Source code for abydos.distance._pearson_heron_ii

# -*- coding: utf-8 -*-

# Copyright 2018-2019 by Christopher C. Little.
# This file is part of Abydos.
#
# 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.
#
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"""abydos.distance._pearson_heron_ii.

Pearson & Heron II correlation
"""

from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)

from math import cos, pi

from ._token_distance import _TokenDistance

__all__ = ['PearsonHeronII']


[docs]class PearsonHeronII(_TokenDistance): r"""Pearson & Heron II correlation. For two sets X and Y and a population N, Pearson & Heron II correlation :cite:`Pearson:1913` is .. math:: corr_{PearsonHeronII}(X, Y) = \cos \Big(\frac{\pi\sqrt{|X \setminus Y| \cdot |Y \setminus X|}} {\sqrt{|X \cap Y| \cdot |(N \setminus X) \setminus Y|} + \sqrt{|X \setminus Y| \cdot |Y \setminus X|}}\Big) In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: corr_{PearsonHeronII} = \cos \Big(\frac{\pi\sqrt{bc}}{\sqrt{ad}+\sqrt{bc}}\Big) .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize PearsonHeronII instance. Parameters ---------- alphabet : Counter, collection, int, or None This represents the alphabet of possible tokens. See :ref:`alphabet <alphabet>` description in :py:class:`_TokenDistance` for details. tokenizer : _Tokenizer A tokenizer instance from the :py:mod:`abydos.tokenizer` package intersection_type : str Specifies the intersection type, and set type as a result: See :ref:`intersection_type <intersection_type>` description in :py:class:`_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. .. versionadded:: 0.4.0 """ super(PearsonHeronII, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, 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 ------- float Pearson & Heron II correlation 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 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 if not src or not tar: return -1.0 self._tokenize(src, tar) root_ad = ( self._intersection_card() * self._total_complement_card() ) ** 0.5 root_bc = (self._src_only_card() * self._tar_only_card()) ** 0.5 num = pi * root_bc return cos((num / (root_ad + root_bc)) if num else 0.0)
[docs] def sim(self, 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 ------- float Pearson & Heron II similarity 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 .. versionadded:: 0.4.0 """ return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__': import doctest doctest.testmod()