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lib/Algorithm/ExpectationMaximization.pm  view on Meta::CPAN

                                      mask                => $mask,
                                      class_priors        => [0.6, 0.2, 0.2],
                                      K                   => 3,
                                      max_em_iterations   => 300,
                                      seeding             => 'manual',
                                      seed_tags           => ['a26', 'b53', 'c49'],
                                      terminal_output     => 1,
                                    );

  #  This example call to the constructor also illustrates how you can inject class
  #  priors into the clustering process. The class priors are the prior probabilities
  #  of the class distributions in your dataset.  As explained later, injecting class
  #  priors in the manner shown above makes statistical sense only for the case of
  #  manual seeding.  When you do inject class priors, the order in which the priors
  #  are expressed must correspond to the manually specified seeds for the clusters.

  #  After the invocation of the constructor, the following calls are mandatory
  #  for reasons that should be obvious from the names of the methods:

  $clusterer->read_data_from_file();
  srand(time);

lib/Algorithm/ExpectationMaximization.pm  view on Meta::CPAN

    $clusterer->read_data_from_file()

This is a required call after the constructor is invoked. As
you would expect, this call reads in the data for
clustering.

=item B<seed_the_clusters():>

    $clusterer->seed_the_clusters();

This is also a required call.  It processes the option you
supplied for C<seeding> in the constructor call to choose
the data elements for seeding the C<K> clusters.

=item B<EM():>

    $clusterer->EM();

This is the workhorse of the module, as you would expect.
The means, the covariances, and the priors estimated by this
method are stored in instance variables that are subsequently