'KRISTOFA'>>> phonet('Niall')'NIAL'>>> phonet('Smith')'SMIT'>>> phonet('Schmidt')'SHMIT'>>> phonet('Christopher', mode=2)'KRIZTUFA'>>> phonet('Niall', mode=2)'NIAL'>>> phonet('Smith', mode=2)'ZNIT'>>> phonet('Schmidt', mode=2)'ZNIT'>>> phonet('Christopher', lang='none')'CHRISTOPHER'>>> phonet('Niall', lang='none')'NIAL'>>> phonet('Smith', lang='none')'SMITH'>>> phonet('Schmidt', lang='none')'SCHMIDT'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Phonet.encode method instead.
class abydos.phonetic.SoundexBR(max_length=4, zero_pad=True)
Bases: abydos.phonetic._phonetic._Phonetic
SoundexBR.
This is based on [Mar15].
New in version 0.3.6.
Initialize SoundexBR instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
New in version 0.4.0.
encode(word)
Return the SoundexBR encoding of a word.
Parameters word (str) -- The word to transform
Returns The SoundexBR code
Return type str

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Examples>>> pe = SoundexBR()>>> pe.encode('Oliveira')'O416'>>> pe.encode('Almeida')'A453'>>> pe.encode('Barbosa')'B612'>>> pe.encode('Araújo')'A620'>>> pe.encode('Gonçalves')'G524'>>> pe.encode('Goncalves')'G524'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic SoundexBR encoding of a word.
Parameters word (str) -- The word to transform
Returns The alphabetic SoundexBR code
Return type str
Examples>>> pe = SoundexBR()>>> pe.encode_alpha('Oliveira')'OLPR'>>> pe.encode_alpha('Almeida')'ALNT'>>> pe.encode_alpha('Barbosa')'BRPK'>>> pe.encode_alpha('Araújo')'ARK'>>> pe.encode_alpha('Gonçalves')'GNKL'>>> pe.encode_alpha('Goncalves')'GNKL'New in version 0.4.0.
abydos.phonetic.soundex_br(word, max_length=4, zero_pad=True)
Return the SoundexBR encoding of a word.
This is a wrapper for SoundexBR.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 4)• zero_pad (bool) -- Pad the end of the return value with 0s to achieve a max_length string
Returns The SoundexBR code
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Return type str
Examples>>> soundex_br('Oliveira')'O416'>>> soundex_br('Almeida')'A453'>>> soundex_br('Barbosa')'B612'>>> soundex_br('Araújo')'A620'>>> soundex_br('Gonçalves')'G524'>>> soundex_br('Goncalves')'G524'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SoundexBR.encode method instead.
class abydos.phonetic.PhoneticSpanish(max_length=-1)
Bases: abydos.phonetic._phonetic._Phonetic
PhoneticSpanish.
This follows the coding described in [AmonME12] and [delPAngelesEGGM15].
New in version 0.3.6.
Initialize PhoneticSpanish instance.
Parameters max_length (int) -- The length of the code returned (defaults to unlimited)
New in version 0.4.0.
encode(word)
Return the PhoneticSpanish coding of word.
Parameters word (str) -- The word to transform
Returns The PhoneticSpanish code
Return type str
Examples>>> pe = PhoneticSpanish()>>> pe.encode('Perez')'094'>>> pe.encode('Martinez')'69364'>>> pe.encode('Gutierrez')'83994'>>> pe.encode('Santiago')'4638'>>> pe.encode('Nicolás')'6454'

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New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic PhoneticSpanish coding of word.
Parameters word (str) -- The word to transform
Returns The alphabetic PhoneticSpanish code
Return type str
Examples>>> pe = PhoneticSpanish()>>> pe.encode_alpha('Perez')'PRS'>>> pe.encode_alpha('Martinez')'NRTNS'>>> pe.encode_alpha('Gutierrez')'GTRRS'>>> pe.encode_alpha('Santiago')'SNTG'>>> pe.encode_alpha('Nicolás')'NSLS'New in version 0.4.0.
abydos.phonetic.phonetic_spanish(word, max_length=-1)
Return the PhoneticSpanish coding of word.
This is a wrapper for PhoneticSpanish.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to unlimited)
Returns The PhoneticSpanish code
Return type str
Examples>>> phonetic_spanish('Perez')'094'>>> phonetic_spanish('Martinez')'69364'>>> phonetic_spanish('Gutierrez')'83994'>>> phonetic_spanish('Santiago')'4638'>>> phonetic_spanish('Nicolás')'6454'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the PhoneticSpanish.encode method instead.

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class abydos.phonetic.SpanishMetaphone(max_length=6, modified=False)
Bases: abydos.phonetic._phonetic._Phonetic
Spanish Metaphone.
This is a quick rewrite of the Spanish Metaphone Algorithm, as presented at https://github.com/amsqr/
Spanish-Metaphone and discussed in [MLM12].
Modified version based on [delPAngelesBailonM16].
New in version 0.3.6.
Initialize AlphaSIS instance.
Parameters• max_length (int) -- The length of the code returned (defaults to 6)• modified (bool) -- Set to True to use del Pilar Angeles & Bailón-Miguel's modified
version of the algorithm
New in version 0.4.0.
encode(word)
Return the Spanish Metaphone of a word.
Parameters word (str) -- The word to transform
Returns The Spanish Metaphone code
Return type str
Examples>>> pe = SpanishMetaphone()>>> pe.encode('Perez')'PRZ'>>> pe.encode('Martinez')'MRTNZ'>>> pe.encode('Gutierrez')'GTRRZ'>>> pe.encode('Santiago')'SNTG'>>> pe.encode('Nicolás')'NKLS'New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.spanish_metaphone(word, max_length=6, modified=False)
Return the Spanish Metaphone of a word.
This is a wrapper for SpanishMetaphone.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to 6)• modified (bool) -- Set to True to use del Pilar Angeles & Bailón-Miguel's modified
version of the algorithm

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Returns The Spanish Metaphone code
Return type str
Examples>>> spanish_metaphone('Perez')'PRZ'>>> spanish_metaphone('Martinez')'MRTNZ'>>> spanish_metaphone('Gutierrez')'GTRRZ'>>> spanish_metaphone('Santiago')'SNTG'>>> spanish_metaphone('Nicolás')'NKLS'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SpanishMetaphone.encode method
instead.
class abydos.phonetic.SfinxBis(max_length=-1)
Bases: abydos.phonetic._phonetic._Phonetic
SfinxBis code.
SfinxBis is a Soundex-like algorithm defined in [Axe09].
This implementation follows the reference implementation: [Sjoo09].
SfinxBis is intended chiefly for Swedish names.
New in version 0.3.6.
Initialize SfinxBis instance.
Parameters max_length (int) -- The length of the code returned (defaults to unlimited)
New in version 0.4.0.
encode(word)
Return the SfinxBis code for a word.
Parameters word (str) -- The word to transform
Returns The SfinxBis value
Return type tuple
Examples>>> pe = SfinxBis()>>> pe.encode('Christopher')('K68376',)>>> pe.encode('Niall')('N4',)>>> pe.encode('Smith')('S53',)>>> pe.encode('Schmidt')('S53',)

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Abydos Documentation, Release 0.5.0>>> pe.encode('Johansson')('J585',)>>> pe.encode('Sjöberg')('#162',)

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classencode_alpha(word)
Return the alphabetic SfinxBis code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic SfinxBis value
Return type tuple
Examples>>> pe = SfinxBis()>>> pe.encode_alpha('Christopher')('KRSTFR',)>>> pe.encode_alpha('Niall')('NL',)>>> pe.encode_alpha('Smith')('SNT',)>>> pe.encode_alpha('Schmidt')('SNT',)>>> pe.encode_alpha('Johansson')('JNSN',)>>> pe.encode_alpha('Sjöberg')('ŠPRK',)

New in version 0.4.0.
abydos.phonetic.sfinxbis(word, max_length=-1)
Return the SfinxBis code for a word.
This is a wrapper for SfinxBis.encode().
Parameters• word (str) -- The word to transform• max_length (int) -- The length of the code returned (defaults to unlimited)
Returns The SfinxBis value
Return type tuple

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Examples>>> sfinxbis('Christopher')('K68376',)>>> sfinxbis('Niall')('N4',)>>> sfinxbis('Smith')('S53',)>>> sfinxbis('Schmidt')('S53',)>>> sfinxbis('Johansson')('J585',)>>> sfinxbis('Sjöberg')('#162',)

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SfinxBis.encode method instead.
class abydos.phonetic.Waahlin(encoder=None)
Bases: abydos.phonetic._phonetic._PhoneticWåhlin code.
Wåhlin's first-letter coding is based on the description in [Eri97].
New in version 0.3.6.
Initialize Waahlin instance.
Parameters encoder (_Phonetic) -- An initialized phonetic algorithm object
New in version 0.4.0.
encode(word, alphabetic=False)
Return the Wåhlin code for a word.
Parameters• word (str) -- The word to transform• alphabetic (bool) -- If True, the encoder will apply its alphabetic form (.encode_alpha rather than .encode)
Returns The Wåhlin code value
Return type str
Examples>>> pe = Waahlin()>>> pe.encode('Christopher')'KRISTOFER'>>> pe.encode('Niall')'NJALL'>>> pe.encode('Smith')'SMITH'>>> pe.encode('Schmidt')'*MIDT'3.1. abydos package575

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New in version 0.4.0.
encode_alpha(word)
Return the alphabetic Wåhlin code for a word.
Parameters word (str) -- The word to transform
Returns The alphabetic Wåhlin code value
Return type str
Examples>>> pe = Waahlin()>>> pe.encode_alpha('Christopher')'KRISTOFER'>>> pe.encode_alpha('Niall')'NJALL'>>> pe.encode_alpha('Smith')'SMITH'>>> pe.encode_alpha('Schmidt')'ŠMIDT'

New in version 0.4.0.
class abydos.phonetic.Norphone
Bases: abydos.phonetic._phonetic._Phonetic
Norphone.
The reference implementation by Lars Marius Garshol is available in [Gar15].
Norphone was designed for Norwegian, but this implementation has been extended to support Swedish vowels
as well. This function incorporates the "not implemented" rules from the above file's rule set.
New in version 0.3.6.
encode(word)
Return the Norphone code.
Parameters word (str) -- The word to transform
Returns The Norphone code
Return type str
Examples>>> pe = Norphone()>>> pe.encode('Hansen')'HNSN'>>> pe.encode('Larsen')'LRSN'>>> pe.encode('Aagaard')'ÅKRT'>>> pe.encode('Braaten')'BRTN'>>> pe.encode('Sandvik')'SNVK'

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New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.phonetic.norphone(word)
Return the Norphone code.
This is a wrapper for Norphone.encode().
Parameters word (str) -- The word to transform
Returns The Norphone code
Return type str
Examples>>> norphone('Hansen')'HNSN'>>> norphone('Larsen')'LRSN'>>> norphone('Aagaard')'ÅKRT'>>> norphone('Braaten')'BRTN'>>> norphone('Sandvik')'SNVK'New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Norphone.encode method instead.
class abydos.phonetic.Ainsworth
Bases: abydos.phonetic._phonetic._Phonetic
Ainsworth's grapheme to phoneme converter.
Based on the ruleset listed in [Ain73].
New in version 0.4.1.
encode(word)
Return the phonemic representation of a word.
Parameters word (str) -- The word to transform
Returns The phonemic representation in IPA
Return type str
Examples>>> pe = Ainsworth()>>> pe.encode('Christopher')'trstof'>>> pe.encode('Niall')'nl'>>> pe.encode('Smith')'smð'>>> pe.encode('Schmidt')'skmdt'3.1. abydos package577

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New in version 0.4.1.
3.1.1.7 abydos.stats package
abydos.stats.
The stats module defines functions for calculating various statistical data about linguistic objects.
Functions are provided for calculating the following means:• arithmetic mean (amean())• geometric mean (gmean())• harmonic mean (hmean())• quadratic mean (qmean())• contraharmonic mean (cmean())• logarithmic mean (lmean())• identric (exponential) mean (imean())• Seiffert's mean (seiffert_mean())• Lehmer mean (lehmer_mean())• Heronian mean (heronian_mean())• Hölder (power/generalized) mean (hoelder_mean())• arithmetic-geometric mean (agmean())• geometric-harmonic mean (ghmean())• arithmetic-geometric-harmonic mean (aghmean())
And for calculating:• midrange (midrange())• median (median())• mode (mode())• variance (var())• standard deviation (std())
Some examples of the basic functions:>>> nums = [16, 49, 55, 49, 6, 40, 23, 47, 29, 85, 76, 20]>>> amean(nums)
41.25>>> aghmean(nums)
32.42167170892585>>> heronian_mean(nums)
37.931508950381925>>> mode(nums)
49>>> std(nums)
22.876935255113754

Two pairwise functions are provided:

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Abydos Documentation, Release 0.5.0• mean pairwise similarity (mean_pairwise_similarity()), which returns the mean similarity (using a
supplied similarity function) among each item in a collection• pairwise similarity statistics (pairwise_similarity_statistics()), which returns the max, min,mean, and standard deviation of pairwise similarities between two collections
The confusion table class (ConfusionTable) can be constructed in a number of ways:• four values, representing true positives, true negatives, false positives, and false negatives, can be passed to theconstructor• a list or tuple with four values, representing true positives, true negatives, false positives, and false negatives,
can be passed to the constructor• a dict with keys 'tp', 'tn', 'fp', 'fn', each assigned to the values for true positives, true negatives, false positives,and false negatives can be passed to the constructor
The ConfusionTable class has methods:• to_tuple() extracts the ConfusionTable values as a tuple: (𝑤, 𝑥, 𝑦, 𝑧)• to_dict() extracts the ConfusionTable values as a dict: {'tp':𝑤, 'tn':𝑥, 'fp':𝑦, 'fn':𝑧}• true_pos() returns the number of true positives• true_neg() returns the number of true negatives• false_pos() returns the number of false positives• false_neg() returns the number of false negatives• correct_pop() returns the correct population• error_pop() returns the error population• pred_pos_pop() returns the test positive population• pred_neg_pop() returns the test negative population• cond_pos_pop() returns the condition positive population• cond_neg_pop() returns the condition negative population• population() returns the total population• precision() returns the precision• precision_gain() returns the precision gain
• recall() returns the recall• specificity() returns the specificity• npv() returns the negative predictive value• fallout() returns the fallout• fdr() returns the false discovery rate
• accuracy() returns the accuracy• accuracy_gain() returns the accuracy gain
• balanced_accuracy() returns the balanced accuracy• informedness() returns the informedness• markedness() returns the markedness• pr_amean() returns the arithmetic mean of precision & recall

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Abydos Documentation, Release 0.5.0• pr_gmean() returns the geometric mean of precision & recall• pr_hmean() returns the harmonic mean of precision & recall• pr_qmean() returns the quadratic mean of precision & recall• pr_cmean() returns the contraharmonic mean of precision & recall• pr_lmean() returns the logarithmic mean of precision & recall• pr_imean() returns the identric mean of precision & recall• pr_seiffert_mean() returns Seiffert's mean of precision & recall• pr_lehmer_mean() returns the Lehmer mean of precision & recall• pr_heronian_mean() returns the Heronian mean of precision & recall• pr_hoelder_mean() returns the Hölder mean of precision & recall• pr_agmean() returns the arithmetic-geometric mean of precision & recall• pr_ghmean() returns the geometric-harmonic mean of precision & recall• pr_aghmean() returns the arithmetic-geometric-harmonic mean of precision & recall• fbeta_score() returns the 𝐹𝑏𝑒𝑡𝑎 score• f2_score() returns the 𝐹2 score• fhalf_score() returns the 𝐹 21 score• e_score() returns the 𝐸 score• f1_score() returns the 𝐹1 score• f_measure() returns the F measure• g_measure() returns the G measure• mcc() returns Matthews correlation coefficient• significance() returns the significance• kappa_statistic() returns the Kappa statistic>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.f1_score()
0.8275862068965518>>> ct.mcc()
0.5367450401216932>>> ct.specificity()
0.75>>> ct.significance()
66.26190476190476

The ConfusionTable class also supports checking for equality with another ConfusionTable and casting tostring with str():>>> (ConfusionTable({'tp':120, 'tn':60, 'fp':20, 'fn':30}) ==... ConfusionTable(120, 60, 20, 30))
True>>> str(ConfusionTable(120, 60, 20, 30))
'tp:120, tn:60, fp:20, fn:30'

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class abydos.stats.ConfusionTable(tp=0, tn=0, fp=0, fn=0)
Bases: object
ConfusionTable object.
This object is initialized by passing either four integers (or a tuple of four integers) representing the squares ofa confusion table: true positives, true negatives, false positives, and false negatives
The object possesses methods for the calculation of various statistics based on the confusion table.
Initialize ConfusionTable.
Parameters• tp (int or a tuple, list, or dict) -- True positives; If a tuple or list is supplied, it must include 4 values in the order [tp, tn, fp, fn]. If a dict is supplied, it must have
4 keys, namely 'tp', 'tn', 'fp', & 'fn'.
• tn (int) -- True negatives• fp (int) -- False positives• fn (int) -- False negatives
Raises AttributeError -- ConfusionTable requires a 4-tuple when being created from a tuple.
Examples>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct == ConfusionTable((120, 60, 20, 30))
True>>> ct == ConfusionTable([120, 60, 20, 30])
True>>> ct == ConfusionTable({'tp': 120, 'tn': 60, 'fp': 20, 'fn': 30})
True

New in version 0.1.0.
accuracy()
Return accuracy.
Accuracy is defined as

𝑡𝑝 + 𝑡𝑛
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
Cf. https://en.wikipedia.org/wiki/Accuracy
Returns The accuracy of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.accuracy()
0.782608695652174

New in version 0.1.0.
accuracy_gain()
Return gain in accuracy.
The gain in accuracy is defined as𝐺(𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦) =𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦
𝑟𝑎𝑛𝑑𝑜𝑚 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦

Cf. https://en.wikipedia.org/wiki/Gain_(information_retrieval)
Returns The gain in accuracy of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.accuracy_gain()
1.4325259515570934

New in version 0.1.0.
actual_entropy()
Return the actual entropy.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns The actual entropy of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.actual_entropy()
0.6460905050608101

New in version 0.4.0.
balanced_accuracy()
Return balanced accuracy.
Balanced accuracy is defined as𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 + 𝑠𝑝𝑒𝑐𝑖𝑓 𝑖𝑐𝑖𝑡𝑦
2
Cf. https://en.wikipedia.org/wiki/Accuracy
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Returns The balanced accuracy of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.balanced_accuracy()
0.775

New in version 0.1.0.
cond_neg_pop()
Return condition negative population.
Returns The condition negative population of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.cond_neg_pop()
80

New in version 0.1.0.
cond_pos_pop()
Return condition positive population.
Returns The condition positive population of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.cond_pos_pop()
150

New in version 0.1.0.
correct_pop()
Return correct population.
Returns The correct population of the confusion table
Return type int

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.correct_pop()180

New in version 0.1.0.
d_measure()
Return D-measure.
𝐷-measure is defined as

1−

1
1
1
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙 − 1

Returns The 𝐷-measure of the confusion table
Return type float
Examples>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.d_measure()
0.2941176470588237

New in version 0.4.0.
dependency()
Return dependency.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns The dependency of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.dependency()
0.12618094145262454

New in version 0.4.0.
diagnostic_odds_ratio()
Return diagnostic odds ratio.
Diagnostic odds ratio is defined as
𝑡𝑝 · 𝑡𝑛
𝑓𝑝 · 𝑓𝑛
Cf. https://en.wikipedia.org/wiki/Diagnostic_odds_ratio

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Returns The negative likelihood ratio of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.diagnostic_odds_ratio()
12.0

New in version 0.4.0.
e_score(beta=1.0)
Return 𝐸-score.
This is Van Rijsbergen's effectiveness measure: 𝐸 = 1 − 𝐹𝛽 .
Cf. https://en.wikipedia.org/wiki/Information_retrieval#F-measure
Parameters beta (float) -- The 𝛽 parameter in the above formula
Returns The 𝐸-score of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.e_score()
0.17241379310344818

New in version 0.1.0.
error_pop()
Return error population.
Returns The error population of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.error_pop()
50

New in version 0.1.0.
error_rate()
Return error rate.
Error rate is defined as

𝑓𝑝 + 𝑓𝑛
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
Returns The error rate of the confusion table
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Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.error_rate()
0.21739130434782608

New in version 0.4.0.
f1_score()
Return 𝐹1 score.
𝐹1 score is the harmonic mean of precision and recall

2·

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙

Cf. https://en.wikipedia.org/wiki/F1_score
Returns The 𝐹1 of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.f1_score()
0.8275862068965518

