Example usage for weka.core Instance isMissingSparse

Introduction

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

Prototype

public boolean isMissingSparse(int indexOfIndex);

Source Link

Document

Tests if a specific value is "missing" in the sparse representation.

Usage

From source file:moa.classifiers.functions.SGDOld.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *///from  ww  w. ja v a 2s  .c  o m
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new double[instance.numAttributes() + 1];
    }

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_weights[m_weights.length - 1]);
        } else {
            y = instance.classValue();
            z = y - (wx + m_weights[m_weights.length - 1]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.length - 1; i++) {
            m_weights[i] *= multiplier;
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[indS] += factor * instance.valueSparse(p1);
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += factor;
        }
        m_t++;
    }
}

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From source file:moa.classifiers.functions.SPegasos.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance the new training instance to include in the model
 */// w ww.j  a  va2s.  c  om
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new double[instance.numAttributes() + 1];
    }
    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++;
    }
}

---

From source file:org.esa.nest.gpf.SGD.java

/**
 * 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.//from ww  w  .j av  a 2 s.c om
 */
@Override
public void updateClassifier(Instance instance) throws Exception {

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_weights[m_weights.length - 1]);
        } else {
            y = instance.classValue();
            z = y - (wx + m_weights[m_weights.length - 1]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.length - 1; i++) {
            m_weights[i] *= multiplier;
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[indS] += factor * instance.valueSparse(p1);
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += factor;
        }
        m_t++;
    }
}

---

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 ww .  ja  v a2 s  .co 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++;
    }
}

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