Example usage for weka.core Instance setClassValue

Introduction

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

Prototype

public void setClassValue(String value);

Source Link

Document

Sets the class value of an instance to the given value.

Usage

From source file:tr.gov.ulakbim.jDenetX.streams.ConceptDriftRealStream.java

License:Open Source License

public Instance nextInstance() {
    numberInstanceStream++;/*ww  w.j  a  v  a  2s  . co  m*/
    double numclass = 0.0;
    double x = -4.0 * (double) (numberInstanceStream - this.positionOption.getValue())
            / (double) this.widthOption.getValue();
    double probabilityDrift = 1.0 / (1.0 + Math.exp(x));
    if (this.random.nextDouble() > probabilityDrift) {
        if (!this.inputStream.hasMoreInstances()) {
            this.inputStream.restart();
        }
        this.inputInstance = this.inputStream.nextInstance();
        numclass = this.inputInstance.classValue();
    } else {
        if (!this.driftStream.hasMoreInstances()) {
            this.driftStream.restart();
        }
        this.driftInstance = this.driftStream.nextInstance();
        numclass = this.driftInstance.classValue();
    }
    int m = 0;
    double[] newVals = new double[this.inputInstance.numAttributes() + this.driftInstance.numAttributes() - 1];
    for (int j = 0; j < this.inputInstance.numAttributes() - 1; j++, m++) {
        newVals[m] = this.inputInstance.value(j);
    }
    for (int j = 0; j < this.driftInstance.numAttributes() - 1; j++, m++) {
        newVals[m] = this.driftInstance.value(j);
    }
    newVals[m] = numclass;
    //return new Instance(1.0, newVals);
    Instance inst = new DenseInstance(1.0, newVals);
    inst.setDataset(this.getHeader());
    inst.setClassValue(numclass);
    return inst;

}

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

License:Open Source License

public Instance nextInstance() {
    double salary = 0, commission = 0, hvalue = 0, loan = 0;
    int age = 0, elevel = 0, car = 0, zipcode = 0, hyears = 0, group = 0;
    boolean desiredClassFound = false;
    while (!desiredClassFound) {
        // generate attributes
        salary = 20000.0 + 130000.0 * this.instanceRandom.nextDouble();
        commission = (salary >= 75000.0) ? 0 : (10000.0 + 65000.0 * this.instanceRandom.nextDouble());
        // true to c implementation:
        // if (instanceRandom.nextDouble() < 0.5 && salary < 75000.0)
        // commission = 10000.0 + 65000.0 * instanceRandom.nextDouble();
        age = 20 + this.instanceRandom.nextInt(61);
        elevel = this.instanceRandom.nextInt(5);
        car = this.instanceRandom.nextInt(20);
        zipcode = this.instanceRandom.nextInt(9);
        hvalue = (9.0 - zipcode) * 100000.0 * (0.5 + this.instanceRandom.nextDouble());
        hyears = 1 + this.instanceRandom.nextInt(30);
        loan = this.instanceRandom.nextDouble() * 500000.0;
        // determine class
        group = classificationFunctions[this.functionOption.getValue() - 1].determineClass(salary, commission,
                age, elevel, car, zipcode, hvalue, hyears, loan);
        if (!this.balanceClassesOption.isSet()) {
            desiredClassFound = true;//from   w  ww.ja  va  2s  . 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
        }
    }
    // perturb values
    if (this.peturbFractionOption.getValue() > 0.0) {
        salary = perturbValue(salary, 20000, 150000);
        if (commission > 0) {
            commission = perturbValue(commission, 10000, 75000);
        }
        age = (int) Math.round(perturbValue(age, 20, 80));
        hvalue = perturbValue(hvalue, (9.0 - zipcode) * 100000.0, 0, 135000);
        hyears = (int) Math.round(perturbValue(hyears, 1, 30));
        loan = perturbValue(loan, 0, 500000);
    }
    // construct instance
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setValue(0, salary);
    inst.setValue(1, commission);
    inst.setValue(2, age);
    inst.setValue(3, elevel);
    inst.setValue(4, car);
    inst.setValue(5, zipcode);
    inst.setValue(6, hvalue);
    inst.setValue(7, hyears);
    inst.setValue(8, loan);
    inst.setDataset(header);
    inst.setClassValue(group);
    return inst;
}

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

License:Open Source License

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 ww  .  j  a v  a  2 s .  c  o  m*/
    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:tr.gov.ulakbim.jDenetX.streams.generators.LEDGenerator.java

License:Open Source License

public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);// www . ja  va2 s.c o  m
    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 {
            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;
}

