code: def test_miislita_high_level(self):
      # construct corpus from file
      miislita = CorpusMiislita(datapath("miIslita.cor"))
  
      # initialize tfidf transformation and similarity index
      tfidf = models.TfidfModel(
          miislita, miislita.dictionary, normalize=False
      )
      index = similarities.SparseMatrixSimilarity(
          tfidf[miislita], num_features=len(miislita.dictionary)
      )
  
      # compare to query
      query = "latent semantic indexing"
      vec_bow = miislita.dictionary.doc2bow(query.lower().split())
      vec_tfidf = tfidf[vec_bow]
  
      # perform a similarity query against the corpus
      sims_tfidf = index[vec_tfidf]
  
      # for the expected results see the article
      expected = [0.0, 0.2560, 0.7022, 0.1524, 0.3334]
      for i, value in enumerate(expected):
          self.assertAlmostEqual(sims_tfidf[i], value, 2)
  
file_path: gensim/gensim/test/test_miislita.py
imports: from __future__ import division
  from __future__ import with_statement
  import logging
  import os
  import unittest
  from gensim import utils, corpora, models, similarities
  from gensim.test.utils import datapath, get_tmpfile
label_desc: Use image node_id 3 for calling the TestMiislita obj's underlying member method code with example usage: obj.test_miislita_high_level() without return types