@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,