Algorithm-ExpectationMaximization

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examples/cleanup_directory.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#  There should be no need to call this script ordinarily.

#  When the Algorithm::KMeans module creates new cluster files,
#  it automatically delete all previously created such files.
#  Such files are named ClusterX.dat for X starting with X = 0.
#  The files __temp_* are created by the visualization script.
#  However, when the program terminates properly, it should 
#  automatically delete those files.


unlink glob "Cluster*.dat";

unlink glob "__temp_*";

unlink glob "__cluster_*.dat";

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

    my @final_output = (0) x @$arr;
    my %hash;
    my @array_of_runs = ([$arr->[0]]);
    foreach my $index (1..@$arr-1) {
        if ($arr->[$index] == $arr->[$index-1]) {
            push @{$array_of_runs[-1]}, $arr->[$index];
        } else {  
            push @array_of_runs, [$arr->[$index]];
        }
    }
    my $runstart_index = 0;
    foreach my $run_index (1..@array_of_runs-2) {
        $runstart_index += @{$array_of_runs[$run_index-1]};
        if ($array_of_runs[$run_index]->[0] > 
            $array_of_runs[$run_index-1]->[0]  &&
            $array_of_runs[$run_index]->[0] > 
            $array_of_runs[$run_index+1]->[0]) {
            my $run_center = @{$array_of_runs[$run_index]} / 2;
            my $assignment_index = $runstart_index + $run_center;
            $output[$assignment_index] = $arr->[$assignment_index];
        }
    }
    if ($array_of_runs[-1]->[0] > $array_of_runs[-2]->[0]) {
        $runstart_index += @{$array_of_runs[-2]};
        my $run_center = @{$array_of_runs[-1]} / 2;
        my $assignment_index = $runstart_index + $run_center;
        $output[$assignment_index] = $arr->[$assignment_index];
    }
    if ($array_of_runs[0]->[0] > $array_of_runs[1]->[0]) {
        my $run_center = @{$array_of_runs[0]} / 2;
        $output[$run_center] = $arr->[$run_center];
    }
    return \@output;
}

sub display_matrix {

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

=head1 SOME RESULTS OBTAINED WITH THIS MODULE

If you would like to see some results that have been obtained with this module, check
out Section 7 of the report
L<https://engineering.purdue.edu/kak/Tutorials/ExpectationMaximization.pdf>.

=head1 THE C<examples> DIRECTORY

Becoming familiar with this directory should be your best
strategy to become comfortable with this module (and its
future versions).  You are urged to start by executing the
following five example scripts:

=over 16

=item I<canned_example1.pl>

This example applies the EM algorithm to the data contained in the datafile
C<mydatafile.dat>.  The mixture data in the file corresponds to three overlapping
Gaussian components in a star-shaped pattern.  The EM based clustering for this data
is shown in the files C<save_example_1_cluster_plot.png> and

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

These were used to generate the synthetic data for which the results are shown in the
C<examples> directory.  Just make a copy of one of these files and edit it if you
would like to generate your own multivariate data for clustering.  Note that you can
generate data with any dimensionality through appropriate entries in the parameter
file.

=head1 CAVEATS

When you run the scripts in the C<examples> directory, your results will NOT always
look like what I have shown in the PNG image files in the directory.  As mentioned
earlier in Description, the EM algorithm starting from randomly chosen initial
guesses for the cluster means can get stuck in a local maximum.

That raises an interesting question of how one judges the correctness of clustering
results when dealing with real experimental data.  For real data, the best approach
is to try the EM algorithm multiple times with all of the seeding options included in
this module.  It would be safe to say that, at least in low dimensional spaces and
with sufficient data, a majority of your runs should yield "correct" results.

Also bear in mind that a pure Perl implementation is not meant for the clustering of
very large data files.  It is really designed more for researching issues related to