New in version 0.1.0.
f2_score()
Return 𝐹2 .
The 𝐹2 score emphasizes recall over precision in comparison to the 𝐹1 score
Cf. https://en.wikipedia.org/wiki/F1_score
Returns The 𝐹2 of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.f2_score()
0.8108108108108109

New in version 0.1.0.
f_measure()
Return 𝐹 -measure.
𝐹 -measure is the harmonic mean of precision and recall

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2·

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙

Cf. https://en.wikipedia.org/wiki/F1_score
Returns The math:F-measure of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.f_measure()
0.8275862068965516

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the ConfusionTable.pr_hmean method
instead.
fallout()
Return fall-out.
Fall-out is defined as𝑓𝑝
𝑓 𝑝 + 𝑡𝑛
AKA false positive rate (FPR)
Cf. https://en.wikipedia.org/wiki/Information_retrieval#Fall-out
Returns The fall-out of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.fallout()
0.25

New in version 0.1.0.
false_neg()
Return false negatives.
AKA Type II error
Returns The false negatives of the confusion table
Return type int

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.false_neg()30

New in version 0.1.0.
false_omission_rate()
Return false omission rate (FOR).
FOR is defined as
𝑓𝑛
𝑡𝑛 + 𝑓 𝑛
Cf. https://en.wikipedia.org/wiki/False_omission_rate
Returns The false omission rate of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.false_omission_rate()
0.3333333333333333

New in version 0.4.0.
false_pos()
Return false positives.
AKA Type I error
Returns The false positives of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.false_pos()
20

New in version 0.1.0.
fbeta_score(beta=1.0)
Return 𝐹𝛽 score.
𝐹𝛽 for a positive real value 𝛽 "measures the effectiveness of retrieval with respect to a user who attaches
𝛽 times as much importance to recall as precision" (van Rijsbergen 1979)
𝐹𝛽 score is defined as(1 + 𝛽 2 ) ·588

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙((𝛽 2 · 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛) + 𝑟𝑒𝑐𝑎𝑙𝑙)
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Cf. https://en.wikipedia.org/wiki/F1_score
Parameters beta (float) -- The 𝛽 parameter in the above formula
Returns The 𝐹𝛽 of the confusion table
Return type float
Raises AttributeError -- Beta must be a positive real value
Examples>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.fbeta_score()
0.8275862068965518>>> ct.fbeta_score(beta=0.1)
0.8565371024734982

New in version 0.1.0.
fdr()
Return false discovery rate (FDR).
False discovery rate is defined as𝑓𝑝
𝑓 𝑝 + 𝑡𝑝
Cf. https://en.wikipedia.org/wiki/False_discovery_rate
Returns The false discovery rate of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.fdr()
0.14285714285714285

New in version 0.1.0.
fhalf_score()
Return 𝐹0.5 score.
The 𝐹0.5 score emphasizes precision over recall in comparison to the 𝐹1 score
Cf. https://en.wikipedia.org/wiki/F1_score
Returns The 𝐹0.5 score of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.fhalf_score()
0.8450704225352114

New in version 0.1.0.
fnr()
Return false negative rate.
False negative rate is defined as

𝑓𝑛
𝑡𝑝 + 𝑓 𝑛
AKA miss rate
Cf. https://en.wikipedia.org/wiki/False_negative_rate
Returns The false negative rate of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> round(ct.fnr(), 8)
0.2

New in version 0.4.0.
g_measure()
Return G-measure.
𝐺-measure is the geometric mean of precision and recall:√︀
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
This is identical to the Fowlkes–Mallows (FM) index for two clusters.
Cf. https://en.wikipedia.org/wiki/F1_score#G-measure
Cf. https://en.wikipedia.org/wiki/Fowlkes%E2%80%93Mallows_index
Returns The 𝐺-measure of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.g_measure()
0.828078671210825

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the ConfusionTable.pr_gmean method
instead.
igr()
Return information gain ratio.
Implementation based on https://github.com/Magnetic/proficiency-metricCf. https://en.wikipedia.org/wiki/Information_gain_ratio
Returns The information gain ratio of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.igr()
0.22019657299448012

New in version 0.4.0.
informedness()
Return informedness.
Informedness is defined as𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 + 𝑠𝑝𝑒𝑐𝑖𝑓 𝑖𝑐𝑖𝑡𝑦 − 1
AKA Youden's J statistic ([You50])
AKA DeltaP'
Cf. https://en.wikipedia.org/wiki/Youden%27s_J_statistic
Returns The informedness of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.informedness()
0.55

New in version 0.1.0.

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jaccard()Return Jaccard index.
The Jaccard index of a confusion table is𝑡𝑝
𝑡𝑝 + 𝑓 𝑝 + 𝑓 𝑛
Returns The Jaccard index of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.jaccard()
0.7058823529411765

New in version 0.4.0.
joint_entropy()
Return the joint entropy.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns The joint entropy of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.joint_entropy()
1.1680347446270396

New in version 0.4.0.
kappa_statistic()
Return statistic.
The statistic is defined as𝜅=𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 − 𝑟𝑎𝑛𝑑𝑜𝑚 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦‘
1 − 𝑟𝑎𝑛𝑑𝑜𝑚 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦

The statistic compares the performance of the classifier relative to the performance of a random classifier.
𝜅 = 0 indicates performance identical to random. 𝜅 = 1 indicates perfect predictive success. 𝜅 = -1 indicates
perfect predictive failure.
Returns The statistic of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.kappa_statistic()
0.5344129554655871

New in version 0.1.0.
lift()
Return lift.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns The lift of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.lift()
1.3142857142857143

New in version 0.4.0.
markedness()
Return markedness.
Markedness is defined as

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑛𝑝𝑣 − 1
AKA DeltaP
Returns The markedness of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.markedness()
0.5238095238095237

New in version 0.1.0.
mcc()
Return Matthews correlation coefficient (MCC).
The Matthews correlation coefficient is defined in [Mat75] as:(𝑡𝑝 · 𝑡𝑛) − (𝑓 𝑝 · 𝑓 𝑛)
√︀
(𝑡𝑝 + 𝑓 𝑝)(𝑡𝑝 + 𝑓 𝑛)(𝑡𝑛 + 𝑓 𝑝)(𝑡𝑛 + 𝑓 𝑛)

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This is equivalent to the geometric mean of informedness and markedness, defined above.
Cf. https://en.wikipedia.org/wiki/Matthews_correlation_coefficient
Returns The Matthews correlation coefficient of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.mcc()
0.5367450401216932

New in version 0.1.0.
mutual_information()
Return the mutual information.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns• float -- The mutual information of the confusion table• Cf. https (//en.wikipedia.org/wiki/Mutual_information)
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.mutual_information()
0.14738372372641576

New in version 0.4.0.
neg_likelihood_ratio()
Return negative likelihood ratio.
Negative likelihood ratio is defined as

1 − 𝑟𝑒𝑐𝑎𝑙𝑙
𝑠𝑝𝑒𝑐𝑖𝑓 𝑖𝑐𝑖𝑡𝑦
Cf. https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testing
Returns The negative likelihood ratio of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.neg_likelihood_ratio()0.2666666666666666

New in version 0.4.0.
npv()
Return negative predictive value (NPV).
NPV is defined as𝑡𝑛
𝑡𝑛 + 𝑓 𝑛
AKA inverse precision
Cf. https://en.wikipedia.org/wiki/Negative_predictive_value
Returns The negative predictive value of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.npv()
0.6666666666666666

New in version 0.1.0.
phi_coefficient()
Return coefficient.
The 𝜑 coefficient is defined as

𝑡𝑝 · 𝑡𝑛 − 𝑓 𝑝 · 𝑡𝑛
𝜑 = √︀
(𝑡𝑝 + 𝑓 𝑝) · (𝑡𝑝 + 𝑓 𝑛) · (𝑡𝑛 + 𝑓 𝑝) · (𝑡𝑛 + 𝑓 𝑛)
Returns The coefficient of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.phi_coefficient()
0.5367450401216932

New in version 0.4.0.
population()
Return population, N.

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Returns The population (N) of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.population()
230

New in version 0.1.0.
pos_likelihood_ratio()
Return positive likelihood ratio.
Positive likelihood ratio is defined as𝑟𝑒𝑐𝑎𝑙𝑙1 − 𝑠𝑝𝑒𝑐𝑖𝑓 𝑖𝑐𝑖𝑡𝑦
Cf. https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testing
Returns The positive likelihood ratio of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pos_likelihood_ratio()
3.2

New in version 0.4.0.
pr_aghmean()
Return arithmetic-geometric-harmonic mean of precision & recall.
Iterates over arithmetic, geometric, & harmonic means until they converge to a single value (rounded to 12
digits), following the method described in [RaissouliLC09].
Returns The arithmetic-geometric-harmonic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_aghmean()
0.8280786712108288

New in version 0.1.0.
pr_agmean()
Return arithmetic-geometric mean of precision & recall.
Iterates between arithmetic & geometric means until they converge to a single value (rounded to 12 digits)
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Cf. https://en.wikipedia.org/wiki/Arithmetic-geometric_mean
Returns The arithmetic-geometric mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_agmean()
0.8283250315702829

New in version 0.1.0.
pr_amean()
Return arithmetic mean of precision & recall.
The arithmetic mean of precision and recall is defined as

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
2
Cf. https://en.wikipedia.org/wiki/Arithmetic_mean
Returns The arithmetic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_amean()
0.8285714285714285

New in version 0.1.0.
pr_cmean()
Return contraharmonic mean of precision & recall.
The contraharmonic mean is𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛2 + 𝑟𝑒𝑐𝑎𝑙𝑙2
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
Cf. https://en.wikipedia.org/wiki/Contraharmonic_mean
Returns The contraharmonic mean of the confusion table's precision & recall
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_cmean()0.8295566502463055

New in version 0.1.0.
pr_ghmean()
Return geometric-harmonic mean of precision & recall.
Iterates between geometric & harmonic means until they converge to a single value (rounded to 12 digits)
Cf. https://en.wikipedia.org/wiki/Geometric-harmonic_mean
Returns The geometric-harmonic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_ghmean()
0.8278323841238441

New in version 0.1.0.
pr_gmean()
Return geometric mean of precision & recall.
The geometric mean of precision and recall is defined as:√︀𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
Cf. https://en.wikipedia.org/wiki/Geometric_mean
Returns The geometric mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_gmean()
0.828078671210825

New in version 0.1.0.
pr_heronian_mean()
Return Heronian mean of precision & recall.
The Heronian mean of precision and recall is defined as

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 +598√

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙 + 𝑟𝑒𝑐𝑎𝑙𝑙
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Cf. https://en.wikipedia.org/wiki/Heronian_mean
Returns The Heronian mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_heronian_mean()
0.8284071761178939

New in version 0.1.0.
pr_hmean()
Return harmonic mean of precision & recall.
The harmonic mean of precision and recall is defined as

2 · 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 · 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
Cf. https://en.wikipedia.org/wiki/Harmonic_mean
Returns The harmonic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_hmean()
0.8275862068965516

New in version 0.1.0.
pr_hoelder_mean(exp=2)
Return Hölder (power/generalized) mean of precision & recall.
The power mean of precision and recall is defined as

√︀
1 𝑒𝑥𝑝
·
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑒𝑥𝑝 + 𝑟𝑒𝑐𝑎𝑙𝑙𝑒𝑥𝑝
2
for 𝑒𝑥𝑝 ̸= 0, and the geometric mean for 𝑒𝑥𝑝 = 0
Cf. https://en.wikipedia.org/wiki/Generalized_mean
Parameters exp (float) -- The exponent of the Hölder mean
Returns The Hölder mean for the given exponent of the confusion table's precision & recall
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_hoelder_mean()0.8290638930598233

New in version 0.1.0.
pr_imean()
Return identric (exponential) mean of precision & recall.
The identric mean is: precision if precision = recall, otherwise√︂
1 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛−𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛·𝑒𝑟𝑒𝑐𝑎𝑙𝑙𝑟𝑒𝑐𝑎𝑙𝑙
Cf. https://en.wikipedia.org/wiki/Identric_mean
Returns The identric mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_imean()
0.8284071826325543

New in version 0.1.0.
pr_lehmer_mean(exp=2.0)
Return Lehmer mean of precision & recall.
The Lehmer mean is𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑒𝑥𝑝 + 𝑟𝑒𝑐𝑎𝑙𝑙𝑒𝑥𝑝
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑒𝑥𝑝−1 + 𝑟𝑒𝑐𝑎𝑙𝑙𝑒𝑥𝑝−1
Cf. https://en.wikipedia.org/wiki/Lehmer_mean
Parameters exp (float) -- The exponent of the Lehmer mean
Returns The Lehmer mean for the given exponent of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_lehmer_mean()0.8295566502463055

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pr_lmean()
Return logarithmic mean of precision & recall.
The logarithmic mean is: 0 if either precision or recall is 0, the precision if they are equal, otherwise

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑐𝑎𝑙𝑙𝑙𝑛(𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛) − 𝑙𝑛(𝑟𝑒𝑐𝑎𝑙𝑙)
Cf. https://en.wikipedia.org/wiki/Logarithmic_mean
Returns The logarithmic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_lmean()
0.8282429171492667

New in version 0.1.0.
pr_qmean()
Return quadratic mean of precision & recall.
The quadratic mean of precision and recall is defined as√︂

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛2 + 𝑟𝑒𝑐𝑎𝑙𝑙2
2

Cf. https://en.wikipedia.org/wiki/Quadratic_mean
Returns The quadratic mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_qmean()0.8290638930598233

New in version 0.1.0.
pr_seiffert_mean()
Return Seiffert's mean of precision & recall.
Seiffert's mean of precision and recall is

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 − 𝑟𝑒𝑐𝑎𝑙𝑙√︁
4 · 𝑎𝑟𝑐𝑡𝑎𝑛 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
𝑟𝑒𝑐𝑎𝑙𝑙 − 𝜋
It is defined in [Sei93].
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Returns Seiffert's mean of the confusion table's precision & recall
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pr_seiffert_mean()
0.8284071696048312

New in version 0.1.0.
precision()
Return precision.
Precision is defined as𝑡𝑝
𝑡𝑝 + 𝑓 𝑝
AKA positive predictive value (PPV)
Cf. https://en.wikipedia.org/wiki/Precision_and_recall
Cf. https://en.wikipedia.org/wiki/Information_retrieval#Precision
Returns The precision of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.precision()
0.8571428571428571

New in version 0.1.0.
precision_gain()
Return gain in precision.
The gain in precision is defined as

𝐺(𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛) =𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
𝑟𝑎𝑛𝑑𝑜𝑚 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛

Cf. https://en.wikipedia.org/wiki/Gain_(information_retrieval)
Returns The gain in precision of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.precision_gain()
1.3142857142857143

New in version 0.1.0.
pred_neg_pop()
Return predicted negative population.
Returns The predicted negative population of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pred_neg_pop()90

New in version 0.1.0.
Changed in version 0.4.0: renamed from test_neg_pop
New in version 0.1.0.
pred_pos_pop()
Return predicted positive population.
Returns The predicted positive population of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.pred_pos_pop()140

New in version 0.1.0.
Changed in version 0.4.0: renamed from test_pos_pop
New in version 0.1.0.
predicted_entropy()
Return the predicted entropy.
Implementation based on https://github.com/Magnetic/proficiency-metric
Returns The predicted entropy of the confusion table
Return type float

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.predicted_entropy()
0.6693279632926457

New in version 0.4.0.
prevalence()
Return prevalence.
Prevalence is defined as𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
Cf. https://en.wikipedia.org/wiki/Prevalence
Returns The prevelence of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.prevalence()
0.6521739130434783

New in version 0.4.0.
proficiency()
Return the proficiency.
Implementation based on https://github.com/Magnetic/proficiency-metric [SLaclavik15]
AKA uncertainty coefficientCf. https://en.wikipedia.org/wiki/Uncertainty_coefficient
Returns The proficiency of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.proficiency()
0.228116219897929

New in version 0.4.0.
recall()Return recall.
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𝑡𝑝
𝑡𝑝 + 𝑓 𝑛
AKA sensitivity
AKA true positive rate (TPR)
Cf. https://en.wikipedia.org/wiki/Precision_and_recall
Cf. https://en.wikipedia.org/wiki/Sensitivity_(test)
Cf. https://en.wikipedia.org/wiki/Information_retrieval#Recall
Returns The recall of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.recall()
0.8

New in version 0.1.0.
significance()
Return the significance, 𝜒2 .
Significance is defined as

𝜒2 =(𝑡𝑝 · 𝑡𝑛 − 𝑓 𝑝 · 𝑓 𝑛)2 (𝑡𝑝 + 𝑡𝑛 + 𝑓 𝑝 + 𝑓 𝑛)‘((𝑡𝑝 + 𝑓 𝑝)(𝑡𝑝 + 𝑓 𝑛)(𝑡𝑛 + 𝑓 𝑝)(𝑡𝑛 + 𝑓 𝑛)

Also: 𝜒2 = 𝑀 𝐶𝐶 2 · 𝑛
Cf. https://en.wikipedia.org/wiki/Pearson%27s_chi-square_test
Returns The significance of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.significance()
66.26190476190476

New in version 0.1.0.
specificity()
Return specificity.
Specificity is defined as𝑡𝑛
𝑡𝑛 + 𝑓 𝑝
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AKA true negative rate (TNR)
AKA inverse recall
Cf. https://en.wikipedia.org/wiki/Specificity_(tests)
Returns The specificity of the confusion table
Return type float
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.specificity()
0.75

New in version 0.1.0.
to_dict()
Cast to dict.
Returns The confusion table as a dictReturn type dict
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> import pprint>>> pprint.pprint(ct.to_dict()){'fn': 30, 'fp': 20, 'tn': 60, 'tp': 120}

New in version 0.1.0.
to_tuple()
Cast to tuple.
Returns The confusion table as a 4-tuple (tp, tn, fp, fn)
Return type tuple
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.to_tuple()(120, 60, 20, 30)

New in version 0.1.0.
true_neg()
Return true negatives.
Returns The true negatives of the confusion table
Return type int

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Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.true_neg()60

New in version 0.1.0.
true_pos()
Return true positives.
Returns The true positives of the confusion table
Return type int
Example>>> ct = ConfusionTable(120, 60, 20, 30)>>> ct.true_pos()120

New in version 0.1.0.
abydos.stats.amean(nums)
Return arithmetic mean.
The arithmetic mean is defined as

∑︀

𝑛𝑢𝑚𝑠
|𝑛𝑢𝑚𝑠|

Cf. https://en.wikipedia.org/wiki/Arithmetic_mean
Parameters nums (list) -- A series of numbers
Returns The arithmetric mean of nums
Return type float
Examples>>> amean([1, 2, 3, 4])
2.5>>> amean([1, 2])
1.5>>> amean([0, 5, 1000])
335.0

New in version 0.1.0.
abydos.stats.gmean(nums)
Return geometric mean.
The geometric mean is defined as

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𝑛𝑢𝑚𝑠𝑖

𝑖

Cf. https://en.wikipedia.org/wiki/Geometric_mean
Parameters nums (list) -- A series of numbers
Returns The geometric mean of nums
Return type float
Examples>>> gmean([1, 2, 3, 4])2.213363839400643>>> gmean([1, 2])
1.4142135623730951>>> gmean([0, 5, 1000])
0.0

New in version 0.1.0.
abydos.stats.hmean(nums)
Return harmonic mean.
The harmonic mean is defined as|𝑛𝑢𝑚𝑠|
∑︀ 1
𝑖

𝑛𝑢𝑚𝑠𝑖

Following the behavior of Wolfram|Alpha: - If one of the values in nums is 0, return 0. - If more than one value
in nums is 0, return NaN.
Cf. https://en.wikipedia.org/wiki/Harmonic_mean
Parameters nums (list) -- A series of numbers
Returns The harmonic mean of nums
Return type float
Raises ValueError -- hmean requires at least one value
Examples>>> hmean([1, 2, 3, 4])
1.9200000000000004>>> hmean([1, 2])
1.3333333333333333>>> hmean([0, 5, 1000])0

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abydos.stats.agmean(nums, prec=12)Return arithmetic-geometric mean.
Iterates between arithmetic & geometric means until they converge to a single value (rounded to 10 digits).
Cf. https://en.wikipedia.org/wiki/Arithmetic-geometric_mean
Parameters nums (list) -- A series of numbers
Returns• float -- The arithmetic-geometric mean of nums
• prec (int) -- Digits of precision when testing convergeance
Examples>>> agmean([1, 2, 3, 4])2.3545004777751077>>> agmean([1, 2])
1.4567910310469068>>> agmean([0, 5, 1000])
2.9753977059954195e-13

New in version 0.1.0.
abydos.stats.ghmean(nums, prec=12)Return geometric-harmonic mean.
Iterates between geometric & harmonic means until they converge to a single value (rounded to 10 digits).
Cf. https://en.wikipedia.org/wiki/Geometric-harmonic_mean
Parameters• nums (list) -- A series of numbers• prec (int) -- Digits of precision when testing convergeance
Returns The geometric-harmonic mean of nums
Return type float
Examples>>> ghmean([1, 2, 3, 4])
2.058868154613003>>> ghmean([1, 2])
1.3728805006183502>>> ghmean([0, 5, 1000])
0.0>>> ghmean([0, 0])
0.0>>> ghmean([0, 0, 5])nan

New in version 0.1.0.