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

License:Open Source License

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  va2s  . co  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: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   www.  j a  va  2s  . com
    }
    inst.setDataset(header);
    inst.setClassValue(classifyInstance(this.treeRoot, attVals));
    return inst;
}

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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 av a 2s .  c  o  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: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;/*  w w w  .j a va2 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;
}

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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);/* w  ww .ja v  a2s. c o  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;
}

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From source file:tubes1.Main.java

/**
 * @param args the command line arguments
 *///from  w  w  w .ja v a 2s.  c o  m
public static void main(String[] args) throws IOException, Exception {
    // TODO code application logic here
    String filename = "weather";

    //Masih belum mengerti tipe .csv yang dapat dibaca seperti apa
    //CsvToArff convert = new CsvToArff(filename+".csv");

    //LOAD FILE
    BufferedReader datafile = readDataFile("src/" + filename + ".arff");
    Instances data = new Instances(datafile);
    data.setClassIndex(data.numAttributes() - 1);
    //END OF LOAD FILE

    CustomFilter fil = new CustomFilter();

    //REMOVE USELESS ATTRIBUTE
    data = fil.removeAttribute(data);
    System.out.println(data);

    Instances[] allData = new Instances[4];
    //data for Id3
    allData[0] = fil.resampling(fil.convertNumericToNominal(data));
    //data for J48
    allData[1] = fil.convertNumericToNominal(fil.resampling(data));
    //data for myId3
    allData[2] = allData[0];
    //data for myC4.5
    allData[3] = fil.resampling(fil.convertNumericToNominal(fil.convertNumericRange(data)));

    data = fil.convertNumericToNominal(data);
    // BUILD CLASSIFIERS
    Classifier[] models = { new Id3(), //C4.5
            new J48(), new myID3(), new myC45() };

    for (int j = 0; j < models.length; j++) {
        FastVector predictions = new FastVector();
        //FOR TEN-FOLD CROSS VALIDATION
        Instances[][] split = crossValidationSplit(allData[j], 10);
        // Separate split into training and testing arrays
        Instances[] trainingSplits = split[0];
        Instances[] testingSplits = split[1];
        System.out.println("\n---------------------------------");
        for (int i = 0; i < trainingSplits.length; i++) {
            try {
                //                    System.out.println("Building for training Split : " + i);
                Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]);

                predictions.appendElements(validation.predictions());

                // Uncomment to see the summary for each training-testing pair.
                //                    System.out.println(models[j].toString());
            } catch (Exception ex) {
                Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex);
            }
            // Calculate overall accuracy of current classifier on all splits
            double accuracy = calculateAccuracy(predictions);

            // Print current classifier's name and accuracy in a complicated,
            // but nice-looking way.
            System.out.println(String.format("%.2f%%", accuracy));
        }
        models[j].buildClassifier(allData[j]);
        Model.save(models[j], models[j].getClass().getSimpleName());
    }

    //test instance
    Instances trainingSet = new Instances("Rel", getFvWekaAttributes(data), 10);
    trainingSet.setClassIndex(data.numAttributes() - 1);

    Instance testInstance = new Instance(data.numAttributes());
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        System.out.print("Masukkan " + data.attribute(i).name() + " : ");
        Scanner in = new Scanner(System.in);
        String att = in.nextLine();
        if (isNumeric(att)) {
            att = fil.convertToFit(att, data, i);
        }
        testInstance.setValue(data.attribute(i), att);
    }

    //        System.out.println(testInstance);
    //        System.out.println(testInstance.classAttribute().index());

    trainingSet.add(testInstance);

    Classifier Id3 = Model.load("Id3");
    Classifier J48 = Model.load("J48");
    Classifier myID3 = Model.load("myID3");
    Classifier MyC45 = Model.load("myC45");
    //        Classifier MyId3 = Model.load("myID3");

    Instance A = trainingSet.instance(0);
    Instance B = trainingSet.instance(0);
    Instance C = trainingSet.instance(0);
    Instance D = trainingSet.instance(0);

    //test with ID3 WEKA
    A.setClassValue(Id3.classifyInstance(trainingSet.instance(0)));
    System.out.println("Id3 Weka : " + A);

    //test with C4.5 WEKA
    B.setClassValue(J48.classifyInstance(trainingSet.instance(0)));
    System.out.println("C4.5 Weka : " + B);

    //test with my C4.5
    C.setClassValue(MyC45.classifyInstance(trainingSet.instance(0)));
    System.out.println("My C4.5 : " + C);

    //test with my ID3
    D.setClassValue(myID3.classifyInstance(trainingSet.instance(0)));
    System.out.println("My ID3 : " + D);
}

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