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

Example 56 with AttributeDataset

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

the class GradientTreeBoostTest method testUSPS.

/**
     * Test of learn method, of class GradientTreeBoost.
     */
@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()]);
        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 57 with AttributeDataset

use of smile.data.AttributeDataset 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 58 with AttributeDataset

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

the class GradientTreeBoostTest method testSegment.

/**
     * Test of learn method, of class GradientTreeBoost.
     */
@Test
public void testSegment() {
    System.out.println("Segment");
    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(19);
    try {
        AttributeDataset train = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
        AttributeDataset test = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
        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()]);
        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);
    //assertEquals(28, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 59 with AttributeDataset

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

the class KNNTest method testSegment.

/**
     * Test of learn method, of class KNN.
     */
@Test
public void testSegment() throws ParseException {
    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]);
        KNN<double[]> knn = KNN.learn(x, y);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (knn.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("Segment error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertEquals(39, error);
    } catch (IOException ex) {
        System.err.println(ex);
    }
}

Example 60 with AttributeDataset

use of smile.data.AttributeDataset 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);
    }
}