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abydos.stats.aghmean(nums, prec=12)Return arithmetic-geometric-harmonic mean.
Iterates over arithmetic, geometric, & harmonic means until they converge to a single value (rounded to 10
digits), following the method described in [RaissouliLC09].
Parameters• nums (list) -- A series of numbers• prec (int) -- Digits of precision when testing convergeance
Returns The arithmetic-geometric-harmonic mean of nums
Return type float
Examples>>> aghmean([1, 2, 3, 4])2.198327159900212>>> aghmean([1, 2])
1.4142135623731884>>> aghmean([0, 5, 1000])
335.0

New in version 0.1.0.
abydos.stats.cmean(nums)
Return contraharmonic mean.
The contraharmonic mean is
∑︀
𝑖
∑︀

𝑥2𝑖
𝑥𝑖

𝑖

Cf. https://en.wikipedia.org/wiki/Contraharmonic_mean
Parameters nums (list) -- A series of numbers
Returns The contraharmonic mean of nums
Return type float
Examples>>> cmean([1, 2, 3, 4])
3.0>>> cmean([1, 2])
1.6666666666666667>>> cmean([0, 5, 1000])
995.0497512437811

New in version 0.1.0.
abydos.stats.imean(nums)
Return identric (exponential) mean.
The identric mean of two numbers x and y is: x if x = y otherwise
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Cf. https://en.wikipedia.org/wiki/Identric_mean
Parameters nums (list) -- A series of numbers
Returns The identric mean of nums
Return type float
Raises ValueError -- imean supports no more than two values
Examples>>> imean([1, 2])
1.4715177646857693>>> imean([1, 0])nan>>> imean([2, 4])2.9430355293715387

New in version 0.1.0.
abydos.stats.lmean(nums)
Return logarithmic mean.
The logarithmic mean of an arbitrarily long series is defined by http://www.survo.fi/papers/logmean.pdf as

𝐿(𝑥1 , 𝑥2 , ..., 𝑥𝑛 ) = (𝑛 − 1)!

𝑛
∑︁
𝑖=1

𝑥𝑖
𝑛
∏︀
𝑗=1
𝑗̸=𝑖

𝑙𝑛 𝑥𝑥𝑗𝑖Cf. https://en.wikipedia.org/wiki/Logarithmic_mean
Parameters nums (list) -- A series of numbers
Returns The logarithmic mean of nums
Return type float
Raises ValueError -- No two values in the nums list may be equal
Examples>>> lmean([1, 2, 3, 4])2.2724242417489258>>> lmean([1, 2])
1.4426950408889634

New in version 0.1.0.
abydos.stats.qmean(nums)
Return quadratic mean.
The quadratic mean is defined as
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Cf. https://en.wikipedia.org/wiki/Quadratic_mean
Parameters nums (list) -- A series of numbers
Returns The quadratic mean of nums
Return type float
Examples>>> qmean([1, 2, 3, 4])
2.7386127875258306>>> qmean([1, 2])
1.5811388300841898>>> qmean([0, 5, 1000])
577.3574860228857

New in version 0.1.0.
abydos.stats.heronian_mean(nums)
Return Heronian mean.
The Heronian mean is:∑︀ √𝑥𝑖 · 𝑥𝑗𝑖,𝑗|𝑛𝑢𝑚𝑠| · |𝑛𝑢𝑚𝑠|+1
2
for 𝑗 ≥ 𝑖
Cf. https://en.wikipedia.org/wiki/Heronian_mean
Parameters nums (list) -- A series of numbers
Returns The Heronian mean of nums
Return type float
Examples>>> heronian_mean([1, 2, 3, 4])2.3888282852609093>>> heronian_mean([1, 2])
1.4714045207910316>>> heronian_mean([0, 5, 1000])
179.28511301977582

New in version 0.1.0.
abydos.stats.hoelder_mean(nums, exp=2)
Return Hölder (power/generalized) mean.
The Hölder mean is defined as:
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√︃
𝑝

∑︁ 𝑝
1
·
𝑥
|𝑛𝑢𝑚𝑠| 𝑖 𝑖for 𝑝 ̸= 0, and the geometric mean for 𝑝 = 0
Cf. https://en.wikipedia.org/wiki/Generalized_mean
Parameters• nums (list) -- A series of numbers• exp (numeric) -- The exponent of the Hölder mean
Returns The Hölder mean of nums for the given exponent
Return type float
Examples>>> hoelder_mean([1, 2, 3, 4])
2.7386127875258306>>> hoelder_mean([1, 2])
1.5811388300841898>>> hoelder_mean([0, 5, 1000])
577.3574860228857

New in version 0.1.0.
abydos.stats.lehmer_mean(nums, exp=2)
Return Lehmer mean.
The Lehmer mean is
𝑥𝑝𝑖
𝑖
∑︀ (
𝑥𝑖 𝑝 − 1)𝑖∑︀Cf. https://en.wikipedia.org/wiki/Lehmer_mean
Parameters• nums (list) -- A series of numbers• exp (numeric) -- The exponent of the Lehmer mean
Returns The Lehmer mean of nums for the given exponent
Return type float

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Examples>>> lehmer_mean([1, 2, 3, 4])
3.0>>> lehmer_mean([1, 2])
1.6666666666666667>>> lehmer_mean([0, 5, 1000])
995.0497512437811

New in version 0.1.0.
abydos.stats.seiffert_mean(nums)
Return Seiffert's mean.
Seiffert's mean of two numbers x and y is𝑥−𝑦√︁
4 · 𝑎𝑟𝑐𝑡𝑎𝑛 𝑥𝑦 − 𝜋
It is defined in [Sei93].
Parameters nums (list) -- A series of numbers
Returns Sieffert's mean of nums
Return type float
Raises ValueError -- seiffert_mean supports no more than two values
Examples>>> seiffert_mean([1, 2])
1.4712939827611637>>> seiffert_mean([1, 0])0.3183098861837907>>> seiffert_mean([2, 4])2.9425879655223275>>> seiffert_mean([2, 1000])
336.84053300118825

New in version 0.1.0.
abydos.stats.median(nums)
Return median.
With numbers sorted by value, the median is the middle value (if there is an odd number of values) or thearithmetic mean of the two middle values (if there is an even number of values).
Cf. https://en.wikipedia.org/wiki/MedianParameters nums (list) -- A series of numbers
Returns The median of nums
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Examples>>> median([1, 2, 3])
2>>> median([1, 2, 3, 4])
2.5>>> median([1, 2, 2, 4])2

New in version 0.1.0.
abydos.stats.midrange(nums)
Return midrange.
The midrange is the arithmetic mean of the maximum & minimum of a series.
Cf. https://en.wikipedia.org/wiki/Midrange
Parameters nums (list) -- A series of numbers
Returns The midrange of nums
Return type float
Examples>>> midrange([1, 2, 3])
2.0>>> midrange([1, 2, 2, 3])
2.0>>> midrange([1, 2, 1000, 3])500.5

New in version 0.1.0.
abydos.stats.mode(nums)
Return the mode.
The mode of a series is the most common element of that series
Cf. https://en.wikipedia.org/wiki/Mode_(statistics)
Parameters nums (list) -- A series of numbers
Returns The mode of nums
Return type int or float
Example>>> mode([1, 2, 2, 3])2

New in version 0.1.0.
abydos.stats.std(nums, mean_func=<function amean>, ddof=0)
Return the standard deviation.
The standard deviation of a series of values is the square root of the variance.

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Cf. https://en.wikipedia.org/wiki/Standard_deviation
Parameters• nums (list) -- A series of numbers• mean_func (function) -- A mean function (amean by default)• ddof (int) -- The degrees of freedom (0 by default)
Returns The standard deviation of the values in the series
Return type float
Examples>>> std([1, 1, 1, 1])
0.0>>> round(std([1, 2, 3, 4]), 12)
1.11803398875>>> round(std([1, 2, 3, 4], ddof=1), 12)1.290994448736

New in version 0.3.0.
abydos.stats.var(nums, mean_func=<function amean>, ddof=0)
Calculate the variance.
The variance (𝜎 2 ) of a series of numbers (𝑥𝑖 ) with mean 𝜇 and population 𝑁 is:

𝑁

𝜎2 =

1 ∑︁
(𝑥𝑖 − 𝜇)2
𝑁 𝑖=1

Cf. https://en.wikipedia.org/wiki/Variance
Parameters• nums (list) -- A series of numbers• mean_func (function) -- A mean function (amean by default)• ddof (int) -- The degrees of freedom (0 by default)
Returns The variance of the values in the series
Return type float
Examples>>> var([1, 1, 1, 1])
0.0>>> var([1, 2, 3, 4])
1.25>>> round(var([1, 2, 3, 4], ddof=1), 12)
1.666666666667

New in version 0.3.0.

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abydos.stats.mean_pairwise_similarity(collection,
metric=<function sim_levenshtein>,mean_func=<function hmean>, symmetric=False)
Calculate the mean pairwise similarity of a collection of strings.
Takes the mean of the pairwise similarity between each member of a collection, optionally in both directions(for asymmetric similarity metrics.
Parameters• collection (list) -- A collection of terms or a string that can be split• metric (function) -- A similarity metric function• mean_func (function) -- A mean function that takes a list of values and returns a float• symmetric (bool) -- Set to True if all pairwise similarities should be calculated in both
directions
Returns The mean pairwise similarity of a collection of strings
Return type float
Raises• ValueError -- mean_func must be a function• ValueError -- metric must be a function• ValueError -- collection is neither a string nor iterable type
• ValueError -- collection has fewer than two members
Examples>>> round(mean_pairwise_similarity(['Christopher', 'Kristof',... 'Christobal']), 12)0.519801980198>>> round(mean_pairwise_similarity(['Niall', 'Neal', 'Neil']), 12)0.545454545455

New in version 0.1.0.
abydos.stats.pairwise_similarity_statistics(src_collection,
tar_collection,
metric=<function
sim_levenshtein>,mean_func=<function
amean>,
symmetric=False)
Calculate the pairwise similarity statistics a collection of strings.
Calculate pairwise similarities among members of two collections, returning the maximum, minimum, mean(according to a supplied function, arithmetic mean, by default), and (population) standard deviation of thosesimilarities.
Parameters• src_collection (list) -- A collection of terms or a string that can be split• tar_collection (list) -- A collection of terms or a string that can be split• metric (function) -- A similarity metric function• mean_func (function) -- A mean function that takes a list of values and returns a float• symmetric (bool) -- Set to True if all pairwise similarities should be calculated in both
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Returns The max, min, mean, and standard deviation of similarities
Return type tuple
Raises• ValueError -- mean_func must be a function• ValueError -- metric must be a function• ValueError -- src_collection is neither a string nor iterable• ValueError -- tar_collection is neither a string nor iterable
Example>>> tuple(round(_, 12) for _ in pairwise_similarity_statistics(... ['Christopher', 'Kristof', 'Christobal'], ['Niall', 'Neal', 'Neil']))(0.2, 0.0, 0.118614718615, 0.075070477184)

New in version 0.3.0.
3.1.1.8 abydos.stemmer package
abydos.stemmer.
The stemmer package collects stemmer classes for a number of languages including:• English stemmers:– Lovins' (Lovins)– Porter (Porter)– Porter2 (i.e. Snowball English) (Porter2)– UEA-Lite (UEALite)– Paice-Husk (PaiceHusk)– S-stemmer (SStemmer)• German stemmers:– Caumanns' (Caumanns)– CLEF German (CLEFGerman)– CLEF German Plus (CLEFGermanPlus)– Snowball German (SnowballGerman)• Swedish stemmers:– CLEF Swedish (CLEFSwedish)– Snowball Swedish (SnowballSwedish)• Latin stemmer:– Schinke (Schinke)• Danish stemmer:– Snowball Danish (SnowballDanish)

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Abydos Documentation, Release 0.5.0• Dutch stemmer:– Snowball Dutch (SnowballDutch)• Norwegian stemmer:– Snowball Norwegian (SnowballNorwegian)Each stemmer has a stem method, which takes a word and returns its stemmed form:>>> stmr = Porter()>>> stmr.stem('democracy')'democraci'>>> stmr.stem('trusted')'trust'class abydos.stemmer.Lovins
Bases: abydos.stemmer._stemmer._Stemmer
Lovins stemmer.
The Lovins stemmer is described in Julie Beth Lovins's article [Lov68].
New in version 0.3.6.
Initialize the stemmer.
New in version 0.3.6.
stem(word)
Return Lovins stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = Lovins()>>> stmr.stem('reading')'read'>>> stmr.stem('suspension')'suspens'>>> stmr.stem('elusiveness')'elus'New in version 0.2.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.lovins(word)
Return Lovins stem.
This is a wrapper for Lovins.stem().
Parameters word (str) -- The word to stem
Returns str
Return type Word stem

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Examples>>> lovins('reading')'read'>>> lovins('suspension')'suspens'>>> lovins('elusiveness')'elus'New in version 0.2.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Lovins.stem method instead.
class abydos.stemmer.PaiceHusk
Bases: abydos.stemmer._stemmer._Stemmer
Paice-Husk stemmer.
Implementation of the Paice-Husk Stemmer, also known as the Lancaster Stemmer, developed by Chris Paice,with the assistance of Gareth Husk
This is based on the algorithm's description in [Pai90].
New in version 0.3.6.
stem(word)
Return Paice-Husk stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = PaiceHusk()>>> stmr.stem('assumption')'assum'>>> stmr.stem('verifiable')'ver'>>> stmr.stem('fancies')'fant'>>> stmr.stem('fanciful')'fancy'>>> stmr.stem('torment')'tor'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.paice_husk(word)
Return Paice-Husk stem.
This is a wrapper for PaiceHusk.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str

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Examples>>> paice_husk('assumption')'assum'>>> paice_husk('verifiable')'ver'>>> paice_husk('fancies')'fant'>>> paice_husk('fanciful')'fancy'>>> paice_husk('torment')'tor'

New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the PaiceHusk.stem method instead.
class abydos.stemmer.UEALite(max_word_length=20, max_acro_length=8, return_rule_no=False,
var='standard')
Bases: abydos.stemmer._stemmer._Stemmer
UEA-Lite stemmer.
The UEA-Lite stemmer is discussed in [JS05].
This is chiefly based on the Java implementation of the algorithm, with variants based on the Perl implementation
and Jason Adams' Ruby port.
Java version: [Chu] Perl version: [JS05] Ruby version: [Ada17]
New in version 0.3.6.
Initialize UEALite instance.
Parameters• max_word_length (int) -- The maximum word length allowed• max_acro_length (int) -- The maximum acronym length allowed• return_rule_no (bool) -- If True, returns the stem along with rule number• var (str) -- Variant rules to use:
– standard to use the original (Java-version) rules– Adams to use Jason Adams' rules– Perl to use the original Perl rules
New in version 0.4.0.
stem(word)
Return UEA-Lite stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str or (str, int)

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Examples>>> uealite('readings')'read'>>> uealite('insulted')'insult'>>> uealite('cussed')'cuss'>>> uealite('fancies')'fancy'>>> uealite('eroded')'erode'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.uealite(word, max_word_length=20, max_acro_length=8, return_rule_no=False,
var='standard')
Return UEA-Lite stem.
This is a wrapper for UEALite.stem().
Parameters• word (str) -- The word to stem
• max_word_length (int) -- The maximum word length allowed• max_acro_length (int) -- The maximum acronym length allowed• return_rule_no (bool) -- If True, returns the stem along with rule number• var (str) -- Variant rules to use:
– Adams to use Jason Adams' rules– Perl to use the original Perl rules
Returns Word stem
Return type str or (str, int)
Examples>>> uealite('readings')'read'>>> uealite('insulted')'insult'>>> uealite('cussed')'cuss'>>> uealite('fancies')'fancy'>>> uealite('eroded')'erode'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the UEALite.stem method instead.

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class abydos.stemmer.SStemmer
Bases: abydos.stemmer._stemmer._Stemmer
S-stemmer.
The S stemmer is defined in [Har91].
New in version 0.3.6.
stem(word)
Return the S-stemmed form of a word.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SStemmer()>>> stmr.stem('summaries')'summary'>>> stmr.stem('summary')'summary'>>> stmr.stem('towers')'tower'>>> stmr.stem('reading')'reading'>>> stmr.stem('census')'census'

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.s_stemmer(word)
Return the S-stemmed form of a word.
This is a wrapper for SStemmer.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> s_stemmer('summaries')'summary'>>> s_stemmer('summary')'summary'>>> s_stemmer('towers')'tower'>>> s_stemmer('reading')'reading'>>> s_stemmer('census')'census'3.1. abydos package623

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New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SStemmer.stem method instead.
class abydos.stemmer.Caumanns
Bases: abydos.stemmer._stemmer._Stemmer
Caumanns stemmer.
Jörg Caumanns' stemmer is described in his article in [Cau99].
This implementation is based on the GermanStemFilter described at [Lan13].
New in version 0.3.6.
stem(word)
Return Caumanns German stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = Caumanns()>>> stmr.stem('lesen')'les'>>> stmr.stem('graues')'grau'>>> stmr.stem('buchstabieren')'buchstabier'

New in version 0.2.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.caumanns(word)
Return Caumanns German stem.
This is a wrapper for Caumanns.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> caumanns('lesen')'les'>>> caumanns('graues')'grau'>>> caumanns('buchstabieren')'buchstabier'

New in version 0.2.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Caumanns.stem method instead.

