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- from simstring.measure.base import BaseMeasure
- from sys import maxsize
- import math
- class OverlapMeasure(BaseMeasure):
- def min_feature_size(self, query_size, alpha):
- return 1
- def max_feature_size(self, query_size, alpha):
- return maxsize
- def minimum_common_feature_count(self, query_size, y_size, alpha):
- return int(math.ceil(alpha * min(query_size, y_size)))
- def similarity(self, X, Y):
- return min(len(set(X)), len(set(Y)))
- def patch_search(self, query_string, alpha):
- features = self.feature_extractor.features(query_string)
- min_feature_size = self.measure.min_feature_size(len(features), alpha)
- max_feature_size = self.measure.max_feature_size(len(features), alpha)
- # -- PATCH START
- max_feature_size = min(max_feature_size, self.db.max_feature_size())
- # -- PATCH END
- results = []
- for candidate_feature_size in range(min_feature_size, max_feature_size + 1):
- tau = self._Searcher__min_overlap(len(features), candidate_feature_size, alpha)
- results.extend(self._Searcher__overlap_join(features, tau, candidate_feature_size))
- return results
- from simstring.searcher import Searcher
- Searcher.search = patch_search
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