Example usage for weka.core Instance setDataset

Introduction

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

Prototype

public void setDataset(Instances instances);

Source Link

Document

Sets the reference to the dataset.

Usage

From source file:moa.streams.generators.cd.AbstractConceptDriftGenerator.java

License:Open Source License

public Instance nextInstance() {
    this.numInstances++;
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);
    double nextValue = this.nextValue();
    if (this.notBinaryStreamOption.isSet()) {
        inst.setValue(0, nextValue);/* w ww.  j  a va 2s  .  c  o  m*/
    } else {
        inst.setValue(0, this.nextbinaryValue(nextValue));
    }
    //Ground truth
    inst.setValue(1, this.getChange() ? 1 : 0);
    if (this.getChange() == true) {
        //this.clusterEvents.add(new ClusterEvent(this, this.numInstances, "Change", "Drift"));
    }
    inst.setValue(2, nextValue);
    return inst;
}

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From source file:moa.streams.generators.HyperplaneGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {

    int numAtts = this.numAttsOption.getValue();
    double[] attVals = new double[numAtts + 1];
    double sum = 0.0;
    double sumWeights = 0.0;
    for (int i = 0; i < numAtts; i++) {
        attVals[i] = this.instanceRandom.nextDouble();
        sum += this.weights[i] * attVals[i];
        sumWeights += this.weights[i];
    }//from  w  w  w  .  j a  v  a2 s  .c om
    int classLabel;
    if (sum >= sumWeights * 0.5) {
        classLabel = 1;
    } else {
        classLabel = 0;
    }
    //Add Noise
    if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
        classLabel = (classLabel == 0 ? 1 : 0);
    }

    Instance inst = new DenseInstance(1.0, attVals);
    inst.setDataset(getHeader());
    inst.setClassValue(classLabel);
    addDrift();
    return inst;
}

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From source file:moa.streams.generators.HyperplaneGeneratorReg.java

License:Open Source License

@Override
public Instance nextInstance() {

    int numAtts = this.numAttsOption.getValue();
    double[] attVals = new double[numAtts + 1];
    double sum = 0.0;
    double sumWeights = 0.0;
    double sumSquareWeights = 0.0;

    for (int i = 0; i < numAtts; i++) {
        attVals[i] = this.instanceRandom.nextDouble();
        sum += this.weights[i] * attVals[i];
        sumWeights += this.weights[i];
        sumSquareWeights += Math.pow(this.weights[i], 2);
    }/*from w  w w  .j  a  v a  2  s .c o m*/
    double classLabel = 0;
    if (targetValueOption.getChosenIndex() == 0)
        if (sum >= sumWeights * 0.5) {
            classLabel = 1;
        } else {
            classLabel = 0;
        }
    else if (targetValueOption.getChosenIndex() == 1)
        classLabel = sum / Math.sqrt(sumSquareWeights);
    else if (targetValueOption.getChosenIndex() == 2)
        classLabel = Math.pow(sum, 2) / sumSquareWeights;
    else if (targetValueOption.getChosenIndex() == 3)
        classLabel = Math.pow(sum / Math.sqrt(sumSquareWeights), 3);

    //Add Noise
    if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {

        if (targetValueOption.getChosenIndex() == 0) {
            if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
                classLabel = (classLabel == 0 ? 1 : 0);
            }
        } else {
            // the range of noise added to the distance increases by the increase of the number of dimentsions  
            double temp = this.instanceRandom.nextInt(1000);
            temp = temp * Math.sqrt(numAtts) / 1000;

            if (targetValueOption.getChosenIndex() == 1)
                temp = Math.pow(temp, 1);
            else if (targetValueOption.getChosenIndex() == 2)
                temp = Math.pow(temp, 2);
            else if (targetValueOption.getChosenIndex() == 3)
                temp = Math.pow(temp, 3);

            if (this.instanceRandom.nextInt(2) == 1)
                classLabel -= temp;
            else
                classLabel += temp;
            classLabel = Math.abs(classLabel);
        }
    }

