Examples with GaussianRadialBasis smile.math.rbf.GaussianRadialBasis used on opensource projects

Example 6 with GaussianRadialBasis

use of smile.math.rbf.GaussianRadialBasis in project smile by haifengl.

the class SmileUtils method learnGaussianRadialBasis.

/**
     * Learns Gaussian RBF function and centers from data. The centers are
     * chosen as the centroids of K-Means. The standard deviation (i.e. width)
     * of Gaussian radial basis function is estimated by the p-nearest neighbors
     * (among centers, not all samples) heuristic. A suggested value for
     * p is 2.
     * @param x the training dataset.
     * @param centers an array to store centers on output. Its length is used as k of k-means.
     * @param p the number of nearest neighbors of centers to estimate the width
     * of Gaussian RBF functions.
     * @return Gaussian RBF functions with parameter learned from data.
     */
public static GaussianRadialBasis[] learnGaussianRadialBasis(double[][] x, double[][] centers, int p) {
    if (p < 1) {
        throw new IllegalArgumentException("Invalid number of nearest neighbors: " + p);
    }
    int k = centers.length;
    KMeans kmeans = new KMeans(x, k, 10);
    System.arraycopy(kmeans.centroids(), 0, centers, 0, k);
    p = Math.min(p, k - 1);
    double[] r = new double[k];
    GaussianRadialBasis[] rbf = new GaussianRadialBasis[k];
    for (int i = 0; i < k; i++) {
        for (int j = 0; j < k; j++) {
            r[j] = Math.distance(centers[i], centers[j]);
        }
        Arrays.sort(r);
        double r0 = 0.0;
        for (int j = 1; j <= p; j++) {
            r0 += r[j];
        }
        r0 /= p;
        rbf[i] = new GaussianRadialBasis(r0);
    }
    return rbf;
}

Example 7 with GaussianRadialBasis

use of smile.math.rbf.GaussianRadialBasis in project smile by haifengl.

the class RBFNetworkTest method testUSPS.

/**
     * Test of learn method, of class RBFNetwork.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    DelimitedTextParser parser = new DelimitedTextParser();
    parser.setResponseIndex(new NominalAttribute("class"), 0);
    try {
        AttributeDataset train = parser.parse("USPS Train", smile.data.parser.IOUtils.getTestDataFile("usps/zip.train"));
        AttributeDataset test = parser.parse("USPS Test", smile.data.parser.IOUtils.getTestDataFile("usps/zip.test"));
        double[][] x = train.toArray(new double[train.size()][]);
        int[] y = train.toArray(new int[train.size()]);
        double[][] testx = test.toArray(new double[test.size()][]);
        int[] testy = test.toArray(new int[test.size()]);
        double[][] centers = new double[200][];
        RadialBasisFunction basis = SmileUtils.learnGaussianRadialBasis(x, centers);
        RBFNetwork<double[]> rbf = new RBFNetwork<>(x, y, new EuclideanDistance(), new GaussianRadialBasis(8.0), centers);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (rbf.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertTrue(error <= 150);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}