Package fr.lip6.jkernelmachines.kernel.typed

Examples of fr.lip6.jkernelmachines.kernel.typed.DoubleLinear

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    mean = mul(mean, 1./list.size());
   
    // compute covariance matrix;
    double[][] cov = new double[dim][dim];
   
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator() {
     
      @Override
      public void doLines(double[][] matrix, int from, int to) {
        for(int i = from ; i < to ; i++) {
          for(int j = 0 ; j < matrix.length ; j++) {
            double sum = 0;
            for(TrainingSample<double[]> t : list) {
              sum += (t.sample[i]-mean[i]) * (t.sample[j]-mean[j]);
            }
            matrix[i][j] = sum/list.size();
          }
        }       
      }
    };
   
    cov = factory.getMatrix(cov);
   
    // eigen decomposition
    double[][][] eig = eig(cov);
   
    //projectors are eigenvectors transposed
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    double[][] matrix = new double[l.size()][l.size()];
   
//    if(gammas[x] == 0)
//      return matrix;
    //computing matrix       
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLines(double[][] matrix, int from, int to) {
        double tmp = 0;
        for(int index = from ; index < to ; index++)
        {
          double s1 = l.get(index).sample[x];
          for(int j = 0 ; j < matrix.length ; j++){
            tmp = s1 - l.get(j).sample[x];
//            matrix[index][j] = gammas[x]*tmp*tmp;
            matrix[index][j] = tmp*tmp;
          }
           
        }
      }
    };
   
    factory.getMatrix(matrix);
   
    return matrix;
  }
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  public double[][] getDistanceMatrix(final List<TrainingSample<T>> l)
  {
    double[][] matrix = new double[l.size()][l.size()];
   
    //computing matrix       
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLines(double[][] matrix, int from, int to) {
        for(int index = from ; index < to ; index++)
        {
          T s1 = l.get(index).sample;
          for(int j = 0 ; j < matrix.length ; j++)
            matrix[index][j] = distanceValueOf(s1, l.get(j).sample);
        }
      }
    };
   
    factory.getMatrix(matrix);
   
    return matrix;
  }
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    double[][] matrix = new double[l.size()][l.size()];
   
//    if(gammas[x] == 0)
//      return matrix;
    //computing matrix       
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLines(double[][] matrix, int from, int to) {
        double tmp = 0;
        for(int index = from ; index < to ; index++)
        {
          double s1 = l.get(index).sample[x];
          for(int j = 0 ; j < matrix.length ; j++){
            tmp = s1 - l.get(j).sample[x];
//            matrix[index][j] = gammas[x]*tmp*tmp;
            matrix[index][j] = tmp*tmp;
          }
           
        }
      }
    };
   
    factory.getMatrix(matrix);
   
    return matrix;
  }
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    //rebuild matrix
    kmatrix = kernel.getKernelMatrix(tlist);
   
    //rebuild gradient
    final double[] tmp = new double[g.length];
    (new ThreadedMatrixOperator(){

      @Override
      public void doLines(double[][] matrix, int from, int to) {
        for(int index = from ; index < to ; index++)
        {
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  LaSVM<double[]> svm;

  @Before
  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);
  }
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  LaSVM<double[]> svm;

  @Before
  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);
  }
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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    g = new GaussianGenerator();
  }
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  /**
   * Test method for {@link fr.lip6.jkernelmachines.util.generators.GaussianGenerator#GaussianGenerator(int)}.
   */
  @Test
  public final void testGaussianGeneratorInt() {
    g = new GaussianGenerator(2);
    assertEquals(2, g.getDimension());
  }
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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    GaussianGenerator gen = new GaussianGenerator(2, 2, 1.0);
    List<TrainingSample<double[]>> list = gen.generateList(100, 100);
    train = new ArrayList<double[]>();
    for (TrainingSample<double[]> t : list) {
      train.add(t.sample);
    }
  }
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Related Classes of fr.lip6.jkernelmachines.kernel.typed.DoubleLinear

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  • 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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