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Generating Levenshtein Distance Matrix and finding Minimum Edit Distance
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import numpy | |
s1 = input('input: ').strip().lower(); l1 = len(s1) + 1 | |
s2 = input('target: ').strip().lower(); l2 = len(s2) + 1 | |
m = numpy.empty([l1, l2], dtype = int) | |
for i in range(l1): | |
m[i][0] = i | |
for j in range(l2): | |
m[0][j] = j | |
for i in range(1, l1): | |
for j in range(1, l2): | |
if s1[i-1] == s2[j-1]: | |
m[i][j] = min(m[i-1, j] + 1, m[i, j-1] + 1, m[i-1, j-1]) | |
else: | |
m[i][j] = min(m[i-1, j] + 1, m[i, j-1] + 1, m[i-1, j-1] + 1) | |
print('\nLevenshtein distance matrix:', m[1:, 1:], sep = '\n') | |
print('\nMinimum edit distance: ', m[-1][-1]) |
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