view release on metacpan or  search on metacpan

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

  # 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();

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

Loads a model from the specified filename.  Returns a false value on failure,
and truth value on success.

  $svm->train(@tset);

Trains the SVM on a set of Algorithm::SVM::DataSet objects.  @tset should
be an array of Algorithm::SVM::DataSet objects.


  $accuracy = $svm->validate(5);

Performs cross validation on the training set.  If an argument is provided,
the set is partioned into n subsets, and validated against one another.
Returns a floating point number representing the accuracy of the validation.

  $num = $svm->getNRClass();

For a classification model, this function gives the number of classes.
For a regression or a one-class model, 2 is returned.

  (@labels) = $svm->getLabels();

For a classification model, this function returns the name of the labels

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

  return _train($self->{svm}, 0);
}

sub retrain {
  my $self = shift;

  return _train($self->{svm}, 1);
}

sub validate {
  my ($self, $nfolds) = @_;

  $nfolds = 5 if(! defined($nfolds));
  croak("NumFolds must be >= 2") if($nfolds < 2);

  return _crossValidate($self->{svm}, $nfolds + 0);
}

sub svm_type {
  my ($self, $type) = @_;