# Copyright 2014-2020 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._gotoh.
Gotoh score
"""
from deprecation import deprecated
from numpy import float32 as np_float32
from numpy import zeros as np_zeros
from ._ident import sim_ident
from ._needleman_wunsch import NeedlemanWunsch
from .. import __version__
__all__ = ['Gotoh', 'gotoh']
[docs]class Gotoh(NeedlemanWunsch):
"""Gotoh score.
The Gotoh score :cite:`Gotoh:1982` is essentially Needleman-Wunsch with
affine gap penalties.
.. versionadded:: 0.3.6
"""
def __init__(self, gap_open=1, gap_ext=0.4, sim_func=None, **kwargs):
"""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
.. versionadded:: 0.4.0
"""
super(Gotoh, self).__init__(**kwargs)
self._gap_open = gap_open
self._gap_ext = gap_ext
self._sim_func = sim_func
if self._sim_func is None:
self._sim_func = NeedlemanWunsch.sim_matrix
[docs] def sim_score(self, src, tar):
"""Return the Gotoh score of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
Gotoh score
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
.. versionadded:: 0.1.0
.. versionchanged:: 0.3.6
Encapsulated in class
"""
d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
p_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
q_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
d_mat[0, 0] = 0
p_mat[0, 0] = float('-inf')
q_mat[0, 0] = float('-inf')
for i in range(1, len(src) + 1):
d_mat[i, 0] = float('-inf')
p_mat[i, 0] = -self._gap_open - self._gap_ext * (i - 1)
q_mat[i, 0] = float('-inf')
if len(tar) > 1:
q_mat[i, 1] = -self._gap_open
for j in range(1, len(tar) + 1):
d_mat[0, j] = float('-inf')
p_mat[0, j] = float('-inf')
if len(src) > 1:
p_mat[1, j] = -self._gap_open
q_mat[0, j] = -self._gap_open - self._gap_ext * (j - 1)
for i in range(1, len(src) + 1):
for j in range(1, len(tar) + 1):
sim_val = self._sim_func(src[i - 1], tar[j - 1])
d_mat[i, j] = max(
d_mat[i - 1, j - 1] + sim_val,
p_mat[i - 1, j - 1] + sim_val,
q_mat[i - 1, j - 1] + sim_val,
)
p_mat[i, j] = max(
d_mat[i - 1, j] - self._gap_open,
p_mat[i - 1, j] - self._gap_ext,
)
q_mat[i, j] = max(
d_mat[i, j - 1] - self._gap_open,
q_mat[i, j - 1] - self._gap_ext,
)
i, j = (n - 1 for n in d_mat.shape)
return max(d_mat[i, j], p_mat[i, j], q_mat[i, j])
[docs] def sim(self, src, tar):
"""Return the normalized Gotoh score of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
Normalized Gotoh score
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
.. versionadded:: 0.4.1
"""
if src == tar:
return 1.0
return max(0.0, self.sim_score(src, tar)) / (
self.sim_score(src, src) ** 0.5 * self.sim_score(tar, tar) ** 0.5
)
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the Gotoh.dist_abs method instead.',
)
def gotoh(src, tar, gap_open=1, gap_ext=0.4, sim_func=sim_ident):
"""Return the Gotoh score of two strings.
This is a wrapper for :py:meth:`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
-------
float
Gotoh score
Examples
--------
>>> gotoh('cat', 'hat')
2.0
>>> gotoh('Niall', 'Neil')
1.0
>>> round(gotoh('aluminum', 'Catalan'), 12)
-0.4
>>> gotoh('cat', 'hat')
2.0
.. versionadded:: 0.1.0
"""
return Gotoh(gap_open, gap_ext, sim_func).sim_score(src, tar)
if __name__ == '__main__':
import doctest
doctest.testmod()