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

Example 1 with GaussianKernel

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

the class SVMDemo method learn.

@Override
public double[][] learn(double[] x, double[] y) {
    try {
        gamma = Double.parseDouble(gammaField.getText().trim());
        if (gamma <= 0) {
            JOptionPane.showMessageDialog(this, "Invalid ˠ: " + gamma, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception ex) {
        JOptionPane.showMessageDialog(this, "Invalid ˠ: " + gammaField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        C = Double.parseDouble(cField.getText().trim());
        if (C < 0) {
            JOptionPane.showMessageDialog(this, "Invalid C: " + C, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception ex) {
        JOptionPane.showMessageDialog(this, "Invalid C: " + cField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
    SVM<double[]> svm = new SVM<>(new GaussianKernel(gamma), C);
    svm.learn(data, label);
    svm.finish();
    for (int i = 0; i < label.length; i++) {
        label[i] = svm.predict(data[i]);
    }
    double trainError = error(label, label);
    System.out.format("training error = %.2f%%\n", 100 * trainError);
    double[][] z = new double[y.length][x.length];
    for (int i = 0; i < y.length; i++) {
        for (int j = 0; j < x.length; j++) {
            double[] p = { x[j], y[i] };
            z[i][j] = svm.predict(p);
        }
    }
    return z;
}

Example 2 with GaussianKernel

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

the class KPCATest method testKPCAK.

/**
     * Test of learn method, of class PCA.
     */
@Test
public void testKPCAK() {
    System.out.println("learn k");
    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()][]);
        KPCA<double[]> kpca = new KPCA(x, new GaussianKernel(Math.sqrt(2.5)), 29);
        assertTrue(Math.equals(latent, kpca.getVariances(), 1E-3));
        double[][] points = kpca.project(x);
        points[0] = kpca.project(x[0]);
        assertTrue(Math.equals(points, kpca.getCoordinates(), 1E-7));
    /*
            for (int j = 0; j < points[0].length; j++) {
                double sign = Math.signum(points[0][j] / scores[0][j]);
                for (int i = 0; i < points.length; i++) {
                    points[i][j] *= sign;
                }
            }

            assertTrue(Math.equals(scores, points, 1E-1));
 */
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 3 with GaussianKernel

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

the class GaussianProcessRegressionTest method testKin8nm.

/**
     * Test of learn method, of class GaussianProcessRegression.
     */
@Test
public void testKin8nm() {
    System.out.println("kin8nm");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(8);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/kin8nm.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        int[] perm = Math.permutate(x.length);
        double[][] datax = new double[4000][];
        double[] datay = new double[datax.length];
        for (int i = 0; i < datax.length; i++) {
            datax[i] = x[perm[i]];
            datay[i] = y[perm[i]];
        }
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 0.0;
        double sparseRSS30 = 0.0;
        for (int i = 0; i < k; i++) {
            double[][] trainx = Math.slice(datax, cv.train[i]);
            double[] trainy = Math.slice(datay, cv.train[i]);
            double[][] testx = Math.slice(datax, cv.test[i]);
            double[] testy = Math.slice(datay, cv.test[i]);
            GaussianProcessRegression<double[]> rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(34.97), 0.1);
            KMeans kmeans = new KMeans(trainx, 30, 10);
            double[][] centers = kmeans.centroids();
            double r0 = 0.0;
            for (int l = 0; l < centers.length; l++) {
                for (int j = 0; j < l; j++) {
                    r0 += Math.distance(centers[l], centers[j]);
                }
            }
            r0 /= (2 * centers.length);
            System.out.println("Kernel width = " + r0);
            GaussianProcessRegression<double[]> sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rkhs.predict(testx[j]);
                rss += r * r;
                r = testy[j] - sparse30.predict(testx[j]);
                sparseRSS30 += r * r;
            }
        }
        System.out.println("Regular 10-CV MSE = " + rss / n);
        System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with GaussianKernel

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

the class GaussianProcessRegressionTest method testCPU.

