Example usage for weka.core Instance setValue

Introduction

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

Prototype

public void setValue(Attribute att, String value);

Source Link

Document

Sets a value of an nominal or string attribute to the given value.

Usage

From source file:tr.gov.ulakbim.jDenetX.streams.generators.multilabel.MetaMultilabelGenerator.java

License:Open Source License

/**
 * GenerateMLInstance./*from w  w w .j a v a 2  s . c o  m*/
 */
private Instance generateMLInstance(ArrayList<Integer> lbls) throws Exception {

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

    // set classes
    for (int i = 0; i < m_N; i++)
        ml_x.setValue(i, 0.0);
    for (int i = 0; i < lbls.size(); i++) {
        ml_x.setValue(lbls.get(i), 1.0);
    }

    // generate binary instances
    Instance binary0 = getNextWithBinary(0);
    Instance binary1 = 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 (lbls.containsAll(m_FeatureEffects[a % m_FeatureEffects.length])) {
            ml_x.setValue(m_N + a, binary1.value(a));
        }
        // The combination is absent: use a negative value
        else {
            ml_x.setValue(m_N + a, binary0.value(a));
        }
    }

    return ml_x;

}

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From source file:tr.gov.ulakbim.jDenetX.streams.generators.RandomTreeGenerator.java

License:Open Source License

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  ww w . j av a2  s. co  m
    inst.setDataset(header);
    inst.setClassValue(classifyInstance(this.treeRoot, attVals));
    return inst;
}

---

From source file:tr.gov.ulakbim.jDenetX.streams.generators.SEAGenerator.java

License:Open Source License

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  a  v a  2 s.  c  om
        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:tr.gov.ulakbim.jDenetX.streams.generators.STAGGERGenerator.java

License:Open Source License

public Instance nextInstance() {

    int size = 0, color = 0, shape = 0, group = 0;
    boolean desiredClassFound = false;
    while (!desiredClassFound) {
        // generate attributes
        size = this.instanceRandom.nextInt(3);
        color = this.instanceRandom.nextInt(3);
        shape = this.instanceRandom.nextInt(3);

        // determine class
        group = classificationFunctions[this.functionOption.getValue() - 1].determineClass(size, color, shape);
        if (!this.balanceClassesOption.isSet()) {
            desiredClassFound = true;/*from w  w w.  jav  a2 s  . c  o  m*/
        } else {
            // balance the classes
            if ((this.nextClassShouldBeZero && (group == 0)) || (!this.nextClassShouldBeZero && (group == 1))) {
                desiredClassFound = true;
                this.nextClassShouldBeZero = !this.nextClassShouldBeZero;
            } // else keep searching
        }
    }

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

---

From source file:tr.gov.ulakbim.jDenetX.streams.generators.WaveformGenerator.java

License:Open Source License

public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);/*from w ww. j av a 2  s  .co m*/
    int waveform = this.instanceRandom.nextInt(NUM_CLASSES);
    int choiceA = 0, choiceB = 0;
    switch (waveform) {
    case 0:
        choiceA = 0;
        choiceB = 1;
        break;
    case 1:
        choiceA = 0;
        choiceB = 2;
        break;
    case 2:
        choiceA = 1;
        choiceB = 2;
        break;
    default:
        break;
    }
    double multiplierA = this.instanceRandom.nextDouble();
    double multiplierB = 1.0 - multiplierA;
    for (int i = 0; i < NUM_BASE_ATTRIBUTES; i++) {
        inst.setValue(i, (multiplierA * hFunctions[choiceA][i]) + (multiplierB * hFunctions[choiceB][i])
                + this.instanceRandom.nextGaussian());
    }
    if (this.addNoiseOption.isSet()) {
        for (int i = NUM_BASE_ATTRIBUTES; i < TOTAL_ATTRIBUTES_INCLUDING_NOISE; i++) {
            inst.setValue(i, this.instanceRandom.nextGaussian());
        }
    }
    inst.setClassValue(waveform);
    return inst;
}

---

From source file:transformation.mimlTOml.ArithmeticTransformation.java

License:Open Source License

@Override
public MultiLabelInstances transformDataset() throws Exception {
    Instances newData = new Instances(template);
    int labelIndices[] = dataset.getLabelIndices();
    Instance newInst = new DenseInstance(newData.numAttributes());
    newInst.setDataset(newData); // Sets the reference to the dataset

    // For all bags in the dataset
    double nBags = dataset.getNumBags();
    for (int i = 0; i < nBags; i++) {
        // retrieves a bag
        Bag bag = dataset.getBag(i);/*from   w ww.j  a  v  a  2  s . co m*/
        // sets the bagLabel
        newInst.setValue(0, bag.value(0));

        // retrieves instances (relational value) for each bag
        Instances instances = bag.getBagAsInstances();
        // for all attributes in bag
        for (int j = 0, attIdx = 1; j < instances.numAttributes(); j++, attIdx++) {
            double value = instances.meanOrMode(j);
            newInst.setValue(attIdx, value);
        }

