Examples of fr.lip6.jkernelmachines.util.generators.GaussianGenerator

Package fr.lip6.jkernelmachines.util.generators

Examples of fr.lip6.jkernelmachines.util.generators.GaussianGenerator

fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2

Class for generating toys subject to binary classification tasks, using Gaussian distribution.

Toys are sampled from 2 Gaussian distributions (one for the positive samples, the other for the negtive samples). The positive samples have a mean of p on each component, while the negative samples have a mean of -p on each component. The number of dimension, the spacing p and the standard deviation of the Gaussian can be adjusted.
Generated lists are shuffled.
@author picard

   * @param kernel
   *            the kernel to be approximated
   */
  public NystromKernel(Kernel<T> kernel) {
    this.kernel = kernel;
    linear = new DoubleLinear();
  }

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   * @param list
   */
  public static void normalizeList(List<TrainingSample<double[]>> list) {
    if(list.isEmpty())
      return;
    DoubleLinear linear = new DoubleLinear();
    
    for(TrainingSample<double[]> t : list) {
      double[] desc = t.sample;
      double norm = Math.sqrt(linear.valueOf(desc, desc));
      for(int x = 0 ; x < desc.length ; x++)
        desc[x] /= norm;
    }
    
  }

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   * @param list
   */
  public static void normalizeDoubleList(List<double[]> list) {
    if(list.isEmpty())
      return;
    DoubleLinear linear = new DoubleLinear();
    
    for(double[] desc : list) {
      double norm = Math.sqrt(linear.valueOf(desc, desc));
      for(int x = 0 ; x < desc.length ; x++)
        desc[x] /= norm;
    }
    
  }

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  @Test
  public final void testProjectListListOfTrainingSampleOfT() {
    double[][] m1 = k.getKernelMatrix(list);
    
    List<TrainingSample<double[]>> plist = pca.projectList(list);
    DoubleLinear lin = new DoubleLinear();
    double[][] m2 = lin.getKernelMatrix(plist);
    
    for(int i = 0 ; i < m1.length ; i++) {
      for(int j = i ; j < m1[0].length ; j++) {
        assertEquals(m1[i][j], m2[i][j]+pca.getMean(), 1e-10);
      }

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          .getSelectedItem().toString())) {
        k = new DoubleTriangleL2(
            Double.parseDouble(kernelParamTextField.getText()));
      } else if ("Polynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      } else if ("HPlolynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleHPolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      }


      LaSVM<double[]> svm = new LaSVM<double[]>(k);
      svm.setC(Double.parseDouble(regularizationField.getText()));
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;
      for (int s = 0; s < alphas.length; s++) {
        if (alphas[s] != 0) {
          sv++;
        }
      }
      svLabel.setText("" + sv);
      validate();
      // save current classifier
      model.classifier = svm;
    } else if ("smo".equalsIgnoreCase(classifierBox.getSelectedItem()
        .toString())) {
      Kernel<double[]> k = new DoubleLinear();
      if ("GaussianL2".equalsIgnoreCase(kernelBox.getSelectedItem()
          .toString())) {
        k = new DoubleGaussL2(Double.parseDouble(kernelParamTextField
            .getText()));
      } else if ("TriangleL2".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleTriangleL2(
            Double.parseDouble(kernelParamTextField.getText()));
      } else if ("Polynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      } else if ("HPlolynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleHPolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      }


      SMOSVM<double[]> svm = new SMOSVM<double[]>(k);
      svm.setC(Double.parseDouble(regularizationField.getText()));
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;
      for (int s = 0; s < alphas.length; s++) {
        if (alphas[s] != 0) {
          sv++;
        }
      }
      svLabel.setText("" + sv);
      validate();
      // save current classifier
      model.classifier = svm;
    } else if ("sag".equalsIgnoreCase(classifierBox.getSelectedItem()
        .toString())) {
      DoubleSAG svm = new DoubleSAG();
      svm.setLambda(1. / (train.size() * Double
          .parseDouble(regularizationField.getText())));
      svm.setE(10);
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      svLabel.setText("N/A");


      // save current classifier
      model.classifier = svm;
    } else if ("pegasos".equalsIgnoreCase(classifierBox.getSelectedItem()
        .toString())) {
      DoublePegasosSVM svm = new DoublePegasosSVM();


      svm.setLambda(1. / (train.size() * Double
          .parseDouble(regularizationField.getText())));
      svm.setK(train.size() / 20);
      svm.setT(10 * train.size());
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      svLabel.setText("N/A");


      // save current classifier
      model.classifier = svm;
    } else if ("simplemkl".equalsIgnoreCase(classifierBox.getSelectedItem()
        .toString())) {
      SimpleMKL<double[]> svm = new SimpleMKL<double[]>();
      svm.setC(Double.parseDouble(regularizationField.getText()));


      double[] G = { 0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 25.6 };
//      int dim = train.get(0).sample.length;
      for (double g : G) {
        svm.addKernel(new DoubleGaussL2(g));
//        // for(int i = 0 ; i < dim ; i++) {
//        // IndexDoubleGaussL2 k = new IndexDoubleGaussL2(i);
//        // k.setGamma(g);
//        // svm.addKernel(k);
//        // }
      }
      for (int d = 1; d < 5; d++) {
        svm.addKernel(new DoublePolynomial(d));
        svm.addKernel(new DoubleHPolynomial(d));
      }
      svm.train(localTrain);


