Example usage for weka.core Instance numAttributes

Introduction

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

Prototype

public int numAttributes();

Source Link

Document

Returns the number of attributes.

Usage

From source file:test20November.ArffFileLoader.java

public ArffFileLoader(String filename) throws FileNotFoundException, IOException {
    BufferedReader reader = new BufferedReader(new FileReader(filename));
    ArffLoader.ArffReader arff = new ArffLoader.ArffReader(reader);
    Instances data = arff.getData();/*w  w  w  .jav a2  s . c o m*/
    data.setClassIndex(data.numAttributes() - 1);

    attributes = new String[data.numInstances()][data.numAttributes() - 1];
    labels = new String[data.numInstances()];

    for (int i = 0; i < data.numInstances(); i++) {
        Instance instance = data.instance(i);
        for (int j = 0; j < instance.numAttributes() - 1; j++) {
            attributes[i][j] = instance.stringValue(j);
        }
        labels[i] = instance.stringValue(instance.numAttributes() - 1);
    }

    attributesLegalValues = new String[data.numAttributes() - 1][];
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        attributesLegalValues[i] = (String[]) Collections.list(data.attribute(i).enumerateValues())
                .toArray(new String[data.attribute(i).numValues()]);
    }

    labelsLegalValues = (String[]) Collections.list(data.attribute(data.numAttributes() - 1).enumerateValues())
            .toArray(new String[data.attribute(data.numAttributes() - 1).numValues()]);
}

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From source file:text_clustering.Cobweb.java

License:Open Source License

/**
 * Adds an instance to the clusterer./*from   w ww .jav  a2s.c o m*/
 *
 * @param newInstance the instance to be added
 * @throws Exception    if something goes wrong
 */
public void updateClusterer(Instance newInstance) throws Exception {
    m_numberOfClustersDetermined = false;

    if (m_cobwebTree == null) {
        m_cobwebTree = new CNode(newInstance.numAttributes(), newInstance);
    } else {
        m_cobwebTree.addInstance(newInstance);
    }
}

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From source file:tml.utils.DistanceLib.java

License:Apache License

public static double kullbackLeibler(Instance inst1, Instance inst2) {

    double divergence = 0.0;
    for (int i = 0; i < inst1.numAttributes(); ++i) {
        if (inst1.value(i) != 0 && inst2.value(i) != 0) {
            divergence += inst1.value(i) * Math.log(inst1.value(i) / inst2.value(i));
        }/* ww w . j  a  v a  2 s .c o  m*/
    }
    divergence /= Math.log(2);
    return divergence;
}

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From source file:tml.utils.DistanceLib.java

License:Apache License

public static double jensenShannon(Instance inst1, Instance inst2) {

    Instance averageInst = new Instance(inst1.numAttributes());
    for (int i = 0; i < inst1.numAttributes(); i++) {
        averageInst.setValue(i, (inst1.value(i) + inst2.value(i)) / 2);
    }/*from www  .  ja v  a  2 s. co m*/

    double divergence = (kullbackLeibler(inst1, averageInst) + kullbackLeibler(inst2, averageInst)) / 2;
    return divergence;
}

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From source file:tr.gov.ulakbim.jDenetX.classifiers.Perceptron.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {

    //Init Perceptron
    if (this.reset) {
        this.reset = false;
        this.numberAttributes = inst.numAttributes();
        this.numberClasses = inst.numClasses();
        this.weightAttribute = new double[inst.numClasses()][inst.numAttributes()];
        for (int i = 0; i < inst.numClasses(); i++) {
            for (int j = 0; j < inst.numAttributes(); j++) {
                weightAttribute[i][j] = 0.2 * Math.random() - 0.1;
            }/*from   w  ww  . j a v a  2s  .c  o  m*/
        }
    }

    double[] preds = new double[inst.numClasses()];
    for (int i = 0; i < inst.numClasses(); i++) {
        preds[i] = prediction(inst, i);
    }
    double learningRatio = learningRatioOption.getValue();

    int actualClass = (int) inst.classValue();
    for (int i = 0; i < inst.numClasses(); i++) {
        double actual = (i == actualClass) ? 1.0 : 0.0;
        double delta = (actual - preds[i]) * preds[i] * (1 - preds[i]);
        for (int j = 0; j < inst.numAttributes() - 1; j++) {
            this.weightAttribute[i][j] += learningRatio * delta * inst.value(j);
        }
        this.weightAttribute[i][inst.numAttributes() - 1] += learningRatio * delta;
    }
}

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From source file:tr.gov.ulakbim.jDenetX.clusterers.CobWeb.java

