Example usage for weka.core Instance value

List of usage examples for weka.core Instance value

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Introduction

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

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:fantail.algorithms.BinaryART.java

License:Open Source License

private Instances[] splitData(Instances data, int attIndex, double splitPoint) throws Exception {

    Instances[] subsets = new Instances[2];
    subsets[0] = new Instances(data, 0);
    subsets[1] = new Instances(data, 0);

    // changed on 7 Feb 2013, because for some LR datasets, the Alpo returns NaN
    int halfPoint = (int) (data.numInstances() * 0.50);
    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = data.instance(i);
        if (inst.value(attIndex) <= splitPoint && subsets[0].numInstances() < halfPoint) {
            subsets[0].add(inst);/*from  w w w.j  a va  2  s  .  co m*/
        } else {
            subsets[1].add(inst);
        }
    }

    if (subsets[1].numInstances() == 0) {
        subsets[1].add(subsets[0].instance(0));
    }

    if (subsets[0].numInstances() == 0) {
        subsets[0].add(subsets[1].instance(0));
    }
    return subsets;

    // following were used before 7 Feb 2013
    //        for (int i = 0; i < data.numInstances(); i++) {
    //            Instance inst = data.instance(i);
    //            if (inst.value(attIndex) <= splitPoint) {
    //                subsets[0].add(inst);
    //            } else {
    //                subsets[1].add(inst);
    //            }
    //        }
    //
    //        if (subsets[1].numInstances() == 0) {
    //            subsets[1].add(subsets[0].instance(0));
    //        }
    //
    //        if (subsets[0].numInstances() == 0) {
    //            subsets[0].add(subsets[1].instance(0));
    //        }
    //        return subsets;
}

From source file:fantail.algorithms.BinaryART.java

License:Open Source License

private double getMedian2(Instances data, int attIndex) throws Exception {
    double[] numArray = new double[data.numInstances()];
    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = data.instance(i);
        numArray[i] = inst.value(attIndex);
    }//from w  w w  .j  a v a  2s.c om
    Arrays.sort(numArray);
    double median;
    if (numArray.length % 2 == 0) {
        median = ((double) numArray[numArray.length / 2] + (double) numArray[numArray.length / 2 + 1]) / 2;
    } else {
        median = (double) numArray[numArray.length / 2];
    }
    return median;
}

From source file:fantail.algorithms.BinaryART.java

License:Open Source License

private double getMedian(Instances data, int attIndex) throws Exception {

    if (false) {/*from  w  w w.j ava2 s.  com*/
        return getMedian2(data, attIndex); // added 07-july 2013; actually they are the same
        // removed 17/07/2013 
    }

    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = (Instance) data.instance(i);
        stats.addValue(inst.value(attIndex));
    }
    double median = stats.getPercentile(50);
    return median;
}

From source file:fantail.algorithms.BinaryART.java

License:Open Source License

@Override
public double[] recommendRanking(Instance metaInst) throws Exception {
    if (m_Attribute == null) {
        return Tools.doubleArrayToRanking(m_Prototype);
    } else {//w  w  w. ja  v a  2s  .  c o  m
        if (metaInst.value(m_Attribute) <= m_SplitPoint) {
            return m_Successors[0].recommendRanking(metaInst);
        } else {
            return m_Successors[1].recommendRanking(metaInst);
        }
    }
}

From source file:fantail.algorithms.RankingViaRegression.java

License:Open Source License

@Override
public void buildRanker(Instances data) throws Exception {

    Instances workingData = new Instances(data);
    //Instance instTemp = workingData.instance(0);

    //m_LastFeatureIndex = workingData.numAttributes() - 1;
    m_NumFeatures = workingData.numAttributes() - 1;
    m_NumTargets = Tools.getNumberTargets(data);
    m_Classifiers = new AbstractClassifier[m_NumTargets];

    for (int i = 0; i < m_NumTargets; i++) {
        weka.classifiers.functions.LinearRegression lr = new weka.classifiers.functions.LinearRegression();
        m_Classifiers[i] = AbstractClassifier.makeCopy(lr);
    }/*from ww  w . ja v  a  2 s  . c  om*/

    Instances[] trainingSets = new Instances[m_NumTargets];

    for (int t = 0; t < m_NumTargets; t++) {

        ArrayList attributes = new ArrayList();
        for (int i = 0; i < m_NumFeatures; i++) {
            attributes.add(new Attribute(workingData.attribute(i).name()));
        }