      // info

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          .toString())) {
        k = new DoubleGaussL2(Double.parseDouble(kernelParamTextField
            .getText()));
      } else if ("TriangleL2".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleTriangleL2(
            Double.parseDouble(kernelParamTextField.getText()));
      } else if ("Polynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      } else if ("HPlolynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleHPolynomial(Integer.parseInt(kernelParamTextField
            .getText()));
      }


      LaSVM<double[]> svm = new LaSVM<double[]>(k);
      svm.setC(Double.parseDouble(regularizationField.getText()));
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;
      for (int s = 0; s < alphas.length; s++) {
        if (alphas[s] != 0) {
          sv++;
        }
      }
      svLabel.setText("" + sv);
      validate();
      // save current classifier
      model.classifier = svm;
    } else if ("smo".equalsIgnoreCase(classifierBox.getSelectedItem()
        .toString())) {
      Kernel<double[]> k = new DoubleLinear();
      if ("GaussianL2".equalsIgnoreCase(kernelBox.getSelectedItem()
          .toString())) {
        k = new DoubleGaussL2(Double.parseDouble(kernelParamTextField
            .getText()));
      } else if ("TriangleL2".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoubleTriangleL2(
            Double.parseDouble(kernelParamTextField.getText()));
      } else if ("Polynomial".equalsIgnoreCase(kernelBox
          .getSelectedItem().toString())) {
        k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
            .getText()));

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//      weights[i] = 1.0/(dim*dim);
    }
    
    
    //1 train first svm
    GeneralizedDoubleGaussL2 kernel = new GeneralizedDoubleGaussL2(weights);
    svm = trainSVM(kernel);
    double[] a = svm.getAlphas();
    //update lambda matrix before objective computation
    updateLambdaMatrix(a, kernel);
    //compute old value of objective function
    oldObjective = computeObj(a);
    debug.println(2, "+ initial objective : "+oldObjective);
    debug.println(3, "+ initial weights : "+Arrays.toString(weights));
    
    //2. big loop
    double gap = 0;
    do
    {            
      //perform one step
      double objEvol = performPKLStep();
      
      if(objEvol < 0)
      {
        debug.println(1, "Error, performPKLStep return wrong value");
        System.exit(0);;
      }
      gap = 1 - objEvol;
      
      debug.println(1, "+ objective_gap : "+(float)gap);
      debug.println(1, "+");
      
    }
    while(gap >= stopGap);
    
    
    //3. get minimal objective svm and weights
    listOfKernelWeights = new ArrayList<Double>();
    for(int i = 0 ; i < weights.length; i++)
      listOfKernelWeights.add(weights[i]);
    kernel = new GeneralizedDoubleGaussL2(weights);
    svm = trainSVM(kernel);
    //update lambdamatrix
    a = svm.getAlphas();
    updateLambdaMatrix(a, kernel);

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    //store new as old for the loop
    double objective = oldObjective;
    double[] oldWeights = weights;
    
    //train new svm
    GeneralizedDoubleGaussL2 k = new GeneralizedDoubleGaussL2(weights);
    LaSVM<double[]> svm = trainSVM(k);
    //update lambdamatrix
    double[] a = svm.getAlphas();
    updateLambdaMatrix(a, k);
    
    //compute grad
    double[] gNew = computeGrad(k);
    
    //estimate B
    double[] B = computeB(gNew);
    
    double lambda = 1.;
    do
    {
      //1. update weights.
      double[] wNew = new double[weights.length];
      double Z = 0;
      for(int x = 0 ; x < wNew.length ; x++) {
        wNew[x] = weights[x] - lambda * B[x] * gNew[x];
        if(wNew[x] < num_cleaning)
          wNew[x] = 0;
        if(hasNorm)
          Z += wNew[x];
      }
      
      if(hasNorm) {
        for(int x = 0 ; x < wNew.length ; x++)
          wNew[x] /= Z;
      }
        
      
      //2. retrain SVM
      k = new GeneralizedDoubleGaussL2(wNew);
      svm = trainSVM(k);
      //update lambdamatrix
      a = svm.getAlphas();
      updateLambdaMatrix(a, k);

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  @Test
  public final void testTrainListOfT() {
    DoubleGaussL2 k = new DoubleGaussL2();
    SimpleMKLDensity<double[]> de = new SimpleMKLDensity<double[]>();
    for(int i = 0 ; i < 2 ; i++) {
      de.addKernel(new IndexDoubleGaussL2(i));
    }
    de.addKernel(k);
    de.train(train);


    for (double[] x : train) {

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    DoubleGaussianMixtureModel gmm = new DoubleGaussianMixtureModel(1);
    gmm.train(train);


    SimpleMKLDensity<double[]> mkl = new SimpleMKLDensity<double[]>();
    for (int x = 0; x < dimension; x++) {
      mkl.addKernel(new IndexDoubleGaussL2(x));
    }
    mkl.setC(100);
    mkl.train(train);


    ArrayList<double[]> test = new ArrayList<double[]>();

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