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

Example 46 with NominalAttribute

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

the class LDATest method testUSPS.

/**
     * Test of learn method, of class LDA.
     */
@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()]);
        LDA lda = new LDA(x, y);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (lda.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertEquals(256, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 47 with NominalAttribute

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

the class LogisticRegressionTest method testUSPS.

/**
     * Test of learn method, of class LogisticRegression.
     */
@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()]);
        LogisticRegression logit = new LogisticRegression(x, y, 0.3, 1E-3, 1000);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (logit.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertEquals(188, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 48 with NominalAttribute

use of smile.data.NominalAttribute 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);
    }
}

Example 49 with NominalAttribute

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

the class RDATest method testUSPS.

/**
     * Test of learn method, of class RDA.
     */
@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()]);
        RDA rda = new RDA(x, y, 0.7);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (rda.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertEquals(235, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 50 with NominalAttribute

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

the class RandomForestTest method testUSPS.

/**
     * Test of learn method, of class RandomForest.
     */
@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()]);
        RandomForest forest = new RandomForest(x, y, 200);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (forest.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.println(error);
        System.out.format("USPS OOB error rate = %.2f%%%n", 100.0 * forest.error());
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertTrue(error <= 140);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}