Example usage for weka.core Instance classValue

List of usage examples for weka.core Instance classValue

  1. HOME
  2. Java
  3. weka
  4. weka.core.*
  5. Instance
  6. classValue

Introduction

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

Prototype

public double classValue();

Source Link

Document

Returns an instance's class value as a floating-point number.

Usage

From source file:moa.classifiers.LeveragingBag.java

License:Open Source License

public double[] getVotesForInstanceBinary(Instance inst) {
    double combinedVote[] = new double[(int) inst.numClasses()];
    Instance weightedInst = (Instance) inst.copy();
    if (this.initMatrixCodes == false) {
        for (int i = 0; i < this.ensemble.length; i++) {
            //Replace class by OC
            weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]);

            double vote[];
            vote = this.ensemble[i].getVotesForInstance(weightedInst);
            //Binary Case
            int voteClass = 0;
            if (vote.length == 2) {
                voteClass = (vote[1] > vote[0] ? 1 : 0);
            }/*from w  w  w  .  jav a2s .  co  m*/
            //Update votes
            for (int j = 0; j < inst.numClasses(); j++) {
                if (this.matrixCodes[i][j] == voteClass) {
                    combinedVote[j] += 1;
                }
            }
        }
    }
    return combinedVote;
}

From source file:moa.classifiers.LeveragingBagHalf.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    //Output Codes
    if (this.initMatrixCodes == true) {
        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;/*w ww  .j a va 2 s. c  o m*/
                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 w = 1.0;
    double mt = 0.0;
    Instance weightedInst = (Instance) inst.copy();
    //Train ensemble of classifiers
    for (int i = 0; i < this.ensemble.length; i++) {
        int k = this.classifierRandom.nextBoolean() ? 0 : (int) this.weightShrinkOption.getValue(); //half bagging
        if (k > 0) {
            if (this.outputCodesOption.isSet()) {
                weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]);
            }
            weightedInst.setWeight(k);
            this.ensemble[i].trainOnInstance(weightedInst);
        }
        boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst);
        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());
        }
    }
}

From source file:moa.classifiers.LeveragingBagME.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    //Output Codes
    if (this.initMatrixCodes == true) {
        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;//w  ww .j  ava2 s.  c om
                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;
    Instance weightedInst = (Instance) inst.copy();
    //Train ensemble of classifiers
    for (int i = 0; i < this.ensemble.length; i++) {
        double error = this.ADError[i].getEstimation();
        double k = !this.ensemble[i].correctlyClassifies(weightedInst) ? 1.0
                : (this.classifierRandom.nextDouble() < (error / (1.0 - error)) ? 1.0 : 0.0);///error);
        if (k > 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);
        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());
        }
    }
}

From source file:moa.classifiers.LeveragingBagWT.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    //Output Codes
    if (this.initMatrixCodes == true) {
        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;//from   ww  w .j  a  v  a  2 s.c  o m
                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 w = 1.0;
    double mt = 0.0;
    Instance weightedInst = (Instance) inst.copy();
    //update w
    w = this.weightShrinkOption.getValue();
    //Train ensemble of classifiers
    for (int i = 0; i < this.ensemble.length; i++) {
        int k = 1 + MiscUtils.poisson(w, this.classifierRandom);
        if (k > 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);
        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());
        }
    }
}

From source file:moa.classifiers.LeveragingSubag.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    //Output Codes
    if (this.initMatrixCodes == true) {
        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;/*from   ww  w . ja  v a  2 s.  co m*/
                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 w = 1.0;
    double mt = 0.0;
    Instance weightedInst = (Instance) inst.copy();

    //Train ensemble of classifiers
    for (int i = 0; i < this.ensemble.length; i++) {
        int k = MiscUtils.poisson(1, this.classifierRandom);
        k = (k > 0) ? (int) this.weightShrinkOption.getValue() : 0;
        if (k > 0) {
            if (this.outputCodesOption.isSet()) {
                weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]);
            }
            weightedInst.setWeight(k);
            this.ensemble[i].trainOnInstance(weightedInst);
        }
        boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst);
        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.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue());
        }
    }
}

From source file:moa.classifiers.LimAttClassifier.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    int numClasses = inst.numClasses();
    //Init Ensemble
    if (this.initClassifiers == true) {
        numberAttributes = numAttributesOption.getValue();
        if (bigTreesOption.isSet()) {
            numberAttributes = inst.numAttributes() - 1 - numAttributesOption.getValue();
        }//from w  ww .  ja v  a 2  s. c  o m
        CombinationGenerator x = new CombinationGenerator(inst.numAttributes() - 1, this.numberAttributes);
        int numberClassifiers = x.getTotal().intValue();
        this.ensemble = new Classifier[numberClassifiers];
        Classifier baseLearner = (Classifier) getPreparedClassOption(this.baseLearnerOption);
        baseLearner.resetLearning();
        for (int i = 0; i < this.ensemble.length; i++) {
            this.ensemble[i] = baseLearner.copy();
        }
        this.ADError = new ADWIN[this.ensemble.length];
        for (int i = 0; i < this.ensemble.length; i++) {
            this.ADError[i] = new ADWIN((double) this.deltaAdwinOption.getValue());
        }
        this.numberOfChangesDetected = 0;
        //Prepare combinations
        int i = 0;
        if (baseLearner instanceof LimAttHoeffdingTree) {
            while (x.hasMore()) {
                ((LimAttHoeffdingTree) this.ensemble[i]).setlistAttributes(x.getNext());
                i++;
            }
        }

        this.initClassifiers = false;
    }

    boolean Change = false;
    Instance weightedInst = (Instance) inst.copy();

