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Implement Damerau-Levenshtein distance.

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jez 2011-06-01 01:38:04 +00:00
parent 544aefa178
commit 4c2517dafa
1 changed files with 16 additions and 9 deletions
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25
autojump
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@ -90,18 +90,25 @@ def clean_dict(sorted_dirs, path_dict):
else: return False else: return False
def approximatch(pat, text): def approximatch(pat, text):
prev_col = list(range(0, len(pat)+1)) cols = [list(range(0, len(pat)+1))]
col = [0] * (len(pat) + 1) cols.extend(copy(col) for col in [[0] * (len(pat) + 1)] * (len(text) + 1))
errors = len(pat) errors = len(pat)
for char1 in text: last_seen_in_text = {}
col[0] = 0 for i, char1 in enumerate(text):
for i, char2 in enumerate(pat): cols[i+1][0] = 0
last_seen_in_pat = 0
for j, char2 in enumerate(pat):
i1 = last_seen_in_text[char2] if char2 in last_seen_in_text else 0
j1 = last_seen_in_pat
if char1 == char2: if char1 == char2:
col[i+1] = prev_col[i] cols[i+1][j+1] = cols[i][j]
last_seen_in_pat = j + 1
else: else:
col[i+1] = 1 + min(col[i], prev_col[i+1], prev_col[i]) cols[i+1][j+1] = 1 + min(cols[i+1][j], cols[i][j+1], cols[i][j])
prev_col = copy(col) if i1 and j1:
errors = min(errors, col[-1]) cols[i+1][j+1] = min(cols[i+1][j+1], 1 + (i - i1) + (j - j1) + cols[i1-1][j1-1])
errors = min(errors, cols[i+1][-1])
last_seen_in_text[char1] = i + 1
return errors return errors
def find_matches(dirs, patterns, result_list, ignore_case, approx, max_matches): def find_matches(dirs, patterns, result_list, ignore_case, approx, max_matches):