view release on metacpan or  search on metacpan

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

        next if (($cluster_size_zero_condition) && ($iteration_index < 100));
        last if $iteration_index == 100;
        # Now do a deep copy of new_clusters into clusters
	$clusters = deep_copy_AoA( $new_clusters );
        last if $assignment_changed_flag == 0;
    }
    $final_cluster_centers = $self->update_cluster_centers( $clusters );
    return ($clusters, $final_cluster_centers);
}

sub update_cluster_centers_and_covariances_mahalanobis {
    my $self = shift;
    my @clusters = @{ shift @_ };
    my @new_cluster_centers;
    my @new_cluster_covariances;
    # During clustering for a fixed K, should a cluster inadvertently become empty,
    # steal a member from the largest cluster to hopefully spawn a new cluster:
    my $largest_cluster;
    foreach my $cluster (@clusters) {
        next if !defined $cluster;
        $largest_cluster = $cluster if !defined $largest_cluster;

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

            display_matrix( $covariance );
        }
        push @new_cluster_covariances, $covariance;
    }
    return [\@new_cluster_centers, \@new_cluster_covariances];
}

# After each new assignment of the data points to the clusters on the basis of the
# current values for the cluster centers, we call the routine shown here for updating
# the values of the cluster centers.
sub update_cluster_centers {
    my $self = shift;
    my @clusters = @{ shift @_ };
    my @new_cluster_centers;
    # During clustering for a fixed K, should a cluster inadvertently become empty,
    # steal a member from the largest cluster to hopefully spawn a new cluster:
    my $largest_cluster;
    foreach my $cluster (@clusters) {
        next if !defined $cluster;
        $largest_cluster = $cluster if !defined $largest_cluster;
        if (@$cluster > @$largest_cluster) {