List of distributionForInstance() Examples

Examples of distributionForInstance()

weka.classifiers.Classifier.distributionForInstance()
Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero. If the class is numeric, the array must consist of only one element, which contains the predicted value. Note that a classifier MUST implement either this or classifyInstance(). @param instance the instance to be classified @return an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction @exception Exception if distribution could not be computed successfully
weka.classifiers.pmml.consumer.PMMLClassifier.distributionForInstance()

Examples of weka.classifiers.Classifier.distributionForInstance()

      for(int i=0;i<base.numAttributes();i++)
      {
        sample.setValue(i, ((double)c[i]));
      }
      
      double []w=cModel.distributionForInstance(sample);
      for(int j=0;j<target.getSenses().size();j++)
      {
        ArrayList<String> dwords=new ArrayList<String>(window.size());
        for(AmbiguousWord word:window)
        {

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Examples of weka.classifiers.Classifier.distributionForInstance()

    for (int k = 0; k < m_Classifiers.length; k++) {
      Classifier classifier = getClassifier(k);
      if (m_BaseFormat.classAttribute().isNumeric()) {
  values[i++] = classifier.classifyInstance(instance);
      } else {
  double[] dist = classifier.distributionForInstance(instance);
  for (int j = 0; j < dist.length; j++) {
    values[i++] = dist[j];
  }
      }
    }

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Examples of weka.classifiers.Classifier.distributionForInstance()

    Classifier classifier = getClassifier(k);


    if (m_BaseFormat.classAttribute().isNumeric()) {
      throw new Exception("Class Attribute must not be numeric!");
    } else {
      double[] dist = classifier.distributionForInstance(instance);
      
      maxIdx=0;
      maxVal=dist[0];
      for (int j = 1; j < dist.length; j++) {
  if (dist[j]>maxVal) {

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Examples of weka.classifiers.Classifier.distributionForInstance()

      double [] sums = new double [instance.numClasses()], newProbs; 
      Classifier curr;
      
      for (int i = 0; i < m_Committee.size(); i++) {
    curr = (Classifier) m_Committee.get(i);
    newProbs = curr.distributionForInstance(instance);
    for (int j = 0; j < newProbs.length; j++)
      sums[j] += newProbs[j];
      }
      if (Utils.eq(Utils.sum(sums), 0)) {
    return sums;

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Examples of weka.classifiers.Classifier.distributionForInstance()

    for (int k = 0; k < m_Classifiers.length; k++) {
      Classifier classifier = getClassifier(k);
      if (m_BaseFormat.classAttribute().isNumeric()) {
  values[i++] = classifier.classifyInstance(instance);
      } else {
  double[] dist = classifier.distributionForInstance(instance);
  for (int j = 0; j < dist.length; j++) {
    values[i++] = dist[j];
  }
      }
    }

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Examples of weka.classifiers.Classifier.distributionForInstance()

    } else {
      Classifier classifier = (Classifier)m_classifiers.get(node.m_left.getString() + "|" +
                  node.m_right.getString());
      double[] leftDist = distributionForInstance(inst, node.m_left);
      double[] rightDist = distributionForInstance(inst, node.m_right);
      double[] dist = classifier.distributionForInstance(inst);


      for (int i = 0; i < inst.numClasses(); i++) {
  if (node.m_right.contains(i)) {
    newDist[i] = dist[1] * rightDist[i];
  } else {

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Examples of weka.classifiers.Classifier.distributionForInstance()

    for (int k = 0; k < m_Classifiers.length; k++) {
      Classifier classifier = getClassifier(k);
      if (m_BaseFormat.classAttribute().isNumeric()) {
  values[i++] = classifier.classifyInstance(instance);
      } else {
  double[] dist = classifier.distributionForInstance(instance);
  for (int j = 0; j < dist.length; j++) {
    values[i++] = dist[j];
  }
      }
    }

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Examples of weka.classifiers.pmml.consumer.PMMLClassifier.distributionForInstance()

      if (temp.classAttribute().isNumeric()) {
        buff.append(temp.value(temp.classIndex()) + " ");
      } else {
        buff.append(temp.classAttribute().value((int)temp.value(temp.classIndex())) + " ");
      }
      preds = classifier.distributionForInstance(temp);
      buff.append(" Predicted: ");
      for (int j = 0; j < preds.length; j++) {
        buff.append("" + preds[j] + " ");
      }
      buff.append("\n");

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