How to use

distributionForInstance

method

in

weka.clusterers.Clusterer

@Override
public double[] distributionForInstance(Instance inst) throws Exception {
 return m_model.distributionForInstance(inst);
}

public Matrix predict(Matrix input, Matrix weight) throws Exception {
  double[] probabilities = null;
  Instance instance = new SampleToInstanceWrapper(input, weight, null, discrete, false);
  instance.setDataset(instances);
  probabilities = wekaClusterer.distributionForInstance(instance);
  double[][] v = new double[1][];
  v[0] = probabilities;
  DenseDoubleMatrix2D output = Matrix.Factory.linkToArray(v);
  return output;
}

/**
 * Classifies a given instance after filtering.
 * 
 * @param instance the instance to be classified
 * @return the class distribution for the given instance
 * @throws Exception if instance could not be classified successfully
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
 if (m_Filter.numPendingOutput() > 0) {
  throw new Exception("Filter output queue not empty!");
 }
 if (!m_Filter.input(instance)) {
  throw new Exception(
   "Filter didn't make the test instance immediately available!");
 }
 m_Filter.batchFinished();
 Instance newInstance = m_Filter.output();
 return m_Clusterer.distributionForInstance(newInstance);
}

/**
 * Classifies a given instance after filtering.
 * 
 * @param instance the instance to be classified
 * @return the class distribution for the given instance
 * @throws Exception if instance could not be classified successfully
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
 if (m_Filter.numPendingOutput() > 0) {
  throw new Exception("Filter output queue not empty!");
 }
 if (!m_Filter.input(instance)) {
  throw new Exception(
   "Filter didn't make the test instance immediately available!");
 }
 m_Filter.batchFinished();
 Instance newInstance = m_Filter.output();
 return m_Clusterer.distributionForInstance(newInstance);
}

 clusterer.distributionForInstance(testSet.instance(i));
for (int j = 0; j < clusterer.numberOfClusters(); j++) {
 newInstances.instance(i).setValue(testSet.numAttributes() + j,

 clusterer.distributionForInstance(testSet.instance(i));
for (int j = 0; j < clusterer.numberOfClusters(); j++) {
 newInstances.instance(i).setValue(testSet.numAttributes() + j,