    //Instance inst = new DenseInstance(1.0, attVals);
    Instance inst = new DenseInstance(1.0, attVals);
    inst.setDataset(getHeader());
    inst.setClassValue(classLabel);
    addDrift();
    return inst;
}

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From source file:moa.streams.generators.InducedRandomNonStationaryDataGenerator.java

License:Apache License

@Override
public Instance nextInstance() {
    // 1.) Pick a concept at weighted random
    DriftingExemplarInstance[] activeConceptsArray = activeConcepts.toArray(new DriftingExemplarInstance[1]);
    int nextConceptIdx = rng.nextInt(activeConceptsArray.length);

    // 2.) Pull the sampled Instance from the concept
    DenseInstance candidateInstance = activeConceptsArray[nextConceptIdx].nextInstance();
    Instance inst = new DenseInstance(candidateInstance);
    inst.setDataset(getHeader());

    // 3.) Add label noise
    if (rng.nextDouble() < this.labelNoiseOption.getValue() / 100.0) {
        inst.setClassValue(rng.nextInt(numClasses));
    }/*  ww w .  ja  v a2 s.  c  o m*/

    // 4.) Shift active concepts
    if (!inactiveConcepts.isEmpty()
            && rng.nextDouble() < this.classActivationProbabilityOption.getValue() / 100.0) {
        DriftingExemplarInstance[] inactiveConceptsArray = inactiveConcepts
                .toArray(new DriftingExemplarInstance[1]);
        int idxToActivate = rng.nextInt(inactiveConceptsArray.length);
        int idxToDeactivate = rng.nextInt(activeConceptsArray.length);
        activeConcepts.add(inactiveConceptsArray[idxToActivate]);
        activeConcepts.remove(activeConceptsArray[idxToDeactivate]);
        inactiveConcepts.add(activeConceptsArray[idxToDeactivate]);
        inactiveConcepts.remove(inactiveConceptsArray[idxToActivate]);
    }

    //5.) Return new instance
    return inst;
}

---

From source file:moa.streams.generators.LEDGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);
    int selected = this.instanceRandom.nextInt(10);
    for (int i = 0; i < 7; i++) {
        if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
            inst.setValue(i, originalInstances[selected][i] == 0 ? 1 : 0);
        } else {/*from www  .j  a  va2  s . c om*/
            inst.setValue(i, originalInstances[selected][i]);
        }
    }
    if (!this.suppressIrrelevantAttributesOption.isSet()) {
        for (int i = 0; i < NUM_IRRELEVANT_ATTRIBUTES; i++) {
            inst.setValue(i + 7, this.instanceRandom.nextInt(2));
        }
    }
    inst.setClassValue(selected);
    return inst;
}

---

From source file:moa.streams.generators.LEDGeneratorDrift.java

License:Open Source License

@Override
public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);
    int selected = this.instanceRandom.nextInt(10);
    for (int i = 0; i < 7; i++) {
        if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
            inst.setValue(this.numberAttribute[i], originalInstances[selected][i] == 0 ? 1 : 0);
        } else {/*  www  . j av  a2s. c  o  m*/
            inst.setValue(this.numberAttribute[i], originalInstances[selected][i]);
        }
    }
    if (!this.suppressIrrelevantAttributesOption.isSet()) {
        for (int i = 0; i < NUM_IRRELEVANT_ATTRIBUTES; i++) {
            inst.setValue(this.numberAttribute[i + 7], this.instanceRandom.nextInt(2));
        }
    }
    inst.setClassValue(selected);
    return inst;
}

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From source file:moa.streams.generators.multilabel.MetaMultilabelGenerator.java

License:Open Source License

/**
 * GenerateMLInstance.//from   www.ja v  a  2  s .  co  m
 *
 * @param   Y   a set of label [indices]
 * @return a multit-labelled example
 */
private Instance generateMLInstance(HashSet<Integer> Y) {

    // create a multi-label instance:
    Instance x_ml = new SparseInstance(this.multilabelStreamTemplate.numAttributes());
    x_ml.setDataset(this.multilabelStreamTemplate);