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class abydos.stemmer.Schinke
Bases: abydos.stemmer._stemmer._Stemmer
Schinke stemmer.
This is defined in [SGRW96].
New in version 0.3.6.
stem(word)
Return the stem of a word according to the Schinke stemmer.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = Schinke()>>> stmr.stem('atque'){'n': 'atque', 'v': 'atque'}>>> stmr.stem('census'){'n': 'cens', 'v': 'censu'}>>> stmr.stem('virum'){'n': 'uir', 'v': 'uiru'}>>> stmr.stem('populusque'){'n': 'popul', 'v': 'populu'}>>> stmr.stem('senatus'){'n': 'senat', 'v': 'senatu'}

New in version 0.3.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.schinke(word)
Return the stem of a word according to the Schinke stemmer.
This is a wrapper for Schinke.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> schinke('atque'){'n': 'atque', 'v': 'atque'}>>> schinke('census'){'n': 'cens', 'v': 'censu'}>>> schinke('virum'){'n': 'uir', 'v': 'uiru'}>>> schinke('populusque'){'n': 'popul', 'v': 'populu'}>>> schinke('senatus'){'n': 'senat', 'v': 'senatu'}

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New in version 0.3.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Schinke.stem method instead.
class abydos.stemmer.Porter(early_english=False)
Bases: abydos.stemmer._stemmer._Stemmer
Porter stemmer.
The Porter stemmer is described in [Por80].
New in version 0.3.6.
Initialize Porter instance.
Parameters early_english (bool) -- Set to True in order to remove -eth & -est (2nd & 3rd
person singular verbal agreement suffixes)
New in version 0.4.0.
stem(word)
Return Porter stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = Porter()>>> stmr.stem('reading')'read'>>> stmr.stem('suspension')'suspens'>>> stmr.stem('elusiveness')'elus'>>> stmr = Porter(early_english=True)>>> stmr.stem('eateth')'eat'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.porter(word, early_english=False)
Return Porter stem.
This is a wrapper for Porter.stem().
Parameters• word (str) -- The word to stem
• early_english (bool) -- Set to True in order to remove -eth & -est (2nd & 3rd person
singular verbal agreement suffixes)
Returns Word stem
Return type str

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Examples>>> porter('reading')'read'>>> porter('suspension')'suspens'>>> porter('elusiveness')'elus'>>> porter('eateth', early_english=True)'eat'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Porter.stem method instead.
class abydos.stemmer.Porter2(early_english=False)
Bases: abydos.stemmer._snowball._Snowball
Porter2 (Snowball English) stemmer.
The Porter2 (Snowball English) stemmer is defined in [Por02].
New in version 0.3.6.
Initialize Porter2 instance.
Parameters early_english (bool) -- Set to True in order to remove -eth & -est (2nd & 3rd
person singular verbal agreement suffixes)
New in version 0.4.0.
stem(word)
Return the Porter2 (Snowball English) stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = Porter2()>>> stmr.stem('reading')'read'>>> stmr.stem('suspension')'suspens'>>> stmr.stem('elusiveness')'elus'>>> stmr = Porter2(early_english=True)>>> stmr.stem('eateth')'eat'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class

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abydos.stemmer.porter2(word, early_english=False)
Return the Porter2 (Snowball English) stem.
This is a wrapper for Porter2.stem().
Parameters• word (str) -- The word to stem
• early_english (bool) -- Set to True in order to remove -eth & -est (2nd & 3rd person
singular verbal agreement suffixes)
Returns Word stem
Return type str
Examples>>> porter2('reading')'read'>>> porter2('suspension')'suspens'>>> porter2('elusiveness')'elus'>>> porter2('eateth', early_english=True)'eat'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the Porter2.stem method instead.
class abydos.stemmer.SnowballDanish
Bases: abydos.stemmer._snowball._Snowball
Snowball Danish stemmer.
The Snowball Danish stemmer is defined at: http://snowball.tartarus.org/algorithms/danish/stemmer.html
New in version 0.3.6.
stem(word)
Return Snowball Danish stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SnowballDanish()>>> stmr.stem('underviser')'undervis'>>> stmr.stem('suspension')'suspension'>>> stmr.stem('sikkerhed')'sikker'

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New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.sb_danish(word)
Return Snowball Danish stem.
This is a wrapper for SnowballDanish.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> sb_danish('underviser')'undervis'>>> sb_danish('suspension')'suspension'>>> sb_danish('sikkerhed')'sikker'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SnowballDanish.stem method instead.
class abydos.stemmer.SnowballDutch
Bases: abydos.stemmer._snowball._Snowball
Snowball Dutch stemmer.
The Snowball Dutch stemmer is defined at: http://snowball.tartarus.org/algorithms/dutch/stemmer.html
New in version 0.3.6.
stem(word)
Return Snowball Dutch stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SnowballDutch()>>> stmr.stem('lezen')'lez'>>> stmr.stem('opschorting')'opschort'>>> stmr.stem('ongrijpbaarheid')'ongrijp'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class

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abydos.stemmer.sb_dutch(word)
Return Snowball Dutch stem.
This is a wrapper for SnowballDutch.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> sb_dutch('lezen')'lez'>>> sb_dutch('opschorting')'opschort'>>> sb_dutch('ongrijpbaarheid')'ongrijp'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SnowballDutch.stem method instead.
class abydos.stemmer.SnowballGerman(alternate_vowels=False)
Bases: abydos.stemmer._snowball._Snowball
Snowball German stemmer.
The Snowball German stemmer is defined at: http://snowball.tartarus.org/algorithms/german/stemmer.html
New in version 0.3.6.
Initialize SnowballGerman instance.
Parameters alternate_vowels (bool) -- Composes ae as ä, oe as ö, and ue as ü before running the algorithm
New in version 0.4.0.
stem(word)
Return Snowball German stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SnowballGerman()>>> stmr.stem('lesen')'les'>>> stmr.stem('graues')'grau'>>> stmr.stem('buchstabieren')'buchstabi'

New in version 0.1.0.
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abydos.stemmer.sb_german(word, alternate_vowels=False)
Return Snowball German stem.
This is a wrapper for SnowballGerman.stem().
Parameters• word (str) -- The word to stem
• alternate_vowels (bool) -- Composes ae as ä, oe as ö, and ue as ü before running
the algorithm
Returns Word stem
Return type str
Examples>>> sb_german('lesen')'les'>>> sb_german('graues')'grau'>>> sb_german('buchstabieren')'buchstabi'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SnowballGerman.stem method instead.
class abydos.stemmer.SnowballNorwegianBases: abydos.stemmer._snowball._Snowball
Snowball Norwegian stemmer.
The Snowball Norwegian stemmer is defined at: http://snowball.tartarus.org/algorithms/norwegian/stemmer.
html
New in version 0.3.6.
stem(word)
Return Snowball Norwegian stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SnowballNorwegian()>>> stmr.stem('lese')'les'>>> stmr.stem('suspensjon')'suspensjon'>>> stmr.stem('sikkerhet')'sikker'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in class

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abydos.stemmer.sb_norwegian(word)
Return Snowball Norwegian stem.
This is a wrapper for SnowballNorwegian.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> sb_norwegian('lese')'les'>>> sb_norwegian('suspensjon')'suspensjon'>>> sb_norwegian('sikkerhet')'sikker'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SnowballNorwegian.stem method instead.
class abydos.stemmer.SnowballSwedish
Bases: abydos.stemmer._snowball._Snowball
Snowball Swedish stemmer.
The Snowball Swedish stemmer is defined at: http://snowball.tartarus.org/algorithms/swedish/stemmer.html
New in version 0.3.6.
stem(word)
Return Snowball Swedish stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = SnowballSwedish()>>> stmr.stem('undervisa')'undervis'>>> stmr.stem('suspension')'suspension'>>> stmr.stem('visshet')'viss'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.sb_swedish(word)
Return Snowball Swedish stem.
This is a wrapper for SnowballSwedish.stem().

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Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> sb_swedish('undervisa')'undervis'>>> sb_swedish('suspension')'suspension'>>> sb_swedish('visshet')'viss'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the SnowballSwedish.stem method instead.
class abydos.stemmer.CLEFGerman
Bases: abydos.stemmer._stemmer._Stemmer
CLEF German stemmer.
The CLEF German stemmer is defined at [Sav05].
New in version 0.3.6.
stem(word)
Return CLEF German stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = CLEFGerman()>>> stmr.stem('lesen')'lese'>>> stmr.stem('graues')'grau'>>> stmr.stem('buchstabieren')'buchstabier'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.clef_german(word)
Return CLEF German stem.
This is a wrapper for CLEFGerman.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str

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Examples>>> clef_german('lesen')'lese'>>> clef_german('graues')'grau'>>> clef_german('buchstabieren')'buchstabier'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the CLEFGerman.stem method instead.
class abydos.stemmer.CLEFGermanPlus
Bases: abydos.stemmer._stemmer._Stemmer
CLEF German stemmer plus.
The CLEF German stemmer plus is defined at [Sav05].
New in version 0.3.6.
stem(word)
Return 'CLEF German stemmer plus' stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> stmr = CLEFGermanPlus()>>> clef_german_plus('lesen')'les'>>> clef_german_plus('graues')'grau'>>> clef_german_plus('buchstabieren')'buchstabi'

New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.clef_german_plus(word)
Return 'CLEF German stemmer plus' stem.
This is a wrapper for CLEFGermanPlus.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str

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Examples>>> stmr = CLEFGermanPlus()>>> clef_german_plus('lesen')'les'>>> clef_german_plus('graues')'grau'>>> clef_german_plus('buchstabieren')'buchstabi'

New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the CLEFGermanPlus.stem method instead.
class abydos.stemmer.CLEFSwedish
Bases: abydos.stemmer._stemmer._Stemmer
CLEF Swedish stemmer.
The CLEF Swedish stemmer is defined at [Sav05].
New in version 0.3.6.
stem(word)
Return CLEF Swedish stem.
Parameters word (str) -- The word to stem
Returns Word stem
Return type str
Examples>>> clef_swedish('undervisa')'undervis'>>> clef_swedish('suspension')'suspensio'>>> clef_swedish('visshet')'viss'New in version 0.1.0.
Changed in version 0.3.6: Encapsulated in classabydos.stemmer.clef_swedish(word)
Return CLEF Swedish stem.
This is a wrapper for CLEFSwedish.stem().
Parameters word (str) -- The word to stem
Returns Word stem
Return type str

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Examples>>> clef_swedish('undervisa')'undervis'>>> clef_swedish('suspension')'suspensio'>>> clef_swedish('visshet')'viss'New in version 0.1.0.
Deprecated since version 0.4.0: This will be removed in 0.6.0. Use the CLEFSwedish.stem method instead.
3.1.1.9 abydos.tokenizer package
abydos.tokenizer.
The tokenizer package collects classes whose purpose is to tokenize text or individual words. Each tokenizer also
supports a scaler attribute when constructed, which adjusts count scaling. The scaler defaults to None, which performs
no scaling. Setting scaler to 'set' is used to convert token counters from multi-sets to sets, so even if multiple instancesof a token are present, they will be counted as one. Additionally, a callable function (of one argument, such as log,
exp, or lambda x: x + 1) may be passed to scaler and this function will be applied to each count value.
The following general tokenizers are provided:• QGrams tokenizes a string into q-grams, substrings of length q. The class supports different values of q, theaddition of start and stop symbols, and skip values. It even supports multiple values for q and skip, using lists
or ranges.
• QSkipgrams tokenizes a string into skipgrams of length q. A skipgram is a sequence of letters from a stringwith q, often discontinuous, characters. For example, the string 'ABCD' has the following 2-skipgrams: 'AB','AC', 'AD', 'BC', 'BD', 'CD'.
• CharacterTokenizer tokenizes a string into individual characters.
• RegexpTokenizer tokenizes a string according to a supplied regular expression.
• WhitespaceTokenizer tokenizes a string by dividing it at instances of whitespace.
• WordpunctTokenizer tokenizes a string by dividing it into strings of letters and strings of punctuation.
Six syllable-oriented tokenizers are provided:• COrVClusterTokenizer tokenizes a string by dividing it into strings of consonants, vowels, or other characters:• COrVClusterTokenizer tokenizes a string by dividing it into strings of consonants (C* clusters), vowels(V* clusters, or non-letter characters:• CVClusterTokenizer tokenizes a string by dividing it into strings of consonants then vowels (C*V* clusters) or non-letter characters:• VCClusterTokenizer tokenizes a string by dividing it into strings of vowels then characters (V*C* clusters) or non-letter characters:• SAPSTokenizer tokenizes a string according to the rules specified by the SAPS syllabification algorithm[RY05]:• SonoriPyTokenizer does syllabification according to the sonority sequencing principle, using SyllabiPy.
It requires that SyllabiPy be installed.

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Abydos Documentation, Release 0.5.0• LegaliPyTokenizer does syllabification according to the onset maximization principle (principle of legality), using SyllabiPy. It requires that SyllabiPy be installed, and works best if it has been trained on a corpus of
text.
Finally, an NLTK tokenizer is provided:• NLTKTokenizer does tokenization using an instantiated NLTK tokenizer. Accordingly, NLTK needs to beinstalled.

class abydos.tokenizer.QGrams(qval=2, start_stop='$#', skip=0, scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
A q-gram class, which functions like a bag/multiset.
A q-gram is here defined as all sequences of q characters. Q-grams are also known as k-grams and n-grams,
but the term n-gram more typically refers to sequences of whitespace-delimited words in a string, where q-gramrefers to sequences of characters in a word or string.
New in version 0.1.0.
Initialize QGrams.
Parameters• qval (int or Iterable) -- The q-gram length (defaults to 2), can be an integer, range
object, or list• start_stop (str) -- A string of length >= 0 indicating start & stop symbols. If thestring is '', q-grams will be calculated without start & stop symbols appended to each end.
Otherwise, the first character of start_stop will pad the beginning of the string and the last
character of start_stop will pad the end of the string before q-grams are calculated. (In thecase that start_stop is only 1 character long, the same symbol will be used for both.)• skip (int or Iterable) -- The number of characters to skip, can be an integer, range
object, or list
• scaler (None, str, or function) -- A scaling function for the Counter:– None : no scaling– 'set' : All non-zero values are set to 1.
– 'length' : Each token has weight equal to its length.
– 'length-log' [Each token has weight equal to the log of its] length + 1.
– 'length-exp' [Each token has weight equal to e raised to its] length.
– a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer
sequences such as the Fibonacci sequence.
Raises ValueError -- Use WhitespaceTokenizer instead of qval=0.

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Examples>>> qg = QGrams().tokenize('AATTATAT')>>> qgQGrams({'AT': 3, 'TA': 2, '$A': 1, 'AA': 1, 'TT': 1, 'T#': 1})>>> qg = QGrams(qval=1, start_stop='').tokenize('AATTATAT')>>> qgQGrams({'A': 4, 'T': 4})>>> qg = QGrams(qval=3, start_stop='').tokenize('AATTATAT')>>> qgQGrams({'TAT': 2, 'AAT': 1, 'ATT': 1, 'TTA': 1, 'ATA': 1})>>> QGrams(qval=2, start_stop='$#').tokenize('interning')
QGrams({'in': 2, '$i': 1, 'nt': 1, 'te': 1, 'er': 1, 'rn': 1,'ni': 1, 'ng': 1, 'g#': 1})>>> QGrams(start_stop='', skip=1).tokenize('AACTAGAAC')
QGrams({'AC': 2, 'AT': 1, 'CA': 1, 'TG': 1, 'AA': 1, 'GA': 1, 'A': 1})>>> QGrams(start_stop='', skip=[0, 1]).tokenize('AACTAGAAC')
QGrams({'AC': 4, 'AA': 3, 'GA': 2, 'CT': 1, 'TA': 1, 'AG': 1,'AT': 1, 'CA': 1, 'TG': 1, 'A': 1})>>> QGrams(qval=range(3), skip=[0, 1]).tokenize('interdisciplinarian')
QGrams({'i': 10, 'n': 7, 'r': 4, 'a': 4, 'in': 3, 't': 2, 'e': 2,'d': 2, 's': 2, 'c': 2, 'p': 2, 'l': 2, 'ri': 2, 'ia': 2, '$i': 1,'nt': 1, 'te': 1, 'er': 1, 'rd': 1, 'di': 1, 'is': 1, 'sc': 1, 'ci': 1,'ip': 1, 'pl': 1, 'li': 1, 'na': 1, 'ar': 1, 'an': 1, 'n#': 1, '$n': 1,'it': 1, 'ne': 1, 'tr': 1, 'ed': 1, 'ds': 1, 'ic': 1, 'si': 1, 'cp': 1,'il': 1, 'pi': 1, 'ln': 1, 'nr': 1, 'ai': 1, 'ra': 1, 'a#': 1})

New in version 0.1.0.
Changed in version 0.4.0: Broke tokenization functions out into tokenize method
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
New in version 0.4.0.
class abydos.tokenizer.QSkipgrams(qval=2, start_stop='$#', scaler=None, ssk_lambda=0.9)
Bases: abydos.tokenizer._tokenizer._Tokenizer
A q-skipgram class, which functions like a bag/multiset.
A q-gram is here defined as all sequences of q characters. Q-grams are also known as k-grams and n-grams,
but the term n-gram more typically refers to sequences of whitespace-delimited words in a string, where q-gramrefers to sequences of characters in a word or string.
New in version 0.4.0.
Initialize QSkipgrams.

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Parameters• qval (int or Iterable) -- The q-gram length (defaults to 2), can be an integer, range
object, or list• start_stop (str) -- A string of length >= 0 indicating start & stop symbols. If thestring is '', q-grams will be calculated without start & stop symbols appended to each end.
Otherwise, the first character of start_stop will pad the beginning of the string and the last
character of start_stop will pad the end of the string before q-grams are calculated. (In thecase that start_stop is only 1 character long, the same symbol will be used for both.)• scaler (None, str, or function) -- A scaling function for the Counter:– None : no scaling– 'set' : All non-zero values are set to 1.
– 'length' : Each token has weight equal to its length.
– 'length-log' [Each token has weight equal to the log of its] length + 1.
– 'length-exp' [Each token has weight equal to e raised to its] length.
– a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer
sequences such as the Fibonacci sequence.
– 'SSK' : Applies weighting according to the substring kernel rules of [LSShaweTaylor+02].
• ssk_lambda (float or Iterable) -- A value in the range (0.0, 1.0) used
for discouting gaps between characters according to the method described in[LSShaweTaylor+02]. To supply multiple values of lambda, provide an Iterable of numericvalues, such as (0.5, 0.05) or np.arange(0.05, 0.5, 0.05)
Raises ValueError -- Use WhitespaceTokenizer instead of qval=0.
Examples>>> QSkipgrams().tokenize('AATTAT')QSkipgrams({'AT': 7, '$A': 3, '$T': 3, 'AA': 3, 'A#': 3, 'TT': 3,'T#': 3, 'TA': 2, '$#': 1})>>> QSkipgrams(qval=1, start_stop='').tokenize('AATTAT')QSkipgrams({'A': 3, 'T': 3})>>> QSkipgrams(qval=3, start_stop='').tokenize('AATTAT')QSkipgrams({'ATT': 6, 'AAT': 5, 'ATA': 4, 'TAT': 2, 'AAA': 1,'TTA': 1, 'TTT': 1})>>> QSkipgrams(start_stop='').tokenize('ABCD')
QSkipgrams({'AB': 1, 'AC': 1, 'AD': 1, 'BC': 1, 'BD': 1, 'CD': 1})>>> QSkipgrams().tokenize('Colin')
QSkipgrams({'$C': 1, '$o': 1, '$l': 1, '$i': 1, '$n': 1, '$#': 1,'Co': 1, 'Cl': 1, 'Ci': 1, 'Cn': 1, 'C#': 1, 'ol': 1, 'oi': 1, 'on': 1,'o#': 1, 'li': 1, 'ln': 1, 'l#': 1, 'in': 1, 'i#': 1, 'n#': 1})

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Abydos Documentation, Release 0.5.0>>> QSkipgrams(qval=3).tokenize('AACTAGAAC')
QSkipgrams({'$AA': 20, '$A#': 20, 'AA#': 20, '$AC': 14, 'AC#': 14,'AAC': 11, 'AAA': 10, '$C#': 8, '$AG': 6, '$CA': 6, '$TA': 6, 'ACA': 6,'ATA': 6, 'AGA': 6, 'AG#': 6, 'CA#': 6, 'TA#': 6, '$$A': 5, 'A##': 5,'$AT': 4, '$T#': 4, '$GA': 4, '$G#': 4, 'AT#': 4, 'GA#': 4, 'AAG': 3,'AGC': 3, 'CTA': 3, 'CAA': 3, 'CAC': 3, 'TAA': 3, 'TAC': 3, '$$C': 2,'$$#': 2, '$CT': 2, '$CG': 2, '$CC': 2, '$TG': 2, '$TC': 2, '$GC': 2,'$##': 2, 'ACT': 2, 'ACG': 2, 'ACC': 2, 'ATG': 2, 'ATC': 2, 'CT#': 2,'CGA': 2, 'CG#': 2, 'CC#': 2, 'C##': 2, 'TGA': 2, 'TG#': 2, 'TC#': 2,'GAC': 2, 'GC#': 2, '$$T': 1, '$$G': 1, 'AAT': 1, 'CTG': 1, 'CTC': 1,'CAG': 1, 'CGC': 1, 'TAG': 1, 'TGC': 1, 'T##': 1, 'GAA': 1, 'G##': 1})

QSkipgrams may also be used to produce weights in accordance with the substring kernel rules of[LSShaweTaylor+02] by passing the scaler value 'SSK':>>> QSkipgrams(scaler='SSK').tokenize('AACTAGAAC')
QSkipgrams({'AA': 6.170192010000001, 'AC': 4.486377699,'$A': 2.8883286990000006, 'A#': 2.6526399291000002, 'TA': 2.05659,'AG': 1.931931, 'CA': 1.850931, 'GA': 1.5390000000000001, 'AT': 1.3851,'C#': 1.2404672100000003, '$C': 1.0047784401000002, 'CT': 0.81,'TG': 0.7290000000000001, 'CG': 0.6561, 'GC': 0.6561,'$T': 0.5904900000000001, 'G#': 0.5904900000000001, 'TC': 0.531441,'$G': 0.4782969000000001, 'CC': 0.4782969000000001,'T#': 0.4782969000000001, '$#': 0.31381059609000006})

New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
New in version 0.4.0.
class abydos.tokenizer.CharacterTokenizer(scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
A character tokenizer.
New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize

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Examples>>> CharacterTokenizer().tokenize('AACTAGAAC')
CharacterTokenizer({'A': 5, 'C': 2, 'T': 1, 'G': 1})

New in version 0.4.0.
class abydos.tokenizer.RegexpTokenizer(scaler=None, regexp='\w+', flags=0)
Bases: abydos.tokenizer._tokenizer._Tokenizer
A regexp tokenizer.
New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> RegexpTokenizer(regexp=r'[^-]+').tokenize('AA-CT-AG-AA-CD')
RegexpTokenizer({'AA': 2, 'CT': 1, 'AG': 1, 'CD': 1})

New in version 0.4.0.
class abydos.tokenizer.WhitespaceTokenizer(scaler=None, flags=0)
Bases: abydos.tokenizer._regexp.RegexpTokenizer
A whitespace tokenizer.

