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

Example 21 with NominalAttribute

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

the class GradientTreeBoostTest method testUSPS2.

/**
     * Test of learn method, of class GradientTreeBoost.
     */
@Test
public void testUSPS2() {
    System.out.println("USPS 2 classes");
    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()]);
        for (int i = 0; i < y.length; i++) {
            if (y[i] != 0) {
                y[i] = 1;
            }
        }
        for (int i = 0; i < testy.length; i++) {
            if (testy[i] != 0) {
                testy[i] = 1;
            }
        }
        GradientTreeBoost boost = new GradientTreeBoost(train.attributes(), x, y, 100);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (boost.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("Gradient Tree Boost error rate = %.2f%%%n", 100.0 * error / testx.length);
        double[] accuracy = boost.test(testx, testy);
        for (int i = 1; i <= accuracy.length; i++) {
            System.out.format("%d trees accuracy = %.2f%%%n", i, 100.0 * accuracy[i - 1]);
        }
        double[] importance = boost.importance();
        int[] index = QuickSort.sort(importance);
        for (int i = importance.length; i-- > 0; ) {
            System.out.format("%s importance is %.4f%n", train.attributes()[index[i]], importance[i]);
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 22 with NominalAttribute

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

the class CLARANSTest method testUSPS.

/**
     * Test of learn method, of class CLARANS.
     */
@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()]);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        CLARANS<double[]> clarans = new CLARANS<>(x, new EuclideanDistance(), 10, 50, 8);
        double r = rand.measure(y, clarans.getClusterLabel());
        double r2 = ari.measure(y, clarans.getClusterLabel());
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.8);
        assertTrue(r2 > 0.28);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = clarans.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 > 0.8);
        assertTrue(r2 > 0.25);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 23 with NominalAttribute

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

the class KMeansTest method testUSPS.

/**
     * Test of learn method, of class KMeans.
     */
@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()]);
        KMeans kmeans = new KMeans(x, 10, 100, 4);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, kmeans.getClusterLabel());
        double r2 = ari.measure(y, kmeans.getClusterLabel());
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.85);
        assertTrue(r2 > 0.45);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = kmeans.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 > 0.85);
        assertTrue(r2 > 0.45);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 24 with NominalAttribute

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

the class MECTest method testUSPS.

/**
     * Test of learn method, of class MEC.
     */
@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()]);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        MEC<double[]> mec = new MEC<>(x, new EuclideanDistance(), 10, 8.0);
        double r = rand.measure(y, mec.getClusterLabel());
        double r2 = ari.measure(y, mec.getClusterLabel());
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.85);
        assertTrue(r2 > 0.35);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = mec.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 > 0.85);
        assertTrue(r2 > 0.35);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 25 with NominalAttribute

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

the class NumericAttributeFeatureTest method testNORMALIZATION.

/**
     * Test of f method, of class NumericAttributeFeature.
     */
@Test
public void testNORMALIZATION() {
    System.out.println("NORMALIZATION");
    DelimitedTextParser parser = new DelimitedTextParser();
    parser.setResponseIndex(new NominalAttribute("class"), 0);
    try {
        AttributeDataset data = parser.parse("USPS Train", smile.data.parser.IOUtils.getTestDataFile("usps/zip.train"));
        double[][] x = data.toArray(new double[data.size()][]);
        double[] min = Math.colMin(x);
        double[] max = Math.colMax(x);
        NumericAttributeFeature naf = new NumericAttributeFeature(data.attributes(), NumericAttributeFeature.Scaling.NORMALIZATION, x);
        Attribute[] attributes = naf.attributes();
        assertEquals(256, attributes.length);
        for (int i = 0; i < x.length; i++) {
            double[] y = new double[attributes.length];
            for (int j = 0; j < y.length; j++) {
                y[j] = naf.f(x[i], j);
                assertEquals((x[i][j] - min[j]) / (max[j] - min[j]), y[j], 1E-7);
            }
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}