        String targetName = "att-" + (t + 1);
        attributes.add(new Attribute(targetName));

        trainingSets[t] = new Instances("data-" + targetName, attributes, 0);

        for (int j = 0; j < workingData.numInstances(); j++) {
            Instance metaInst = workingData.instance(j);
            double[] ranking = Tools.getTargetVector(metaInst);
            double[] values = new double[trainingSets[t].numAttributes()];

            for (int m = 0; m < (trainingSets[t].numAttributes() - 1); m++) {
                values[m] = metaInst.value(m);
            }
            values[values.length - 1] = ranking[t];
            trainingSets[t].add(new DenseInstance(1.0, values));
        }

        trainingSets[t].setClassIndex(trainingSets[t].numAttributes() - 1);
        m_Classifiers[t].buildClassifier(trainingSets[t]);
    }

    m_TempHeader = new Instances(trainingSets[0], 0);
}

From source file:fantail.algorithms.RankingViaRegression.java

License:Open Source License

@Override
public double[] recommendRanking(Instance metaInst) throws Exception {

    double[] values = new double[m_NumFeatures + 1];
    for (int i = 0; i < values.length - 1; i++) {
        values[i] = metaInst.value(i);
    }//from   ww  w .  j a  va  2 s.c o  m
    values[values.length - 1] = 0;

    Instance inst = new DenseInstance(1.0, values);
    inst.setDataset(m_TempHeader);

    //
    double[] preds = new double[m_NumTargets];

    for (int t = 0; t < m_NumTargets; t++) {

        double pred = m_Classifiers[t].classifyInstance(inst);
        if (pred <= 0) {
            pred = 0;
        }
        if (pred >= m_NumTargets) {
            pred = m_NumTargets;
        }
        preds[t] = pred;
    }

    return Tools.doubleArrayToRanking(preds);
}

From source file:fantail.algorithms.RankingWithBinaryPCT.java

License:Open Source License

private Instances[] splitData(Instances data, int attIndex, double splitPoint) throws Exception {
    Instances[] subsets = new Instances[2];
    subsets[0] = new Instances(data, 0);
    subsets[1] = new Instances(data, 0);

    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = data.instance(i);
        if (inst.value(attIndex) <= splitPoint && (subsets[0].numInstances() <= 0.5 * data.numInstances())) {
            subsets[0].add(inst);/*from w ww. j ava 2  s. c om*/
        } else {
            subsets[1].add(inst);
        }
    }
    // TODO: 
    if (subsets[1].numInstances() == 0) {
        subsets[1].add(subsets[0].instance(0));
    }
    if (subsets[0].numInstances() == 0) {
        subsets[0].add(subsets[1].instance(0));
    }
    return subsets;
}

From source file:fantail.algorithms.RankingWithBinaryPCT.java

License:Open Source License

private Instances[] splitData2(Instances data, int attIndex, double splitPoint) throws Exception {
    Instances[] subsets = new Instances[2];
    subsets[0] = new Instances(data, 0);
    subsets[1] = new Instances(data, 0);
    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = data.instance(i);
        if (inst.value(attIndex) <= splitPoint) {
            subsets[0].add(inst);/*from   w w w  .  j a  v  a 2s  .  c  o m*/
        } else {
            subsets[1].add(inst);
        }
    }
    // TODO: 
    if (subsets[1].numInstances() == 0) {
        subsets[1].add(subsets[0].instance(0));
    }
    if (subsets[0].numInstances() == 0) {
        subsets[0].add(subsets[1].instance(0));
    }
    return subsets;
}

From source file:fantail.algorithms.RankingWithBinaryPCT.java

License:Open Source License

private double getMedian(Instances data, int attIndex) throws Exception {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (int i = 0; i < data.numInstances(); i++) {
        Instance inst = (Instance) data.instance(i);
        stats.addValue(inst.value(attIndex));
    }/*from   w w  w.j av  a  2  s  .  c  o  m*/
    double median = stats.getPercentile(50);
    return median;
}

From source file:fantail.algorithms.RankingWithBinaryPCT.java

License:Open Source License

@Override
public double[] recommendRanking(Instance metaInst) throws Exception {
    if (m_Attribute == null) {
        return m_Prototype;
    } else {/*from  w w w.  j a  v a2 s .  c om*/
        if (metaInst.value(m_Attribute) <= m_SplitPoint) {
            return m_Successors[0].recommendRanking(metaInst);
        } else {
            return m_Successors[1].recommendRanking(metaInst);
        }
    }
}