    // set classes
    for (int j = 0; j < m_L; j++) {
        x_ml.setValue(j, 0.0);
    }
    for (int l : Y) {
        x_ml.setValue(l, 1.0);
    }

    // generate binary instances
    Instance x_0 = getNextWithBinary(0);
    Instance x_1 = getNextWithBinary(1);

    // Loop through each feature attribute @warning: assumes class is last index
    for (int a = 0; a < m_A; a++) {

        // The combination is present: use a positive value
        if (Y.containsAll(m_TopCombinations[a])) {
            x_ml.setValue(m_L + a, x_1.value(a));
            //x_ml.setValue(m_L+a,1.0);
        } // The combination is absent: use a negative value
        else {
            x_ml.setValue(m_L + a, x_0.value(a));
            //x_ml.setValue(m_L+a,0.0);
        }
    }

    return x_ml;
}

---

From source file:moa.streams.generators.MySEAGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    double attrib1 = 0, attrib2 = 0, attrib3 = 0;
    int group = 0;
    boolean desiredClassFound = false;
    while (!desiredClassFound) {
        // generate attributes
        attrib1 = 10 * this.instanceRandom.nextDouble();
        attrib2 = 10 * this.instanceRandom.nextDouble();
        attrib3 = 10 * this.instanceRandom.nextDouble();

        // determine class
        group = classificationFunctions[this.functionOption.getValue() - 1].determineClass(attrib1, attrib2,
                attrib3);//from w w w .j av  a 2s.  co m
        if (!this.balanceClassesOption.isSet()) {
            desiredClassFound = true;
        } else {
            // balance the classes
            if ((this.nextClassShouldBeZero && (group == 0)) || (!this.nextClassShouldBeZero && (group == 1))) {
                desiredClassFound = true;
                this.nextClassShouldBeZero = !this.nextClassShouldBeZero;
            } // else keep searching
        }
    }
    //Add Noise
    if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
        group = (group == 0 ? 1 : 0);
    }

    // construct instance
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setValue(0, attrib1);
    inst.setValue(1, attrib2);
    inst.setValue(2, attrib3);
    inst.setDataset(header);
    inst.setClassValue(group);
    return inst;
}

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From source file:moa.streams.generators.RandomRBFGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    Centroid centroid = this.centroids[MiscUtils.chooseRandomIndexBasedOnWeights(this.centroidWeights,
            this.instanceRandom)];
    int numAtts = this.numAttsOption.getValue();
    double[] attVals = new double[numAtts + 1];
    for (int i = 0; i < numAtts; i++) {
        attVals[i] = (this.instanceRandom.nextDouble() * 2.0) - 1.0;
    }/*from   w  ww . j a  va  2s.  c o  m*/
    double magnitude = 0.0;
    for (int i = 0; i < numAtts; i++) {
        magnitude += attVals[i] * attVals[i];
    }
    magnitude = Math.sqrt(magnitude);
    double desiredMag = this.instanceRandom.nextGaussian() * centroid.stdDev;
    double scale = desiredMag / magnitude;
    for (int i = 0; i < numAtts; i++) {
        attVals[i] = centroid.centre[i] + attVals[i] * scale;
    }
    Instance inst = new DenseInstance(1.0, attVals);
    inst.setDataset(getHeader());
    inst.setClassValue(centroid.classLabel);
    return inst;
}

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From source file:moa.streams.generators.RandomTreeGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    double[] attVals = new double[this.numNominalsOption.getValue() + this.numNumericsOption.getValue()];
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    for (int i = 0; i < attVals.length; i++) {
        attVals[i] = i < this.numNominalsOption.getValue()
                ? this.instanceRandom.nextInt(this.numValsPerNominalOption.getValue())
                : this.instanceRandom.nextDouble();
        inst.setValue(i, attVals[i]);//from   www .ja v a2s.c  o  m
    }
    inst.setDataset(header);
    inst.setClassValue(classifyInstance(this.treeRoot, attVals));
    return inst;
}

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