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Examples>>> WhitespaceTokenizer().tokenize('a b c f a c g e a b')
WhitespaceTokenizer({'a': 3, 'b': 2, 'c': 2, 'f': 1, 'g': 1, 'e': 1})

New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
class abydos.tokenizer.WordpunctTokenizer(scaler=None, flags=0)
Bases: abydos.tokenizer._regexp.RegexpTokenizer
A wordpunct tokenizer.
Examples>>> WordpunctTokenizer().tokenize("Can't stop the feelin'!")WordpunctTokenizer({'Can': 1, "'": 1, 't': 1, 'stop': 1, 'the': 1,'feelin': 1, "'!": 1})

New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
class abydos.tokenizer.COrVClusterTokenizer(scaler=None,
els=None)Bases: abydos.tokenizer._tokenizer._Tokenizer

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A C- or V-cluster tokenizer.
New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> COrVClusterTokenizer().tokenize('seven-twelfths')
COrVClusterTokenizer({'e': 3, 's': 1, 'v': 1, 'n': 1, '-': 1,'tw': 1, 'lfths': 1})>>> COrVClusterTokenizer().tokenize('character')
COrVClusterTokenizer({'a': 2, 'r': 2, 'ch': 1, 'ct': 1, 'e': 1})

New in version 0.4.0.
class abydos.tokenizer.CVClusterTokenizer(scaler=None,
els=None)Bases: abydos.tokenizer._tokenizer._Tokenizer

consonants=None,vow-

A C*V*-cluster tokenizer.
New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.

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Abydos Documentation, Release 0.5.0• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> CVClusterTokenizer().tokenize('seven-twelfths')
CVClusterTokenizer({'se': 1, 've': 1, 'n': 1, '-': 1, 'twe': 1,'lfths': 1})>>> CVClusterTokenizer().tokenize('character')
CVClusterTokenizer({'cha': 1, 'ra': 1, 'cte': 1, 'r': 1})

New in version 0.4.0.
class abydos.tokenizer.VCClusterTokenizer(scaler=None,
els=None)Bases: abydos.tokenizer._tokenizer._Tokenizer

consonants=None,vow-

A V*C*-cluster tokenizer.
New in version 0.4.0.
Initialize tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize

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Examples>>> VCClusterTokenizer().tokenize('seven-twelfths')
VCClusterTokenizer({'s': 1, 'ev': 1, 'en': 1, '-': 1, 'tw': 1,'elfths': 1})>>> VCClusterTokenizer().tokenize('character')
VCClusterTokenizer({'ch': 1, 'ar': 1, 'act': 1, 'er': 1})

New in version 0.4.0.
class abydos.tokenizer.SAPSTokenizer(scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
Syllable Alignment Pattern Searching tokenizer.
This is the syllabifier described on p. 917 of [RY05].
New in version 0.4.0.
Initialize Tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> SAPSTokenizer().tokenize('seven-twelfths')
SAPSTokenizer({'t': 2, 'se': 1, 'ven': 1, '-': 1, 'wel': 1, 'f': 1,'h': 1, 's': 1})>>> SAPSTokenizer().tokenize('character')
SAPSTokenizer({'c': 1, 'ha': 1, 'rac': 1, 'ter': 1})

New in version 0.4.0.
class abydos.tokenizer.SonoriPyTokenizer(scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
SonoriPy tokenizer.
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New in version 0.4.0.
Initialize Tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> SonoriPyTokenizer().tokenize('seven-twelfths')
SonoriPyTokenizer({'se': 1, 'ven-': 1, 'twelfths': 1})>>> SonoriPyTokenizer().tokenize('character')
SonoriPyTokenizer({'cha': 1, 'rac': 1, 'ter': 1})

New in version 0.4.0.
class abydos.tokenizer.LegaliPyTokenizer(scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
LegaliPy tokenizer.
New in version 0.4.0.
Initialize Tokenizer.
Parameters scaler (None, str, or function) -- A scaling function for the Counter:• None : no scaling• 'set' : All non-zero values are set to 1.
• 'length' : Each token has weight equal to its length.
• 'length-log' [Each token has weight equal to the log of its] length + 1.
• 'length-exp' [Each token has weight equal to e raised to its] length.
• a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer sequences such as the Fibonacci sequence.
New in version 0.4.0.

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tokenize(string, ipa=False)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters• string (str) -- The string to tokenize• ipa (bool) -- If True, indicates that the string is in IPA
Examples>>> LegaliPyTokenizer().tokenize('seven-twelfths')
LegaliPyTokenizer({'s': 1, 'ev': 1, 'en-tw': 1, 'elfths': 1})>>> LegaliPyTokenizer().tokenize('character')
LegaliPyTokenizer({'ch': 1, 'ar': 1, 'act': 1, 'er': 1})

New in version 0.4.0.
train_onsets(text, threshold=0.0002, clean=True, append=False)
Train the onsets on a text.
Parameters• text (str) -- The text on which to train• threshold (float) -- Threshold proportion above which to include onset into onset
list• clean (bool) -- If True, the text is stripped of numerals and punctuation• append (bool) -- If True, the current onset list is extended
New in version 0.4.0.
class abydos.tokenizer.NLTKTokenizer(nltk_tokenizer=None, scaler=None)
Bases: abydos.tokenizer._tokenizer._Tokenizer
NLTK tokenizer wrapper class.
New in version 0.4.0.
Initialize Tokenizer.
Parameters• scaler (None, str, or function) -- A scaling function for the Counter:– None : no scaling– 'set' : All non-zero values are set to 1.
– 'length' : Each token has weight equal to its length.
– 'length-log' [Each token has weight equal to the log of its] length + 1.
– 'length-exp' [Each token has weight equal to e raised to its] length.
– a callable function : The function is applied to each value in the Counter. Some usefulfunctions include math.exp, math.log1p, math.sqrt, and indexes into interesting integer
sequences such as the Fibonacci sequence.
• nltk_tokenizer (Object) -- An instantiated tokenizer from NLTK.

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New in version 0.4.0.
tokenize(string)
Tokenize the term and store it.
The tokenized term is stored as an ordered list and as a Counter object.
Parameters string (str) -- The string to tokenize
Examples>>> from nltk.tokenize.casual import TweetTokenizer>>> nltk_tok = TweetTokenizer()>>> NLTKTokenizer(nltk_tokenizer=nltk_tok).tokenize(... '.@Twitter Today is #lit!')NLTKTokenizer({'.': 1, '@Twitter': 1, 'Today': 1, 'is': 1, '#lit': 1,'!': 1})

New in version 0.4.0.
3.1.1.10 abydos.util package
abydos.util.
The util module defines various utility functions for other modules within Abydos, including:• _prod -- computes the product of a collection of numbers (akin to sum)
These functions are not intended for use by users.
abydos.util.download_package(resource_name,silent=False)
Download and install a package or collection.

url=None,

data_path=None,

force=False,

abydos.util.list_available_packages(url=None)
List all data packages available for install.
abydos.util.list_installed_packages(path=None)
List all installed data packages.
abydos.util.package_path(resource_name)
Given a resource name, returns the path to the package.

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FOUR

RELEASE HISTORY

4.1 0.5.0 (2020-01-10) ecgtheow
doi:10.5281/zenodo.3603514
Changes:• Support for Python 2.7 was removed.

4.2 0.4.1 (2020-01-07) distant dietrich
doi:10.5281/zenodo.3600548
Changes:• Support for Python 3.4 was removed. (3.4 reached end-of-life on March 18, 2019)• Fuzzy intersections were corrected to avoid over-counting partial intersection instances.
• Levenshtein can now return an optimal alignment• Added the following distance measures:– Indice de Similitude-Guth (ISG)– INClusion Programme– Guth– Victorian Panel Study (VPS) score– LIG3 similarity– Discounted Levenshtein– Relaxed Hamming– String subsequence kernel (SSK) similarity– Phonetic edit distance– Henderson-Heron dissimilarity– Raup-Crick similarity– Millar's binomial deviance dissimilarity– Morisita similarity– Horn-Morisita similarity649

Abydos Documentation, Release 0.5.0– Clark's coefficient of divergence– Chao's Jaccard similarity– Chao's Dice similarity– Cao's CY similarity (CYs) and dissimilarity (CYd)• Added the following fingerprint classes:– Taft's Consonant coding– Taft's Extract - letter list
– Taft's Extract - position & frequency– L.A. County Sheriff's System– Library of Congres Cutter table encoding• Added the following phonetic algorithms:– Ainsworth's grapheme-to-phoneme– PHONIC

4.3 0.4.0 (2019-05-30) dietrich
doi:10.5281/zenodo.3235034
Version 0.4.0 focuses on distance measures, adding 211 new measures. Attempts were made to provide normalizedversion for measure that did not inherently range from 0 to 1. The other major focus was the addition of 12 tokenizers,
in service of expanding distance measure options.
Changes:• Support for Python 3.3 was dropped.
• Deprecated functions that merely wrap class methods to maintain API compatibility, for removal in 0.6.0• Added methods to ConfusionTable to return:– its internal representation– false negative rate
– false omission rate
– positive & negative likelihood ratios– diagnostic odds ratio– error rate
– prevalence– Jaccard index– D-measure– Phi coefficient– joint, actual, & predicted entropies– mutual information– proficiency (uncertainty coefficient)

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Abydos Documentation, Release 0.5.0– information gain ratio– dependency– lift• Deprecated f-measure & g-measure from ConfusionTable for removal in 0.6.0• Added notes to indicate when functions, classes, & methods were added• Added the following 12 tokenizers:– QSkipgrams– CharacterTokenizer– RegexpTokenizer, WhitespaceTokenizer, & WordpunctTokenizer– COrVClusterTokenizer, CVClusterTokenizer, & VCClusterTokenizer– SonoriPyTokenizer & LegaliPyTokenizer– NLTKTokenizer– SAPSTokenizer• Added the UnigramCorpus class & a facility for downloading data, such as pre-processed/trained data, fromstorage on GitHub• Added the Wåhlin phonetic encoding• Added the following 211 similarity/distance/correlation measures:– ALINE– AMPLE– Anderberg– Andres & Marzo's Delta– Average Linkage– AZZOO– Baroni-Urbani & Buser I & II
– Batagelj & Bren
– Baulieu I-XV
– Benini I & II
– Bennet– Bhattacharyya– BI-SIM– BLEU– Block Levenshtein– Brainerd-Robinson– Braun-Blanquet– Canberra– Chord4.3. 0.4.0 (2019-05-30) dietrich

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Abydos Documentation, Release 0.5.0– Clement– Cohen's Kappa– Cole– Complete Linkage– Consonni & Todeschini I-V– Cormode's LZ– Covington– Dennis– Dice Asymmetric I & II
– Digby– Dispersion– Doolittle– Dunning– Eyraud– Fager & McGowan– Faith– Fellegi-Sunter– Fidelity– Fleiss– Fleiss-Levin-Paik– FlexMetric– Forbes I & II
– Fossum– FuzzyWuzzy Partial String– FuzzyWuzzy Token Set– FuzzyWuzzy Token Sort
– Generalized Fleiss– Gilbert– Gilbert & Wells– Gini I & II
– Goodall– Goodman & Kruskal's Lambda– Goodman & Kruskal's Lambda-r– Goodman & Kruskal's Tau A & B– Gower & Legendre– Guttman's Lambda A & B

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Abydos Documentation, Release 0.5.0– Gwet's AC– Hamann– Harris & Lahey– Hassanat– Hawkins & Dotson– Hellinger– Higuera & Mico
– Hurlbert– Iterative SubString– Jaccard-NM– Jensen-Shannon– Johnson– Kendall's Tau– Kent & Foster I & II
– Koppen I & II
– Kuder & Richardson– Kuhns I-XII– Kulczynski I & II
– Longest Common Prefix– Longest Common Suffix– Lorentzian– Maarel– Marking– Marking Metric– MASI– Matusita– Maxwell & Pilliner– McConnaughey– McEwen & Michael– MetaLevenshtein– Michelet– MinHash– Mountford– Mean Squared Contingency– Mutual Information– NCD with LZSS

4.3. 0.4.0 (2019-05-30) dietrich

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Abydos Documentation, Release 0.5.0– NCD with PAQ9a– Ozbay– Pattern– Pearson's Chi-Squared– Pearson & Heron II– Pearson II & III– Pearson's Phi– Peirce– Positional Q-Gram Dice, Jaccard, & Overlap– Q-Gram– Quantitative Cosine, Dice, & Jaccard– Rees-Levenshtein– Roberts– Rogers & Tanimoto– Rogot & Goldberg– Rouge-L, -S, -SU, & -W
– Russell & Rao– SAPS– Scott's Pi– Shape– Shapira & Storer I– Sift4 Extended– Single Linkage– Size– Soft Cosine– SoftTF-IDF– Sokal & Michener– Sokal & Sneath I-V– Sorgenfrei– Steffensen– Stiles– Stuart's Tau– Tarantula– Tarwid– Tetrachoric– TF-IDF

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Abydos Documentation, Release 0.5.0– Tichy– Tulloss's R, S, T, & U– Unigram Subtuple– Unknown A-M– Upholt– Warrens I-V– Weighted Jaccard– Whittaker– Yates' Chi-Squared– YJHHR– Yujian & Bo
– Yule's Q, Q II, & Y• Four intersection types are now supported for all distance measure that are based on _TokenDistance. In addition
to basic crisp intersections, soft, fuzzy, and group linkage intersections have been provided.

4.4 0.3.6 (2018-11-17) classy carldoi:10.5281/zenodo.1490537
Changes:• Most functions were encapsulated into classes.
• Each class is broken out into its own file, with test files paralleling library files.
• Documentation was converted from Sphinx markup to Numpy style.
• A tutorial was written for each subpackage.
• Documentation was cleaned up, with math markup corrections and many additional links.

4.5 0.3.5 (2018-10-31) cantankerous carldoi:10.5281/zenodo.1463204
Version 0.3.5 focuses on refactoring the whole project. The API itself remains largely the same as in previous versions,but underlyingly modules have been split up. Essentially no new features are added (bugfixes aside) in this version.
Changes:• Refactored library and tests into smaller modules• Broke compression distances (NCD) out into separate functions• Adopted Black code style• Added pyproject.toml to use Poetry for packaging (but will continue using setuptools and setup.py for thepresent)• Minor bug fixes

4.4. 0.3.6 (2018-11-17) classy carl655

Abydos Documentation, Release 0.5.0

4.6 0.3.0 (2018-10-15) carldoi:10.5281/zenodo.1462443
Version 0.3.0 focuses on additional phonetic algorithms, but does add numerous distance measures, fingerprints, andeven a few stemmers. Another focus was getting everything to build again (including docs) and to move to more
standard modern tools (flake8, tox, etc.).
Changes:• Fixed implementation of Bag distance• Updated BMPM to version 3.10
• Fixed Sphinx documentation on readthedocs.org• Split string fingerprints out of clustering into their own module• Added support for q-grams to skip-n characters• New phonetic algorithms:– Statistics Canada– Lein– Roger Root
– Oxford Name Compression Algorithm (ONCA)– Eudex phonetic hash
– Haase Phonetik– Reth-Schek Phonetik– FONEM– Parmar-Kumbharana– Davidson's Consonant Code– SoundD– PSHP Soundex/Viewex Coding– an early version of Henry Code– Norphone– Dolby Code– Phonetic Spanish– Spanish Metaphone– MetaSoundex– SoundexBR– NRL English-to-phoneme• New string fingerprints:– Cisłak & Grabowski's occurrence fingerprint– Cisłak & Grabowski's occurrence halved fingerprint– Cisłak & Grabowski's count fingerprint

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Abydos Documentation, Release 0.5.0– Cisłak & Grabowski's position fingerprint– Synoname Toolcode• New distance measures:– Minkowski distance & similarity– Manhattan distance & similarity– Euclidean distance & similarity– Chebyshev distance & similarity– Eudex distances– Sift4 distance– Baystat distance & similarity– Typo distance– Indel distance– Synoname• New stemmers:– UEA-Lite Stemmer– Paice-Husk Stemmer– Schinke Latin stemmer– S stemmer• Eliminated ._compat submodule in favor of six• Transitioned from PEP8 to flake8, etc.
• Phonetic algorithms now consistently use max_length=-1 to indicate that there should be no length limit• Added example notebooks in binder directory

4.7 0.2.0 (2015-05-27) berthold• Added Caumanns' German stemmer• Added Lovins' English stemmer• Updated Beider-Morse Phonetic Matching to 3.04
• Added Sphinx documentation

4.7. 0.2.0 (2015-05-27) berthold

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Abydos Documentation, Release 0.5.0

4.8 0.1.1 (2015-05-12) albrecht• First Beta release to PyPI

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CHAPTERFIVE

INDICES• genindex• modindex• search659

Abydos Documentation, Release 0.5.0

660

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Bibliography

PYTHON MODULE INDEXaabydos, 9abydos.compression, 9abydos.corpus, 18
abydos.distance, 26
abydos.fingerprint, 477
abydos.phones, 496
abydos.phonetic, 503
abydos.stats, 578
abydos.stemmer, 618
abydos.tokenizer, 636
abydos.util, 648

679

Abydos Documentation, Release 0.5.0

680

Python Module Index

INDEXAAZZOO (class in abydos.distance), 84

abydos (module), 9
abydos.compression (module), 9
abydos.corpus (module), 18
abydos.distance (module), 26
abydos.fingerprint (module), 477
abydos.phones (module), 496
abydos.phonetic (module), 503
abydos.stats (module), 578
abydos.stemmer (module), 618
abydos.tokenizer (module), 636
abydos.util (module), 648
ac_decode() (in module abydos.compression), 12
ac_encode() (in module abydos.compression), 12
ac_train() (in module abydos.compression), 13
accuracy() (abydos.stats.ConfusionTable method),581
accuracy_gain()(abydos.stats.ConfusionTable
method), 582
actual_entropy()(abydos.stats.ConfusionTable
method), 582
add_document()(abydos.corpus.UnigramCorpus
method), 25
aghmean() (in module abydos.stats), 609
agmean() (in module abydos.stats), 608
Ainsworth (class in abydos.phonetic), 577
alignment() (abydos.distance.ALINE method), 58
alignment() (abydos.distance.Covington method), 55
alignment() (abydos.distance.Levenshtein method),35alignments() (abydos.distance.ALINE method), 58
alignments() (abydos.distance.Covington method),
55
ALINE (class in abydos.distance), 57
alpha_sis() (in module abydos.phonetic), 531
AlphaSIS (class in abydos.phonetic), 530
amean() (in module abydos.stats), 607
AMPLE (class in abydos.distance), 83
Anderberg (class in abydos.distance), 86
AndresMarzoDelta (class in abydos.distance), 88
Arithmetic (class in abydos.compression), 10
AverageLinkage (class in abydos.distance), 379

B
Bag (class in abydos.distance), 383
bag() (in module abydos.distance), 384
balanced_accuracy()(abydos.stats.ConfusionTable method), 582
BaroniUrbaniBuserI (class in abydos.distance), 90
BaroniUrbaniBuserII (class in abydos.distance),
91
BatageljBren (class in abydos.distance), 93
BaulieuI (class in abydos.distance), 95
BaulieuII (class in abydos.distance), 96
BaulieuIII (class in abydos.distance), 97
BaulieuIV (class in abydos.distance), 98
BaulieuIX (class in abydos.distance), 105
BaulieuV (class in abydos.distance), 100
BaulieuVI (class in abydos.distance), 101
BaulieuVII (class in abydos.distance), 103
BaulieuVIII (class in abydos.distance), 104
BaulieuX (class in abydos.distance), 106
BaulieuXI (class in abydos.distance), 108
BaulieuXII (class in abydos.distance), 109
BaulieuXIII (class in abydos.distance), 110
BaulieuXIV (class in abydos.distance), 112
BaulieuXV (class in abydos.distance), 113
Baystat (class in abydos.distance), 450
BeiderMorse (class in abydos.phonetic), 548
BeniniI (class in abydos.distance), 114
BeniniII (class in abydos.distance), 116
Bennet (class in abydos.distance), 118
Bhattacharyya (class in abydos.distance), 372
BISIM (class in abydos.distance), 61
BLEU (class in abydos.distance), 396
BlockLevenshtein (class in abydos.distance), 74
bmpm() (in module abydos.phonetic), 550
BrainerdRobinson (class in abydos.distance), 373
BraunBlanquet (class in abydos.distance), 119
BWT (class in abydos.compression), 13
bwt_decode() (in module abydos.compression), 15
bwt_encode() (in module abydos.compression), 15
BWTF (class in abydos.fingerprint), 494

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BWTRLEF (class in abydos.fingerprint), 495