/**
     * Test of learn method, of class GaussianProcessRegression.
     */
@Test
public void testCPU() {
    System.out.println("CPU");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] datay = data.toArray(new double[data.size()]);
        double[][] datax = data.toArray(new double[data.size()][]);
        Math.standardize(datax);
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 0.0;
        double sparseRSS30 = 0.0;
        double nystromRSS30 = 0.0;
        for (int i = 0; i < k; i++) {
            double[][] trainx = Math.slice(datax, cv.train[i]);
            double[] trainy = Math.slice(datay, cv.train[i]);
            double[][] testx = Math.slice(datax, cv.test[i]);
            double[] testy = Math.slice(datay, cv.test[i]);
            GaussianProcessRegression<double[]> rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(47.02), 0.1);
            KMeans kmeans = new KMeans(trainx, 30, 10);
            double[][] centers = kmeans.centroids();
            double r0 = 0.0;
            for (int l = 0; l < centers.length; l++) {
                for (int j = 0; j < l; j++) {
                    r0 += Math.distance(centers[l], centers[j]);
                }
            }
            r0 /= (2 * centers.length);
            System.out.println("Kernel width = " + r0);
            GaussianProcessRegression<double[]> sparse30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1);
            GaussianProcessRegression<double[]> nystrom30 = new GaussianProcessRegression<>(trainx, trainy, centers, new GaussianKernel(r0), 0.1, true);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rkhs.predict(testx[j]);
                rss += r * r;
                r = testy[j] - sparse30.predict(testx[j]);
                sparseRSS30 += r * r;
                r = testy[j] - nystrom30.predict(testx[j]);
                nystromRSS30 += r * r;
            }
        }
        System.out.println("Regular 10-CV MSE = " + rss / n);
        System.out.println("Sparse (30) 10-CV MSE = " + sparseRSS30 / n);
        System.out.println("Nystrom (30) 10-CV MSE = " + nystromRSS30 / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 5 with GaussianKernel

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

the class GaussianProcessRegressionTest method testLearn.

/**
     * Test of learn method, of class RKHSRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    double[][] longley = { { 234.289, 235.6, 159.0, 107.608, 1947, 60.323 }, { 259.426, 232.5, 145.6, 108.632, 1948, 61.122 }, { 258.054, 368.2, 161.6, 109.773, 1949, 60.171 }, { 284.599, 335.1, 165.0, 110.929, 1950, 61.187 }, { 328.975, 209.9, 309.9, 112.075, 1951, 63.221 }, { 346.999, 193.2, 359.4, 113.270, 1952, 63.639 }, { 365.385, 187.0, 354.7, 115.094, 1953, 64.989 }, { 363.112, 357.8, 335.0, 116.219, 1954, 63.761 }, { 397.469, 290.4, 304.8, 117.388, 1955, 66.019 }, { 419.180, 282.2, 285.7, 118.734, 1956, 67.857 }, { 442.769, 293.6, 279.8, 120.445, 1957, 68.169 }, { 444.546, 468.1, 263.7, 121.950, 1958, 66.513 }, { 482.704, 381.3, 255.2, 123.366, 1959, 68.655 }, { 502.601, 393.1, 251.4, 125.368, 1960, 69.564 }, { 518.173, 480.6, 257.2, 127.852, 1961, 69.331 }, { 554.894, 400.7, 282.7, 130.081, 1962, 70.551 } };
    double[] y = { 83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9 };
    Math.standardize(longley);
    int n = longley.length;
    LOOCV loocv = new LOOCV(n);
    double rss = 0.0;
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(longley, loocv.train[i]);
        double[] trainy = Math.slice(y, loocv.train[i]);
        GaussianProcessRegression<double[]> rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(8.0), 0.2);
        double r = y[loocv.test[i]] - rkhs.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
}