Ejemplo n.º 1
0
  @Override
  public void doWork() throws OperatorException {
    ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);

    // only use numeric attributes
    Tools.onlyNumericalAttributes(exampleSet, "KernelPCA");
    Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this);

    Attributes attributes = exampleSet.getAttributes();
    int numberOfExamples = exampleSet.size();

    // calculating means for later zero centering
    exampleSet.recalculateAllAttributeStatistics();
    double[] means = new double[exampleSet.getAttributes().size()];
    int i = 0;
    for (Attribute attribute : exampleSet.getAttributes()) {
      means[i] = exampleSet.getStatistics(attribute, Statistics.AVERAGE);
      i++;
    }

    // kernel
    Kernel kernel = Kernel.createKernel(this);

    // copying zero centered exampleValues
    ArrayList<double[]> exampleValues = new ArrayList<double[]>(numberOfExamples);
    i = 0;
    for (Example columnExample : exampleSet) {
      double[] columnValues = getAttributeValues(columnExample, attributes, means);
      exampleValues.add(columnValues);
      i++;
    }

    // filling kernel matrix
    Matrix kernelMatrix = new Matrix(numberOfExamples, numberOfExamples);
    for (i = 0; i < numberOfExamples; i++) {
      for (int j = 0; j < numberOfExamples; j++) {
        kernelMatrix.set(
            i, j, kernel.calculateDistance(exampleValues.get(i), exampleValues.get(j)));
      }
    }

    // calculating eigenVectors
    EigenvalueDecomposition eig = kernelMatrix.eig();
    Model model = new KernelPCAModel(exampleSet, means, eig.getV(), exampleValues, kernel);

    if (exampleSetOutput.isConnected()) {
      exampleSetOutput.deliver(model.apply(exampleSet));
    }
    originalOutput.deliver(exampleSet);
    modelOutput.deliver(model);
  }
 @Override
 public void init(ExampleSet exampleSet) throws OperatorException {
   super.init(exampleSet);
   Tools.onlyNumericalAttributes(exampleSet, "value based similarities");
   Attributes attributes = exampleSet.getAttributes();
   if (attributes.size() != 1)
     throw new OperatorException(
         "The bregman divergence you've choosen is not applicable for the dataset! Proceeding with the 'Squared Euclidean distance' bregman divergence.");
   for (Example example : exampleSet) {
     for (Attribute attribute : attributes) {
       if (example.getValue(attribute) <= 0)
         throw new OperatorException(
             "The bregman divergence you've choosen is not applicable for the dataset! Proceeding with the 'Squared Euclidean distance' bregman divergence.");
       ;
     }
   }
 }
 @Override
 public void init(ExampleSet exampleSet) throws OperatorException {
   super.init(exampleSet);
   Tools.onlyNumericalAttributes(exampleSet, "value based similarities");
 }