Example usage for weka.core Instances instance

Introduction

In this page you can find the example usage for weka.core Instances instance.

Prototype

publicInstance instance(int index) 

Source Link

Document

Returns the instance at the given position.

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  ww. ja v  a  2  s  .c  o 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 ww  .jav a2 s .co  m
    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) {// ww w.ja  va  2 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.RankingByPairwiseComparison.java

License:Open Source License

@Override
public void buildRanker(Instances data) throws Exception {
    m_Classifiers = new ArrayList<weka.classifiers.AbstractClassifier>();
    m_AlgoPairs = new ArrayList<String>();
    m_NumLabels = Tools.getNumberTargets(data);

    // build pb datasets
    for (int a = 0; a < m_NumLabels; a++) {
        for (int b = 0; b < m_NumLabels; b++) {

            String pairStr = a + "|" + b;
            if (!hasPair(m_AlgoPairs, pairStr) && a != b) {
                m_AlgoPairs.add(pairStr);

                Instances d = new Instances(data);
                d.setClassIndex(-1);//w ww .  j  a  va2 s  .co m
                d.deleteAttributeAt(d.numAttributes() - 1);

                weka.filters.unsupervised.attribute.Add add = new weka.filters.unsupervised.attribute.Add();
                add.setInputFormat(d);
                add.setOptions(weka.core.Utils
                        .splitOptions("-T NOM -N class -L " + ((int) a) + "," + ((int) b) + " -C last"));

                d = Filter.useFilter(d, add);
                d.setClassIndex(d.numAttributes() - 1);

                for (int i = 0; i < d.numInstances(); i++) {

                    Instance metaInst = (Instance) data.instance(i);
                    Instance inst = d.instance(i);

                    double[] rankVector = Tools.getTargetVector(metaInst);

                    double rank_a = rankVector[a];
                    double rank_b = rankVector[b];

                    if (rank_a < rank_b) {
                        inst.setClassValue(0.0);
                    } else {
                        inst.setClassValue(1.0);
                    }
                }

                //weka.classifiers.functions.SMO cls = new weka.classifiers.functions.SMO();
                //String ops = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01\"";
                //cls.setOptions(weka.core.Utils.splitOptions(ops));                   
                //cls.buildClassifier(d);
                //weka.classifiers.functions.Logistic cls = new weka.classifiers.functions.Logistic();
                //weka.classifiers.trees.J48 cls = new weka.classifiers.trees.J48();
                //weka.classifiers.rules.ZeroR cls = new weka.classifiers.rules.ZeroR();
                weka.classifiers.trees.DecisionStump cls = new weka.classifiers.trees.DecisionStump();
                cls.buildClassifier(d);
                m_Classifiers.add(cls);
                m_BaseClassifierName = cls.getClass().getSimpleName();
                m_Add = add;
            }
        }
    }
}

---

From source file:fantail.algorithms.RankingByPairwiseComparison.java

License:Open Source License

@Override
public double[] recommendRanking(Instance testInst) throws Exception {
    Instances tempData = new Instances(testInst.dataset(), 0);
    tempData.add((Instance) testInst.copy());
    // remove the relation att
    tempData.setClassIndex(-1);//from  ww  w  .j  a va2s .  c om
    tempData.deleteAttributeAt(tempData.numAttributes() - 1);
    tempData = Filter.useFilter(tempData, m_Add);
    tempData.setClassIndex(tempData.numAttributes() - 1);
    double predRanking[] = new double[m_NumLabels];
    for (int i = 0; i < predRanking.length; i++) {
        predRanking[i] = m_NumLabels - 1;
    }
    for (int i = 0; i < m_Classifiers.size(); i++) {
        double predIndex = m_Classifiers.get(i).classifyInstance(tempData.instance(0));
        String algoPair = m_AlgoPairs.get(i);
        String[] parts = algoPair.split("\\|");
        int trueIndex = Integer.parseInt(parts[(int) predIndex]);
        predRanking[trueIndex] -= 1;
    }
    predRanking = Tools.doubleArrayToRanking(predRanking);
    return predRanking;
}

---

From source file:fantail.algorithms.RankingByPairwiseComparison.java

License:Open Source License

public double[] recommendRanking2(Instance testInst) throws Exception {
    Instances tempData = new Instances(testInst.dataset(), 0);
    tempData.add((Instance) testInst.copy());
    // remove the relation att
    tempData.setClassIndex(-1);/*from  w w  w  .j  a v a  2s .  c om*/
    tempData.deleteAttributeAt(tempData.numAttributes() - 1);
    tempData = Filter.useFilter(tempData, m_Add);
    tempData.setClassIndex(tempData.numAttributes() - 1);
    double predRanking[] = new double[m_NumLabels];
    for (int i = 0; i < m_Classifiers.size(); i++) {
        double predIndex = m_Classifiers.get(i).classifyInstance(tempData.instance(0));
        double predProb = m_Classifiers.get(i).distributionForInstance(tempData.instance(0))[0];
        String algoPair = m_AlgoPairs.get(i);
        String[] parts = algoPair.split("\\|");
        int trueIndex = Integer.parseInt(parts[(int) predIndex]);
        predRanking[trueIndex] -= predProb;
    }
    return Tools.doubleArrayToRanking(predRanking);
}

---

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   w  w w  . j  av a 2  s. com*/

    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.RankingWithBinaryPCT.java

License:Open Source License

private double computeVariance(Instances data) throws Exception {
    double[][] targets = new double[data.numInstances()][];
    for (int i = 0; i < data.numInstances(); i++) {
        targets[i] = Tools.getTargetVector(data.instance(i));
    }/*from  www  .  j  a  va 2 s. com*/
    double sumVar = 0;
    for (int i = 0; i < m_NumTargetLabels; i++) {
        double[] target_i = new double[data.numInstances()];

        for (int j = 0; j < data.numInstances(); j++) {
            Instance metaInst = (Instance) data.instance(j);
            target_i[j] = targets[j][i] * metaInst.weight();
        }
        sumVar += weka.core.Utils.variance(target_i);
    }
    return sumVar / m_NumTargetLabels;
}

---

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);/*  w w  w .j a  v a 2 s .  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 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);// ww w . j  ava2 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;
}

---

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