# Source code for abydos.distance._bisim

# 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._bisim.

BI-SIM similarity
"""

from numpy import float as np_float
from numpy import zeros as np_zeros

from ._distance import _Distance

__all__ = ['BISIM']

[docs]class BISIM(_Distance):
r"""BI-SIM similarity.

BI-SIM similarity :cite:Kondrak:2003 is an n-gram based, edit-distance
derived similarity measure.

.. versionadded:: 0.4.0
"""

def __init__(self, qval=2, **kwargs):
"""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

.. versionadded:: 0.4.0

"""
super(BISIM, self).__init__(**kwargs)
self._qval = qval

[docs]    def sim(self, src, tar):
"""Return the BI-SIM similarity of two strings.

Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison

Returns
-------
float
BI-SIM similarity

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

.. versionadded:: 0.4.0

"""
src_len = len(src)
tar_len = len(tar)

if src == tar:
return 1.0
if not src or not tar:
return 0.0

def _id(src_pos, tar_pos):
s = 0
for i in range(self._qval):
s += int(src[src_pos + i] == tar[tar_pos + i])
return s / self._qval

src = src[0].swapcase() * (self._qval - 1) + src
tar = tar[0].swapcase() * (self._qval - 1) + tar

d_mat = np_zeros((src_len + 1, tar_len + 1), dtype=np_float)

for i in range(1, src_len + 1):
for j in range(1, tar_len + 1):
d_mat[i, j] = max(
d_mat[i - 1, j - 1] + _id(i - 1, j - 1),  # sub/==
d_mat[i - 1, j],  # ins
d_mat[i, j - 1],  # del
)
return d_mat[src_len, tar_len] / max(src_len, tar_len)

if __name__ == '__main__':
import doctest

doctest.testmod()