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Space Optimal Fuzzy Matching: This gist do a fuzzy matching of documents on the basis of levenshtein distance and returns a similarity score. This do the same as my previous gist https://gist.github.com/greatsharma/eeb22285a837a9b29431179451d0ba7f with a time complexity of O(mn) but the space complexity is now reduced to O(m) i.e., linear time,…
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import gc | |
import numpy as np | |
from pprint import pprint | |
def levenshtein_distance_optimal(pattern, docs, ignore_case=True) -> dict: | |
"""Do a fuzzy matching of documents using levenshtein distance in linear space complexity | |
Parameters | |
---------- | |
pattern : str | |
The document which you want to match | |
docs : list | |
The documents which you want to match with | |
ignore_case : bool, optional | |
Whether the matching is case sensitive or not (default is True) | |
Returns | |
------- | |
dict | |
a dictionary of similarity scores of all documents with pattern in the order passed in docs | |
""" | |
if ignore_case: | |
pattern = pattern.lower() | |
pattern_len = len(pattern) | |
similarity_score = {} | |
count = 1 | |
for doc in docs: | |
if ignore_case: | |
doc = doc.lower() | |
if pattern == doc: | |
similarity_score['doc' + str(count)] = 1.0 | |
count += 1 | |
continue | |
doc_len = len(doc) | |
cache = [0] * (pattern_len+1) | |
space_penalty = 1 | |
for i in range(pattern_len+1): | |
cache[i] = space_penalty*i | |
for i in range(1, doc_len+1): | |
temp_store = [0] * (pattern_len+1) | |
temp_store[0] = cache[0] + space_penalty | |
for j in range(1, pattern_len+1): | |
miss_penalty = cache[j-1] | |
if pattern[j-1] != doc[i-1]: | |
miss_penalty += 1 | |
temp_store[j] = min([space_penalty+cache[j], | |
space_penalty+temp_store[j-1], | |
miss_penalty]) | |
cache = temp_store | |
del temp_store | |
gc.collect | |
lev_dist = cache[pattern_len] | |
similarity_score['doc' + str(count)] = (pattern_len + doc_len - | |
lev_dist) / float(pattern_len + doc_len) | |
count += 1 | |
return similarity_score | |
if __name__ == '__main__': | |
pattern = 'this is a test for fuzzy wuzzy match' | |
docs = ['a test for fuzzy match', 'test fuzzy matching', 'this is a test for fuzzy wuzzy match', | |
'this is test for fuzy wuzy match', 'this is a for fuzzy wuzzy match'] | |
similarity_score = levenshtein_distance_optimal(pattern, docs) | |
pprint(similarity_score) | |
# output -> | |
# {'doc1': 0.7586206896551724, | |
# 'doc2': 0.5818181818181818, | |
# 'doc3': 1.0, | |
# 'doc4': 0.9411764705882353, | |
# 'doc5': 0.9253731343283582} |
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