Example usage for weka.core Instance numClasses

Introduction

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

Prototype

public int numClasses();

Source Link

Document

Returns the number of class labels.

Usage

From source file:machinelearningproject.RandomForest.java

@Override
public double classifyInstance(Instance instance) throws Exception {
    HashMap<String, Integer> classMap = new HashMap<>();

    for (int i = 0; i < dtrees.size(); i++) {
        String key = dtrees.get(i).traverseTree(instance);
        if (classMap.isEmpty() || !classMap.containsKey(key)) {
            classMap.put(key, 1);/*www .  j  a  va 2  s.c o m*/
        } else {
            if (classMap.containsKey(key)) {
                classMap.put(key, classMap.get(key) + 1);
            }
        }
    }
    Iterator<String> keySetIterator = classMap.keySet().iterator();
    String modeClass = "";
    int count = 0;
    while (keySetIterator.hasNext()) {
        String key = keySetIterator.next();
        if (count < classMap.get(key)) {
            modeClass = key;
            count = classMap.get(key);
        }
    }

    double result = 0.0;
    for (int i = 0; i < instance.numClasses(); i++) {
        if (modeClass.equals(instance.attribute(instance.classIndex()).value(i))) {
            result = (double) i;
        }
    }

    return result;
}

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From source file:ml.ann.BackPropagation.java

License:Open Source License

private void initInputAndTarget(Instance instance) {
    int classAttributeIdx = neuronTopology.instances.classIndex();
    if (neuronTopology.instances.classAttribute().isNumeric()) {
        neuronTopology.target = new double[1];
        neuronTopology.target[0] = instance.value(classAttributeIdx);
    } else if (neuronTopology.instances.classAttribute().isNominal()) {
        neuronTopology.target = new double[instance.numClasses()];
        for (int i = 0; i < instance.numClasses(); i++) {
            neuronTopology.target[i] = 0;
        }/*from  www.j  av  a2 s  .  co m*/
        int idxClassValue = (int) instance.classValue();
        neuronTopology.target[idxClassValue] = 1;
    }
    for (int i = 0; i < instance.numAttributes(); i++) {
        if (i == 0) {
            neuronTopology.input[i] = 1;
            neuronTopology.output[0][i] = 1;
        } else {
            neuronTopology.input[i] = instance.value(i - 1);
            neuronTopology.output[0][i] = instance.value(i - 1);
        }
    }
    //        System.out.println(neuronTopology.originInstances.instance(instancesIdx).toString());
}

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From source file:ml.engine.LibSVM.java

License:Open Source License

/**
 * Computes the distribution for a given instance. In case of 1-class
 * classification, 1 is returned at index 0 if libsvm returns 1 and NaN (=
 * missing) if libsvm returns -1./*  ww w .  j a  v a 2  s .c  o  m*/
 * 
 * @param instance the instance for which distribution is computed
 * @return the distribution
 * @throws Exception if the distribution can't be computed successfully
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    int[] labels = new int[instance.numClasses()];
    double[] prob_estimates = null;

    if (m_ProbabilityEstimates) {
        invokeMethod(Class.forName(CLASS_SVM).newInstance(), "svm_get_labels",
                new Class[] { Class.forName(CLASS_SVMMODEL),
                        Array.newInstance(Integer.TYPE, instance.numClasses()).getClass() },
                new Object[] { m_Model, labels });

        prob_estimates = new double[instance.numClasses()];
    }

    if (!getDoNotReplaceMissingValues()) {
        m_ReplaceMissingValues.input(instance);
        m_ReplaceMissingValues.batchFinished();
        instance = m_ReplaceMissingValues.output();
    }

    if (m_Filter != null) {
        m_Filter.input(instance);
        m_Filter.batchFinished();
        instance = m_Filter.output();
    }

    m_NominalToBinary.input(instance);
    m_NominalToBinary.batchFinished();
    instance = m_NominalToBinary.output();

    Object x = instanceToArray(instance);
    double v;
    double[] result = new double[instance.numClasses()];
    if (m_ProbabilityEstimates && ((m_SVMType == SVMTYPE_C_SVC) || (m_SVMType == SVMTYPE_NU_SVC))) {
        v = ((Double) invokeMethod(Class.forName(CLASS_SVM).newInstance(), "svm_predict_probability",
                new Class[] { Class.forName(CLASS_SVMMODEL),
                        Array.newInstance(Class.forName(CLASS_SVMNODE), Array.getLength(x)).getClass(),
                        Array.newInstance(Double.TYPE, prob_estimates.length).getClass() },
                new Object[] { m_Model, x, prob_estimates })).doubleValue();

        // Return order of probabilities to canonical weka attribute order
        for (int k = 0; k < prob_estimates.length; k++) {
            result[labels[k]] = prob_estimates[k];
        }
    } else {
        v = ((Double) invokeMethod(Class.forName(CLASS_SVM).newInstance(), "svm_predict",
                new Class[] { Class.forName(CLASS_SVMMODEL),
                        Array.newInstance(Class.forName(CLASS_SVMNODE), Array.getLength(x)).getClass() },
                new Object[] { m_Model, x })).doubleValue();

        if (instance.classAttribute().isNominal()) {
            if (m_SVMType == SVMTYPE_ONE_CLASS_SVM) {
                if (v > 0) {
                    result[0] = 1;
                } else {
                    // outlier (interface for Classifier specifies that unclassified
                    // instances
                    // should return a distribution of all zeros)
                    result[0] = 0;
                }
            } else {
                result[(int) v] = 1;
            }
        } else {
            result[0] = v;
        }
    }

    return result;
}

---

From source file:moa.classifiers.active.ActiveClassifier.java

License:Open Source License

private void labelSelSampling(double incomingPosterior, Instance inst) {
    double p = Math.abs(incomingPosterior - 1.0 / (inst.numClasses()));
    double budget = this.budgetOption.getValue() / (this.budgetOption.getValue() + p);
    if (this.classifierRandom.nextDouble() < budget) {
        this.classifier.trainOnInstance(inst);
        this.costLabeling++;
    }// ww  w. ja v a  2s . c om
}

---

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 ww. jav a 2s . 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.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 .jav  a 2 s.c om
        observedClassDistribution.addToValue(((int) i.classValue() + 1) % i.numClasses(), i.weight());

    }
    return observedClassDistribution.getArrayCopy();
}

---

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

License:Open Source License

public double[] getVotesForInstance(Instance i) {
    double[] votes = new double[i.numClasses()];
    votes[(int) lastSeenClass] = 1.0;
    return votes;
}

---

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;
            }// ww w  .  ja  va2s.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;
    }
}

---

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.functions.SGD.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance.//from  ww w .  j a  v  a  2 s . co m
 *
 * @param instance    the instance to be classified
 * @return       predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance inst) {

    if (m_weights == null) {
        return new double[inst.numClasses()];
    }
    double[] result = (inst.classAttribute().isNominal()) ? new double[2] : new double[1];

    double wx = dotProd(inst, m_weights, inst.classIndex());// * m_wScale;
    double z = (wx + m_bias);

    if (inst.classAttribute().isNumeric()) {
        result[0] = z;
        return result;
    }

    if (z <= 0) {
        //  z = 0;
        if (m_loss == LOGLOSS) {
            result[0] = 1.0 / (1.0 + Math.exp(z));
            result[1] = 1.0 - result[0];
        } else {
            result[0] = 1;
        }
    } else {
        if (m_loss == LOGLOSS) {
            result[1] = 1.0 / (1.0 + Math.exp(-z));
            result[0] = 1.0 - result[1];
        } else {
            result[1] = 1;
        }
    }
    return result;
}

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