License:Open Source License

/**
 * Adds an instance to the clusterer.//from   w  ww  .  j a v  a 2  s.c  o m
 *
 * @param newInstance the instance to be added
 * @throws Exception if something goes wrong
 */
// public void updateClusterer(Instance newInstance) throws Exception {
@Override
public void trainOnInstanceImpl(Instance newInstance) { //throws Exception {
    m_numberOfClustersDetermined = false;

    if (m_cobwebTree == null) {
        m_cobwebTree = new CNode(newInstance.numAttributes(), newInstance);
    } else {
        m_cobwebTree.addInstance(newInstance);
    }
}

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From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java

License:Open Source License

protected void selfTrain(Instance testInst) {
    int maxInstances = this.maxInstancesOption.getValue();
    int poolSizeRatio = poolSizeOption.getValue();
    int poolLimit = maxInstances / poolSizeRatio;
    int poolCount = 0;
    VotedInstancePool vInstPool = SelfOzaBoostID.getVotedInstancePool();
    noOfClassesInPool = vInstPool.getNoOfClasses();

    System.out.println("No of instances in the pool: " + vInstPool.getSize());
    System.out.println("No of classes in the pool: " + noOfClassesInPool);

    if (vInstPool.getSize() > 10) {
        ArrayList<Attribute> attrs = new ArrayList<Attribute>();
        for (int i = 0; i < testInst.numAttributes(); i++) {
            attrs.add(testInst.attribute(i));
        }// w  w  w  . j a va 2  s  .  c  o  m
        Instances instances = new Instances("instances", attrs, vInstPool.getSize());
        Iterator instanceIt = vInstPool.iterator();
        System.out.println("Size of pool: " + vInstPool.getSize());

        while (instanceIt.hasNext() && poolCount < poolLimit) {
            VotedInstance vInstance = (VotedInstance) instanceIt.next();
            ((Instances) instances).add(vInstance.getInstance());
            poolCount++;
        }

        System.out.println("Size of instances: " + instances.size());
        instances = clusterInstances(instances);
        InstanceStream activeStream = new CachedInstancesStream((Instances) instances);

        System.out.println("Selftraining have been started");
        System.out.println("Number of self training instances: " + instances.numInstances());

        long treeSize = vInstPool.getSize();
        long limit = treeSize / SAMPLING_LIMIT;
        Instance inst = null;

        for (long j = 0; j < limit && activeStream.hasMoreInstances(); j++) {
            inst = activeStream.nextInstance();
            if (inst.numAttributes() == attrs.size()) {
                model.trainOnInstance(inst);
            }
        }
    }

}

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From source file:tr.gov.ulakbim.jDenetX.streams.filters.AddNoiseFilter.java

License:Open Source License

public Instance nextInstance() {
    Instance inst = (Instance) this.inputStream.nextInstance().copy();
    for (int i = 0; i < inst.numAttributes(); i++) {
        double noiseFrac = i == inst.classIndex() ? this.classNoiseFractionOption.getValue()
                : this.attNoiseFractionOption.getValue();
        if (inst.attribute(i).isNominal()) {
            DoubleVector obs = (DoubleVector) this.attValObservers.get(i);
            if (obs == null) {
                obs = new DoubleVector();
                this.attValObservers.set(i, obs);
            }//from ww  w .  j a v  a  2 s.  c om
            int originalVal = (int) inst.value(i);
            if (!inst.isMissing(i)) {
                obs.addToValue(originalVal, inst.weight());
            }
            if ((this.random.nextDouble() < noiseFrac) && (obs.numNonZeroEntries() > 1)) {
                do {
                    inst.setValue(i, this.random.nextInt(obs.numValues()));
                } while (((int) inst.value(i) == originalVal) || (obs.getValue((int) inst.value(i)) == 0.0));
            }
        } else {
            GaussianEstimator obs = (GaussianEstimator) this.attValObservers.get(i);
            if (obs == null) {
                obs = new GaussianEstimator();
                this.attValObservers.set(i, obs);
            }
            obs.addObservation(inst.value(i), inst.weight());
            inst.setValue(i, inst.value(i) + this.random.nextGaussian() * obs.getStdDev() * noiseFrac);
        }
    }
    return inst;
}

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From source file:tubes2.myClusterers.EuclideanDistance.java

@Override
public double distanceOf(Instance a, Instance b) throws Exception {
    if (a.numAttributes() != b.numAttributes())
        throw new Exception("number of attributes doesn't match");
    double sumSquares = 0;
    for (int i = 0; i < a.numAttributes(); i++) {
        double Error = a.value(i) - b.value(i);
        sumSquares += Error * Error;
    }//from   w w  w  . jav a2 s. c  o m
    return Math.sqrt(sumSquares);
}

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From source file:tubes2.myClusterers.myKMeans.java

public boolean instanceDifferent(Instance a, Instance b) {
    for (int i = 0; i < a.numAttributes(); i++) {
        if (a.value(i) != b.value(i))
            return true;
    }//w  ww  . ja  v  a 2  s . c  o m
    return false;
}

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