Example usage for weka.core Instance index

Introduction

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

Prototype

public int index(int position);

Source Link

Document

Returns the index of the attribute stored at the given position in the sparse representation.

Usage

From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance.//from  w  w  w . ja v  a 2s. c  om
 *
 * @param instance the instance to be classified
 * @return predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance instance) {
    if (this.reset == true) {
        return new double[2];
    }
    double[] probOfClassGivenDoc = new double[m_numClasses];
    double totalSize = totalSize(instance);

    for (int i = 0; i < m_numClasses; i++) {
        probOfClassGivenDoc[i] = Math.log(m_probOfClass[i]) - totalSize * Math.log(m_classTotals[i]);
    }

    for (int i = 0; i < instance.numValues(); i++) {

        int index = instance.index(i);
        if (index == instance.classIndex() || instance.isMissing(i)) {
            continue;
        }

        double wordCount = instance.valueSparse(i);
        for (int c = 0; c < m_numClasses; c++) {
            double value = m_wordTotalForClass[c].getValue(index);
            probOfClassGivenDoc[c] += wordCount
                    * Math.log(value == 0 ? this.laplaceCorrectionOption.getValue() : value);
        }
    }

    return Utils.logs2probs(probOfClassGivenDoc);
}

---

From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java

License:Open Source License

public double totalSize(Instance instance) {
    int classIndex = instance.classIndex();
    double total = 0.0;
    for (int i = 0; i < instance.numValues(); i++) {
        int index = instance.index(i);
        if (index == classIndex || instance.isMissing(i)) {
            continue;
        }//w w  w.j ava 2  s  .co  m
        double count = instance.valueSparse(i);
        if (count >= 0) {
            total += count;
        } else {
            //throw new Exception("Numeric attribute value is not >= 0. " + i + " " + index + " " +
            //          instance.valueSparse(i) + " " + " " + instance);
        }
    }
    return total;
}

---

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

License:Open Source License

protected static double dotProd(Instance inst1, DoubleVector weights, int classIndex) {
    double result = 0;

    int n1 = inst1.numValues();
    int n2 = weights.numValues();

    for (int p1 = 0, p2 = 0; p1 < n1 && p2 < n2;) {
        int ind1 = inst1.index(p1);
        int ind2 = p2;
        if (ind1 == ind2) {
            if (ind1 != classIndex && !inst1.isMissingSparse(p1)) {
                result += inst1.valueSparse(p1) * weights.getValue(p2);
            }/*from   w  w w  .  j  av a 2  s  .  c  o  m*/
            p1++;
            p2++;
        } else if (ind1 > ind2) {
            p2++;
        } else {
            p1++;
        }
    }
    return (result);
}

---

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

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *//*from w w  w . j a  v a2 s  .  c om*/
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new DoubleVector();
        m_bias = 0.0;
    }

    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_bias);
        } else {
            y = instance.classValue();
            z = y - (wx + m_bias);
            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.numValues(); i++) {
            m_weights.setValue(i, m_weights.getValue(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.addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias += factor;
        }
        m_t++;
    }
}

---

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

License:Open Source License

public void trainOnInstanceImpl(Instance instance, int classLabel) {
    if (!instance.classIsMissing()) {

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

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() != classLabel) ? -1 : 1;
            z = y * (wx + m_bias[classLabel]);
        } else {//from  w  w  w . j a  v  a2s  .  c  om
            y = instance.classValue();
            z = y - (wx + m_bias[classLabel]);
            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[classLabel].numValues(); i++) {
            m_weights[classLabel].setValue(i, m_weights[classLabel].getValue(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[classLabel].addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias[classLabel] += factor;
        }

    }
}

---

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

License:Open Source License

protected static double dotProd(Instance inst1, double[] weights, int classIndex) {
    double result = 0;

    int n1 = inst1.numValues();
    int n2 = weights.length - 1;

    for (int p1 = 0, p2 = 0; p1 < n1 && p2 < n2;) {
        int ind1 = inst1.index(p1);
        int ind2 = p2;
        if (ind1 == ind2) {
            if (ind1 != classIndex && !inst1.isMissingSparse(p1)) {
                result += inst1.valueSparse(p1) * weights[p2];
            }//from   w ww  .  ja  v  a  2s.  c  om
            p1++;
            p2++;
        } else if (ind1 > ind2) {
            p2++;
        } else {
            p1++;
        }
    }
    return (result);
}

---

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
 *//*  w  ww  . ja  v  a2  s  .  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++;
    }
}

---

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
 *///from   w  ww  .  j  a  va 2 s .com
@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:moa.classifiers.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *//*from   w ww  .  j  a v a2  s.c  om*/
@Override
public void trainOnInstanceImpl(Instance inst) {
    if (this.reset == true) {
        this.m_numClasses = inst.numClasses();
        double laplace = this.laplaceCorrectionOption.getValue();
        int numAttributes = inst.numAttributes();

        m_probOfClass = new double[m_numClasses];
        Arrays.fill(m_probOfClass, laplace);

        m_classTotals = new double[m_numClasses];
        Arrays.fill(m_classTotals, laplace * numAttributes);

        m_wordTotalForClass = new double[numAttributes][m_numClasses];
        for (double[] wordTotal : m_wordTotalForClass) {
            Arrays.fill(wordTotal, laplace);
        }
        this.reset = false;
    }
    // Update classifier
    int classIndex = inst.classIndex();
    int classValue = (int) inst.value(classIndex);

    double w = inst.weight();
    m_probOfClass[classValue] += w;

    m_classTotals[classValue] += w * totalSize(inst);
    double total = m_classTotals[classValue];

    for (int i = 0; i < inst.numValues(); i++) {
        int index = inst.index(i);
        if (index != classIndex && !inst.isMissing(i)) {
            m_wordTotalForClass[index][classValue] += w * inst.valueSparse(i);
        }
    }
}

---

From source file:moa.classifiers.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance.//  w w w. java  2 s.  c  o m
 *
 * @param instance    the instance to be classified
 * @return       predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance instance) {
    if (this.reset == true) {
        return new double[2];
    }
    double[] probOfClassGivenDoc = new double[m_numClasses];
    double totalSize = totalSize(instance);

    for (int i = 0; i < m_numClasses; i++) {
        probOfClassGivenDoc[i] = Math.log(m_probOfClass[i]) - totalSize * Math.log(m_classTotals[i]);
    }

    for (int i = 0; i < instance.numValues(); i++) {

        int index = instance.index(i);
        if (index == instance.classIndex() || instance.isMissing(i)) {
            continue;
        }

        double wordCount = instance.valueSparse(i);
        for (int c = 0; c < m_numClasses; c++) {
            probOfClassGivenDoc[c] += wordCount * Math.log(m_wordTotalForClass[index][c]);
        }
    }

    return Utils.logs2probs(probOfClassGivenDoc);
}

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