Examples with AttributeDataset smile.data.AttributeDataset used on opensource projects

Example 41 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class SVMTest method testLearn.

/**
     * Test of learn method, of class SVM.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    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()][]);
        int[] y = iris.toArray(new int[iris.size()]);
        SVM<double[]> svm = new SVM<>(new LinearKernel(), 10.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        int error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("Linear ONE vs. ALL error = " + error);
        assertTrue(error <= 10);
        svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        svm.trainPlattScaling(x, y);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
            double[] prob = new double[3];
            int yp = svm.predict(x[i], prob);
        //System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
        }
        System.out.println("Gaussian ONE vs. ALL error = " + error);
        assertTrue(error <= 5);
        svm = new SVM<>(new GaussianKernel(1), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ONE);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        svm.trainPlattScaling(x, y);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
            double[] prob = new double[3];
            int yp = svm.predict(x[i], prob);
        //System.out.format("%d %d %.2f, %.2f %.2f\n", y[i], yp, prob[0], prob[1], prob[2]);
        }
        System.out.println("Gaussian ONE vs. ONE error = " + error);
        assertTrue(error <= 5);
        svm = new SVM<>(new PolynomialKernel(2), 1.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.learn(x, y);
        svm.finish();
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (svm.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("Polynomial ONE vs. ALL error = " + error);
        assertTrue(error <= 5);
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

Example 42 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class BIRCHTest method testUSPS.

/**
     * Test of learn method, of class BIRCH.
     */
@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()]);
        BIRCH birch = new BIRCH(x[0].length, 5, 16.0);
        for (int i = 0; i < 20; i++) {
            int[] index = Math.permutate(x.length);
            for (int j = 0; j < x.length; j++) {
                birch.add(x[index[j]]);
            }
        }
        birch.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        int[] p = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            p[i] = birch.predict(x[i]);
        }
        double r = rand.measure(y, p);
        double r2 = ari.measure(y, p);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.65);
        assertTrue(r2 > 0.20);
        p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = birch.predict(testx[i]);
        }
        r = rand.measure(testy, p);
        r2 = ari.measure(testy, p);
        System.out.format("Testing rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.65);
        assertTrue(r2 > 0.20);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 43 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class GaussianProcessRegressionTest method test2DPlanes.

/**
     * Test of learn method, of class GaussianProcessRegression.
     */
@Test
public void test2DPlanes() {
    System.out.println("2dplanes");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(10);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/2dplanes.arff"));
        double[][] x = data.toArray(new double[data.size()][]);
        double[] y = 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(34.866), 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 44 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class GradientTreeBoostTest method test.

public void test(GradientTreeBoost.Loss loss, String dataset, String url, int response) {
    System.out.println(dataset + "\t" + loss);
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(response);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile(url));
        double[] datay = data.toArray(new double[data.size()]);
        double[][] datax = data.toArray(new double[data.size()][]);
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 0.0;
        double ad = 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]);
            GradientTreeBoost boost = new GradientTreeBoost(data.attributes(), trainx, trainy, loss, 100, 6, 0.05, 0.7);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - boost.predict(testx[j]);
                ad += Math.abs(r);
                rss += r * r;
            }
        }
        System.out.format("10-CV RMSE = %.4f \t AbsoluteDeviation = %.4f%n", Math.sqrt(rss / n), ad / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 45 with AttributeDataset

use of smile.data.AttributeDataset 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);
    }
}