C
c_features (abydos.distance.ALINE attribute), 59
Canberra (class in abydos.distance), 121
Cao (class in abydos.distance), 122
Caumanns (class in abydos.stemmer), 624
caumanns() (in module abydos.stemmer), 624
Caverphone (class in abydos.phonetic), 528
caverphone() (in module abydos.phonetic), 530
ChaoDice (class in abydos.distance), 124
ChaoJaccard (class in abydos.distance), 125
CharacterTokenizer (class in abydos.tokenizer),
640
Chebyshev (class in abydos.distance), 126
chebyshev() (in module abydos.distance), 128
Chord (class in abydos.distance), 128
Clark (class in abydos.distance), 130
clef_german() (in module abydos.stemmer), 633
clef_german_plus() (in module abydos.stemmer),634
clef_swedish() (in module abydos.stemmer), 635
CLEFGerman (class in abydos.stemmer), 633
CLEFGermanPlus (class in abydos.stemmer), 634
CLEFSwedish (class in abydos.stemmer), 635
Clement (class in abydos.distance), 131
cmean() (in module abydos.stats), 610
cmp_features() (in module abydos.phones), 502
CohenKappa (class in abydos.distance), 132
Cole (class in abydos.distance), 133
CompleteLinkage (class in abydos.distance), 381
cond_neg_pop()(abydos.stats.ConfusionTable
method), 583
cond_pos_pop()(abydos.stats.ConfusionTable
method), 583
ConfusionTable (class in abydos.stats), 580
Consonant (class in abydos.fingerprint), 491
ConsonniTodeschiniI (class in abydos.distance),
135
ConsonniTodeschiniII (class in abydos.distance),
137
ConsonniTodeschiniIII(classinabydos.distance), 138
ConsonniTodeschiniIV (class in abydos.distance),
139
ConsonniTodeschiniV (class in abydos.distance),
140
CormodeLZ (class in abydos.distance), 75
Corpus (class in abydos.corpus), 19
corpus_importer() (abydos.corpus.NGramCorpus
method), 23
corr() (abydos.distance.AndresMarzoDelta method),89682(abydos.distance.BaroniUrbaniBuserII
method), 92
corr() (abydos.distance.BeniniI method), 115corr() (abydos.distance.BeniniII method), 117corr() (abydos.distance.Bennet method), 118corr() (abydos.distance.Cole method), 134corr()(abydos.distance.ConsonniTodeschiniV
method), 141corr() (abydos.distance.Dennis method), 145corr() (abydos.distance.Digby method), 153corr() (abydos.distance.Dispersion method), 154corr() (abydos.distance.Fleiss method), 168corr() (abydos.distance.ForbesII method), 172corr() (abydos.distance.GeneralizedFleiss method),177
corr() (abydos.distance.Gilbert method), 179corr() (abydos.distance.GiniI method), 182corr() (abydos.distance.GiniII method), 184corr() (abydos.distance.GoodmanKruskalLambdaR
method), 188corr() (abydos.distance.GwetAC method), 196corr() (abydos.distance.Hamann method), 198corr() (abydos.distance.Hurlbert method), 207corr() (abydos.distance.IterativeSubString method),82
corr() (abydos.distance.KendallTau method), 215corr() (abydos.distance.KoppenI method), 221corr() (abydos.distance.KuderRichardson method),224
corr() (abydos.distance.KuhnsI method), 226corr() (abydos.distance.KuhnsII method), 228corr() (abydos.distance.KuhnsIII method), 230corr() (abydos.distance.KuhnsIV method), 232corr() (abydos.distance.KuhnsIX method), 242
corr() (abydos.distance.KuhnsV method), 234
corr() (abydos.distance.KuhnsVI method), 236
corr() (abydos.distance.KuhnsVII method), 238corr() (abydos.distance.KuhnsVIII method), 240corr() (abydos.distance.KuhnsX method), 244
corr() (abydos.distance.KuhnsXI method), 246corr() (abydos.distance.Maarel method), 254corr() (abydos.distance.MaxwellPilliner method), 269
corr() (abydos.distance.McConnaughey method), 270corr() (abydos.distance.McEwenMichael method), 272
corr() (abydos.distance.MSContingency method), 277
corr() (abydos.distance.PearsonChiSquared method),288
corr() (abydos.distance.PearsonHeronII method), 282
corr() (abydos.distance.PearsonIII method), 286
corr() (abydos.distance.PearsonPhi method), 290corr() (abydos.distance.Peirce method), 292
corr() (abydos.distance.ScottPi method), 301
corr() (abydos.distance.Stiles method), 316
corr() (abydos.distance.StuartTau method), 318
corr()

Index

Abydos Documentation, Release 0.5.0

corr() (abydos.distance.Tarwid method), 321corr() (abydos.distance.Tetrachoric method), 322
corr() (abydos.distance.UnknownA method), 333corr() (abydos.distance.UnknownE method), 339
corr() (abydos.distance.WarrensI method), 354
corr() (abydos.distance.WarrensIII method), 357
corr() (abydos.distance.YuleQ method), 366
corr() (abydos.distance.YuleY method), 370
correct_pop()(abydos.stats.ConfusionTable
method), 583
COrVClusterTokenizer(classinabydos.tokenizer), 642
Cosine (class in abydos.distance), 142
Count (class in abydos.fingerprint), 487
count_fingerprint()(inmoduleabydos.fingerprint), 487
Covington (class in abydos.distance), 54
CVClusterTokenizer (class in abydos.tokenizer),643

Dd_measure() (abydos.stats.ConfusionTable method),584
DaitchMokotoff (class in abydos.phonetic), 512
damerau_levenshtein()(inmoduleabydos.distance), 41
DamerauLevenshtein (class in abydos.distance), 39
Davidson (class in abydos.phonetic), 532
davidson() (in module abydos.phonetic), 533
decode() (abydos.compression.Arithmetic method), 10
decode() (abydos.compression.BWT method), 14
decode() (abydos.compression.RLE method), 16
Dennis (class in abydos.distance), 145
dependency() (abydos.stats.ConfusionTable method),584
diagnostic_odds_ratio()(abydos.stats.ConfusionTable method), 584
Dice (class in abydos.distance), 147
DiceAsymmetricI (class in abydos.distance), 149
DiceAsymmetricII (class in abydos.distance), 151
Digby (class in abydos.distance), 152
DiscountedLevenshtein(classinabydos.distance), 62
Dispersion (class in abydos.distance), 153
dist() (abydos.distance.AverageLinkage method), 379
dist() (abydos.distance.Bag method), 383
dist() (abydos.distance.BatageljBren method), 94
dist() (abydos.distance.BaulieuI method), 95
dist() (abydos.distance.BaulieuIII method), 98
dist() (abydos.distance.BaulieuIV method), 99
dist() (abydos.distance.BaulieuIX method), 106dist() (abydos.distance.BaulieuV method), 101dist() (abydos.distance.BaulieuVI method), 102dist() (abydos.distance.BaulieuVII method), 104
Indexdist() (abydos.distance.BaulieuVIII method), 105dist() (abydos.distance.BaulieuX method), 107dist() (abydos.distance.BaulieuXI method), 109dist() (abydos.distance.BaulieuXII method), 110dist() (abydos.distance.BaulieuXIII method), 111dist() (abydos.distance.BaulieuXIV method), 113dist() (abydos.distance.BaulieuXV method), 114
dist() (abydos.distance.Bhattacharyya method), 373
dist() (abydos.distance.BlockLevenshtein method), 74
dist() (abydos.distance.Canberra method), 121dist() (abydos.distance.Chebyshev method), 127dist() (abydos.distance.Chord method), 129dist() (abydos.distance.Clark method), 130
dist() (abydos.distance.CompleteLinkage method),382
dist() (abydos.distance.CormodeLZ method), 75
dist() (abydos.distance.Covington method), 56
dist()(abydos.distance.DamerauLevenshtein
method), 40
dist()(abydos.distance.DiscountedLevenshtein
method), 63
dist() (abydos.distance.Editex method), 447
dist() (abydos.distance.Euclidean method), 159
dist() (abydos.distance.Eudex method), 452
dist() (abydos.distance.FlexMetric method), 60
dist() (abydos.distance.Hamming method), 66
dist() (abydos.distance.Hassanat method), 201dist() (abydos.distance.Hellinger method), 203dist() (abydos.distance.HendersonHeron method),204
dist() (abydos.distance.HigueraMico method), 48
dist() (abydos.distance.Inclusion method), 472
dist() (abydos.distance.Indel method), 49
dist() (abydos.distance.JensenShannon method), 392
dist() (abydos.distance.KulczynskiI method), 250
dist() (abydos.distance.Levenshtein method), 36
dist() (abydos.distance.Lorentzian method), 252dist() (abydos.distance.Manhattan method), 257
dist() (abydos.distance.Marking method), 44
dist() (abydos.distance.MarkingMetric method), 45
dist() (abydos.distance.Matusita method), 267
dist() (abydos.distance.MetaLevenshtein method), 53
dist() (abydos.distance.Millar method), 261
dist() (abydos.distance.Minkowski method), 262
dist() (abydos.distance.Morisita method), 255dist() (abydos.distance.NCDarith method), 433
dist() (abydos.distance.NCDbwtrle method), 435
dist() (abydos.distance.NCDbz2 method), 430dist() (abydos.distance.NCDlzma method), 431
dist() (abydos.distance.NCDlzss method), 439
dist() (abydos.distance.NCDpaq9a method), 438
dist() (abydos.distance.NCDrle method), 437
dist() (abydos.distance.NCDzlib method), 428
dist() (abydos.distance.Ozbay method), 470

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dist() (abydos.distance.Pattern method), 281
dist() (abydos.distance.PhoneticDistance method),443
dist()(abydos.distance.PhoneticEditDistancemethod), 65
dist() (abydos.distance.QGram method), 293dist() (abydos.distance.ReesLevenshtein method), 295
dist() (abydos.distance.RelaxedHamming method), 71
dist() (abydos.distance.Shape method), 303
dist() (abydos.distance.ShapiraStorerI method), 43
dist() (abydos.distance.Sift4 method), 457
dist() (abydos.distance.SingleLinkage method), 380dist() (abydos.distance.Size method), 304
dist() (abydos.distance.SokalSneathIII method), 309
dist() (abydos.distance.Synoname method), 468
dist() (abydos.distance.Tichy method), 73
dist() (abydos.distance.Typo method), 463
dist() (abydos.distance.UnknownF method), 341
dist() (abydos.distance.UnknownK method), 348
dist() (abydos.distance.YJHHR method), 371
dist() (abydos.distance.YujianBo method), 47
dist() (abydos.distance.YuleQII method), 368
dist() (in module abydos.distance), 34
dist_abs() (abydos.distance.Bag method), 384
dist_abs() (abydos.distance.BatageljBren method),94
dist_abs() (abydos.distance.BaulieuIV method), 100
dist_abs() (abydos.distance.Bhattacharyya method),373
dist_abs()(abydos.distance.BlockLevenshtein
method), 74
dist_abs() (abydos.distance.Cao method), 123
dist_abs() (abydos.distance.Chebyshev method), 127
dist_abs() (abydos.distance.Chord method), 129
dist_abs()(abydos.distance.CompleteLinkage
method), 382
dist_abs() (abydos.distance.CormodeLZ method), 76
dist_abs() (abydos.distance.Covington method), 56
dist_abs() (abydos.distance.DamerauLevenshtein
method), 40
dist_abs() (abydos.distance.DiscountedLevenshtein
method), 63
dist_abs() (abydos.distance.Editex method), 448
dist_abs() (abydos.distance.Euclidean method), 159
dist_abs() (abydos.distance.Eudex method), 453
dist_abs() (abydos.distance.FlexMetric method), 61
dist_abs() (abydos.distance.Hamming method), 67
dist_abs() (abydos.distance.Hassanat method), 201
dist_abs() (abydos.distance.Hellinger method), 204
dist_abs() (abydos.distance.HigueraMico method),49
dist_abs()(abydos.distance.JensenShannon
method), 393
dist_abs() (abydos.distance.LCPrefix method), 416

684

dist_abs() (abydos.distance.LCSuffix method), 418
dist_abs() (abydos.distance.Levenshtein method), 37
dist_abs() (abydos.distance.Lorentzian method), 253
dist_abs() (abydos.distance.Manhattan method),257
dist_abs() (abydos.distance.Marking method), 45
dist_abs()(abydos.distance.MarkingMetricmethod), 46
dist_abs() (abydos.distance.Matusita method), 267
dist_abs()(abydos.distance.MetaLevenshtein
method), 54
dist_abs() (abydos.distance.Millar method), 261
dist_abs() (abydos.distance.Minkowski method), 263
dist_abs() (abydos.distance.MRA method), 444
dist_abs() (abydos.distance.Ozbay method), 470
dist_abs()(abydos.distance.PhoneticDistancemethod), 443
dist_abs() (abydos.distance.PhoneticEditDistancemethod), 65
dist_abs() (abydos.distance.QGram method), 294
dist_abs()(abydos.distance.ReesLevenshtein
method), 296
dist_abs()(abydos.distance.RelaxedHamming
method), 72
dist_abs() (abydos.distance.ShapiraStorerI method),43
dist_abs() (abydos.distance.Sift4 method), 458
dist_abs() (abydos.distance.Sift4Extended method),459
dist_abs() (abydos.distance.Sift4Simplest method),458
dist_abs() (abydos.distance.SingleLinkage method),381
dist_abs() (abydos.distance.Synoname method), 468
dist_abs() (abydos.distance.Tichy method), 73
dist_abs() (abydos.distance.Typo method), 464
dist_abs() (abydos.distance.UnknownK method),349
dist_abs() (abydos.distance.YJHHR method), 372
dist_abs() (abydos.distance.YujianBo method), 47
dist_abs() (abydos.distance.YuleQII method), 368
dist_bag() (in module abydos.distance), 385
dist_baystat() (in module abydos.distance), 451
dist_cosine() (in module abydos.distance), 143
dist_damerau() (in module abydos.distance), 41
dist_dice() (in module abydos.distance), 148
dist_editex() (in module abydos.distance), 449
dist_euclidean() (in module abydos.distance), 160
dist_eudex() (in module abydos.distance), 456
dist_hamming() (in module abydos.distance), 68
dist_ident() (in module abydos.distance), 422
dist_indel() (in module abydos.distance), 50
dist_jaccard() (in module abydos.distance), 209

IndexAbydos Documentation, Release 0.5.0

dist_jaro_winkler()(in
moduleabydos.distance), 78
dist_lcsseq() (in module abydos.distance), 412
dist_lcsstr() (in module abydos.distance), 415
dist_length() (in module abydos.distance), 423
dist_levenshtein() (in module abydos.distance),38
dist_manhattan() (in module abydos.distance), 258
dist_minkowski() (in module abydos.distance), 264
dist_mlipns() (in module abydos.distance), 70
dist_monge_elkan() (in module abydos.distance),388
dist_mra() (in module abydos.distance), 446
dist_ncd_arith() (in module abydos.distance), 434
dist_ncd_bwtrle() (in module abydos.distance),436
dist_ncd_bz2() (in module abydos.distance), 430
dist_ncd_lzma() (in module abydos.distance), 432
dist_ncd_rle() (in module abydos.distance), 437
dist_ncd_zlib() (in module abydos.distance), 428
dist_overlap() (in module abydos.distance), 279
dist_prefix() (in module abydos.distance), 425
dist_ratcliff_obershelp() (in module abydos.distance), 420
dist_sift4() (in module abydos.distance), 462
dist_strcmp95() (in module abydos.distance), 80
dist_suffix() (in module abydos.distance), 427
dist_tversky() (in module abydos.distance), 330
dist_typo() (in module abydos.distance), 466
dm_soundex() (in module abydos.phonetic), 513
docs() (abydos.corpus.Corpus method), 20
docs_of_words() (abydos.corpus.Corpus method),
20
Dolby (class in abydos.phonetic), 533
dolby() (in module abydos.phonetic), 535
Doolittle (class in abydos.distance), 155
double_metaphone() (in module abydos.phonetic),
546
DoubleMetaphone (class in abydos.phonetic), 545
download_package() (in module abydos.util), 648
Dunning (class in abydos.distance), 156

encode() (abydos.phonetic.DaitchMokotoff method),
512
encode() (abydos.phonetic.Davidson method), 532
encode() (abydos.phonetic.Dolby method), 534
encode()(abydos.phonetic.DoubleMetaphonemethod), 546
encode() (abydos.phonetic.Eudex method), 547
encode() (abydos.phonetic.FONEM method), 557
encode() (abydos.phonetic.FuzzySoundex method),514
encode() (abydos.phonetic.Haase method), 562
encode() (abydos.phonetic.HenryEarly method), 558
encode() (abydos.phonetic.Koelner method), 559
encode() (abydos.phonetic.LEIN method), 516
encode() (abydos.phonetic.Metaphone method), 544
encode() (abydos.phonetic.MetaSoundex method), 553
encode() (abydos.phonetic.MRA method), 527
encode() (abydos.phonetic.Norphone method), 576
encode() (abydos.phonetic.NRL method), 552
encode() (abydos.phonetic.NYSIIS method), 526
encode() (abydos.phonetic.ONCA method), 555
encode()(abydos.phonetic.ParmarKumbharana
method), 543
encode() (abydos.phonetic.Phonem method), 565
encode() (abydos.phonetic.Phonet method), 566
encode() (abydos.phonetic.PhoneticSpanish method),570
encode() (abydos.phonetic.Phonex method), 517
encode() (abydos.phonetic.PHONIC method), 519
encode() (abydos.phonetic.Phonix method), 520
encode()(abydos.phonetic.PSHPSoundexFirst
method), 522
encode()(abydos.phonetic.PSHPSoundexLast
method), 524
encode() (abydos.phonetic.RefinedSoundex method),
510
encode() (abydos.phonetic.RethSchek method), 564
encode() (abydos.phonetic.RogerRoot method), 539
encode() (abydos.phonetic.RussellIndex method), 505
encode() (abydos.phonetic.SfinxBis method), 573
encode() (abydos.phonetic.SoundD method), 542
encode() (abydos.phonetic.Soundex method), 508
E
encode() (abydos.phonetic.SoundexBR method), 568(abydos.phonetic.SpanishMetaphone
e_score() (abydos.stats.ConfusionTable method), 585 encode()
method), 572
Editex (class in abydos.distance), 447
encode() (abydos.phonetic.SPFC method), 537
editex() (in module abydos.distance), 448
encode() (abydos.compression.Arithmetic method), 10 encode() (abydos.phonetic.StatisticsCanada method),541
encode() (abydos.compression.BWT method), 14
encode()(abydos.phonetic.Waahlin method), 575
encode() (abydos.compression.RLE method), 16
encode_alpha()(abydos.phonetic.AlphaSIS
encode() (abydos.phonetic.Ainsworth method), 577
method),531
encode() (abydos.phonetic.AlphaSIS method), 530(abydos.phonetic.Caverphone
encode() (abydos.phonetic.BeiderMorse method), 549 encode_alpha()method),529
encode() (abydos.phonetic.Caverphone method), 528

Index

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Abydos Documentation, Release 0.5.0

encode_alpha() (abydos.phonetic.DaitchMokotoff
method), 513
encode_alpha() (abydos.phonetic.Dolby method),535
encode_alpha()(abydos.phonetic.DoubleMetaphonemethod),546
encode_alpha()(abydos.phonetic.FuzzySoundex
method), 514
encode_alpha() (abydos.phonetic.Haase method),562
encode_alpha() (abydos.phonetic.Koelner method),560
encode_alpha() (abydos.phonetic.LEIN method),516
encode_alpha()(abydos.phonetic.MetaSoundex
method), 554
encode_alpha() (abydos.phonetic.ONCA method),556
encode_alpha() (abydos.phonetic.PhoneticSpanish
method), 571
encode_alpha() (abydos.phonetic.Phonex method),518
encode_alpha()(abydos.phonetic.PHONIC
method), 519
encode_alpha() (abydos.phonetic.Phonix method),
520
encode_alpha()(abydos.phonetic.PSHPSoundexFirst
method),522
encode_alpha()(abydos.phonetic.PSHPSoundexLast
method),524
encode_alpha() (abydos.phonetic.RefinedSoundex
method), 511
encode_alpha()(abydos.phonetic.RogerRoot
method), 539
encode_alpha()(abydos.phonetic.RussellIndex
method), 505
encode_alpha() (abydos.phonetic.SfinxBis method),574
encode_alpha() (abydos.phonetic.SoundD method),542
encode_alpha() (abydos.phonetic.Soundex method),509
encode_alpha()(abydos.phonetic.SoundexBR
method), 569
encode_alpha() (abydos.phonetic.SPFC method),537
encode_alpha() (abydos.phonetic.Waahlin method),576
error_pop() (abydos.stats.ConfusionTable method),585
error_rate() (abydos.stats.ConfusionTable method),

686

585
Euclidean (class in abydos.distance), 158
euclidean() (in module abydos.distance), 160
Eudex (class in abydos.distance), 452
Eudex (class in abydos.phonetic), 547
eudex() (in module abydos.phonetic), 548
eudex_hamming() (in module abydos.distance), 454
Extract (class in abydos.fingerprint), 492
ExtractPositionFrequency (class in abydos.fingerprint), 492
Eyraud (class in abydos.distance), 161

