Example usage for weka.classifiers.evaluation NominalPrediction NominalPrediction

Introduction

In this page you can find the example usage for weka.classifiers.evaluation NominalPrediction NominalPrediction.

Prototype

public NominalPrediction(double actual, double[] distribution, double weight) 

Source Link

Document

Creates the NominalPrediction object.

Usage

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Evaluates the classifier on a single instance and records the prediction
 * (if the class is nominal).//  w  w  w .j  a  v a2 s .  c  o m
 * 
 * @param classifier machine learning classifier
 * @param instance the test instance to be classified
 * @return the prediction made by the clasifier
 * @throws Exception if model could not be evaluated successfully or the data
 *           contains string attributes
 */
public double evaluateModelOnceAndRecordPrediction(List<LibSVM> classifier, List<Double> classifierWeight,
        Instance instance) throws Exception {
    Instance classMissing = (Instance) instance.copy();
    double pred = 0;
    classMissing.setDataset(instance.dataset());
    classMissing.setClassMissing();
    if (m_ClassIsNominal) {
        if (m_Predictions == null) {
            m_Predictions = new FastVector();
        }
        List<double[]> prob = new ArrayList<double[]>();//
        double[] finalProb = new double[instance.numClasses()];
        for (int i = 0; i < classifier.size(); i++) {
            double[] dist = classifier.get(i).distributionForInstance(classMissing);//
            prob.add(dist);
        }
        for (int i = 0; i < finalProb.length; i++) {
            for (int j = 0; j < classifier.size(); j++) {
                finalProb[i] += prob.get(j)[i] * classifierWeight.get(j);
            }
        }
        double sum = 0;
        for (int i = 0; i < finalProb.length; i++) {
            sum += finalProb[i];
        }
        for (int i = 0; i < finalProb.length; i++) {
            finalProb[i] = finalProb[i] / sum;
        }
        pred = Utils.maxIndex(finalProb);
        if (finalProb[(int) pred] <= 0) {
            pred = Instance.missingValue();
        }
        updateStatsForClassifier(finalProb, instance);
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), finalProb, instance.weight()));
    } else {

        pred = classifier.get(0).classifyInstance(classMissing);
        updateStatsForPredictor(pred, instance);
    }
    return pred;
}

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From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Evaluates the supplied distribution on a single instance.
 * //from   www  .ja  v  a  2 s .  c  o m
 * @param dist the supplied distribution
 * @param instance the test instance to be classified
 * @return the prediction
 * @throws Exception if model could not be evaluated successfully
 */
public double evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance) throws Exception {
    double pred;
    if (m_ClassIsNominal) {
        if (m_Predictions == null) {
            m_Predictions = new FastVector();
        }
        pred = Utils.maxIndex(dist);
        if (dist[(int) pred] <= 0) {
            pred = Instance.missingValue();
        }
        updateStatsForClassifier(dist, instance);
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, instance.weight()));
    } else {
        pred = dist[0];
        updateStatsForPredictor(pred, instance);
    }
    return pred;
}

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From source file:com.evaluation.ConfidenceLabelBasedMeasures.java

License:Open Source License

private void computeMeasures(MultiLabelOutput[] output, boolean[][] trueLabels) {
    int numLabels = trueLabels[0].length;

    // AUC/*from   w  w  w .  j a va  2s . c o m*/
    FastVector[] m_Predictions = new FastVector[numLabels];
    for (int j = 0; j < numLabels; j++)
        m_Predictions[j] = new FastVector();
    FastVector all_Predictions = new FastVector();

    int numInstances = output.length;
    for (int instanceIndex = 0; instanceIndex < numInstances; instanceIndex++) {
        double[] confidences = output[instanceIndex].getConfidences();
        for (int labelIndex = 0; labelIndex < numLabels; labelIndex++) {

            int classValue;
            boolean actual = trueLabels[instanceIndex][labelIndex];
            if (actual)
                classValue = 1;
            else
                classValue = 0;

            double[] dist = new double[2];
            dist[1] = confidences[labelIndex];
            dist[0] = 1 - dist[1];

            m_Predictions[labelIndex].addElement(new NominalPrediction(classValue, dist, 1));
            all_Predictions.addElement(new NominalPrediction(classValue, dist, 1));
        }
    }

    labelAUC = new double[numLabels];
    for (int i = 0; i < numLabels; i++) {
        ThresholdCurve tc = new ThresholdCurve();
        Instances result = tc.getCurve(m_Predictions[i], 1);
        labelAUC[i] = ThresholdCurve.getROCArea(result);
    }
    auc[Averaging.MACRO.ordinal()] = Utils.mean(labelAUC);
    ThresholdCurve tc = new ThresholdCurve();
    Instances result = tc.getCurve(all_Predictions, 1);
    auc[Averaging.MICRO.ordinal()] = ThresholdCurve.getROCArea(result);
}

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From source file:cotraining.copy.Evaluation_D.java

License:Open Source License

/**
 * Evaluates the classifier on a single instance and records the
 * prediction (if the class is nominal).
 *
 * @param classifier machine learning classifier
 * @param instance the test instance to be classified
 * @return the prediction made by the clasifier
 * @throws Exception if model could not be evaluated 
 * successfully or the data contains string attributes
 *//*  www  .j a  v  a  2s .  c o  m*/
public double evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) throws Exception {

    Instance classMissing = (Instance) instance.copy();
    double pred = 0;
    classMissing.setDataset(instance.dataset());
    classMissing.setClassMissing();
    if (m_ClassIsNominal) {
        if (m_Predictions == null) {
            m_Predictions = new FastVector();
        }
        double[] dist = classifier.distributionForInstance(classMissing);
        pred = Utils.maxIndex(dist);
        if (dist[(int) pred] <= 0) {
            pred = Instance.missingValue();
        }
        updateStatsForClassifier(dist, instance);
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, instance.weight()));
    } else {
        pred = classifier.classifyInstance(classMissing);
        updateStatsForPredictor(pred, instance);
    }
    return pred;
}

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From source file:mulan.evaluation.measure.LabelBasedAUC.java

License:Open Source License

protected void updateConfidence(double[] confidences, boolean[] truth) {
    for (int labelIndex = 0; labelIndex < numOfLabels; labelIndex++) {

        int classValue;
        boolean actual = truth[labelIndex];
        if (actual) {
            classValue = 1;//from  w  ww .  ja v  a2s.  c  o m
        } else {
            classValue = 0;
        }

        double[] dist = new double[2];
        dist[1] = confidences[labelIndex];
        dist[0] = 1 - dist[1];

        m_Predictions[labelIndex].addElement(new NominalPrediction(classValue, dist, 1));
        all_Predictions.addElement(new NominalPrediction(classValue, dist, 1));
    }
}

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