Examples with DelimitedTextParser smile.data.parser.DelimitedTextParser used on opensource projects

Example 36 with DelimitedTextParser

use of smile.data.parser.DelimitedTextParser in project smile by haifengl.

the class LinearSearchSpeedTest method testUSPS.

/**
     * Test of nearest method, of class LinearSearch.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    double[][] x = null;
    double[][] testx = null;
    long start = System.currentTimeMillis();
    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"));
        x = train.toArray(new double[train.size()][]);
        testx = test.toArray(new double[test.size()][]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
    double time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Loading USPS: %.2fs%n", time);
    LinearSearch<double[]> naive = new LinearSearch<>(x, new EuclideanDistance());
    start = System.currentTimeMillis();
    for (int i = 0; i < testx.length; i++) {
        naive.nearest(testx[i]);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    for (int i = 0; i < testx.length; i++) {
        naive.knn(testx[i], 10);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("10-NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    List<Neighbor<double[], double[]>> n = new ArrayList<>();
    for (int i = 0; i < testx.length; i++) {
        naive.range(testx[i], 8.0, n);
        n.clear();
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Range: %.2fs%n", time);
}

Example 37 with DelimitedTextParser

use of smile.data.parser.DelimitedTextParser in project smile by haifengl.

the class MPLSHSpeedTest method testUSPS.

/**
     * Test of nearest method, of class KDTree.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    double[][] x = null;
    double[][] testx = null;
    long start = System.currentTimeMillis();
    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"));
        x = train.toArray(new double[train.size()][]);
        testx = test.toArray(new double[test.size()][]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
    double time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Loading USPS: %.2fs%n", time);
    start = System.currentTimeMillis();
    MPLSH<double[]> lsh = new MPLSH<>(256, 100, 3, 4.0);
    for (double[] xi : x) {
        lsh.put(xi, xi);
    }
    double[][] train = new double[500][];
    int[] index = Math.permutate(x.length);
    for (int i = 0; i < train.length; i++) {
        train[i] = x[index[i]];
    }
    LinearSearch<double[]> naive = new LinearSearch<>(x, new EuclideanDistance());
    lsh.learn(naive, train, 8.0);
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Building LSH: %.2fs%n", time);
    start = System.currentTimeMillis();
    for (int i = 0; i < testx.length; i++) {
        lsh.nearest(testx[i]);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    for (int i = 0; i < testx.length; i++) {
        lsh.knn(testx[i], 10);
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("10-NN: %.2fs%n", time);
    start = System.currentTimeMillis();
    List<Neighbor<double[], double[]>> n = new ArrayList<>();
    for (int i = 0; i < testx.length; i++) {
        lsh.range(testx[i], 8.0, n);
        n.clear();
    }
    time = (System.currentTimeMillis() - start) / 1000.0;
    System.out.format("Range: %.2fs%n", time);
}

Example 38 with DelimitedTextParser

use of smile.data.parser.DelimitedTextParser in project smile by haifengl.

the class ValidationTest method testTest_4args_1.

/**
     * Test of test method, of class Validation.
     */
@Test
public void testTest_4args_1() {
    System.out.println("test");
    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);
        ClassificationMeasure[] measures = { new Accuracy() };
        double[] accuracy = Validation.test(lda, testx, testy, measures);
        System.out.println("accuracy = " + accuracy[0]);
        assertEquals(0.8724, accuracy[0], 1E-4);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 39 with DelimitedTextParser

use of smile.data.parser.DelimitedTextParser in project smile by haifengl.

the class NeuralGasTest method testUSPS.

/**
     * Test of learn method, of class NeuralGas.
     */
@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()]);
        NeuralGas gas = new NeuralGas(x, 10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, gas.getClusterLabel());
        double r2 = ari.measure(y, gas.getClusterLabel());
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.88);
        assertTrue(r2 > 0.45);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = gas.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 > 0.88);
        assertTrue(r2 > 0.45);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 40 with DelimitedTextParser

use of smile.data.parser.DelimitedTextParser in project smile by haifengl.

the class DeterministicAnnealingTest method testUSPS.

/**
     * Test of learn method, of class DeterministicAnnealing.
     */
@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()]);
        DeterministicAnnealing annealing = new DeterministicAnnealing(x, 10, 0.8);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, annealing.getClusterLabel());
        double r2 = ari.measure(y, annealing.getClusterLabel());
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.75);
        assertTrue(r2 > 0.25);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = annealing.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 > 0.75);
        assertTrue(r2 > 0.3);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}