Examples of fr.lip6.jkernelmachines.kernel.extra.CustomTrainTestMatrixKernel

Package fr.lip6.jkernelmachines.kernel.extra

Examples of fr.lip6.jkernelmachines.kernel.extra.CustomTrainTestMatrixKernel

fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator

Kernel with provided custom train and test matrices.

The datatype of input space is integer relative to row/column indices. Therefore, the similarity between elements i and j is matrix[i][j].
Two matrices have to be provided: the first is train[nb_train][nb_train], and the second is test[nb_train][nb_test].

We suppose the train indices start at 0, and the test indices start at nb_train (the number of rows in the train matrix). For example, if you have 5 training samples and 10 testing samples, the train matrix is 5x5 and the test matrix is 5x10. This means that valid training indices are {0, 1, 2, 3, 4} and valid testing indices are {5, 6, 7, 8, 9, 10, 11, 12, 13, 14}.
To get the similarity between the first training sample and the first testing sample, you thus have to call valueOf(0, 5+0). The kernel automatically translates the second index for the test matrix and returns test[0][0].
@author dpicard

  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
    train = g.generateList(100);
    
    svm = new DoubleLLSVM();
    svm.setK(4);
    svm.setNn(2);
  }

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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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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    GaussianGenerator g = new GaussianGenerator(2, 50.0f, 1.0);
    train = g.generateList(10);


    svm = new S3VMLightPegasos();
  }

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   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    
    GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
    train = g.generateList(10);
    
    svm = new GradMKL<double[]>();
    svm.addKernel(new DoubleGaussL2());
  }

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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    GaussianGenerator g = new GaussianGenerator(2, 10.0f, 1.0);
    train = g.generateList(10);
    test = g.generateList(10);


    svm = new S3VMLightSGDQN();
  }

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   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    
    GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
    train = g.generateList(10);
    
    svm = new SimpleMKL<double[]>();
    svm.addKernel(new DoubleGaussL2());
  }

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    //set debug for visibility
    DebugPrinter.DEBUG_LEVEL = 2;
    
    // new generator with 10 classes and stddev of 0.5
    MultiClassGaussianGenerator mcg = new MultiClassGaussianGenerator();
    mcg.setSigma(0.5);
    mcg.setNbclasses(10);
    
    //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);

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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    mcg = new MultiClassGaussianGenerator();
  }

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  /**
   * Test method for {@link fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator#MultiClassGaussianGenerator(int)}.
   */
  @Test
  public final void testMultiClassGaussianGeneratorInt() {
    mcg = new MultiClassGaussianGenerator(5);
    assertEquals(5, mcg.getNbclasses());
  }

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  /**
   * @throws java.lang.Exception
   */
  @Before
  public void setUp() throws Exception {
    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);

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fr.lip6.jkernelmachines.classifier.DoublePegasosSVM

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fr.lip6.jkernelmachines.classifier.DoubleSAG

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fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel

fr.lip6.jkernelmachines.density.SimpleMKLDensity

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