Examples with BIRCH smile.clustering.BIRCH used on opensource projects

Example 1 with BIRCH

use of smile.clustering.BIRCH in project smile by haifengl.

the class BIRCHDemo method learn.

@Override
public JComponent learn() {
    try {
        B = Integer.parseInt(BNumberField.getText().trim());
        if (B < 2) {
            JOptionPane.showMessageDialog(this, "Invalid B: " + B, ERROR, JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(null, "Invalid B: " + BNumberField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        T = Double.parseDouble(TNumberField.getText().trim());
        if (T <= 0) {
            JOptionPane.showMessageDialog(this, "Invalid T: " + T, ERROR, JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid T: " + TNumberField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        minPts = Integer.parseInt(minPtsNumberField.getText().trim());
        if (minPts < 0) {
            JOptionPane.showMessageDialog(this, "Invalid minPts: " + minPts, ERROR, JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid minPts: " + minPtsNumberField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    BIRCH birch = new BIRCH(2, B, T);
    for (int i = 0; i < dataset[datasetIndex].length; i++) birch.add(dataset[datasetIndex][i]);
    if (birch.partition(clusterNumber, minPts) < clusterNumber) {
        JOptionPane.showMessageDialog(this, "The number of non-outlier leaves is less than " + clusterNumber + ". Try larger T.", "ERROR", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    int[] membership = new int[dataset[datasetIndex].length];
    int[] clusterSize = new int[clusterNumber];
    for (int i = 0; i < dataset[datasetIndex].length; i++) {
        membership[i] = birch.predict(dataset[datasetIndex][i]);
        if (membership[i] != Clustering.OUTLIER) {
            clusterSize[membership[i]]++;
        }
    }
    System.out.format("BIRCH clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(birch.centroids(), '@');
    for (int k = 0; k < clusterNumber; k++) {
        if (clusterSize[k] > 0) {
            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.points(birch.centroids(), '@');
    return plot;
}