ce5da48b8586d7cfdb392b8cad1988e6bfdd17d5,lexos/processors/analyze/similarity.py,,similarity_maker,#,8

Before Change

    // 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

    final_score_array = np.round(sorted_score_array, decimals=4)

    // sort the
    final_name_array = docs_name_array[docs_score_array.argsort()]

    // pack the scores and names in data_frame
    score_name_data_frame = pd.DataFrame(final_score_array,
                                         index=final_name_array,

Instances