Class OneVSOne

JSAT/src/jsat/classifiers/OneVSOne.java:25–220

A One VS One classifier extends binary decision classifiers into multi-class decision classifiers. This is done by creating a binary-classification problem for every possible pair of classes, and then classifying by taking the result of all possible combinations and choosing the class that got the m

 * @author Edward Raff
 */
public class OneVSOne implements Classifier, Parameterized
{

    private static final long serialVersionUID = 733202830281869416L;
    /**
     * Main binary classifier
     */
    @ParameterHolder
    protected Classifier baseClassifier;
    /**
     * Uper-diagonal matrix of classifiers sans the first index since a 
     * classifier vs itself is useless. First index is the first dimensions is 
     * the first class, 2nd index + the value of the first + 1 is the opponent 
     * class
     */
    protected Classifier[][] oneVone;
    private boolean concurrentTrain;
    protected CategoricalData predicting;

    /**
     * Creates a new One-vs-One classifier
     * @param baseClassifier the binary classifier to extend
     */
    public OneVSOne(Classifier baseClassifier)
    {
        this(baseClassifier, false);
    }
    
    /**
     * Creates a new One-vs-One classifier 
     * @param baseClassifier the binary classifier to extend
     * @param concurrentTrain <tt>true</tt> to have training of individual 
     * classifiers occur in parallel, <tt>false</tt> to have them use their 
     * native parallel training method. 
     */
    public OneVSOne(Classifier baseClassifier, boolean concurrentTrain)
    {
        this.baseClassifier = baseClassifier;
        this.concurrentTrain = concurrentTrain;
    }

    /**
     * Controls whether or not training of the several classifiers occurs concurrently or sequentually. 
     * @param concurrentTrain <tt>true</tt> to have training of individual 
     * classifiers occur in parallel, <tt>false</tt> to have them use their 
     * native parallel training method. 
     */
    public void setConcurrentTraining(boolean concurrentTrain)
    {
        this.concurrentTrain = concurrentTrain;
    }

    /**
     * 
     * @return <tt>true</tt> if training of individual 
     * classifiers occur in parallel, <tt>false</tt> they use their 
     * native parallel training method.
     */