Example usage for weka.core Instance 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.functions.NoChange.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.lastSeenClass = inst.classValue();
}

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

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {

    //Init Perceptron
    if (this.reset == true) {
        this.reset = false;
        this.numberAttributes = inst.numAttributes();
        this.numberClasses = inst.numClasses();
        this.weightAttribute = new double[inst.numClasses()][inst.numAttributes()];
        for (int i = 0; i < inst.numClasses(); i++) {
            for (int j = 0; j < inst.numAttributes(); j++) {
                weightAttribute[i][j] = 0.2 * this.classifierRandom.nextDouble() - 0.1;
            }//from   ww  w  .  j ava2 s  . c o  m
        }
    }

    double[] preds = new double[inst.numClasses()];
    for (int i = 0; i < inst.numClasses(); i++) {
        preds[i] = prediction(inst, i);
    }
    double learningRatio = learningRatioOption.getValue();

    int actualClass = (int) inst.classValue();
    for (int i = 0; i < inst.numClasses(); i++) {
        double actual = (i == actualClass) ? 1.0 : 0.0;
        double delta = (actual - preds[i]) * preds[i] * (1 - preds[i]);
        for (int j = 0; j < inst.numAttributes() - 1; j++) {
            this.weightAttribute[i][j] += learningRatio * delta * inst.value(j);
        }
        this.weightAttribute[i][inst.numAttributes() - 1] += learningRatio * delta;
    }
}

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

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *///from w ww.  j  a  v a2s  .  c  o  m
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new DoubleVector();
        m_bias = 0.0;
    }

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_bias);
        } else {
            y = instance.classValue();
            z = y - (wx + m_bias);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.numValues(); i++) {
            m_weights.setValue(i, m_weights.getValue(i) * multiplier);
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights.addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias += factor;
        }
        m_t++;
    }
}

---

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

License:Open Source License

public void trainOnInstanceImpl(Instance instance, int classLabel) {
    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights[classLabel], instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() != classLabel) ? -1 : 1;
            z = y * (wx + m_bias[classLabel]);
        } else {// w  w  w  . java2 s.c  om
            y = instance.classValue();
            z = y - (wx + m_bias[classLabel]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights[classLabel].numValues(); i++) {
            m_weights[classLabel].setValue(i, m_weights[classLabel].getValue(i) * multiplier);
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[classLabel].addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias[classLabel] += factor;
        }

    }
}

---

From source file:moa.classifiers.functions.SGDOld.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 v a  2s.co m
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new double[instance.numAttributes() + 1];
    }

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_weights[m_weights.length - 1]);
        } else {
            y = instance.classValue();
            z = y - (wx + m_weights[m_weights.length - 1]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.length - 1; i++) {
            m_weights[i] *= multiplier;
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[indS] += factor * instance.valueSparse(p1);
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += factor;
        }
        m_t++;
    }
}

---

From source file:moa.classifiers.functions.SPegasos.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 v a 2 s .co  m*/
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new double[instance.numAttributes() + 1];
    }
    if (!instance.classIsMissing()) {

        double learningRate = 1.0 / (m_lambda * m_t);
        //double scale = 1.0 - learningRate * m_lambda;
        double scale = 1.0 - 1.0 / m_t;
        double y = (instance.classValue() == 0) ? -1 : 1;
        double wx = dotProd(instance, m_weights, instance.classIndex());
        double z = y * (wx + m_weights[m_weights.length - 1]);

        for (int j = 0; j < m_weights.length - 1; j++) {
            if (j != instance.classIndex()) {
                m_weights[j] *= scale;
            }
        }

        if (m_loss == LOGLOSS || (z < 1)) {
            double loss = dloss(z);
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    double m = learningRate * loss * (instance.valueSparse(p1) * y);
                    m_weights[indS] += m;
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += learningRate * loss * y;
        }

        double norm = 0;
        for (int k = 0; k < m_weights.length - 1; k++) {
            if (k != instance.classIndex()) {
                norm += (m_weights[k] * m_weights[k]);
            }
        }

        double scale2 = Math.min(1.0, (1.0 / (m_lambda * norm)));
        if (scale2 < 1.0) {
            scale2 = Math.sqrt(scale2);
            for (int j = 0; j < m_weights.length - 1; j++) {
                if (j != instance.classIndex()) {
                    m_weights[j] *= scale2;
                }
            }
        }
        m_t++;
    }
}

---

From source file:moa.classifiers.lazy.kNN.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    if (inst.classValue() > C)
        C = (int) inst.classValue();
    if (this.window == null) {
        this.window = new Instances(inst.dataset());
    }//  w w  w .  j ava  2s  . co m
    if (this.limitOption.getValue() <= this.window.numInstances()) {
        this.window.delete(0);
    }
    this.window.add(inst);
}

---

From source file:moa.classifiers.lazy.kNNwithPAW.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    if (inst.classValue() > C) {
        C = (int) inst.classValue();
    }//from ww w  .ja  v  a2 s  .c  om
    if (this.window == null) {
        this.window = new Instances(inst.dataset());
    }

    for (int i = 0; i < this.window.size(); i++) {
        if (this.classifierRandom.nextDouble() > this.prob) {
            this.window.delete(i);
        }
    }
    this.window.add(inst);

}

---

From source file:moa.classifiers.lazy.kNNwithPAWandADWIN.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    if (inst.classValue() > C) {
        C = (int) inst.classValue();
    }/* ww w.j  a v  a2 s .  c o m*/
    // ADWIN
    if (this.window == null) {
        this.window = new Instances(inst.dataset());
    }

    if (this.timeStamp == null) {
        this.timeStamp = new ArrayList<Integer>(10);
    }
    for (int i = 0; i < this.window.size(); i++) {
        if (this.classifierRandom.nextDouble() > this.prob) {
            this.window.delete(i);
            this.timeStamp.remove(i);
        }
    }
    this.window.add(inst);
    this.timeStamp.add(this.time);
    this.time++;
    boolean correctlyClassifies = this.correctlyClassifies(inst);
    if (this.adwin.setInput(correctlyClassifies ? 0 : 1)) {
        //Change
        int size = (int) this.adwin.getWidth();
        for (int i = 0; i < this.window.size(); i++) {
            if (this.timeStamp.get(i) < this.time - size) {
                this.window.delete(i);
                this.timeStamp.remove(i);
            }
        }
    }

}

---

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 w ww  .j ava 2s .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;
    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());
        }
    }
}

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