code: def testRandomSelf(self):
      dim = 16
      num_elements = 10000
  
      # Generating sample data
      data = np.float32(np.random.random((num_elements, dim)))
  
      # Declaring index
      hnsw_index = hnswlib.Index(
          space="l2", dim=dim
      )  # possible options are l2, cosine or ip
      bf_index = hnswlib.BFIndex(space="l2", dim=dim)
  
      # Initiating index
      # max_elements - the maximum number of elements, should be known beforehand
      #     (probably will be made optional in the future)
      #
      # ef_construction - controls index search speed/build speed tradeoff
      # M - is tightly connected with internal dimensionality of the data
      #     strongly affects the memory consumption
  
      hnsw_index.init_index(
          max_elements=num_elements, ef_construction=100, M=16
      )
      bf_index.init_index(max_elements=num_elements)
  
      # Controlling the recall by setting ef:
      # higher ef leads to better accuracy, but slower search
      hnsw_index.set_ef(10)
  
      hnsw_index.set_num_threads(
          4
      )  # by default using all available cores
  
      print("Adding %d elements" % (len(data)))
      hnsw_index.add_items(data)
      bf_index.add_items(data)
  
      # Query the elements for themselves and measure recall:
      labels, distances = hnsw_index.knn_query(data, k=1)
      self.assertAlmostEqual(
          np.mean(labels.reshape(-1) == np.arange(len(data))), 1.0, 3
      )
  
      print("Querying only even elements")
      # Query the even elements for themselves and measure recall:
      filter_function = lambda id: id % 2 == 0
      # Warning: search with a filter works slow in python in multithreaded mode, therefore we set num_threads=1
      labels, distances = hnsw_index.knn_query(
          data, k=1, num_threads=1, filter=filter_function
      )
      self.assertAlmostEqual(
          np.mean(labels.reshape(-1) == np.arange(len(data))), 0.5, 3
      )
      # Verify that there are only even elements:
      self.assertTrue(np.max(np.mod(labels, 2)) == 0)
  
      labels, distances = bf_index.knn_query(
          data, k=1, filter=filter_function
      )
      self.assertEqual(
          np.mean(labels.reshape(-1) == np.arange(len(data))), 0.5
      )
  
imports: import os
  import unittest
  import numpy
  import hnswlib
label_desc: Use image node_id 1 for calling the RandomSelfTestCase obj's underlying member method code with example usage: obj.testRandomSelf() without return types