code: def word2vec_similarity(
      segmented_topics, accumulator, with_std=False, with_support=False
  ):
      """For each topic segmentation, compute average cosine similarity using a
      :class:`~gensim.topic_coherence.text_analysis.WordVectorsAccumulator`.
  
      Parameters
      ----------
      segmented_topics : list of lists of (int, `numpy.ndarray`)
          Output from the :func:`~gensim.topic_coherence.segmentation.s_one_set`.
      accumulator : :class:`~gensim.topic_coherence.text_analysis.WordVectorsAccumulator` or
                    :class:`~gensim.topic_coherence.text_analysis.InvertedIndexAccumulator`
          Word occurrence accumulator.
      with_std : bool, optional
          True to also include standard deviation across topic segment sets
          in addition to the mean coherence for each topic.
      with_support : bool, optional
          True to also include support across topic segments. The support is defined as
          the number of pairwise similarity comparisons were used to compute the overall topic coherence.
  
      Returns
      -------
      list of (float[, float[, int]])
          Сosine word2vec similarities per topic (with std/support if `with_std`, `with_support`).
  
      Examples
      --------
      .. sourcecode:: pycon
  
          >>> import numpy as np
          >>> from gensim.corpora.dictionary import Dictionary
          >>> from gensim.topic_coherence import indirect_confirmation_measure
          >>> from gensim.topic_coherence import text_analysis
          >>>
          >>> # create segmentation
          >>> segmentation = [[(1, np.array([1, 2])), (2, np.array([1, 2]))]]
          >>>
          >>> # create accumulator
          >>> dictionary = Dictionary()
          >>> dictionary.id2token = {1: 'fake', 2: 'tokens'}
          >>> accumulator = text_analysis.WordVectorsAccumulator({1, 2}, dictionary)
          >>> _ = accumulator.accumulate([['fake', 'tokens'], ['tokens', 'fake']], 5)
          >>>
          >>> # should be (0.726752426218 0.00695475919227)
          >>> mean, std = indirect_confirmation_measure.word2vec_similarity(segmentation, accumulator, with_std=True)[0]
  
      """
      topic_coherences = []
      total_oov = 0
  
      for topic_index, topic_segments in enumerate(segmented_topics):
          segment_sims = []
          num_oov = 0
          for w_prime, w_star in topic_segments:
              if not hasattr(w_prime, "__iter__"):
                  w_prime = [w_prime]
              if not hasattr(w_star, "__iter__"):
                  w_star = [w_star]
  
              try:
                  segment_sims.append(
                      accumulator.ids_similarity(w_prime, w_star)
                  )
              except ZeroDivisionError:
                  num_oov += 1
  
          if num_oov > 0:
              total_oov += 1
              logger.warning(
                  "%d terms for topic %d are not in word2vec model vocabulary",
                  num_oov,
                  topic_index,
              )
          topic_coherences.append(
              aggregate_segment_sims(
                  segment_sims, with_std, with_support
              )
          )
  
      if total_oov > 0:
          logger.warning(
              "%d terms for are not in word2vec model vocabulary",
              total_oov,
          )
      return topic_coherences
  
file_path: gensim/gensim/topic_coherence/indirect_confirmation_measure.py
imports: import itertools
  import logging
  import numpy
  import scipy.sparse
  from gensim.topic_coherence.direct_confirmation_measure import aggregate_segment_sims, log_ratio_measure
label_desc: Use image node_id 1 for calling a global function with example usage: word2vec_similarity(segmented_topics, accumulator, with_std, with_support) and returns: topic_coherences