Examples with EuclideanDistance smile.math.distance.EuclideanDistance used on opensource projects

Example 6 with EuclideanDistance

use of smile.math.distance.EuclideanDistance in project smile by haifengl.

the class LinearSearchSpeedTest method testToy.

/**
     * Test of nearest method, of class LinearSearch.
     */
@Test
public void testToy() {
    System.out.println("toy data");
    long start = System.currentTimeMillis();
    double[] mu1 = { 1.0, 1.0, 1.0 };
    double[][] sigma1 = { { 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 } };
    double[] mu2 = { -2.0, -2.0, -2.0 };
    double[][] sigma2 = { { 1.0, 0.3, 0.8 }, { 0.3, 1.0, 0.5 }, { 0.8, 0.5, 1.0 } };
    double[] mu3 = { 4.0, 2.0, 3.0 };
    double[][] sigma3 = { { 1.0, 0.8, 0.3 }, { 0.8, 1.0, 0.5 }, { 0.3, 0.5, 1.0 } };
    double[] mu4 = { 3.0, 5.0, 1.0 };
    double[][] sigma4 = { { 1.0, 0.5, 0.5 }, { 0.5, 1.0, 0.5 }, { 0.5, 0.5, 1.0 } };
    double[][] data = new double[10000][];
    MultivariateGaussianDistribution g1 = new MultivariateGaussianDistribution(mu1, sigma1);
    for (int i = 0; i < 2000; i++) {
        data[i] = g1.rand();
    }
    MultivariateGaussianDistribution g2 = new MultivariateGaussianDistribution(mu2, sigma2);
    for (int i = 0; i < 3000; i++) {
        data[2000 + i] = g2.rand();
    }
    MultivariateGaussianDistribution g3 = new MultivariateGaussianDistribution(mu3, sigma3);
    for (int i = 0; i < 3000; i++) {
        data[5000 + i] = g3.rand();
    }
    MultivariateGaussianDistribution g4 = new MultivariateGaussianDistribution(mu4, sigma4);
    for (int i = 0; i < 2000; i++) {
        data[8000 + i] = g4.rand();
    }
    double time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Generating toy data (four Gaussians): %.2fs%n", time);
    LinearSearch<double[]> naive = new LinearSearch<>(data, new EuclideanDistance());
    start = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        naive.nearest(data[Math.randomInt(data.length)]);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        naive.knn(data[Math.randomInt(data.length)], 10);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("10-NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    List<Neighbor<double[], double[]>> n = new ArrayList<>();
    for (int i = 0; i < 1000; i++) {
        naive.range(data[Math.randomInt(data.length)], 1.0, n);
        n.clear();
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Range: %.2fs%n", time);
}

Example 7 with EuclideanDistance

use of smile.math.distance.EuclideanDistance 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 8 with EuclideanDistance

use of smile.math.distance.EuclideanDistance 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 9 with EuclideanDistance

use of smile.math.distance.EuclideanDistance 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 10 with EuclideanDistance

use of smile.math.distance.EuclideanDistance 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);
    }
}