        // inserts label information into the instance
        for (int j = 0; j < labelIndices.length; j++) {
            newInst.setValue(updatedLabelIndices[j], dataset.getBag(i).value(labelIndices[j]));
        }

        newData.add(newInst);
    }

    return new MultiLabelInstances(newData, dataset.getLabelsMetaData());
}

---

From source file:transformation.mimlTOml.ArithmeticTransformation.java

License:Open Source License

@Override
public Instance transformInstance(Bag bag) throws Exception {

    int labelIndices[] = dataset.getLabelIndices();
    Instance newInst = new DenseInstance(template.numAttributes());

    // sets the bagLabel
    newInst.setDataset(bag.dataset()); // Sets the reference to the dataset
    newInst.setValue(0, bag.value(0));

    // retrieves instances (relational value)
    Instances instances = bag.getBagAsInstances();
    // For all attributes in bag
    for (int j = 0, attIdx = 1; j < instances.numAttributes(); j++, attIdx++) {
        double value = instances.meanOrMode(j);
        newInst.setValue(attIdx, value);
    }/* www .  j a v  a 2s. com*/

    // Insert label information into the instance
    for (int j = 0; j < labelIndices.length; j++) {
        newInst.setValue(updatedLabelIndices[j], bag.value(labelIndices[j]));
    }

    return newInst;
}

---

From source file:transformation.mimlTOml.GeometricTransformation.java

License:Open Source License

@Override
public MultiLabelInstances transformDataset() throws Exception {

    Instances newData = new Instances(template);
    int labelIndices[] = dataset.getLabelIndices();
    Instance newInst = new DenseInstance(newData.numAttributes());
    newInst.setDataset(newData); // Sets the reference to the dataset

    // For all bags in the dataset
    double nBags = dataset.getNumBags();
    for (int i = 0; i < nBags; i++) {
        // retrieves a bag
        Bag bag = dataset.getBag(i);/* w  w w. ja v a2s.  c  o  m*/
        // sets the bagLabel
        newInst.setValue(0, bag.value(0));

        // retrieves instances (relational value) for each bag
        Instances instances = bag.getBagAsInstances();
        // for all attributes in bag
        for (int j = 0, attIdx = 1; j < instances.numAttributes(); j++, attIdx++) {
            double[] minimax = minimax(instances, j);
            double value = (minimax[0] + minimax[1]) / 2.0;
            newInst.setValue(attIdx, value);
        }

        // inserts label information into the instance
        for (int j = 0; j < labelIndices.length; j++) {
            newInst.setValue(updatedLabelIndices[j], dataset.getBag(i).value(labelIndices[j]));
        }

        newData.add(newInst);
    }
    return new MultiLabelInstances(newData, dataset.getLabelsMetaData());
}

---

From source file:transformation.mimlTOml.GeometricTransformation.java

License:Open Source License

@Override
public Instance transformInstance(Bag bag) throws Exception {
    int labelIndices[] = dataset.getLabelIndices();
    Instance newInst = new DenseInstance(template.numAttributes());

    // sets the bagLabel
    newInst.setDataset(bag.dataset()); // Sets the reference to the dataset
    newInst.setValue(0, bag.value(0));

    // retrieves instances (relational value)
    Instances instances = bag.getBagAsInstances();
    // For all attributes in bag
    for (int j = 0, attIdx = 1; j < instances.numAttributes(); j++, attIdx++) {
        double[] minimax = minimax(instances, j);
        double value = (minimax[0] + minimax[1]) / 2.0;
        newInst.setValue(attIdx, value);
    }/*from w ww. jav  a2  s .  c  o m*/

    // Insert label information into the instance
    for (int j = 0; j < labelIndices.length; j++) {
        newInst.setValue(updatedLabelIndices[j], bag.value(labelIndices[j]));
    }

    return newInst;
}

---

From source file:transformation.mimlTOml.MiniMaxTransformation.java

License:Open Source License

@Override
public MultiLabelInstances transformDataset() throws Exception {

    Instances newData = new Instances(template);
    int labelIndices[] = dataset.getLabelIndices();
    Instance newInst = new DenseInstance(newData.numAttributes());
    newInst.setDataset(newData); // Sets the reference to the dataset

    // For all bags in the dataset
    double nBags = dataset.getNumBags();
    for (int i = 0; i < nBags; i++) {
        // retrieves a bag
        Bag bag = dataset.getBag(i);/*  w w  w. j a  va  2  s .c  o  m*/
        // sets the bagLabel
        newInst.setValue(0, bag.value(0));

        // retrieves instances (relational value) for each bag
        Instances instances = bag.getBagAsInstances();
        // For all attributes in bag
        for (int j = 0, attIdx = 1; j < instances.numAttributes(); j++, attIdx++) {
            double[] minimax = minimax(instances, j);
            newInst.setValue(attIdx, minimax[0]);// minima value
            newInst.setValue(attIdx + instances.numAttributes(), minimax[1]);// maxima
            // value);
        }
        // Copy label information into the dataset
        for (int j = 0; j < labelIndices.length; j++) {
            newInst.setValue(updatedLabelIndices[j], bag.value(labelIndices[j]));
        }
        newData.add(newInst);

    }
    return new MultiLabelInstances(newData, dataset.getLabelsMetaData());
}

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