Examples with AdjustedRandIndex smile.validation.AdjustedRandIndex used on opensource projects

Example 1 with AdjustedRandIndex

use of smile.validation.AdjustedRandIndex in project smile by haifengl.

the class GrowingNeuralGasTest method testUSPS.

/**
     * Test of learn method, of class GrowingNeuralGas.
     */
@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()]);
        GrowingNeuralGas gng = new GrowingNeuralGas(x[0].length);
        for (int i = 0; i < 10; i++) {
            int[] index = Math.permutate(x.length);
            for (int j = 0; j < x.length; j++) {
                gng.update(x[index[j]]);
            }
        }
        gng.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        int[] p = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            p[i] = gng.predict(x[i]);
        }
        double r = rand.measure(y, p);
        double r2 = ari.measure(y, p);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.85);
        assertTrue(r2 > 0.40);
        p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = gng.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 > 0.40);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 2 with AdjustedRandIndex

use of smile.validation.AdjustedRandIndex in project smile by haifengl.

the class NeuralMapTest method testUSPS.

/**
     * Test of learn method, of class NeuralMap.
     */
@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()]);
        NeuralMap cortex = new NeuralMap(x[0].length, 8.0, 0.05, 0.0006, 5, 3);
        for (int i = 0; i < 5; i++) {
            for (double[] xi : x) {
                cortex.update(xi);
            }
        }
        cortex.purge(16);
        cortex.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        int[] p = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            p[i] = cortex.predict(x[i]);
        }
        double r = rand.measure(y, p);
        double r2 = ari.measure(y, p);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        //assertTrue(r > 0.65);
        //assertTrue(r2 > 0.18);
        p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = cortex.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 > 0.65);
    //assertTrue(r2 > 0.18);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 3 with AdjustedRandIndex

use of smile.validation.AdjustedRandIndex in project smile by haifengl.

the class SOMTest method testUSPS.

/**
     * Test of learn method, of class SOM.
     */
@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()]);
        SOM som = new SOM(x, 10, 10);
        int[] label = som.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, label);
        double r2 = ari.measure(y, label);
        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] = som.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 4 with AdjustedRandIndex

use of smile.validation.AdjustedRandIndex in project smile by haifengl.

the class SIBTest method testParseNG20.

/**
     * Test of parse method, of class SIB.
     */
@Test
public void testParseNG20() throws Exception {
    System.out.println("NG20");
    LibsvmParser parser = new LibsvmParser();
    try {
        SparseDataset train = parser.parse("NG20 Train", smile.data.parser.IOUtils.getTestDataFile("libsvm/news20.dat"));
        SparseDataset test = parser.parse("NG20 Test", smile.data.parser.IOUtils.getTestDataFile("libsvm/news20.t.dat"));
        int[] y = train.toArray(new int[train.size()]);
        int[] testy = test.toArray(new int[test.size()]);
        SIB sib = new SIB(train, 20, 100, 8);
        System.out.println(sib);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, sib.getClusterLabel());
        double r2 = ari.measure(y, sib.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 > 0.2);
        int[] p = new int[test.size()];
        for (int i = 0; i < test.size(); i++) {
            p[i] = sib.predict(test.get(i).x);
        }
        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 > 0.2);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 5 with AdjustedRandIndex

use of smile.validation.AdjustedRandIndex 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);
    }
}