Examples with LinearKernel smile.math.kernel.LinearKernel used on opensource projects

Example 1 with LinearKernel

use of smile.math.kernel.LinearKernel in project smile by haifengl.

the class SVMTest method testLearn.

/**
     * Test of learn method, of class SVM.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    try {
        AttributeDataset iris = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/iris.arff"));
        double[][] x = iris.toArray(new double[iris.size()][]);
        int[] y = iris.toArray(new int[iris.size()]);
        SVM<double[]> svm = new SVM<>(new LinearKernel(), 10.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        int error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("Linear ONE vs. ALL error = " + error);
        assertTrue(error <= 10);
        svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        svm.trainPlattScaling(x, y);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
            double[] prob = new double[3];
            int yp = svm.predict(x[i], prob);
        //System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
        }
        System.out.println("Gaussian ONE vs. ALL error = " + error);
        assertTrue(error <= 5);
        svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ONE);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        svm.trainPlattScaling(x, y);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
            double[] prob = new double[3];
            int yp = svm.predict(x[i], prob);
        //System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
        }
        System.out.println("Gaussian ONE vs. ONE error = " + error);
        assertTrue(error <= 5);
        svm = new SVM<>(new PolynomialKernel(2), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("Polynomial ONE vs. ALL error = " + error);
        assertTrue(error <= 5);
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}