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		 'epsilon-SVR' => 3,
		 'nu-SVR'      => 4);
my %SVM_TYPESR = (0 => 'C-SVC',
		  1 => 'nu-SVC',
		  2 => 'one-class',
		  3 => 'epsilon-SVR',
		  4 => 'nu-SVR');

# Kernel types
my %KERNEL_TYPES = ('linear'     => 0,
		    'polynomial' => 1,
		    'radial'     => 2,
		    'sigmoid'    => 3);
my %KERNEL_TYPESR = (0 => 'linear',
		     1 => 'polynomial',
		     2 => 'radial',
		     3 => 'sigmoid');

use vars qw(@ISA %EXPORT_TAGS @EXPORT_OK @EXPORT $VERSION);

@ISA = qw(Exporter DynaLoader);

%EXPORT_TAGS = ( 'all' => [ qw( ) ] );
@EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
@EXPORT = qw( );

$VERSION = '0.13';

sub AUTOLOAD {
  my $constname;
  use vars qw($AUTOLOAD);
  ($constname = $AUTOLOAD) =~ s/.*:://;
  croak "& not defined" if $constname eq 'constant';
  my $val = constant($constname, @_ ? $_[0] : 0);
  if ($! != 0) {
    if ($! =~ /Invalid/ || $!{EINVAL}) {
      $AutoLoader::AUTOLOAD = $AUTOLOAD;
      goto &AutoLoader::AUTOLOAD;
    }
    else {
      croak "Your vendor has not defined Algorithm::SVM macro $constname";
    }
  }
  {
    no strict 'refs';
    # Fixed between 5.005_53 and 5.005_61
    if ($] >= 5.00561) {
      *$AUTOLOAD = sub () { $val };
    }
    else {
      *$AUTOLOAD = sub { $val };
    }
  }
  goto &$AUTOLOAD;
}

bootstrap Algorithm::SVM $VERSION;

=head1 NAME

Algorithm::SVM - Perl bindings for the libsvm Support Vector Machine library.

=head1 SYNOPSIS

  use Algorithm::SVM;

  # Load the model stored in the file 'sample.model'
  $svm = new Algorithm::SVM(Model => 'sample.model');

  # Classify a dataset.
  $ds1 = new Algorithm::SVM::DataSet(Label => 1,
                                     Data  => [0.12, 0.25, 0.33, 0.98]);
  $res = $svm->predict($ds);

  # Train a new SVM on some new datasets.
  $svm->train(@tset);

  # Change some of the SVM parameters.
  $svm->gamma(64);
  $svm->C(8);
  # Retrain the SVM with the new parameters.
  $svm->retrain();

  # Perform cross validation on the training set.
  $accuracy = $svm->validate(5);

  # Save the model to a file.
  $svm->save('new-sample.model');

  # Load a saved model from a file.
  $svm->load('new-sample.model');

  # Retreive the number of classes.
  $num = $svm->getNRClass();

  # Retreive labels for dataset classes
  (@labels) = $svm->getLabels();

  # Probabilty for regression models, see below for details
  $prob = $svm->getSVRProbability();

=head1 DESCRIPTION

Algorithm::SVM implements a Support Vector Machine for Perl.  Support Vector
Machines provide a method for creating classifcation functions from a set of
labeled training data, from which predictions can be made for subsequent data
sets.

=head1 CONSTRUCTOR

  # Load an existing SVM.
  $svm = new Algorithm::SVM(Model  => 'sample.model');

  # Create a new SVM with the specified parameters.
  $svm = new Algorithm::SVM(Type   => 'C-SVC',
                            Kernel => 'radial',
                            Gamma  => 64,
                            C      => 8);

An Algorithm::SVM object can be created in one of two ways - an existing
SVM can be loaded from a file, or a new SVM can be created an trained on