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lib/Algorithm/LibLinear/Model.pm  view on Meta::CPAN

This class represents a classifier or an estimated function generated as a return value of L<Algorithm::LibLinear>'s C<train> method. 

If you have model files generated by LIBLINEAR's C<train> command or this class's C<save> method, you can C<load> them.

=head1 METHOD

Note that the constructor C<new> is B<not> a part of public API. You can get a instance via C<< Algorithm::LibLinaear->train >>. i.e., C<Algorithm::LibLinear> is a factory class.

=head2 load(filename => $path)

Class method. Loads a LIBLINEAR's model file and returns an instance of this class.

=head2 bias([$index])

Returns value of the bias term corresponding to the C<$index>-th class. In case of one-class SVM (i.e., when C<is_oneclass_model> is true,) the C<$index> is ignored.

Recall that a trained model can be represented as a function f(x) = W^t x + b, where W is a F x C matrix, b is a C-sized vector and C and F are the numbers of classes and features, respectively. This method returns b(C<$index>) in this notation.

Note that C<$index> is 1-based, unlike LIBLINEAR's C<get_decfun_bias()> function.

=head2 class_labels

Returns an ArrayRef of class labels, each of them could be returned by C<predict> and C<predict_values>.

=head2 coefficient($feature_index, $label_index)

Returns value of the coefficient of classifier matrix. i.e., W(C<$feature_index>, C<$label_index>) (see C<bias> method description above.)

Be careful that both indices are 1-based just same as C<bias>.

=head2 is_oneclass_model

Returns true if the model is trained for one-class SVM, false otherwise.

=head2 is_probability_model

Returns true if the model is trained for logistic regression, false otherwise.

=head2 is_regression_model

Returns true if the model is trained for support vector regression (SVR), false otherwise.

=head2 num_classes

The number of class labels.

=head2 num_features

The number of features contained in training set.

=head2 predict(feature => $hashref)

In case of classification, returns predicted class label.

In case of regression, returns value of estimated function given feature.

=head2 predict_probabilities(feature => $hashref)

Returns an ArrayRef of probabilities of the feature belonging to corresponding class.

This method will raise an error if the model is not a classifier based on logistic regression (i.e., C<< not $classifier->is_probability_model >>.)

=head2 predict_values(feature => $hashref)

Returns an ArrayRef of decision values of each class (higher is better).

=head2 save(filename => $path)

Writes the model out as a LIBLINEAR model file.

=cut