/**
* The nr_feature*nr_class array w gives feature weights. We use one against the rest for
* multi-class classification, so each feature index corresponds to nr_class weight values.
* Weights are organized in the following way
*
* <pre>
* +------------------+------------------+------------+
* | nr_class weights | nr_class weights | ...
* | for 1st feature | for 2nd feature |
* +------------------+------------------+------------+
* </pre>
*
* If bias >= 0, x becomes [x; bias]. The number of features is increased by one, so w is a
* (nr_feature+1)*nr_class array. The value of bias is stored in the variable bias.
*
* @see #getBias()
* @return a <b>copy of</b> the feature weight array as described
*/
public double[] getFeatureWeights() {
return Linear.copyOf(w, w.length);
}
/** see {@link Linear#loadModel(java.io.Reader)} */
public static Model load(Reader inputReader) throws IOException {
return Linear.loadModel(inputReader);
}
/** see {@link Linear#saveModel(java.io.Writer, org.apache.lib.Model)} */
public void save(Writer writer) throws IOException {
Linear.saveModel(writer, this);
}
/** see {@link Linear#loadModel(java.io.File)} */
public static Model load(String file) throws IOException {
return Linear.loadModel(file);
}
/** see {@link Linear#saveModel(java.io.File, org.apache.lib.Model)} */
public void save(File file) throws IOException {
Linear.saveModel(file, this);
}
