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.DoubleGaussL2

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

      System.out
          .println("usage: VOCExample trainfile.voc trainfile.fvec testfile.voc testfile.fvec");
      return;
    }


    FvecImporter fvecimp = new FvecImporter();


    // read train features
    List<double[]> feat = null;
    try {
      feat = fvecimp.readFile(args[1]);
    } catch (IOException e1) {
      System.out.println("Unable to read train features: " + args[1]);
      return;
    }


    // read voc train file
    List<TrainingSample<double[]>> train = new ArrayList<TrainingSample<double[]>>();
    try {
      LineNumberReader lin = new LineNumberReader(new FileReader(args[0]));
      String line;
      int i = 0;
      while ((line = lin.readLine()) != null) {
        // get label from second field. ex: "000012 -1"
        int label = Integer.parseInt(line.split("[ ]+")[1]);
        train.add(new TrainingSample<double[]>(feat.get(i), label));
        i++;
      }
      lin.close();
    } catch (FileNotFoundException e) {
      System.out
          .println("trainfile.voc : " + args[0] + " was not found.");
      return;
    } catch (IOException e) {
      System.out
          .println("Error while parsing trainfile.voc : " + args[0]);
      return;
    }
    System.out.println("Train features loaded.");


    // load test features
    try {
      feat = fvecimp.readFile(args[3]);
    } catch (IOException e1) {
      System.out.println("Unable to read test features: " + args[3]);
      return;
    }

View Full Code Here

   * Test method for {@link fr.lip6.jkernelmachines.io.FvecImporter#readFile(java.lang.String)}.
   */
  @Test
  public final void testReadFile() {
    try {
      FvecImporter fvecimp = new FvecImporter();
      List<double[]> l = fvecimp.readFile("resources/dict.fvec");
      assertEquals(32, l.size());
      assertEquals(40, l.get(0).length);
    }
    catch(IOException e) {

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   * Test method for {@link fr.lip6.jkernelmachines.io.FvecImporter#writeFile(java.lang.String, java.util.List<double[]>)}.
   */
  @Test
  public final void testWriteFile() {
    try {
      FvecImporter fvecimp = new FvecImporter();
      
      List<double[]> l = new ArrayList<double[]>();
      double[] d = new double[10];
      for(int i = 0 ; i < 10 ; i++)
        d[i] = i;
      l.add(d);
      
      fvecimp.writeFile("resources/testwrite.fvec", l);
      
      File f = new File("resources/testwrite.fvec");
      
      assertEquals(4+4*10, f.length());
      
      
      l = fvecimp.readFile("resources/testwrite.fvec");
      assertEquals(10, l.get(0).length);
      for(int i = 0 ; i < 10 ; i++)
        assertEquals(i, l.get(0)[i], 1e-7);
      
      f.delete();

View Full Code Here

    if ("lasvm"
        .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()));
      }


      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);
//        // }

View Full Code Here

    
    //generate the list
    List<TrainingSample<double[]>> list = mcg.generateList(100);
    
    //build classifier based on GaussL2 lasvm with c=10
    DoubleGaussL2 k =new DoubleGaussL2(2);
    LaSVM<double[]> svm = new LaSVM<double[]>(k);
    svm.setC(10);
    OneAgainstAll<double[]> mcsvm = new OneAgainstAll<double[]>(svm);

View Full Code Here

      System.out.println("Error parsing file "+args[1]);
      return;
    }
    
    //setting kernel
    DoubleGaussL2 kernel = new DoubleGaussL2();
    kernel.setGamma(2.0);
    
    //setting SVM parameters
    LaSVM<double[]> svm = new LaSVM<double[]>(kernel);
    svm.setC(10); //C hyperparameter

View Full Code Here

        // kernel type
        else if (args[i].equalsIgnoreCase("-k")) {
          i++;


          if (args[i].equalsIgnoreCase("gauss")) {
            kernel = new DoubleGaussL2();
          } else {
            kernel = new DoubleLinear();
          }
        }
        // algorithm

View Full Code Here

  public void setUp() throws Exception {
    
    GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
    train = g.generateList(10);
    
    DoubleGaussL2 k = new DoubleGaussL2(1.0);
    svm = new LaSVM<double[]>(k);
  }

View Full Code Here

    MultiClassGaussianGenerator mcgg = new MultiClassGaussianGenerator(4);
    mcgg.setP(10);
    mcgg.setSigma(1);
    train = mcgg.generateList(5);
    
    DoubleGaussL2 k = new DoubleGaussL2();
    k.setGamma(0.5);
    LaSVM<double[]> svm = new LaSVM<double[]>(k);
    svm.setC(10);
    multisvm = new OneAgainstAll<double[]>(svm);
  }

View Full Code Here

  public void setUp() throws Exception {
    
    GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
    train = g.generateList(10);
    
    DoubleGaussL2 k = new DoubleGaussL2(1.0);
    svm = new LaSVM<double[]>(k);
  }

View Full Code Here

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Related Classes of fr.lip6.jkernelmachines.util.generators.GaussianGenerator

fr.lip6.jkernelmachines.classifier.DoubleLLSVM

fr.lip6.jkernelmachines.classifier.DoublePegasosSVM

fr.lip6.jkernelmachines.classifier.DoubleQNPKL

fr.lip6.jkernelmachines.classifier.DoubleSAG

fr.lip6.jkernelmachines.classifier.DoubleSGDQN

fr.lip6.jkernelmachines.classifier.SimpleMKL

fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos

fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN

fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel

fr.lip6.jkernelmachines.density.SimpleMKLDensity

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