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

Example 31 with AttributeDataset

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

the class SpectralClusteringTest method testUSPSNystrom.

/**
     * Test of learn method, of class SpectralClustering.
     */
@Test
public void testUSPSNystrom() {
    System.out.println("USPS Nystrom approximation");
    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"));
        double[][] x = train.toArray(new double[train.size()][]);
        int[] y = train.toArray(new int[train.size()]);
        SpectralClustering spectral = new SpectralClustering(x, 10, 100, 8.0);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, spectral.getClusterLabel());
        double r2 = ari.measure(y, spectral.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 > 0.35);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 32 with AttributeDataset

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

the class SpectralClusteringTest method testUSPS.

/**
     * Test of learn method, of class SpectralClustering.
     */
@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"));
        double[][] x = train.toArray(new double[train.size()][]);
        int[] y = train.toArray(new int[train.size()]);
        SpectralClustering spectral = new SpectralClustering(x, 10, 8.0);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, spectral.getClusterLabel());
        double r2 = ari.measure(y, spectral.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);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 33 with AttributeDataset

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

the class DateFeatureTest method testAttributes.

/**
     * Test of attributes method, of class DateFeature.
     */
@Test
public void testAttributes() {
    System.out.println("attributes");
    try {
        ArffParser parser = new ArffParser();
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/date.arff"));
        DateFeature.Type[] features = { DateFeature.Type.YEAR, DateFeature.Type.MONTH, DateFeature.Type.DAY_OF_MONTH, DateFeature.Type.DAY_OF_WEEK, DateFeature.Type.HOURS, DateFeature.Type.MINUTES, DateFeature.Type.SECONDS };
        DateFeature df = new DateFeature(data.attributes(), features);
        Attribute[] attributes = df.attributes();
        assertEquals(features.length, attributes.length);
        for (int i = 0; i < attributes.length; i++) {
            System.out.println(attributes[i]);
            assertEquals(Attribute.Type.NUMERIC, attributes[i].getType());
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 34 with AttributeDataset

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

the class FeatureSetTest method testF.

/**
     * Test of f method, of class FeatureSet.
     */
@Test
public void testF() {
    System.out.println("f");
    try {
        ArffParser parser = new ArffParser();
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/abalone.arff"));
        double[][] x = data.toArray(new double[data.size()][]);
        FeatureSet<double[]> features = new FeatureSet<>();
        features.add(new Nominal2Binary(data.attributes()));
        features.add(new NumericAttributeFeature(data.attributes(), 0.05, 0.95, x));
        AttributeDataset dataset = features.f(data);
        assertEquals(data.size(), dataset.size());
        assertEquals(data.getName(), dataset.getName());
        assertEquals(data.getDescription(), dataset.getDescription());
        Attribute[] attributes = features.attributes();
        for (int i = 0; i < attributes.length; i++) {
            assertEquals(attributes[i].getName(), dataset.attributes()[i].getName());
            assertEquals(attributes[i].getType(), dataset.attributes()[i].getType());
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 35 with AttributeDataset

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

the class GAFeatureSelectionTest method testLearn.

/**
     * Test of learn method, of class GAFeatureSelection.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    int size = 100;
    int generation = 20;
    ClassifierTrainer<double[]> trainer = new LDA.Trainer();
    ClassificationMeasure measure = new Accuracy();
    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()]);
        GAFeatureSelection instance = new GAFeatureSelection();
        BitString[] result = instance.learn(size, generation, trainer, measure, x, y, testx, testy);
        for (BitString bits : result) {
            System.out.format("%.2f%% %d ", 100 * bits.fitness(), Math.sum(bits.bits()));
            for (int i = 0; i < x[0].length; i++) {
                System.out.print(bits.bits()[i] + " ");
            }
            System.out.println();
        }
        assertTrue(result[result.length - 1].fitness() > 0.88);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}