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Example 1 with UPGMCLinkage

use of smile.clustering.linkage.UPGMCLinkage in project smile by haifengl.

the class HierarchicalClusteringTest method testUSPS.

/**
     * Test of learn method, of class GMeans.
     */
@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()]);
        int n = x.length;
        double[][] proximity = new double[n][];
        for (int i = 0; i < n; i++) {
            proximity[i] = new double[i + 1];
            for (int j = 0; j < i; j++) {
                proximity[i][j] = Math.distance(x[i], x[j]);
            }
        }
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        HierarchicalClustering hc = new HierarchicalClustering(new SingleLinkage(proximity));
        int[] label = hc.partition(10);
        double r = rand.measure(y, label);
        double r2 = ari.measure(y, label);
        System.out.format("SingleLinkage rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.1);
        hc = new HierarchicalClustering(new CompleteLinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("CompleteLinkage rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.75);
        hc = new HierarchicalClustering(new UPGMALinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("UPGMA rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.1);
        hc = new HierarchicalClustering(new WPGMALinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("WPGMA rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.2);
        hc = new HierarchicalClustering(new UPGMCLinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("UPGMC rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.1);
        hc = new HierarchicalClustering(new WPGMCLinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("WPGMC rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.1);
        hc = new HierarchicalClustering(new WardLinkage(proximity));
        label = hc.partition(10);
        r = rand.measure(y, label);
        r2 = ari.measure(y, label);
        System.out.format("Ward rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.9);
        assertTrue(r2 > 0.5);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}
Also used : DelimitedTextParser(smile.data.parser.DelimitedTextParser) WPGMCLinkage(smile.clustering.linkage.WPGMCLinkage) AttributeDataset(smile.data.AttributeDataset) CompleteLinkage(smile.clustering.linkage.CompleteLinkage) AdjustedRandIndex(smile.validation.AdjustedRandIndex) RandIndex(smile.validation.RandIndex) WardLinkage(smile.clustering.linkage.WardLinkage) NominalAttribute(smile.data.NominalAttribute) SingleLinkage(smile.clustering.linkage.SingleLinkage) WPGMALinkage(smile.clustering.linkage.WPGMALinkage) AdjustedRandIndex(smile.validation.AdjustedRandIndex) UPGMALinkage(smile.clustering.linkage.UPGMALinkage) UPGMCLinkage(smile.clustering.linkage.UPGMCLinkage) Test(org.junit.Test)

Example 2 with UPGMCLinkage

use of smile.clustering.linkage.UPGMCLinkage in project smile by haifengl.

the class HierarchicalClusteringDemo method learn.

@Override
public JComponent learn() {
    long clock = System.currentTimeMillis();
    double[][] data = dataset[datasetIndex];
    int n = data.length;
    double[][] proximity = new double[n][];
    for (int i = 0; i < n; i++) {
        proximity[i] = new double[i + 1];
        for (int j = 0; j < i; j++) proximity[i][j] = Math.distance(data[i], data[j]);
    }
    HierarchicalClustering hac = null;
    switch(linkageBox.getSelectedIndex()) {
        case 0:
            hac = new HierarchicalClustering(new SingleLinkage(proximity));
            break;
        case 1:
            hac = new HierarchicalClustering(new CompleteLinkage(proximity));
            break;
        case 2:
            hac = new HierarchicalClustering(new UPGMALinkage(proximity));
            break;
        case 3:
            hac = new HierarchicalClustering(new WPGMALinkage(proximity));
            break;
        case 4:
            hac = new HierarchicalClustering(new UPGMCLinkage(proximity));
            break;
        case 5:
            hac = new HierarchicalClustering(new WPGMCLinkage(proximity));
            break;
        case 6:
            hac = new HierarchicalClustering(new WardLinkage(proximity));
            break;
        default:
            throw new IllegalStateException("Unsupported Linkage");
    }
    System.out.format("Hierarchical clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    int[] membership = hac.partition(clusterNumber);
    int[] clusterSize = new int[clusterNumber];
    for (int i = 0; i < membership.length; i++) {
        clusterSize[membership[i]]++;
    }
    JPanel pane = new JPanel(new GridLayout(1, 3));
    PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], pointLegend);
    plot.setTitle("Data");
    pane.add(plot);
    for (int k = 0; k < clusterNumber; k++) {
        double[][] cluster = new double[clusterSize[k]][];
        for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
            if (membership[i] == k) {
                cluster[j++] = dataset[datasetIndex][i];
            }
        }
        plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
    }
    plot = Dendrogram.plot("Dendrogram", hac.getTree(), hac.getHeight());
    plot.setTitle("Dendrogram");
    pane.add(plot);
    return pane;
}
Also used : WPGMCLinkage(smile.clustering.linkage.WPGMCLinkage) JPanel(javax.swing.JPanel) CompleteLinkage(smile.clustering.linkage.CompleteLinkage) WardLinkage(smile.clustering.linkage.WardLinkage) HierarchicalClustering(smile.clustering.HierarchicalClustering) GridLayout(java.awt.GridLayout) SingleLinkage(smile.clustering.linkage.SingleLinkage) WPGMALinkage(smile.clustering.linkage.WPGMALinkage) UPGMALinkage(smile.clustering.linkage.UPGMALinkage) UPGMCLinkage(smile.clustering.linkage.UPGMCLinkage) PlotCanvas(smile.plot.PlotCanvas)

Aggregations

CompleteLinkage (smile.clustering.linkage.CompleteLinkage)2 SingleLinkage (smile.clustering.linkage.SingleLinkage)2 UPGMALinkage (smile.clustering.linkage.UPGMALinkage)2 UPGMCLinkage (smile.clustering.linkage.UPGMCLinkage)2 WPGMALinkage (smile.clustering.linkage.WPGMALinkage)2 WPGMCLinkage (smile.clustering.linkage.WPGMCLinkage)2 WardLinkage (smile.clustering.linkage.WardLinkage)2 GridLayout (java.awt.GridLayout)1 JPanel (javax.swing.JPanel)1 Test (org.junit.Test)1 HierarchicalClustering (smile.clustering.HierarchicalClustering)1 AttributeDataset (smile.data.AttributeDataset)1 NominalAttribute (smile.data.NominalAttribute)1 DelimitedTextParser (smile.data.parser.DelimitedTextParser)1 PlotCanvas (smile.plot.PlotCanvas)1 AdjustedRandIndex (smile.validation.AdjustedRandIndex)1 RandIndex (smile.validation.RandIndex)1