Example usage for weka.core Instance weight

Introduction

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

Prototype

public double weight();

Source Link

Document

Returns the instance's weight.

Usage

From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance the new training instance to include in the model
 *//*w w  w. j a va 2  s .  c  o  m*/
@Override
public void trainOnInstanceImpl(Instance inst) {
    if (this.reset == true) {
        this.m_numClasses = inst.numClasses();
        double laplace = this.laplaceCorrectionOption.getValue();
        int numAttributes = inst.numAttributes();

        m_probOfClass = new double[m_numClasses];
        Arrays.fill(m_probOfClass, laplace);

        m_classTotals = new double[m_numClasses];
        Arrays.fill(m_classTotals, laplace * numAttributes);

        m_wordTotalForClass = new DoubleVector[m_numClasses];
        for (int i = 0; i < m_numClasses; i++) {
            //Arrays.fill(wordTotal, laplace);
            m_wordTotalForClass[i] = new DoubleVector();
        }
        this.reset = false;
    }
    // Update classifier
    int classIndex = inst.classIndex();
    int classValue = (int) inst.value(classIndex);

    double w = inst.weight();
    m_probOfClass[classValue] += w;

    m_classTotals[classValue] += w * totalSize(inst);
    double total = m_classTotals[classValue];

    for (int i = 0; i < inst.numValues(); i++) {
        int index = inst.index(i);
        if (index != classIndex && !inst.isMissing(i)) {
            //m_wordTotalForClass[index][classValue] += w * inst.valueSparse(i);
            double laplaceCorrection = 0.0;
            if (m_wordTotalForClass[classValue].getValue(index) == 0) {
                laplaceCorrection = this.laplaceCorrectionOption.getValue();
            }
            m_wordTotalForClass[classValue].addToValue(index, w * inst.valueSparse(i) + laplaceCorrection);
        }
    }
}

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From source file:moa.classifiers.DecisionStump.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
    for (int i = 0; i < inst.numAttributes() - 1; i++) {
        int instAttIndex = modelAttIndexToInstanceAttIndex(i, inst);
        AttributeClassObserver obs = this.attributeObservers.get(i);
        if (obs == null) {
            obs = inst.attribute(instAttIndex).isNominal() ? newNominalClassObserver()
                    : newNumericClassObserver();
            this.attributeObservers.set(i, obs);
        }//from  w ww  .j av  a 2  s  .  c o  m
        obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight());
    }
    if (this.trainingWeightSeenByModel - this.weightSeenAtLastSplit >= this.gracePeriodOption.getValue()) {
        this.bestSplit = findBestSplit((SplitCriterion) getPreparedClassOption(this.splitCriterionOption));
        this.weightSeenAtLastSplit = this.trainingWeightSeenByModel;
    }
}

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From source file:moa.classifiers.functions.AbsurdOracleClassifier.java

License:Apache License

@Override
public double[] getVotesForInstance(Instance i) {
    DoubleVector observedClassDistribution = new DoubleVector();
    if (this.randNumGen.nextFloat() < this.desiredAccuracyOption.getValue()) {
        observedClassDistribution.addToValue((int) i.classValue(), i.weight());
    } else {//  w w  w.  j a v  a2 s  .co m
        observedClassDistribution.addToValue(((int) i.classValue() + 1) % i.numClasses(), i.weight());

    }
    return observedClassDistribution.getArrayCopy();
}

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From source file:moa.classifiers.functions.MajorityClass.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
}

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From source file:moa.classifiers.functions.RandomGuess.java

License:Apache License

@Override
public double[] getVotesForInstance(Instance i) {
    DoubleVector observedClassDistribution = new DoubleVector();
    int classToGuess = this.randNumGen.nextInt(i.numClasses());
    //double weightToGuess = this.randNumGen.nextDouble();
    observedClassDistribution.addToValue(classToGuess, i.weight());
    return observedClassDistribution.getArrayCopy();
}

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From source file:moa.classifiers.imbalanced.SamplingClassifier.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    if (inst.classIndex() == 0) {
        this.rareCount += 1.0;
    }//w ww .j a  v a 2 s .com
    this.count += 1.0;
    double w;

    if (this.overSampleOption.isSet() && inst.classIndex() == 0) {
        w = 1.0 / (this.rareCount / this.count);
        if (this.logTransformOption.isSet()) {
            w = Math.log(w);
        }
    } else if (this.underSampleOption.isSet() && inst.classIndex() != 0) {
        w = 1.0 - this.rareCount / this.count;
    } else {
        w = 1.0;
    }
    int k = MiscUtils.poisson(w, this.classifierRandom);
    Instance weightedInst = (Instance) inst.copy();
    weightedInst.setWeight(inst.weight() * k);
    this.classifier.trainOnInstance(weightedInst);
}

---

From source file:moa.classifiers.LeveragingBag.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  va2s. 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();
    /*for (int i = 0; i < this.ensemble.length; i++) {
    if (this.outputCodesOption.isSet()) {
    weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()] );
    }
    if(!this.ensemble[i].correctlyClassifies(weightedInst)) {
    mt++;
    }
    }*/
    //update w
    w = this.weightShrinkOption.getValue(); //1.0 +mt/2.0;
    //Train ensemble of classifiers
    for (int i = 0; i < this.ensemble.length; i++) {
        int k = 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.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 w w.j  av  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;
    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 w w w  .jav  a2 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.macros.TACNB.java

License:Open Source License

public Instance extendWithOldLabels(Instance instance) {
    if (this.header == null) {
        initHeader(instance.dataset());/*from  w  w  w  . j ava  2 s .c o m*/
    }
    int numLabels = this.oldLabels.length;
    if (numLabels == 0) {
        return instance;
    }
    double[] x = instance.toDoubleArray();
    double[] x2 = Arrays.copyOfRange(this.oldLabels, 0, numLabels + x.length);
    System.arraycopy(x, 0, x2, numLabels, x.length);
    Instance extendedInstance = new DenseInstance(instance.weight(), x2);
    extendedInstance.setDataset(this.header);
    //System.out.println( extendedInstance);
    return extendedInstance;
}

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