F
f1_score() (abydos.stats.ConfusionTable method),586
f2_score() (abydos.stats.ConfusionTable method),586
f_measure() (abydos.stats.ConfusionTable method),586
FagerMcGowan (class in abydos.distance), 163
Faith (class in abydos.distance), 165
fallout() (abydos.stats.ConfusionTable method), 587
false_neg() (abydos.stats.ConfusionTable method),587
false_omission_rate()(abydos.stats.ConfusionTable method), 588
false_pos() (abydos.stats.ConfusionTable method),588
fbeta_score()(abydos.stats.ConfusionTable
method), 588
fdr() (abydos.stats.ConfusionTable method), 589
feature_weights(abydos.distance.ALINE
attribute), 59
FellegiSunter (class in abydos.distance), 393
fhalf_score()(abydos.stats.ConfusionTable
method), 589
Fidelity (class in abydos.distance), 166
fingerprint() (abydos.fingerprint.BWTF method),494
fingerprint()(abydos.fingerprint.BWTRLEF
method), 495
fingerprint()(abydos.fingerprint.Consonant
method), 491
fingerprint() (abydos.fingerprint.Count method),487
fingerprint() (abydos.fingerprint.Extract method),492
fingerprint()(abydos.fingerprint.ExtractPositionFrequency
method), 493
fingerprint() (abydos.fingerprint.LACSS method),493
fingerprint()(abydos.fingerprint.LCCutter
method), 494
IndexAbydos Documentation, Release 0.5.0

fingerprint()(abydos.fingerprint.Occurrence
method), 484
fingerprint()(abydos.fingerprint.OccurrenceHalved
method),485
fingerprint()(abydos.fingerprint.OmissionKey
method), 482
fingerprint()(abydos.fingerprint.Phonetic
method), 481
fingerprint() (abydos.fingerprint.Position method),488
fingerprint() (abydos.fingerprint.QGram method),479
fingerprint()(abydos.fingerprint.SkeletonKey
method), 483
fingerprint() (abydos.fingerprint.String method),478
fingerprint()(abydos.fingerprint.SynonameToolcode
method),490
Fleiss (class in abydos.distance), 167
FleissLevinPaik (class in abydos.distance), 169
FlexMetric (class in abydos.distance), 60
fnr() (abydos.stats.ConfusionTable method), 590
FONEM (class in abydos.phonetic), 557
fonem() (in module abydos.phonetic), 557
ForbesI (class in abydos.distance), 170
ForbesII (class in abydos.distance), 172
Fossum (class in abydos.distance), 173
fuzzy_soundex() (in module abydos.phonetic), 515
FuzzySoundex (class in abydos.phonetic), 514
FuzzyWuzzyPartialString (class in abydos.distance), 440
FuzzyWuzzyTokenSet (class in abydos.distance),
441
FuzzyWuzzyTokenSort (class in abydos.distance),
440

GiniI (class in abydos.distance), 181
GiniII (class in abydos.distance), 183
gmean() (in module abydos.stats), 607
gng_importer()(abydos.corpus.NGramCorpus
method), 24
gng_importer()(abydos.corpus.UnigramCorpus
method), 25
Goodall (class in abydos.distance), 185
GoodmanKruskalLambda (class in abydos.distance),186
GoodmanKruskalLambdaR(classinabydos.distance), 187
GoodmanKruskalTauA (class in abydos.distance),
189
GoodmanKruskalTauB (class in abydos.distance),190
Gotoh (class in abydos.distance), 408
gotoh() (in module abydos.distance), 409
GowerLegendre (class in abydos.distance), 192
Guth (class in abydos.distance), 473
GuttmanLambdaA (class in abydos.distance), 193
GuttmanLambdaB (class in abydos.distance), 194
GwetAC (class in abydos.distance), 195

HHaase (class in abydos.phonetic), 562
haase_phonetik() (in module abydos.phonetic),
563
Hamann (class in abydos.distance), 197
Hamming (class in abydos.distance), 66
hamming() (in module abydos.distance), 67
HarrisLahey (class in abydos.distance), 199
Hassanat (class in abydos.distance), 200
HawkinsDotson (class in abydos.distance), 201
Hellinger (class in abydos.distance), 203
HendersonHeron (class in abydos.distance), 204
henry_early() (in module abydos.phonetic), 559
HenryEarly (class in abydos.phonetic), 558
G
heronian_mean() (in module abydos.stats), 612
g_measure() (abydos.stats.ConfusionTable method), HigueraMico (class in abydos.distance), 48
590
hmean() (in module abydos.stats), 608
gen_exponential() (abydos.distance.Eudex static hoelder_mean() (in module abydos.stats), 612
method), 454
HornMorisita (class in abydos.distance), 205
gen_fibonacci() (abydos.distance.Eudex static Hurlbert (class in abydos.distance), 206
method), 454
GeneralizedFleiss (class in abydos.distance), 175 I
get_count() (abydos.corpus.NGramCorpus method), Ident (class in abydos.distance), 421
23
idf() (abydos.corpus.Corpus method), 20
get_feature() (in module abydos.phones), 501
idf() (abydos.corpus.UnigramCorpus method), 25
get_probs()(abydos.compression.Arithmetic igr() (abydos.stats.ConfusionTable method), 591
method), 11
imean() (in module abydos.stats), 610
ghmean() (in module abydos.stats), 609
Inclusion (class in abydos.distance), 472
Gilbert (class in abydos.distance), 178
Indel (class in abydos.distance), 49
GilbertWells (class in abydos.distance), 179
indel() (in module abydos.distance), 50
Index

687

Abydos Documentation, Release 0.5.0

informedness()(abydos.stats.ConfusionTable
method), 591
ipa_to_feature_dicts() (in module abydos.phones), 499
ipa_to_features() (in module abydos.phones),498
ISG (class in abydos.distance), 471
IterativeSubString (class in abydos.distance), 82J
Jaccard (class in abydos.distance), 208
jaccard() (abydos.stats.ConfusionTable method), 591
JaccardNM (class in abydos.distance), 211
JaroWinkler (class in abydos.distance), 76
JensenShannon (class in abydos.distance), 392
Johnson (class in abydos.distance), 213
joint_entropy()(abydos.stats.ConfusionTable
method), 592

K
kappa_statistic() (abydos.stats.ConfusionTable
method), 592
KendallTau (class in abydos.distance), 214
KentFosterI (class in abydos.distance), 216
KentFosterII (class in abydos.distance), 218
Koelner (class in abydos.phonetic), 559
koelner_phonetik() (in module abydos.phonetic),
560
koelner_phonetik_alpha() (in module abydos.phonetic), 561
koelner_phonetik_num_to_alpha() (in module abydos.phonetic), 561
KoppenI (class in abydos.distance), 220
KoppenII (class in abydos.distance), 222
KuderRichardson (class in abydos.distance), 223
KuhnsI (class in abydos.distance), 225
KuhnsII (class in abydos.distance), 227
KuhnsIII (class in abydos.distance), 229
KuhnsIV (class in abydos.distance), 231
KuhnsIX (class in abydos.distance), 241
KuhnsV (class in abydos.distance), 233
KuhnsVI (class in abydos.distance), 235
KuhnsVII (class in abydos.distance), 237
KuhnsVIII (class in abydos.distance), 239
KuhnsX (class in abydos.distance), 243
KuhnsXI (class in abydos.distance), 245
KuhnsXII (class in abydos.distance), 247
KulczynskiI (class in abydos.distance), 249
KulczynskiII (class in abydos.distance), 251

L
LACSS (class in abydos.fingerprint), 493
LCCutter (class in abydos.fingerprint), 494
LCPrefix (class in abydos.distance), 416688

lcprefix() (abydos.distance.LCPrefix method), 416
LCSseq (class in abydos.distance), 410
lcsseq() (abydos.distance.LCSseq method), 410
lcsseq() (in module abydos.distance), 411
LCSstr (class in abydos.distance), 413
lcsstr() (abydos.distance.LCSstr method), 413
lcsstr() (in module abydos.distance), 414
LCSuffix (class in abydos.distance), 417
lcsuffix() (abydos.distance.LCSuffix method), 418
LegaliPyTokenizer (class in abydos.tokenizer), 646
lehmer_mean() (in module abydos.stats), 613
LEIN (class in abydos.phonetic), 515
lein() (in module abydos.phonetic), 516
Length (class in abydos.distance), 423
Levenshtein (class in abydos.distance), 35
levenshtein() (in module abydos.distance), 37
lift() (abydos.stats.ConfusionTable method), 593
LIG3 (class in abydos.distance), 475
list_available_packages() (in module abydos.util), 648
list_installed_packages() (in module abydos.util), 648
lmean() (in module abydos.stats), 611
load_corpus()(abydos.corpus.UnigramCorpus
method), 25
longer_transpositions_are_more_costly()(abydos.distance.Sift4Extended static method),
460
Lorentzian (class in abydos.distance), 252
Lovins (class in abydos.stemmer), 619
lovins() (in module abydos.stemmer), 619

M
Maarel (class in abydos.distance), 253
Manhattan (class in abydos.distance), 256
manhattan() (in module abydos.distance), 258
markedness() (abydos.stats.ConfusionTable method),593
Marking (class in abydos.distance), 44
MarkingMetric (class in abydos.distance), 45
MASI (class in abydos.distance), 265
Matusita (class in abydos.distance), 266
MaxwellPilliner (class in abydos.distance), 268
mcc() (abydos.stats.ConfusionTable method), 593
McConnaughey (class in abydos.distance), 270
McEwenMichael (class in abydos.distance), 271
mean_pairwise_similarity() (in module abydos.stats), 616
median() (in module abydos.stats), 614
MetaLevenshtein (class in abydos.distance), 53
Metaphone (class in abydos.phonetic), 544
metaphone() (in module abydos.phonetic), 545
MetaSoundex (class in abydos.phonetic), 553
metasoundex() (in module abydos.phonetic), 554
IndexAbydos Documentation, Release 0.5.0

Michelet (class in abydos.distance), 259
midrange() (in module abydos.stats), 615
Millar (class in abydos.distance), 261
MinHash (class in abydos.distance), 395
Minkowski (class in abydos.distance), 262
minkowski() (in module abydos.distance), 263
MLIPNS (class in abydos.distance), 69
mode() (in module abydos.stats), 615
MongeElkan (class in abydos.distance), 387
Morisita (class in abydos.distance), 255
Mountford (class in abydos.distance), 273
MRA (class in abydos.distance), 444
MRA (class in abydos.phonetic), 527
mra() (in module abydos.phonetic), 528
mra_compare() (in module abydos.distance), 445
MSContingency (class in abydos.distance), 276
mutual_information()(abydos.stats.ConfusionTable method), 594
MutualInformation (class in abydos.distance), 274

onca() (in module abydos.phonetic), 556
Overlap (class in abydos.distance), 277
Ozbay (class in abydos.distance), 469

P

package_path() (in module abydos.util), 648
paice_husk() (in module abydos.stemmer), 620
PaiceHusk (class in abydos.stemmer), 620
pairwise_similarity_statistics() (in module abydos.stats), 617
paras() (abydos.corpus.Corpus method), 21
parmar_kumbharana()(inmoduleabydos.phonetic), 544
ParmarKumbharana (class in abydos.phonetic), 543
Pattern (class in abydos.distance), 280
PearsonChiSquared (class in abydos.distance), 287
PearsonHeronII (class in abydos.distance), 281
PearsonII (class in abydos.distance), 283
PearsonIII (class in abydos.distance), 285
PearsonPhi (class in abydos.distance), 289
N
Peirce (class in abydos.distance), 291
NCDarith (class in abydos.distance), 433
phi_coefficient() (abydos.stats.ConfusionTable
NCDbwtrle (class in abydos.distance), 435
method), 595
NCDbz2 (class in abydos.distance), 429
Phonem (class in abydos.phonetic), 565
NCDlzma (class in abydos.distance), 431
phonem() (in module abydos.phonetic), 565
NCDlzss (class in abydos.distance), 439
phones_ipa (abydos.distance.ALINE attribute), 59
NCDpaq9a (class in abydos.distance), 438
phones_kondrak (abydos.distance.ALINE attribute),
NCDrle (class in abydos.distance), 43659
NCDzlib (class in abydos.distance), 428
Phonet (class in abydos.phonetic), 566
needleman_wunsch() (in module abydos.distance), phonet() (in module abydos.phonetic), 567
405
Phonetic (class in abydos.fingerprint), 480
NeedlemanWunsch (class in abydos.distance), 403
phonetic_fingerprint() (in module abyneg_likelihood_ratio()(abydos.fingerprint), 481
dos.stats.ConfusionTable method), 594
phonetic_spanish() (in module abydos.phonetic),
NGramCorpus (class in abydos.corpus), 23571
NLTKTokenizer (class in abydos.tokenizer), 647
PhoneticDistance (class in abydos.distance), 442
Norphone (class in abydos.phonetic), 576
PhoneticEditDistance (class in abydos.distance),
norphone() (in module abydos.phonetic), 577
64
npv() (abydos.stats.ConfusionTable method), 595
PhoneticSpanish (class in abydos.phonetic), 570
NRL (class in abydos.phonetic), 552
Phonex (class in abydos.phonetic), 517
nrl() (in module abydos.phonetic), 552
phonex() (in module abydos.phonetic), 518
NYSIIS (class in abydos.phonetic), 525
PHONIC (class in abydos.phonetic), 518
nysiis() (in module abydos.phonetic), 526
Phonix (class in abydos.phonetic), 520
phonix() (in module abydos.phonetic), 521
Opopulation() (abydos.stats.ConfusionTable method),595
Occurrence (class in abydos.fingerprint), 484
Porter(classin abydos.stemmer), 626
occurrence_fingerprint() (in module abyporter()(inmoduleabydos.stemmer), 626
dos.fingerprint), 484
Porter2(classinabydos.stemmer),627
occurrence_halved_fingerprint() (in modporter2()(inmoduleabydos.stemmer),627
ule abydos.fingerprint), 486
pos_likelihood_ratio()(abyOccurrenceHalved (class in abydos.fingerprint), 485
dos.stats.ConfusionTable
method),596
omission_key() (in module abydos.fingerprint), 482
Position (class in abydos.fingerprint), 488
OmissionKey (class in abydos.fingerprint), 482
ONCA (class in abydos.phonetic), 555

Index

689

Abydos Documentation, Release 0.5.0

position_fingerprint() (in module abydos.fingerprint), 489
PositionalQGramDice (class in abydos.distance),
400
PositionalQGramJaccard(classinabydos.distance), 401
PositionalQGramOverlap(classinabydos.distance), 402
pr_aghmean() (abydos.stats.ConfusionTable method),596
pr_agmean() (abydos.stats.ConfusionTable method),596
pr_amean() (abydos.stats.ConfusionTable method),597
pr_cmean() (abydos.stats.ConfusionTable method),597
pr_ghmean() (abydos.stats.ConfusionTable method),598
pr_gmean() (abydos.stats.ConfusionTable method),598
pr_heronian_mean() (abydos.stats.ConfusionTable
method), 598
pr_hmean() (abydos.stats.ConfusionTable method),599
pr_hoelder_mean() (abydos.stats.ConfusionTable
method), 599
pr_imean() (abydos.stats.ConfusionTable method),600
pr_lehmer_mean()(abydos.stats.ConfusionTable
method), 600
pr_lmean() (abydos.stats.ConfusionTable method),600
pr_qmean() (abydos.stats.ConfusionTable method),601
pr_seiffert_mean() (abydos.stats.ConfusionTable
method), 601
precision() (abydos.stats.ConfusionTable method),602
precision_gain()(abydos.stats.ConfusionTable
method), 602
pred_neg_pop()(abydos.stats.ConfusionTable
method), 603
pred_pos_pop()(abydos.stats.ConfusionTable
method), 603
predicted_entropy()(abydos.stats.ConfusionTable method), 603
Prefix (class in abydos.distance), 424
prevalence() (abydos.stats.ConfusionTable method),604
proficiency()(abydos.stats.ConfusionTable
method), 604
pshp_soundex_first()(inmoduleabydos.phonetic), 523
pshp_soundex_last()(inmoduleaby-

690

dos.phonetic), 525
PSHPSoundexFirst (class in abydos.phonetic), 521
PSHPSoundexLast (class in abydos.phonetic), 524

Q
QGram (class in abydos.distance), 292
QGram (class in abydos.fingerprint), 479
qgram_fingerprint()(inmoduleabydos.fingerprint), 480
QGrams (class in abydos.tokenizer), 637
qmean() (in module abydos.stats), 611
QSkipgrams (class in abydos.tokenizer), 638
QuantitativeCosine (class in abydos.distance),
375
QuantitativeDice (class in abydos.distance), 376
QuantitativeJaccard (class in abydos.distance),
377

R
RatcliffObershelp (class in abydos.distance), 419
RaupCrick (class in abydos.distance), 294
raw() (abydos.corpus.Corpus method), 21
recall() (abydos.stats.ConfusionTable method), 604
ReesLevenshtein (class in abydos.distance), 295
refined_soundex() (in module abydos.phonetic),
511
RefinedSoundex (class in abydos.phonetic), 510
RegexpTokenizer (class in abydos.tokenizer), 641
RelaxedHamming (class in abydos.distance), 71
reth_schek_phonetik()(inmoduleabydos.phonetic), 564
RethSchek (class in abydos.phonetic), 563
reward_length_evaluator()(abydos.distance.Sift4Extended static method),460
reward_length_evaluator_exp()(abydos.distance.Sift4Extended static method),460
RLE (class in abydos.compression), 16
rle_decode() (in module abydos.compression), 17
rle_encode() (in module abydos.compression), 18
Roberts (class in abydos.distance), 378
roger_root() (in module abydos.phonetic), 540
RogerRoot (class in abydos.phonetic), 539
RogersTanimoto (class in abydos.distance), 296
RogotGoldberg (class in abydos.distance), 298
RougeL (class in abydos.distance), 397
RougeS (class in abydos.distance), 399
RougeSU (class in abydos.distance), 400
RougeW (class in abydos.distance), 398
russell_index() (in module abydos.phonetic), 506
russell_index_alpha()(inmoduleabydos.phonetic), 507

IndexAbydos Documentation, Release 0.5.0

russell_index_num_to_alpha() (in module
abydos.phonetic), 506
RussellIndex (class in abydos.phonetic), 505
RussellRao (class in abydos.distance), 299Ss_stemmer() (in module abydos.stemmer), 623
salience (abydos.distance.ALINE attribute), 59
SAPS (class in abydos.distance), 51
SAPSTokenizer (class in abydos.tokenizer), 645
save_corpus()(abydos.corpus.UnigramCorpus
method), 25
sb_danish() (in module abydos.stemmer), 629
sb_dutch() (in module abydos.stemmer), 629
sb_german() (in module abydos.stemmer), 631
sb_norwegian() (in module abydos.stemmer), 631
sb_swedish() (in module abydos.stemmer), 632
Schinke (class in abydos.stemmer), 624
schinke() (in module abydos.stemmer), 625
ScottPi (class in abydos.distance), 300
seiffert_mean() (in module abydos.stats), 614
sents() (abydos.corpus.Corpus method), 22
set_probs()(abydos.compression.Arithmetic
method), 11
SfinxBis (class in abydos.phonetic), 573
sfinxbis() (in module abydos.phonetic), 574
Shape (class in abydos.distance), 302
ShapiraStorerI (class in abydos.distance), 42
Sift4 (class in abydos.distance), 457
sift4_common() (in module abydos.distance), 461
sift4_matching_evaluator()(abydos.distance.Sift4Extended static method),460
sift4_simplest() (in module abydos.distance), 461
sift4_token_matcher()(abydos.distance.Sift4Extended static method),461
Sift4Extended (class in abydos.distance), 459
Sift4Simplest (class in abydos.distance), 458
significance()(abydos.stats.ConfusionTable
method), 605
sim() (abydos.distance.ALINE method), 59
sim() (abydos.distance.AMPLE method), 84
sim() (abydos.distance.Anderberg method), 87
sim() (abydos.distance.AndresMarzoDelta method), 89
sim() (abydos.distance.AZZOO method), 85
sim() (abydos.distance.BaroniUrbaniBuserI method),91
sim() (abydos.distance.BaroniUrbaniBuserII method),92
sim() (abydos.distance.BaulieuII method), 97
sim() (abydos.distance.Baystat method), 450
sim() (abydos.distance.BeniniI method), 115
sim() (abydos.distance.BeniniII method), 117
Indexsim() (abydos.distance.Bennet method), 119
sim() (abydos.distance.BISIM method), 62
sim() (abydos.distance.BLEU method), 396
sim() (abydos.distance.BrainerdRobinson method), 374
sim() (abydos.distance.BraunBlanquet method), 120
sim() (abydos.distance.Cao method), 123
sim() (abydos.distance.ChaoDice method), 124
sim() (abydos.distance.ChaoJaccard method), 125
sim() (abydos.distance.Chebyshev method), 128
sim() (abydos.distance.Clement method), 131
sim() (abydos.distance.CohenKappa method), 133
sim() (abydos.distance.Cole method), 135
sim() (abydos.distance.ConsonniTodeschiniI method),136
sim() (abydos.distance.ConsonniTodeschiniII method),
137
sim()
(abydos.distance.ConsonniTodeschiniIII
method), 139
sim() (abydos.distance.ConsonniTodeschiniIV method),140
sim() (abydos.distance.ConsonniTodeschiniV method),142
sim() (abydos.distance.Cosine method), 143
sim() (abydos.distance.Dennis method), 146
sim() (abydos.distance.Dice method), 148
sim() (abydos.distance.DiceAsymmetricI method), 150
sim() (abydos.distance.DiceAsymmetricII method), 151
sim() (abydos.distance.Digby method), 153
sim() (abydos.distance.Dispersion method), 155
sim() (abydos.distance.Doolittle method), 156
sim() (abydos.distance.Dunning method), 157
sim() (abydos.distance.Eyraud method), 162
sim() (abydos.distance.FagerMcGowan method), 164
sim() (abydos.distance.Faith method), 166
sim() (abydos.distance.FellegiSunter method), 394
sim() (abydos.distance.Fidelity method), 166
sim() (abydos.distance.Fleiss method), 168
sim() (abydos.distance.FleissLevinPaik method), 170
sim() (abydos.distance.ForbesI method), 171
sim() (abydos.distance.ForbesII method), 173
sim() (abydos.distance.Fossum method), 174
sim()(abydos.distance.FuzzyWuzzyPartialString
method), 440
sim() (abydos.distance.FuzzyWuzzyTokenSet method),442
sim() (abydos.distance.FuzzyWuzzyTokenSort method),441
sim() (abydos.distance.GeneralizedFleiss method), 177
sim() (abydos.distance.Gilbert method), 179
sim() (abydos.distance.GilbertWells method), 180
sim() (abydos.distance.GiniI method), 183
sim() (abydos.distance.GiniII method), 184
sim() (abydos.distance.Goodall method), 186

