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# -*- coding: utf-8 -*- |
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# Copyright 2014-2018 by Christopher C. Little. |
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# This file is part of Abydos. |
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# |
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# Abydos is free software: you can redistribute it and/or modify |
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# it under the terms of the GNU General Public License as published by |
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# the Free Software Foundation, either version 3 of the License, or |
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# (at your option) any later version. |
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# |
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# Abydos is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU General Public License for more details. |
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# |
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# You should have received a copy of the GNU General Public License |
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# along with Abydos. If not, see <http://www.gnu.org/licenses/>. |
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"""abydos.distance._smith_waterman. |
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Smith-Waterman score |
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""" |
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from __future__ import ( |
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absolute_import, |
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division, |
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print_function, |
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unicode_literals, |
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) |
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from numpy import float32 as np_float32 |
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from numpy import zeros as np_zeros |
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from six.moves import range |
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from ._ident import sim_ident |
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from ._needleman_wunsch import NeedlemanWunsch |
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__all__ = ['SmithWaterman', 'smith_waterman'] |
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class SmithWaterman(NeedlemanWunsch): |
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"""Smith-Waterman score. |
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The Smith-Waterman score :cite:`Smith:1981` is a standard edit distance |
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measure, differing from Needleman-Wunsch in that it focuses on local |
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alignment and disallows negative scores. |
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""" |
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View Code Duplication |
def dist_abs(self, src, tar, gap_cost=1, sim_func=sim_ident): |
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"""Return the Smith-Waterman score of two strings. |
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Parameters |
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---------- |
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src : str |
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Source string for comparison |
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tar : str |
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Target string for comparison |
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gap_cost : float |
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The cost of an alignment gap (1 by default) |
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sim_func : function |
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A function that returns the similarity of two characters (identity |
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similarity by default) |
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Returns |
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------- |
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float |
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Smith-Waterman score |
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Examples |
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-------- |
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>>> cmp = SmithWaterman() |
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>>> cmp.dist_abs('cat', 'hat') |
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2.0 |
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>>> cmp.dist_abs('Niall', 'Neil') |
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1.0 |
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>>> cmp.dist_abs('aluminum', 'Catalan') |
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0.0 |
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>>> cmp.dist_abs('ATCG', 'TAGC') |
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1.0 |
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""" |
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d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32) |
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for i in range(len(src) + 1): |
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d_mat[i, 0] = 0 |
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for j in range(len(tar) + 1): |
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d_mat[0, j] = 0 |
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for i in range(1, len(src) + 1): |
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for j in range(1, len(tar) + 1): |
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match = d_mat[i - 1, j - 1] + sim_func(src[i - 1], tar[j - 1]) |
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delete = d_mat[i - 1, j] - gap_cost |
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insert = d_mat[i, j - 1] - gap_cost |
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d_mat[i, j] = max(0, match, delete, insert) |
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return d_mat[d_mat.shape[0] - 1, d_mat.shape[1] - 1] |
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def smith_waterman(src, tar, gap_cost=1, sim_func=sim_ident): |
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"""Return the Smith-Waterman score of two strings. |
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This is a wrapper for :py:meth:`SmithWaterman.dist_abs`. |
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Parameters |
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---------- |
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src : str |
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Source string for comparison |
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tar : str |
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Target string for comparison |
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gap_cost : float |
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The cost of an alignment gap (1 by default) |
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sim_func : function |
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A function that returns the similarity of two characters (identity |
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similarity by default) |
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Returns |
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------- |
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float |
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Smith-Waterman score |
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Examples |
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-------- |
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>>> smith_waterman('cat', 'hat') |
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2.0 |
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>>> smith_waterman('Niall', 'Neil') |
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1.0 |
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>>> smith_waterman('aluminum', 'Catalan') |
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0.0 |
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>>> smith_waterman('ATCG', 'TAGC') |
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1.0 |
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""" |
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return SmithWaterman().dist_abs(src, tar, gap_cost, sim_func) |
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if __name__ == '__main__': |
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import doctest |
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doctest.testmod() |
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chrislit /
abydos
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