    //Train Perceptron
    double[][] votes = new double[this.ensemble.length + 1][numClasses];
    for (int i = 0; i < this.ensemble.length; i++) {
        double[] v = new double[numClasses];
        for (int j = 0; j < v.length; j++) {
            v[j] = (double) this.oddsOffsetOption.getValue();
        }
        double[] vt = this.ensemble[i].getVotesForInstance(inst);
        double sum = Utils.sum(vt);
        if (!Double.isNaN(sum) && (sum > 0)) {
            for (int j = 0; j < vt.length; j++) {
                vt[j] /= sum;
            }
        } else {
            // Just in case the base learner returns NaN
            for (int k = 0; k < vt.length; k++) {
                vt[k] = 0.0;
            }
        }
        sum = numClasses * (double) this.oddsOffsetOption.getValue();
        for (int j = 0; j < vt.length; j++) {
            v[j] += vt[j];
            sum += vt[j];
        }
        for (int j = 0; j < vt.length; j++) {
            votes[i][j] = Math.log(v[j] / (sum - v[j]));
        }
    }

    if (adwinReplaceWorstClassifierOption.isSet() == false) {
        //Train ensemble of classifiers
        for (int i = 0; i < this.ensemble.length; i++) {
            boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst);
            double ErrEstim = this.ADError[i].getEstimation();
            if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) {
                numInstances = initialNumInstancesOption.getValue();
                if (this.ADError[i].getEstimation() > ErrEstim) {
                    Change = true;
                    //Replace classifier if ADWIN has detected change
                    numberOfChangesDetected++;
                    this.ensemble[i].resetLearning();
                    this.ADError[i] = new ADWIN((double) this.deltaAdwinOption.getValue());
                    for (int ii = 0; ii < inst.numClasses(); ii++) {
                        weightAttribute[ii][i] = 0.0;// 0.2 * Math.random() - 0.1;
                    }
                }
            }
        }
    } else {
        //Train ensemble of classifiers
        for (int i = 0; i < this.ensemble.length; i++) {
            boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst);
            double ErrEstim = this.ADError[i].getEstimation();
            if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) {
                if (this.ADError[i].getEstimation() > ErrEstim) {
                    Change = true;
                }
            }
        }
        //Replace classifier with higher error if ADWIN has detected change
        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.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue());
                for (int ii = 0; ii < inst.numClasses(); ii++) {
                    weightAttribute[ii][imax] = 0.0;
                }
            }
        }
    }

    trainOnInstanceImplPerceptron(inst.numClasses(), (int) inst.classValue(), votes);

    for (int i = 0; i < this.ensemble.length; i++) {
        this.ensemble[i].trainOnInstance(inst);
    }
}

From source file:moa.classifiers.macros.TACNB.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance instance) {
    this.baseLearner.trainOnInstance(extendWithOldLabels(instance));
    if (this.labelDelayOption.isSet() == false) {
        // Use true old Labels to add attributes to instances
        addOldLabel(instance.classValue());
    }//w w w .java 2 s.  c o  m
}

From source file:moa.classifiers.meta.AccuracyUpdatedEnsemble.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.initVariables();

    this.classDistributions[(int) inst.classValue()]++;
    this.currentChunk.add(inst);
    this.processedInstances++;

    if (this.processedInstances % this.chunkSizeOption.getValue() == 0) {
        this.processChunk();
    }//from   w  w w.j a va 2s . c  o m
}

From source file:moa.classifiers.meta.AccuracyUpdatedEnsemble2.java

License:Open Source License

 @Override
public void trainOnInstanceImpl(Instance inst) {
   this.initVariables();

   this.classDistributions[(int) inst.classValue()]++;
   this.currentChunk.add(inst);
   this.processedInstances++;

   if (this.processedInstances % this.chunkSizeOption.getValue() == 0) {
      this.processChunk();
   }/*from w w w . j a v  a  2 s . c o m*/
}

From source file:moa.classifiers.meta.AccuracyUpdatedEnsemble2.java

License:Open Source License

@Override
    public void trainOnInstanceImpl(Instance inst) {
        this.initVariables();

        this.classDistributions[(int) inst.classValue()]++;
        this.currentChunk.add(inst);
        this.processedInstances++;

        if (this.processedInstances % this.chunkSizeOption.getValue() == 0) {
            this.processChunk();
        }//from w ww.ja  v  a2  s .  co m
    }