# Copyright 2019-2020 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.
#
# Abydos is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Abydos. If not, see <http://www.gnu.org/licenses/>.
"""abydos.distance._millar.
Millar's binomial deviance dissimilarity
"""
from math import log
from ._token_distance import _TokenDistance
__all__ = ['Millar']
[docs]class Millar(_TokenDistance):
r"""Millar's binomial deviance dissimilarity.
For two sets X and Y drawn from a population S, Millar's binomial deviance
dissimilarity :cite:`Anderson:2004` is:
.. math::
dist_{Millar}(X, Y) = \sum_{i=0}^{|S|} \frac{1}{x_i+y_i}
\bigg\{x_i log(\frac{x_i}{x_i+y_i}) + y_i log(\frac{y_i}{x_i+y_i})
- (x_i+y_i) log(\frac{1}{2})\bigg\}
.. versionadded:: 0.4.1
"""
def __init__(self, **kwargs):
"""Initialize Millar instance.
Parameters
----------
**kwargs
Arbitrary keyword arguments
.. versionadded:: 0.4.1
"""
super(Millar, self).__init__(**kwargs)
[docs] def dist_abs(self, 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
-------
float
Millar's binomial deviance dissimilarity
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
.. versionadded:: 0.4.1
"""
self._tokenize(src, tar)
src_tok = self._src_tokens
tar_tok = self._tar_tokens
alphabet = set(src_tok.keys() | tar_tok.keys())
log2 = log(2)
score = 0
for tok in alphabet:
n_k = src_tok[tok] + tar_tok[tok]
src_val = 0
if src_tok[tok]:
src_val = src_tok[tok] * log(src_tok[tok] / n_k)
tar_val = 0
if tar_tok[tok]:
tar_val = tar_tok[tok] * log(tar_tok[tok] / n_k)
score += (src_val + tar_val + n_k * log2) / n_k
if score > 0:
return score
return 0.0
[docs] def sim(self, *args, **kwargs):
"""Raise exception when called.
Parameters
----------
*args
Variable length argument list
**kwargs
Arbitrary keyword arguments
Raises
------
NotImplementedError
Method disabled for Millar dissimilarity.
.. versionadded:: 0.3.6
"""
raise NotImplementedError('Method disabled for Millar dissimilarity.')
[docs] def dist(self, *args, **kwargs):
"""Raise exception when called.
Parameters
----------
*args
Variable length argument list
**kwargs
Arbitrary keyword arguments
Raises
------
NotImplementedError
Method disabled for Millar dissimilarity.
.. versionadded:: 0.3.6
"""
raise NotImplementedError('Method disabled for Millar dissimilarity.')
if __name__ == '__main__':
import doctest
doctest.testmod()