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:ml.engine.LibSVM.java

License:Open Source License

/**
 * builds the classifier//from w  ww.  j a  v a 2  s. com
 * 
 * @param insts the training instances
 * @throws Exception if libsvm classes not in classpath or libsvm encountered
 *           a problem
 */
@Override
public void buildClassifier(Instances insts) throws Exception {
    m_Filter = null;

    if (!isPresent()) {
        throw new Exception("libsvm classes not in CLASSPATH!");
    }

    // remove instances with missing class
    insts = new Instances(insts);
    insts.deleteWithMissingClass();

    if (!getDoNotReplaceMissingValues()) {
        m_ReplaceMissingValues = new ReplaceMissingValues();
        m_ReplaceMissingValues.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_ReplaceMissingValues);
    }

    // can classifier handle the data?
    // we check this here so that if the user turns off
    // replace missing values filtering, it will fail
    // if the data actually does have missing values
    getCapabilities().testWithFail(insts);

    if (getNormalize()) {
        m_Filter = new Normalize();
        m_Filter.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_Filter);
    }

    // nominal to binary
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(insts);
    insts = Filter.useFilter(insts, m_NominalToBinary);

    Vector vy = new Vector();
    Vector vx = new Vector();
    int max_index = 0;

    for (int d = 0; d < insts.numInstances(); d++) {
        Instance inst = insts.instance(d);
        Object x = instanceToArray(inst);
        int m = Array.getLength(x);

        if (m > 0) {
            max_index = Math.max(max_index, ((Integer) getField(Array.get(x, m - 1), "index")).intValue());
        }
        vx.addElement(x);
        vy.addElement(new Double(inst.classValue()));
    }

    // calculate actual gamma
    if (getGamma() == 0) {
        m_GammaActual = 1.0 / max_index;
    } else {
        m_GammaActual = m_Gamma;
    }

    // check parameter
    String error_msg = (String) invokeMethod(Class.forName(CLASS_SVM).newInstance(), "svm_check_parameter",
            new Class[] { Class.forName(CLASS_SVMPROBLEM), Class.forName(CLASS_SVMPARAMETER) },
            new Object[] { getProblem(vx, vy), getParameters() });

    if (error_msg != null) {
        throw new Exception("Error: " + error_msg);
    }

    // make probability estimates deterministic from run to run
    Class svmClass = Class.forName(CLASS_SVM);
    Field randF = svmClass.getField("rand");
    Random rand = (Random) randF.get(null); // static field
    rand.setSeed(m_Seed);

    // train model
    m_Model = invokeMethod(Class.forName(CLASS_SVM).newInstance(), "svm_train",
            new Class[] { Class.forName(CLASS_SVMPROBLEM), Class.forName(CLASS_SVMPARAMETER) },
            new Object[] { getProblem(vx, vy), getParameters() });
}

---

From source file:moa.classifiers.AbstractClassifier.java

License:Open Source License

@Override
public boolean correctlyClassifies(Instance inst) {
    return Utils.maxIndex(getVotesForInstance(inst)) == (int) inst.classValue();
}

---

From source file:moa.classifiers.AccuracyWeightedEnsemble.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.chunkSize == 0) {
        this.processChunk();
    }/*w  w w.j a v  a 2  s  . c  o  m*/
}

---

From source file:moa.classifiers.bayes.NaiveBayes.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);
        }/*w w  w .  ja v a  2  s .  com*/
        obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight());
    }
}

---

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 w  w.j  a v 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;
    }
}

---

From source file:moa.classifiers.drift.SingleClassifierDrift.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    //this.numberInstances++;
    int trueClass = (int) inst.classValue();
    boolean prediction;
    if (Utils.maxIndex(this.classifier.getVotesForInstance(inst)) == trueClass) {
        prediction = true;/*from  w  ww . ja  v a 2  s.c om*/
    } else {
        prediction = false;
    }
    this.ddmLevel = this.driftDetectionMethod.computeNextVal(prediction);
    switch (this.ddmLevel) {
    case DriftDetectionMethod.DDM_WARNING_LEVEL:
        //System.out.println("1 0 W");
        //System.out.println("DDM_WARNING_LEVEL");
        this.warningDetected++;
        if (newClassifierReset == true) {
            this.newclassifier.resetLearning();
            newClassifierReset = false;
        }
        this.newclassifier.trainOnInstance(inst);
        break;

    case DriftDetectionMethod.DDM_OUTCONTROL_LEVEL:
        //System.out.println("0 1 O");
        //System.out.println("DDM_OUTCONTROL_LEVEL");
        this.changeDetected++;
        this.classifier = null;
        this.classifier = this.newclassifier;
        if (this.classifier instanceof WEKAClassifier) {
            ((WEKAClassifier) this.classifier).buildClassifier();
        }
        this.newclassifier = ((Classifier) getPreparedClassOption(this.baseLearnerOption)).copy();
        this.newclassifier.resetLearning();
        break;

    case DriftDetectionMethod.DDM_INCONTROL_LEVEL:
        //System.out.println("0 0 I");
        //System.out.println("DDM_INCONTROL_LEVEL");
        newClassifierReset = true;
        break;
    default:
        //System.out.println("ERROR!");

