// construct an array of names
docs_np_name = np.asarray([temp_labels[i] for i in other_file_indexes])
docs_np = np.column_stack((docs_np_name, docs_np_score))
// sort by score
sorted_docs_np = docs_np[docs_np[:, 1].argsort()]
// extract the array of name and score out from sorted_docs_list
docs_name = sorted_docs_np[:, 0]
docs_score = np.round(sorted_docs_np[:, 1].astype(float), decimals=4)
// pack the scores and names in data_frame
score_name_data_frame = pd.DataFrame(docs_score.reshape(
docs_score.size, 1), index=docs_name, columns=["Cosine similarity"])
After Change
dist = 1 - cosine_similarity(final_matrix)
// get an array of file index in filemanager.files
num_row = len(dtm_data_frame.index)
other_file_indexes = np.asarray([file_index for file_index in range(
num_row)if file_index != comp_file_index])
// construct an array of scores
