def trim_prediction(prediction, reference):
l08 = int(0.8 * len(reference))
l12 = int(1.2 * len(reference))
trimmed_prediction = prediction[:l12]
if len(trimmed_prediction) > l08 and \
reference[-1] in trimmed_prediction[l08:]:
end_pos = l08 + trimmed_prediction[l08:].index(reference[-1]) + 1
trimmed_prediction = trimmed_prediction[:end_pos]
return trimmed_prediction
def levenshtein_distance(s1, s2):
if len(s1) < len(s2):
return levenshtein_distance(s2, s1)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]