    }

    this.classifier.trainOnInstance(inst);
}

---

From source file:moa.classifiers.featureselection.OFSL.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    double y_t, m_bias_p1, m_bias_p2, m_bias;
    double[] m_weights_p1, m_weights_p2, m_weights;

    if (this.weights == null) {
        this.weights = new double[inst.numValues()];
        for (int i = 0; i < this.weights.length; i++)
            this.weights[i] = 0.0;
        this.bias = 0.0;
    }/*  w w w  .  j  a  v  a2s  . co m*/
    if (inst.classAttribute().isNominal()) {
        y_t = (inst.classValue() == 0) ? -1 : 1;
    } else {
        y_t = inst.classValue();
    }

    double f_t = dot(inst.toDoubleArray(), this.weights);
    f_t += this.bias;

    if (y_t * f_t < 0) {
        m_weights_p1 = scalar_vector(1.0 - this.stepSizeOption.getValue() * this.learningRateOption.getValue(),
                this.weights);
        m_bias_p1 = (1.0 - this.stepSizeOption.getValue() * this.learningRateOption.getValue()) * this.bias;
        m_weights_p2 = scalar_vector(this.learningRateOption.getValue() * y_t, inst.toDoubleArray());
        m_bias_p2 = this.learningRateOption.getValue() * y_t;
        m_weights = vector_add(m_weights_p1, m_weights_p2);
        m_bias = m_bias_p1 + m_bias_p2;

        m_weights = l2_projection(m_weights, m_bias, this.learningRateOption.getValue());
        m_weights = truncate(m_weights, this.numSelectOption.getValue());

        for (int i = 0; i < m_weights_p1.length; i++)
            this.weights[i] = m_weights[i];
        this.bias = m_weights[m_weights.length - 1];
    } else {
        this.weights = scalar_vector(1.0 - this.stepSizeOption.getValue() * this.learningRateOption.getValue(),
                this.weights);
        this.bias = (1.0 - this.stepSizeOption.getValue() * this.learningRateOption.getValue()) * this.bias;
    }

}

---

From source file:moa.classifiers.featureselection.OFSP.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    double y_t, f_t, denom, m_bias;
    int[] indices = new int[this.numSelectOption.getValue()];
    double[] m_weights;

    if (this.weights == null) {

        this.weights = new double[inst.numValues()];
        for (int i = 0; i < this.weights.length; i++)
            this.weights[i] = this.rand.nextGaussian();
        this.bias = 0.0;
        this.weights = truncate(this.weights, this.numSelectOption.getValue());
    }/* ww w .  ja  v  a2s .  co  m*/

    if (inst.classAttribute().isNominal()) {
        y_t = (inst.classValue() == 0) ? -1 : 1;
    } else {
        y_t = inst.classValue();
    }
    double[] x_t = inst.toDoubleArray();
    double[] x_hat = inst.toDoubleArray();

    if (this.rand.nextDouble() < this.searchOption.getValue()) {
        int[] indices_perm = perm(inst.numAttributes());
        for (int i = 0; i < this.numSelectOption.getValue(); i++)
            indices[i] = indices_perm[i];

    } else {
        int[] sorted_indices = bubblesort_index(abs_vector(this.weights));

        for (int i = 0; i < inst.numAttributes() - this.numSelectOption.getValue(); i++)
            x_hat[sorted_indices[i]] = 0.0;

        for (int i = 0; i < this.numSelectOption.getValue(); i++)
            indices[i] = sorted_indices[sorted_indices.length - i - 1];
    }

    f_t = 0;
    for (int i = 0; i < this.numSelectOption.getValue(); i++)
        f_t += this.weights[indices[i]] * x_t[indices[i]];
    f_t += this.bias;

    if (f_t * y_t < 0) {

        for (int i = 0; i < x_hat.length; i++) {
            denom = this.numSelectOption.getValue() / x_hat.length * this.searchOption.getValue();
            if (this.weights[i] != 0)
                denom += (1 - this.searchOption.getValue()) * this.weights[i];
            x_hat[i] /= denom;
        }

        m_weights = scalar_vector(y_t * this.stepSizeOption.getValue(), x_hat);
        m_bias = y_t * this.stepSizeOption.getValue() * this.bias;
        m_weights = vector_add(m_weights, this.weights);
        m_bias += m_bias + this.bias;

        m_weights = l2_projection(m_weights, m_bias, this.boundOption.getValue());
        m_weights = truncate(m_weights, this.numSelectOption.getValue());

        for (int i = 0; i < m_weights.length - 1; i++)
            this.weights[i] = m_weights[i];
        this.bias = m_weights[m_weights.length - 1];
    }

}

---

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 {//from w  w  w.  j  a v  a  2  s. c  om
        observedClassDistribution.addToValue(((int) i.classValue() + 1) % i.numClasses(), i.weight());

    }
    return observedClassDistribution.getArrayCopy();
}

---

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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