def remove_false_positives(qstring, candidate_strings, candidate_string_attrs=None):
start_time = int(round(time() * 1000000))
qlength = len(qstring)
if candidate_string_attrs and len(candidate_strings) > CAND_STRINGS_THRESHOLD:
qelements = get_string_elements(qstring)
filtered_candidate_strings = list()
for string in candidate_strings:
elements, length = candidate_string_attrs[string]
if ed_property_is_satisfied(qelements, elements, qlength == length):
filtered_candidate_strings.append(string)
#print '# of candidate strings before filtering: %s' % len(candidate_strings)
#print '# of candidate strings after filtering: %s' % len(filtered_candidate_strings)
candidate_strings = filtered_candidate_strings
approximate_matches = list()
for string in candidate_strings:
length = len(string)
is_not_false_positive = strings_are_within_distance_K(qstring, string, qlength, length, K=ED_THRESHOLD+1)
if is_not_false_positive:
approximate_matches.append(string)
end_time = int(round(time() * 1000000))
return approximate_matches, end_time - start_time
def _create_dense_index(strings):
dense_index = dict()
for i, string in enumerate(strings):
string_elements = get_string_elements(string)
dense_index[i] = (string, string_elements, len(string))
set_dense_index(dense_index)
if VERBOSITY:
print 'Created dense index'