Example usage for weka.core Instance setWeight

Introduction

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

Prototype

public void setWeight(double weight);

Source Link

Document

Sets the weight of an instance.

Usage

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

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    // Set log (k-1) and (k-1) for SAMME Method
    if (this.sammeOption.isSet()) {
        this.Km1 = numClasses - 1;
        this.logKm1 = Math.log(this.Km1);
        this.initKm1 = false;
    }/*from  w  w w .  j a va2 s  .  c  o  m*/
    //Output Codes
    if (this.initMatrixCodes) {

        this.matrixCodes = new int[this.ensemble.length][inst.numClasses()];
        for (int i = 0; i < this.ensemble.length; i++) {
            int numberOnes;
            int numberZeros;

            do { // until we have the same number of zeros and ones
                numberOnes = 0;
                numberZeros = 0;
                for (int j = 0; j < numClasses; j++) {
                    int result = 0;
                    if (j == 1 && numClasses == 2) {
                        result = 1 - this.matrixCodes[i][0];
                    } else {
                        result = (this.classifierRandom.nextBoolean() ? 1 : 0);
                    }
                    this.matrixCodes[i][j] = result;
                    if (result == 1)
                        numberOnes++;
                    else
                        numberZeros++;
                }
            } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2));

        }
        this.initMatrixCodes = false;
    }

    boolean Change = false;
    double lambda_d = 1.0;
    Instance weightedInst = (Instance) inst.copy();
    for (int i = 0; i < this.ensemble.length; i++) {
        double k = this.pureBoostOption.isSet() ? lambda_d
                : MiscUtils.poisson(lambda_d * this.Km1, this.classifierRandom);
        if (k > 0.0) {
            if (this.outputCodesOption.isSet()) {
                weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]);
            }
            weightedInst.setWeight(inst.weight() * k);
            this.ensemble[i].trainOnInstance(weightedInst);
        }
        boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst);
        if (correctlyClassifies) {
            this.scms[i] += lambda_d;
            lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]);
        } else {
            this.swms[i] += lambda_d;
            lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]);
        }

        double ErrEstim = this.ADError[i].getEstimation();
        if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1))
            if (this.ADError[i].getEstimation() > ErrEstim)
                Change = true;
    }
    if (Change) {
        numberOfChangesDetected++;
        double max = 0.0;
        int imax = -1;
        for (int i = 0; i < this.ensemble.length; i++) {
            if (max < this.ADError[i].getEstimation()) {
                max = this.ADError[i].getEstimation();
                imax = i;
            }
        }
        if (imax != -1) {
            this.ensemble[imax].resetLearning();
            //this.ensemble[imax].trainOnInstance(inst);
            this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue());
            this.scms[imax] = 0;
            this.swms[imax] = 0;
        }
    }
}