Example usage for weka.core Instance classIsMissing

Introduction

In this page you can find the example usage for weka.core Instance classIsMissing.

Prototype

public boolean classIsMissing();

Source Link

Document

Tests if an instance's class is missing.

Usage

From source file:xlong.urlclassify.others.SPegasos.java

License:Open Source License

/**
 * Updates the classifier with the given instance.
 *
 * @param instance the new training instance to include in the model 
 * @exception Exception if the instance could not be incorporated in
 * the model./* w w  w  . j  a v a 2  s.c o  m*/
 */
public void updateClassifier(Instance instance) throws Exception {
    if (!instance.classIsMissing()) {

        double learningRate = 1.0 / (m_lambda * m_t);
        //double scale = 1.0 - learningRate * m_lambda;
        double scale = 1.0 - 1.0 / m_t;
        double y = (instance.classValue() == 0) ? -1 : 1;
        double wx = dotProd(instance, m_weights, instance.classIndex());
        double z = y * (wx + m_weights[m_weights.length - 1]);

        for (int j = 0; j < m_weights.length - 1; j++) {
            if (j != instance.classIndex()) {
                m_weights[j] *= scale;
            }
        }

        if (m_loss == LOGLOSS || (z < 1)) {
            double loss = dloss(z);
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    double m = learningRate * loss * (instance.valueSparse(p1) * y);
                    m_weights[indS] += m;
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += learningRate * loss * y;
        }

        double norm = 0;
        for (int k = 0; k < m_weights.length - 1; k++) {
            if (k != instance.classIndex()) {
                norm += (m_weights[k] * m_weights[k]);
            }
        }

        double scale2 = Math.min(1.0, (1.0 / (m_lambda * norm)));
        if (scale2 < 1.0) {
            scale2 = Math.sqrt(scale2);
            for (int j = 0; j < m_weights.length - 1; j++) {
                if (j != instance.classIndex()) {
                    m_weights[j] *= scale2;
                }
            }
        }
        m_t++;
    }
}