new_contents: import numpy as np
  from sklearn.metrics.pairwise import cosine_similarity
  
  
  final_matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15]]
  raw_matrix = np.delete(np.delete(final_matrix, 0, 0), 0, 1)
  dist = 1 - cosine_similarity(raw_matrix)
  comp_file_index = 1
  temp_labels = np.array(['one', 'two', 'three', 'four'])
  other_file_indexes = np.asarray([file_index for file_index in range(
          raw_matrix.shape[0]) if file_index != comp_file_index])
  docs_np_score = np.asarray([dist[file_index, comp_file_index]
                             for file_index in other_file_indexes])
  docs_np_name = np.asarray([temp_labels[i] for i in range(
          other_file_indexes.size)])
  docs_np = np.column_stack((docs_np_name, docs_np_score))
  sorted_docs_np = docs_np[docs_np[:, 1].astype(float).argsort()]
  # extract the list 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)
  
  print(raw_matrix)
  
subject: create a test file for numpy array and add content to it