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

Example 1 with RadialBasisFunction

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

the class ValidationTest method testTest_4args_2.

/**
     * Test of test method, of class Validation.
     */
@Test
public void testTest_4args_2() {
    System.out.println("test");
    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 m = 3 * n / 4;
        double[][] x = new double[m][];
        double[] y = new double[m];
        double[][] testx = new double[n - m][];
        double[] testy = new double[n - m];
        int[] index = Math.permutate(n);
        for (int i = 0; i < m; i++) {
            x[i] = datax[index[i]];
            y[i] = datay[index[i]];
        }
        for (int i = m; i < n; i++) {
            testx[i - m] = datax[index[i]];
            testy[i - m] = datay[index[i]];
        }
        double[][] centers = new double[20][];
        RadialBasisFunction[] rbf = SmileUtils.learnGaussianRadialBasis(x, centers, 2);
        RBFNetwork<double[]> rkhs = new RBFNetwork<>(x, y, new EuclideanDistance(), rbf, centers);
        RegressionMeasure[] measures = { new RMSE(), new AbsoluteDeviation() };
        double[] results = Validation.test(rkhs, testx, testy, measures);
        System.out.println("RMSE = " + results[0]);
        System.out.println("Absolute Deviation = " + results[1]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 2 with RadialBasisFunction

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

the class RBFNetworkTest method testSegment.

/**
     * Test of learn method, of class RBFNetwork.
     */
@Test
public void testSegment() {
    System.out.println("Segment");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(19);
    try {
        AttributeDataset train = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
        AttributeDataset test = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
        double[][] x = train.toArray(new double[0][]);
        int[] y = train.toArray(new int[0]);
        double[][] testx = test.toArray(new double[0][]);
        int[] testy = test.toArray(new int[0]);
        double[][] centers = new double[100][];
        RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(x, centers, 5.0);
        RBFNetwork<double[]> rbf = new RBFNetwork<>(x, y, new EuclideanDistance(), basis, centers);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (rbf.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("Segment error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertTrue(error <= 210);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 3 with RadialBasisFunction

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

the class RBFNetworkTest method testAilerons.

/**
     * Test of learn method, of class RBFNetwork.
     */
@Test
public void testAilerons() {
    System.out.println("ailerons");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(40);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/ailerons.arff"));
        double[][] datax = data.toArray(new double[data.size()][]);
        Math.standardize(datax);
        double[] datay = data.toArray(new double[data.size()]);
        for (int i = 0; i < datay.length; i++) {
            datay[i] *= 10000;
        }
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 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]);
            double[][] centers = new double[20][];
            RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
            RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rbf.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with RadialBasisFunction

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

the class RBFNetworkTest method testLearn.

/**
     * Test of learn method, of class RKHSRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    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]);
        double[][] centers = new double[10][];
        RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
        RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
        double r = y[loocv.test[i]] - rbf.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
}

Example 5 with RadialBasisFunction

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

the class RBFNetworkTest method testBank32nh.

/**
     * Test of learn method, of class RBFNetwork.
     */
@Test
public void testBank32nh() {
    System.out.println("bank32nh");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(31);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/bank32nh.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;
        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]);
            double[][] centers = new double[20][];
            RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
            RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rbf.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}