Example usage for weka.core Utils logs2probs

Introduction

In this page you can find the example usage for weka.core Utils logs2probs.

Prototype

public static double[] logs2probs(double[] a) 

Source Link

Document

Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.

Usage

From source file:gyc.OverBoostM1.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test instance.
 *
 * @param instance the instance to be classified
 * @return predicted class probability distribution
 * @throws Exception if instance could not be classified
 * successfully/*www .  j  av  a2 s.  c  om*/
 */
public double[] distributionForInstance(Instance instance) throws Exception {

    // default model?
    if (m_ZeroR != null) {
        return m_ZeroR.distributionForInstance(instance);
    }

    if (m_NumIterationsPerformed == 0) {
        throw new Exception("No model built");
    }
    double[] sums = new double[instance.numClasses()];

    if (m_NumIterationsPerformed == 1) {
        return m_Classifiers[0].distributionForInstance(instance);
    } else {
        for (int i = 0; i < m_NumIterationsPerformed; i++) {
            sums[(int) m_Classifiers[i].classifyInstance(instance)] += m_Betas[i];
        }
        return Utils.logs2probs(sums);
    }
}

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

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance.//  w  w  w.j  av  a  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++) {
            double value = m_wordTotalForClass[c].getValue(index);
            probOfClassGivenDoc[c] += wordCount
                    * Math.log(value == 0 ? this.laplaceCorrectionOption.getValue() : value);
        }
    }

    return Utils.logs2probs(probOfClassGivenDoc);
}

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From source file:moa.classifiers.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance.//from   w w w. j av  a 2  s  .  co 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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