List of numClasses() Examples

Examples of numClasses()

weka.core.Instances.numClasses()
Returns the number of class labels. @return the number of class labels as an integer if the classattribute is nominal, 1 otherwise. @throws UnassignedClassException if the class is not set

Examples of weka.core.Instances.numClasses()

          for(int x = 0; x < n.numInstances(); x++) {
             if(n.instance(sorted[x]).isMissing(a))
                continue;


             // zero the counts
             for(int c = 0; c < n.numClasses(); c++)
                counts[0][c] = 0;


             double theval = n.instance(sorted[x]).value(a);
             counts[0][(int)n.instance(sorted[x]).classValue()] 
               += iindex[1][sorted[x]];

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Examples of weka.core.Instances.numClasses()

             if(!prohibit[(int)theval]) {
                // work out best laplace for > theval
                double total = Utils.sum(counts[0]);
                bestLaplace = leafLaplace;
                bestClass = Double.NaN;
                for(int c = 0; c < n.numClasses(); c++) {
                   double temp = (counts[0][c]+1.0)/(total+2.0);
                   if(temp > bestLaplace
                    && biprob(counts[0][c],total,leafLaplace) > m_BiProbCrit) {
                      bestLaplace = temp;
                      bestClass = c;

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Examples of weka.core.Instances.numClasses()

     * @return predicted class probability distribution
     * @throws Exception if there is a problem generating the prediction
     */
    public double[] distributionForInstance(BayesNet bayesNet, Instance instance) throws Exception {
        Instances instances = bayesNet.m_Instances;
        int nNumClasses = instances.numClasses();
        double[] fProbs = new double[nNumClasses];


        for (int iClass = 0; iClass < nNumClasses; iClass++) {
            fProbs[iClass] = 1.0;
        }

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Examples of weka.core.Instances.numClasses()

    getCapabilities().testWithFail(data);
    // remove instances with missing class
    Instances instances =  new Instances(data);
    instances.deleteWithMissingClass();


    m_binaryClassifiers = new DNBBinary[instances.numClasses()];
    m_numClasses=instances.numClasses();
    m_headerInfo = new Instances(instances, 0);
    for (int i = 0; i < instances.numClasses(); i++) {
      m_binaryClassifiers[i] = new DNBBinary();
      m_binaryClassifiers[i].setTargetClass(i);

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Examples of weka.core.Instances.numClasses()

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


    m_binaryClassifiers = new DNBBinary[instances.numClasses()];
    m_numClasses=instances.numClasses();
    m_headerInfo = new Instances(instances, 0);
    for (int i = 0; i < instances.numClasses(); i++) {
      m_binaryClassifiers[i] = new DNBBinary();
      m_binaryClassifiers[i].setTargetClass(i);
      m_binaryClassifiers[i].initClassifier(instances);

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Examples of weka.core.Instances.numClasses()

    instances.deleteWithMissingClass();


    m_binaryClassifiers = new DNBBinary[instances.numClasses()];
    m_numClasses=instances.numClasses();
    m_headerInfo = new Instances(instances, 0);
    for (int i = 0; i < instances.numClasses(); i++) {
      m_binaryClassifiers[i] = new DNBBinary();
      m_binaryClassifiers[i].setTargetClass(i);
      m_binaryClassifiers[i].initClassifier(instances);
    }

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Examples of weka.core.Instances.numClasses()

     * @return predicted class probability distribution
     * @throws Exception if there is a problem generating the prediction
     */
    public double[] distributionForInstance(BayesNet bayesNet, Instance instance) throws Exception {
        Instances instances = bayesNet.m_Instances;
        int nNumClasses = instances.numClasses();
        double[] fProbs = new double[nNumClasses];


        for (int iClass = 0; iClass < nNumClasses; iClass++) {
            fProbs[iClass] = 1.0;
        }

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Examples of weka.core.Instances.numClasses()

  m_ClassCounts = tmp2;
      }
      
      // Change the class values
      FastVector values = new FastVector(data.classAttribute().numValues());
      for (int i = 0; i < data.numClasses(); i++) {
  values.addElement(data.classAttribute().value(m_Converter[i]));
      }
      FastVector newVec = new FastVector(data.numAttributes());
      for (int i = 0; i < data.numAttributes(); i++) {
  if (i == data.classIndex()) {

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Examples of weka.core.Instances.numClasses()

      throw new Exception("A base classifier has not been specified!");
    }


    if (getDebug())
      System.out.println("Start training ...");
    m_NumClasses = train.numClasses();


    //convert the training dataset into single-instance dataset
    m_ConvertToProp.setWeightMethod(getWeightMethod());
    m_ConvertToProp.setInputFormat(train);
    train = Filter.useFilter(train, m_ConvertToProp);

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Examples of weka.core.Instances.numClasses()


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


    m_NumClasses = train.numClasses();
    m_NumIterations = m_MaxIterations;


    if (m_Classifier == null)
      throw new Exception("A base classifier has not been specified!");
    if(!(m_Classifier instanceof WeightedInstancesHandler))

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