Examples with ArffParser smile.data.parser.ArffParser used on opensource projects

Example 41 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class SignalNoiseRatioTest method testRank.

/**
     * Test of rank method, of class SignalNoiseRatio.
     */
@Test
public void testRank() {
    System.out.println("rank");
    try {
        ArffParser arffParser = new ArffParser();
        arffParser.setResponseIndex(4);
        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()]);
        for (int i = 0; i < y.length; i++) {
            if (y[i] < 2)
                y[i] = 0;
            else
                y[i] = 1;
        }
        SignalNoiseRatio s2n = new SignalNoiseRatio();
        double[] ratio = s2n.rank(x, y);
        assertEquals(4, ratio.length);
        assertEquals(0.8743107, ratio[0], 1E-7);
        assertEquals(0.1502717, ratio[1], 1E-7);
        assertEquals(1.3446912, ratio[2], 1E-7);
        assertEquals(1.4757334, ratio[3], 1E-7);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 42 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class SumSquaresRatioTest method testRank.

/**
     * Test of rank method, of class SumSquaresRatio.
     */
@Test
public void testRank() {
    System.out.println("rank");
    try {
        ArffParser arffParser = new ArffParser();
        arffParser.setResponseIndex(4);
        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()]);
        SumSquaresRatio ssr = new SumSquaresRatio();
        double[] ratio = ssr.rank(x, y);
        assertEquals(4, ratio.length);
        assertEquals(1.6226463, ratio[0], 1E-7);
        assertEquals(0.6444144, ratio[1], 1E-7);
        assertEquals(16.0412833, ratio[2], 1E-7);
        assertEquals(13.0520327, ratio[3], 1E-7);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 43 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class KPCATest method testKPCAThreshold.

/**
     * Test of learn method, of class PCA.
     */
@Test
public void testKPCAThreshold() {
    System.out.println("learn threshold");
    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)), 1E-4);
        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 44 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class GaussianProcessRegressionTest method testAilerons.

/**
     * Test of learn method, of class GaussianProcessRegression.
     */
@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[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        double[] y = data.toArray(new double[data.size()]);
        for (int i = 0; i < y.length; i++) {
            y[i] *= 10000;
        }
        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(183.96), 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 45 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class GaussianProcessRegressionTest method testBank32nh.

/**
     * Test of learn method, of class GaussianProcessRegression.
     */
@Test
public void testBank32nh() {
    System.out.println("bank32nh");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(32);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/bank32nh.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        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(55.3), 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);
    }
}