Example usage for weka.filters.supervised.instance Resample setNoReplacement

Introduction

In this page you can find the example usage for weka.filters.supervised.instance Resample setNoReplacement.

Prototype

public void setNoReplacement(boolean value) 

Source Link

Document

Sets whether instances are drawn with or with out replacement.

Usage

From source file:de.ugoe.cs.cpdp.dataselection.LACE2.java

License:Apache License

@Override
public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
    Instances selectedData = new Instances(testdata);
    selectedData.clear();/*from w ww .  j a v  a  2s .com*/

    LinkedList<Instances> traindataCopy = new LinkedList<>(traindataSet);
    Collections.shuffle(traindataCopy);

    CLIFF cliff = new CLIFF();
    cliff.setParameter(Double.toString(percentage));
    MORPH morph = new MORPH();
    Median median = new Median();
    double minDist = Double.MIN_VALUE;

    for (Instances traindata : traindataCopy) {
        Instances cliffedData = cliff.applyCLIFF(traindata);
        if (minDist == Double.MIN_VALUE) {
            // determine distance for leader-follower algorithm
            Instances sample;
            if (traindata.size() > 100) {
                Resample resample = new Resample();
                resample.setSampleSizePercent(100.0 / traindata.size() * 100.0);
                resample.setBiasToUniformClass(0.0);
                resample.setNoReplacement(true);
                try {
                    resample.setInputFormat(traindata);
                    sample = Filter.useFilter(traindata, resample);
                } catch (Exception e) {
                    throw new RuntimeException(e);
                }
            } else {
                sample = new Instances(traindata);
            }
            double[] distances = new double[sample.size()];
            for (int i = 0; i < sample.size(); i++) {
                Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(sample.get(i), sample);
                distances[i] = MathArrays.distance(WekaUtils.instanceValues(sample.get(i)),
                        WekaUtils.instanceValues(unlikeNeighbor));
            }
            minDist = median.evaluate(distances);
        }
        for (int i = 0; i < cliffedData.size(); i++) {
            Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(cliffedData.get(i), selectedData);
            if (unlikeNeighbor == null) {
                selectedData.add(cliffedData.get(i));
            } else {
                double distance = MathArrays.distance(WekaUtils.instanceValues(cliffedData.get(i)),
                        WekaUtils.instanceValues(unlikeNeighbor));
                if (distance > minDist) {
                    morph.morphInstance(cliffedData.get(i), cliffedData);
                    selectedData.add(cliffedData.get(i));
                }
            }
        }
    }
}

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From source file:function.FilterResample.java

public static Instances filterResample(Instances inst) {
    Resample filter = new Resample();
    Instances instResample = null;/*from w  ww  .  j a  va2s .  c om*/
    filter.setBiasToUniformClass(1.0);
    try {
        filter.setInputFormat(inst);
        filter.setNoReplacement(false);
        filter.setSampleSizePercent(100);
        instResample = Filter.useFilter(inst, filter);
    } catch (Exception e) {
        System.out.println("Error when resampling input data!");
        e.printStackTrace();
    }

    return instResample;
}

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From source file:Helper.CustomFilter.java

public Instances resampling(Instances structure) {
    Resample filter = new Resample();
    Instances filteredIns = null;//from  w  w w.j av a 2s.  c o m
    filter.setBiasToUniformClass(1.0);
    try {
        filter.setInputFormat(structure);
        filter.setNoReplacement(false);
        filter.setSampleSizePercent(100);
        filteredIns = Filter.useFilter(structure, filter);
    } catch (Exception e) {
        e.printStackTrace();
    }
    return filteredIns;
}

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From source file:meansagnes.MeansAgnes.java

public void resample(double b, double z, int seed) {
    try {//from w w w.j  av a 2  s  .com
        System.out.println(data.toString() + "\n");
        Resample resampleFilter = new Resample();

        resampleFilter.setInputFormat(data);
        resampleFilter.setNoReplacement(false);
        resampleFilter.setBiasToUniformClass(b); // Uniform distribution of class
        resampleFilter.setSampleSizePercent(z);
        resampleFilter.setRandomSeed(seed);

        data = Filter.useFilter(data, resampleFilter);

        /*Random R = new Random();
        data.resample(R);*/
        System.out.println("HASIL RESAMPLE\n\n");
        System.out.println(data.toString() + "\n");
    } catch (Exception ex) {
        Logger.getLogger(MeansAgnes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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From source file:trainableSegmentation.WekaSegmentation.java

License:GNU General Public License

/**
 * Homogenize number of instances per class
 *
 * @param data input set of instances//w  w w.  j a v  a  2s  . co m
 * @return resampled set of instances
 */
public static Instances homogenizeTrainingData(Instances data) {
    final Resample filter = new Resample();
    Instances filteredIns = null;
    filter.setBiasToUniformClass(1.0);
    try {
        filter.setInputFormat(data);
        filter.setNoReplacement(false);
        filter.setSampleSizePercent(100);
        filteredIns = Filter.useFilter(data, filter);
    } catch (Exception e) {
        IJ.log("Error when resampling input data!");
        e.printStackTrace();
    }
    return filteredIns;

}

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From source file:trainableSegmentation.WekaSegmentation.java

License:GNU General Public License

/**
 * Homogenize number of instances per class (in the loaded training data)
 *//*from w w w. j a  va 2s . c  o  m*/
public void homogenizeTrainingData() {
    final Resample filter = new Resample();
    Instances filteredIns = null;
    filter.setBiasToUniformClass(1.0);
    try {
        filter.setInputFormat(this.loadedTrainingData);
        filter.setNoReplacement(false);
        filter.setSampleSizePercent(100);
        filteredIns = Filter.useFilter(this.loadedTrainingData, filter);
    } catch (Exception e) {
        IJ.log("Error when resampling input data!");
        e.printStackTrace();
    }
    this.loadedTrainingData = filteredIns;
}

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