@@ -86,7 +86,7 @@ def cosine_similarity(from_vector: np.ndarray,
8686 similarity_matrix = similarity_matrix .tocsr ()
8787
8888 indices = _top_n_idx_sparse (similarity_matrix , top_n )
89- similarities = _top_n_similarities_sparse (similarity_matrix , top_n , indices )
89+ similarities = _top_n_similarities_sparse (similarity_matrix , indices )
9090 indices = np .array (np .nan_to_num (np .array (indices , dtype = np .float ), nan = 0 ), dtype = np .int )
9191
9292 # Faster than knn and slower than sparse but uses more memory
@@ -132,11 +132,11 @@ def _top_n_idx_sparse(matrix, n):
132132 return np .array (top_n_idx )
133133
134134
135- def _top_n_similarities_sparse (matrix , n , indices ):
135+ def _top_n_similarities_sparse (matrix , indices ):
136136 """ Return similarity scores of top n values in each row of a sparse matrix """
137137 similarity_scores = []
138138 for row , values in enumerate (indices ):
139- scores = [round (matrix [row , value ], n ) if value is not None else 0 for value in values ]
139+ scores = [round (matrix [row , value ], 3 ) if value is not None else 0 for value in values ]
140140 similarity_scores .append (scores )
141141 similarity_scores = np .array (similarity_scores ).T
142142 return similarity_scores
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