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@@ 2313-2350 (lines=38) @@
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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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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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:param str src, tar: two strings to be compared |
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:param float gap_cost: the cost of an alignment gap (1 by default) |
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:param function sim_func: a function that returns the similarity of two |
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characters (identity similarity by default) |
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:returns: Smith-Waterman score |
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:rtype: int (in fact dependent on the gap_cost & return value of sim_func) |
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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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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 gotoh(src, tar, gap_open=1, gap_ext=0.4, sim_func=sim_ident): |
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"""Return the Gotoh score of two strings. |
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@@ 2276-2312 (lines=37) @@
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return mismatch_cost |
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def needleman_wunsch(src, tar, gap_cost=1, sim_func=sim_ident): |
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"""Return the Needleman-Wunsch score of two strings. |
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The Needleman-Wunsch score :cite:`Needleman:1970` is a standard edit |
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distance measure. |
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:param str src, tar: two strings to be compared |
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:param float gap_cost: the cost of an alignment gap (1 by default) |
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:param function sim_func: a function that returns the similarity of two |
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characters (identity similarity by default) |
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:returns: Needleman-Wunsch score |
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:rtype: int (in fact dependent on the gap_cost & return value of sim_func) |
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>>> needleman_wunsch('cat', 'hat') |
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2.0 |
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>>> needleman_wunsch('Niall', 'Neil') |
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1.0 |
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>>> needleman_wunsch('aluminum', 'Catalan') |
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-1.0 |
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>>> needleman_wunsch('ATCG', 'TAGC') |
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0.0 |
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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] = -(i * gap_cost) |
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for j in range(len(tar)+1): |
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d_mat[0, j] = -(j * gap_cost) |
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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(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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chrislit /
abydos
