class_docstr: The Gaussian EM clusterer models the vectors as being produced by
  a mixture of k Gaussian sources. The parameters of these sources
  (prior probability, mean and covariance matrix) are then found to
  maximise the likelihood of the given data. This is done with the
  expectation maximisation algorithm. It starts with k arbitrarily
  chosen means, priors and covariance matrices. It then calculates
  the membership probabilities for each vector in each of the
  clusters; this is the 'E' step. The cluster parameters are then
  updated in the 'M' step using the maximum likelihood estimate from
  the cluster membership probabilities. This process continues until
  the likelihood of the data does not significantly increase.
code: def num_clusters(self):
      return self._num_clusters
  
file_path: nltk/nltk/cluster/em.py
imports: from nltk.cluster.util import VectorSpaceClusterer
label_desc: Use image node_id 2 for calling the EMClusterer obj's underlying member method code with example usage: obj.num_clusters() and returns: self