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

    print "Number of Gaussians used for the synthetic data: $K\n";
    my @means;
    my @covariances;
    my $data_dimension;
    foreach my $i (0..$K-1) {
        my @num_strings = split /  /, $cluster_strings[$i];
        my @cluster_mean = map {/$_num_regex/;$_} split / /, $num_strings[0];
        $data_dimension = @cluster_mean;
        push @means, \@cluster_mean;
        my @covariance_nums = map {/$_num_regex/;$_} split / /, $num_strings[1];
        croak "dimensionality error" if @covariance_nums != 
                                      ($data_dimension ** 2);
        my $cluster_covariance;
        foreach my $j (0..$data_dimension-1) {
            foreach my $k (0..$data_dimension-1) {        
                $cluster_covariance->[$j]->[$k] = 
                         $covariance_nums[$j*$data_dimension + $k];
            }
        }
        push @covariances, $cluster_covariance;
    }

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

  Algorithm::KMeans->cluster_data_generator(
                          input_parameter_file => $parameter_file,
                          output_datafile => $out_datafile,
                          number_data_points_per_cluster => $N );

=head1 CHANGES

Version 2.05 removes the restriction on the version of Perl that is required.  This
is based on Srezic's recommendation.  He had no problem building and testing the
previous version with Perl 5.8.9.  Version 2.05 also includes a small augmentation of
the code in the method C<read_data_from_file_csv()> for guarding against user errors
in the specification of the mask that tells the module which columns of the data file
are to be used for clustering.

Version 2.04 allows you to use CSV data files for clustering.

Version 2.03 incorporates minor code cleanup.  The main implementation of the module
remains unchanged.

Version 2.02 downshifts the version of Perl that is required for this module.  The
module should work with versions 5.10 and higher of Perl.  The implementation code
for the module remains unchanged.

Version 2.01 removes many errors in the documentation. The changes made to the module
in Version 2.0 were not reflected properly in the documentation page for that
version.  The implementation code remains unchanged.

Version 2.0 includes significant additional functionality: (1) You now have the
option to cluster using the Mahalanobis distance metric (the default is the Euclidean
metric); and (2) With the two C<which_cluster> methods that have been added to the
module, you can now determine the best cluster for a new data sample after you have
created the clusters with the previously available data.  Finding the best cluster
for a new data sample can be done using either the Euclidean metric or the
Mahalanobis metric.