691

Abydos Documentation, Release 0.5.0(abydos.distance.GoodmanKruskalLambda
method), 187
sim()(abydos.distance.GoodmanKruskalLambdaR
method), 188
sim()(abydos.distance.GoodmanKruskalTauA
method), 190
sim()(abydos.distance.GoodmanKruskalTauB
method), 191
sim() (abydos.distance.Gotoh method), 408
sim() (abydos.distance.GowerLegendre method), 192
sim() (abydos.distance.Guth method), 473
sim() (abydos.distance.GuttmanLambdaA method),193
sim() (abydos.distance.GuttmanLambdaB method),195
sim() (abydos.distance.GwetAC method), 196
sim() (abydos.distance.Hamann method), 198
sim() (abydos.distance.HarrisLahey method), 200
sim() (abydos.distance.HawkinsDotson method), 202
sim() (abydos.distance.HornMorisita method), 205
sim() (abydos.distance.Hurlbert method), 207
sim() (abydos.distance.Ident method), 421
sim() (abydos.distance.ISG method), 471
sim() (abydos.distance.IterativeSubString method), 83
sim() (abydos.distance.Jaccard method), 209
sim() (abydos.distance.JaccardNM method), 212
sim() (abydos.distance.JaroWinkler method), 77
sim() (abydos.distance.Johnson method), 213
sim() (abydos.distance.KendallTau method), 216
sim() (abydos.distance.KentFosterI method), 217
sim() (abydos.distance.KentFosterII method), 219
sim() (abydos.distance.KoppenI method), 221
sim() (abydos.distance.KoppenII method), 222
sim() (abydos.distance.KuderRichardson method), 225
sim() (abydos.distance.KuhnsI method), 227
sim() (abydos.distance.KuhnsII method), 229
sim() (abydos.distance.KuhnsIII method), 231
sim() (abydos.distance.KuhnsIV method), 233
sim() (abydos.distance.KuhnsIX method), 243
sim() (abydos.distance.KuhnsV method), 235
sim() (abydos.distance.KuhnsVI method), 237
sim() (abydos.distance.KuhnsVII method), 239
sim() (abydos.distance.KuhnsVIII method), 241
sim() (abydos.distance.KuhnsX method), 245
sim() (abydos.distance.KuhnsXI method), 247
sim() (abydos.distance.KuhnsXII method), 248
sim() (abydos.distance.KulczynskiI method), 250
sim() (abydos.distance.KulczynskiII method), 251
sim() (abydos.distance.LCPrefix method), 417
sim() (abydos.distance.LCSseq method), 411
sim() (abydos.distance.LCSstr method), 414
sim() (abydos.distance.LCSuffix method), 419
sim() (abydos.distance.Length method), 423
sim() (abydos.distance.LIG3 method), 475
sim()

692

sim() (abydos.distance.Maarel method), 254
sim() (abydos.distance.MASI method), 266
sim() (abydos.distance.MaxwellPilliner method), 269
sim() (abydos.distance.McConnaughey method), 271
sim() (abydos.distance.McEwenMichael method), 272
sim() (abydos.distance.Michelet method), 260
sim() (abydos.distance.Millar method), 262
sim() (abydos.distance.MinHash method), 395
sim() (abydos.distance.MLIPNS method), 69
sim() (abydos.distance.MongeElkan method), 388
sim() (abydos.distance.Morisita method), 256
sim() (abydos.distance.Mountford method), 274
sim() (abydos.distance.MRA method), 445
sim() (abydos.distance.MSContingency method), 277
sim() (abydos.distance.MutualInformation method),275
sim() (abydos.distance.NeedlemanWunsch method),403
sim() (abydos.distance.Overlap method), 278
sim() (abydos.distance.PearsonChiSquared method),288
sim() (abydos.distance.PearsonHeronII method), 282
sim() (abydos.distance.PearsonII method), 284
sim() (abydos.distance.PearsonIII method), 286
sim() (abydos.distance.PearsonPhi method), 290
sim() (abydos.distance.Peirce method), 292
sim() (abydos.distance.PositionalQGramDice method),
401
sim()
(abydos.distance.PositionalQGramJaccard
method), 402
sim()(abydos.distance.PositionalQGramOverlap
method), 402
sim() (abydos.distance.Prefix method), 425
sim() (abydos.distance.QuantitativeCosine method),375
sim() (abydos.distance.QuantitativeDice method), 376
sim() (abydos.distance.QuantitativeJaccard method),377
sim() (abydos.distance.RatcliffObershelp method), 420
sim() (abydos.distance.RaupCrick method), 295
sim() (abydos.distance.Roberts method), 378
sim() (abydos.distance.RogersTanimoto method), 297
sim() (abydos.distance.RogotGoldberg method), 299
sim() (abydos.distance.RougeL method), 397
sim() (abydos.distance.RougeS method), 399
sim() (abydos.distance.RougeSU method), 400
sim() (abydos.distance.RougeW method), 398
sim() (abydos.distance.RussellRao method), 300
sim() (abydos.distance.SAPS method), 52
sim() (abydos.distance.ScottPi method), 302
sim() (abydos.distance.SmithWaterman method), 406
sim() (abydos.distance.SoftCosine method), 387
sim() (abydos.distance.SoftTFIDF method), 391
sim() (abydos.distance.SokalMichener method), 305

Index

Abydos Documentation, Release 0.5.0

sim() (abydos.distance.SokalSneathI method), 307
sim() (abydos.distance.SokalSneathII method), 308
sim() (abydos.distance.SokalSneathIII method), 309
sim() (abydos.distance.SokalSneathIV method), 311
sim() (abydos.distance.SokalSneathV method), 312
sim() (abydos.distance.Sorgenfrei method), 313
sim() (abydos.distance.SSK method), 476
sim() (abydos.distance.Steffensen method), 315
sim() (abydos.distance.Stiles method), 316
sim() (abydos.distance.Strcmp95 method), 80
sim() (abydos.distance.StuartTau method), 318
sim() (abydos.distance.Suffix method), 426
sim() (abydos.distance.Tarantula method), 320
sim() (abydos.distance.Tarwid method), 321
sim() (abydos.distance.Tetrachoric method), 323
sim() (abydos.distance.TFIDF method), 390
sim() (abydos.distance.TullossR method), 324
sim() (abydos.distance.TullossS method), 325
sim() (abydos.distance.TullossT method), 326
sim() (abydos.distance.TullossU method), 328
sim() (abydos.distance.Tversky method), 329
sim() (abydos.distance.UnigramSubtuple method), 332
sim() (abydos.distance.UnknownA method), 334
sim() (abydos.distance.UnknownB method), 335
sim() (abydos.distance.UnknownC method), 336
sim() (abydos.distance.UnknownD method), 338
sim() (abydos.distance.UnknownE method), 339
sim() (abydos.distance.UnknownF method), 341
sim() (abydos.distance.UnknownG method), 342
sim() (abydos.distance.UnknownH method), 344
sim() (abydos.distance.UnknownI method), 345
sim() (abydos.distance.UnknownJ method), 347
sim() (abydos.distance.UnknownL method), 350
sim() (abydos.distance.UnknownM method), 352
sim() (abydos.distance.Upholt method), 353
sim() (abydos.distance.VPS method), 474
sim() (abydos.distance.WarrensI method), 355
sim() (abydos.distance.WarrensII method), 356
sim() (abydos.distance.WarrensIII method), 358
sim() (abydos.distance.WarrensIV method), 359
sim() (abydos.distance.WarrensV method), 360
sim() (abydos.distance.WeightedJaccard method), 362
sim() (abydos.distance.Whittaker method), 363
sim() (abydos.distance.YatesChiSquared method), 364
sim() (abydos.distance.YuleQ method), 366
sim() (abydos.distance.YuleY method), 370
sim() (in module abydos.distance), 34
sim_bag() (in module abydos.distance), 385
sim_baystat() (in module abydos.distance), 451
sim_cosine() (in module abydos.distance), 144
sim_damerau() (in module abydos.distance), 42
sim_dice() (in module abydos.distance), 149
sim_editex() (in module abydos.distance), 449
sim_euclidean() (in module abydos.distance), 161

Index

sim_eudex() (in module abydos.distance), 456
sim_hamming() (in module abydos.distance), 68
sim_ident() (in module abydos.distance), 422
sim_indel() (in module abydos.distance), 51
sim_jaccard() (in module abydos.distance), 210
sim_jaro_winkler() (in module abydos.distance),79
sim_lcsseq() (in module abydos.distance), 412
sim_lcsstr() (in module abydos.distance), 415
sim_length() (in module abydos.distance), 424
sim_levenshtein() (in module abydos.distance),38
sim_manhattan() (in module abydos.distance), 259
sim_matrix() (abydos.distance.NeedlemanWunschstatic method), 404
sim_minkowski() (in module abydos.distance), 265
sim_mlipns() (in module abydos.distance), 70
sim_monge_elkan() (in module abydos.distance),389
sim_mra() (in module abydos.distance), 446
sim_ncd_arith() (in module abydos.distance), 434
sim_ncd_bwtrle() (in module abydos.distance), 436
sim_ncd_bz2() (in module abydos.distance), 431
sim_ncd_lzma() (in module abydos.distance), 432
sim_ncd_rle() (in module abydos.distance), 438
sim_ncd_zlib() (in module abydos.distance), 429
sim_overlap() (in module abydos.distance), 279
sim_prefix() (in module abydos.distance), 426
sim_ratcliff_obershelp() (in module abydos.distance), 421
sim_score() (abydos.distance.ALINE method), 59
sim_score() (abydos.distance.Anderberg method), 88
sim_score() (abydos.distance.AZZOO method), 86
sim_score()(abydos.distance.BrainerdRobinson
method), 374
sim_score() (abydos.distance.ChaoDice method),124
sim_score() (abydos.distance.ChaoJaccard method),126
sim_score() (abydos.distance.Dennis method), 146
sim_score() (abydos.distance.Dunning method), 158
sim_score() (abydos.distance.Eyraud method), 163
sim_score()(abydos.distance.FagerMcGowan
method), 164
sim_score() (abydos.distance.FellegiSunter method),394
sim_score() (abydos.distance.ForbesI method), 171
sim_score() (abydos.distance.Fossum method), 175
sim_score() (abydos.distance.GilbertWells method),181
sim_score() (abydos.distance.Gotoh method), 409
sim_score() (abydos.distance.Guth method), 474
sim_score() (abydos.distance.JaccardNM method),
212

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sim_score() (abydos.distance.Johnson method), 214
sim_score() (abydos.distance.KentFosterI method),217
sim_score() (abydos.distance.KentFosterII method),219
sim_score() (abydos.distance.KoppenII method), 223
sim_score() (abydos.distance.KuhnsXII method),249
sim_score() (abydos.distance.KulczynskiI method),250
sim_score() (abydos.distance.Morisita method), 256
sim_score()(abydos.distance.MutualInformation
method), 275
sim_score()(abydos.distance.NeedlemanWunschmethod), 405
sim_score() (abydos.distance.PearsonChiSquared
method), 288
sim_score() (abydos.distance.PearsonII method),284
sim_score() (abydos.distance.SAPS method), 52
sim_score()(abydos.distance.SmithWaterman
method), 407
sim_score()(abydos.distance.SokalSneathIII
method), 309
sim_score() (abydos.distance.SSK method), 477
sim_score() (abydos.distance.Stiles method), 317
sim_score()(abydos.distance.UnigramSubtuple
method), 332
sim_score() (abydos.distance.UnknownF method),341
sim_score() (abydos.distance.UnknownH method),344
sim_score() (abydos.distance.UnknownJ method),347
sim_score() (abydos.distance.UnknownM method),352
sim_score() (abydos.distance.WarrensV method),361
sim_score()(abydos.distance.YatesChiSquared
method), 365
sim_sift4() (in module abydos.distance), 462
sim_strcmp95() (in module abydos.distance), 81
sim_suffix() (in module abydos.distance), 427
sim_tversky() (in module abydos.distance), 330
sim_typo() (in module abydos.distance), 467
SingleLinkage (class in abydos.distance), 380
Size (class in abydos.distance), 303
skeleton_key() (in module abydos.fingerprint), 483
SkeletonKey (class in abydos.fingerprint), 483
smith_waterman() (in module abydos.distance), 407
SmithWaterman (class in abydos.distance), 406
SnowballDanish (class in abydos.stemmer), 628
SnowballDutch (class in abydos.stemmer), 629
SnowballGerman (class in abydos.stemmer), 630

694

SnowballNorwegian (class in abydos.stemmer), 631
SnowballSwedish (class in abydos.stemmer), 632
SoftCosine (class in abydos.distance), 386
SoftTFIDF (class in abydos.distance), 390
SokalMichener (class in abydos.distance), 305
SokalSneathI (class in abydos.distance), 306
SokalSneathII (class in abydos.distance), 307
SokalSneathIII (class in abydos.distance), 308
SokalSneathIV (class in abydos.distance), 310
SokalSneathV (class in abydos.distance), 311
SonoriPyTokenizer (class in abydos.tokenizer), 645
Sorgenfrei (class in abydos.distance), 312
sound_d() (in module abydos.phonetic), 543
SoundD (class in abydos.phonetic), 541
Soundex (class in abydos.phonetic), 507
soundex() (in module abydos.phonetic), 509
soundex_br() (in module abydos.phonetic), 569
SoundexBR (class in abydos.phonetic), 568
spanish_metaphone()(inmoduleabydos.phonetic), 572
SpanishMetaphone (class in abydos.phonetic), 571
specificity()(abydos.stats.ConfusionTable
method), 605
SPFC (class in abydos.phonetic), 536
spfc() (in module abydos.phonetic), 538
SSK (class in abydos.distance), 476
SStemmer (class in abydos.stemmer), 622
statistics_canada()(inmoduleabydos.phonetic), 541
StatisticsCanada (class in abydos.phonetic), 540
std() (in module abydos.stats), 615
Steffensen (class in abydos.distance), 314
stem() (abydos.stemmer.Caumanns method), 624
stem() (abydos.stemmer.CLEFGerman method), 633
stem() (abydos.stemmer.CLEFGermanPlus method),634
stem() (abydos.stemmer.CLEFSwedish method), 635
stem() (abydos.stemmer.Lovins method), 619
stem() (abydos.stemmer.PaiceHusk method), 620
stem() (abydos.stemmer.Porter method), 626
stem() (abydos.stemmer.Porter2 method), 627
stem() (abydos.stemmer.Schinke method), 625
stem() (abydos.stemmer.SnowballDanish method), 628
stem() (abydos.stemmer.SnowballDutch method), 629
stem() (abydos.stemmer.SnowballGerman method),630
stem() (abydos.stemmer.SnowballNorwegian method),631
stem() (abydos.stemmer.SnowballSwedish method),632
stem() (abydos.stemmer.SStemmer method), 623
stem() (abydos.stemmer.UEALite method), 621
Stiles (class in abydos.distance), 315

Index

Abydos Documentation, Release 0.5.0

str_fingerprint() (in module abydos.fingerprint),
479
Strcmp95 (class in abydos.distance), 80
String (class in abydos.fingerprint), 478
StuartTau (class in abydos.distance), 317
Suffix (class in abydos.distance), 426
Synoname (class in abydos.distance), 467
synoname() (in module abydos.distance), 469
synoname_toolcode()(inmoduleabydos.fingerprint), 490
SynonameToolcode (class in abydos.fingerprint), 489

TullossS (class in abydos.distance), 325
TullossT (class in abydos.distance), 326
TullossU (class in abydos.distance), 327
Tversky (class in abydos.distance), 328
Typo (class in abydos.distance), 463
typo() (in module abydos.distance), 465

U

UEALite (class in abydos.stemmer), 621
uealite() (in module abydos.stemmer), 622
UnigramCorpus (class in abydos.corpus), 24
UnigramSubtuple (class in abydos.distance), 331
T
UnknownA (class in abydos.distance), 332
tanimoto() (in module abydos.distance), 211
UnknownB (class in abydos.distance), 334
tanimoto_coeff()(abydos.distance.Jaccard UnknownC (class in abydos.distance), 335
method), 209
UnknownD (class in abydos.distance), 337
Tarantula (class in abydos.distance), 319
UnknownE (class in abydos.distance), 338
Tarwid (class in abydos.distance), 320
UnknownF (class in abydos.distance), 340
Tetrachoric (class in abydos.distance), 322
UnknownG (class in abydos.distance), 342
TFIDF (class in abydos.distance), 389
UnknownH (class in abydos.distance), 343
Tichy (class in abydos.distance), 72
UnknownI (class in abydos.distance), 345
to_dict() (abydos.stats.ConfusionTable method), 606 UnknownJ (class in abydos.distance), 346
to_tuple() (abydos.stats.ConfusionTable method), UnknownK (class in abydos.distance), 348
606
UnknownL (class in abydos.distance), 349
tokenize()(abydos.tokenizer.CharacterTokenizer UnknownM (class in abydos.distance), 351
method), 640
Upholt (class in abydos.distance), 352
tokenize() (abydos.tokenizer.COrVClusterTokenizerVmethod), 643
tokenize()(abydos.tokenizer.CVClusterTokenizer v_features (abydos.distance.ALINE attribute), 60
method), 644
var() (in module abydos.stats), 616
tokenize()(abydos.tokenizer.LegaliPyTokenizer VCClusterTokenizer (class in abydos.tokenizer),
method), 646
644
tokenize()(abydos.tokenizer.NLTKTokenizer VPS (class in abydos.distance), 474
method), 648
tokenize() (abydos.tokenizer.QGrams method), 638
Wtokenize() (abydos.tokenizer.QSkipgrams method), Waahlin (class in abydos.phonetic), 575
640
WarrensI (class in abydos.distance), 354
tokenize()(abydos.tokenizer.RegexpTokenizer WarrensII (class in abydos.distance), 355
method), 641
WarrensIII (class in abydos.distance), 356
tokenize()(abydos.tokenizer.SAPSTokenizer WarrensIV (class in abydos.distance), 358
method), 645
WarrensV (class in abydos.distance), 359
tokenize()(abydos.tokenizer.SonoriPyTokenizer WeightedJaccard (class in abydos.distance), 361
method), 646
WhitespaceTokenizer (class in abydos.tokenizer),
tokenize()(abydos.tokenizer.VCClusterTokenizer
641
method), 644
Whittaker (class in abydos.distance), 362
train() (abydos.compression.Arithmetic method), 11
wlcs() (abydos.distance.RougeW method), 399
train_onsets()(aby- WordpunctTokenizer (class in abydos.tokenizer),
dos.tokenizer.LegaliPyTokenizer
method),642
647
words() (abydos.corpus.Corpus method), 22
true_neg() (abydos.stats.ConfusionTable method),606
Y
true_pos() (abydos.stats.ConfusionTable method),
YatesChiSquared (class in abydos.distance), 363
607
YJHHR (class in abydos.distance), 370
TullossR (class in abydos.distance), 323

Index

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Abydos Documentation, Release 0.5.0

YujianBo (class in abydos.distance), 46
YuleQ (class in abydos.distance), 365
YuleQII (class in abydos.distance), 367
YuleY (class in abydos.distance), 369

696

Index