Examples with GridLayout java.awt.GridLayout used on opensource projects

Example 86 with GridLayout

use of java.awt.GridLayout in project smile by haifengl.

the class MDSDemo method learn.

/**
     * Execute the MDS algorithm and return a swing JComponent representing
     * the clusters.
     */
public JComponent learn() {
    JPanel pane = new JPanel(new GridLayout(1, 2));
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    String[] labels = dataset[datasetIndex].toArray(new String[dataset[datasetIndex].size()]);
    if (labels[0] == null) {
        Attribute[] attr = dataset[datasetIndex].attributes();
        labels = new String[attr.length];
        for (int i = 0; i < labels.length; i++) {
            labels[i] = attr[i].getName();
        }
    }
    long clock = System.currentTimeMillis();
    MDS mds = new MDS(data, 2);
    System.out.format("Learn MDS (k=2) from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(mds.getCoordinates(), labels);
    plot.setTitle("MDS (k = 2)");
    pane.add(plot);
    clock = System.currentTimeMillis();
    mds = new MDS(data, 3);
    System.out.format("Learn MDS (k=3) from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    plot = ScatterPlot.plot(mds.getCoordinates(), labels);
    plot.setTitle("MDS (k = 3)");
    pane.add(plot);
    return pane;
}

Example 87 with GridLayout

use of java.awt.GridLayout in project smile by haifengl.

the class ApproximateStringSearchDemo method run.

@Override
public void run() {
    startButton.setEnabled(false);
    knnField.setEnabled(false);
    if (data == null) {
        System.out.print("Loading dataset...");
        List<String> words = new ArrayList<>();
        try {
            FileInputStream stream = new FileInputStream(smile.data.parser.IOUtils.getTestDataFile("index.noun"));
            BufferedReader input = new BufferedReader(new InputStreamReader(stream));
            String line = input.readLine();
            while (line != null) {
                if (!line.startsWith(" ")) {
                    String[] w = line.split("\\s");
                    words.add(w[0].replace('_', ' '));
                }
                line = input.readLine();
            }
        } catch (Exception e) {
            System.err.println(e);
        }
        data = words.toArray(new String[1]);
        System.out.println(words.size() + " words");
        System.out.println("Building searching data structure...");
        long time = System.currentTimeMillis();
        naive = new LinearSearch<>(data, new EditDistance(50, true));
        int naiveBuild = (int) (System.currentTimeMillis() - time) / 1000;
        time = System.currentTimeMillis();
        bktree = new BKTree<>(new EditDistance(50, true));
        bktree.add(data);
        int bktreeBuild = (int) (System.currentTimeMillis() - time) / 1000;
        time = System.currentTimeMillis();
        cover = new CoverTree<>(data, new EditDistance(50, true));
        int coverBuild = (int) (System.currentTimeMillis() - time) / 1000;
        double[] buildTime = { naiveBuild, bktreeBuild, coverBuild };
        PlotCanvas build = BarPlot.plot(buildTime, label);
        build.setTitle("Build Time");
        canvas.add(build);
        validate();
    }
    int[] perm = Math.permutate(data.length);
    System.out.println("Perform 1000 searches...");
    long time = System.currentTimeMillis();
    List<Neighbor<String, String>> neighbors = new ArrayList<>();
    for (int i = 0; i < 1000; i++) {
        naive.range(data[perm[i]], knn, neighbors);
        neighbors.clear();
    }
    int naiveSearch = (int) (System.currentTimeMillis() - time) / 1000;
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        bktree.range(data[perm[i]], knn, neighbors);
        neighbors.clear();
    }
    int kdtreeSearch = (int) (System.currentTimeMillis() - time) / 1000;
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        cover.range(data[perm[i]], knn, neighbors);
        neighbors.clear();
    }
    int coverSearch = (int) (System.currentTimeMillis() - time) / 1000;
    double[] searchTime = { naiveSearch, kdtreeSearch, coverSearch };
    PlotCanvas search = BarPlot.plot(searchTime, label);
    search.setTitle("Search Time of k = " + knn);
    canvas.add(search);
    if (canvas.getComponentCount() > 3)
        canvas.setLayout(new GridLayout(2, 2));
    validate();
    startButton.setEnabled(true);
    knnField.setEnabled(true);
}

Example 88 with GridLayout

use of java.awt.GridLayout 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;
}

Example 89 with GridLayout

use of java.awt.GridLayout in project smile by haifengl.

the class LaplacianEigenmapDemo method learn.

@Override
public JComponent learn() {
    JPanel pane = new JPanel(new GridLayout(1, 2));
    try {
        sigma = Double.parseDouble(sigmaField.getText().trim());
    } catch (Exception ex) {
        JOptionPane.showMessageDialog(this, "Invalid t: " + sigmaField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return pane;
    }
    sigmaField.setEnabled(false);
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    if (data.length > 1000) {
        double[][] x = new double[1000][];
        for (int i = 0; i < 1000; i++) {
            x[i] = data[i];
        }
        data = x;
    }
    long clock = System.currentTimeMillis();
    LaplacianEigenmap eigenmap = new LaplacianEigenmap(data, 2, k, sigma);
    System.out.format("Learn Laplacian Eigenmap from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    double[][] y = eigenmap.getCoordinates();
    PlotCanvas plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
    plot.points(y, 'o', Color.RED);
    int n = y.length;
    Graph graph = eigenmap.getNearestNeighborGraph();
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < i; j++) {
            if (graph.hasEdge(i, j)) {
                plot.line(y[i], y[j]);
            }
        }
    }
    plot.setTitle("Laplacian Eigenmap");
    pane.add(plot);
    sigmaField.setEnabled(true);
    return pane;
}

Example 90 with GridLayout

use of java.awt.GridLayout in project smile by haifengl.

the class DBScanDemo method learn.

@Override
public JComponent learn() {
    try {
        minPts = Integer.parseInt(minPtsField.getText().trim());
        if (minPts < 1) {
            JOptionPane.showMessageDialog(this, "Invalid MinPts: " + minPts, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid MinPts: " + minPtsField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        range = Double.parseDouble(rangeField.getText().trim());
        if (range <= 0) {
            JOptionPane.showMessageDialog(this, "Invalid Range: " + range, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid range: " + rangeField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    DBScan<double[]> dbscan = new DBScan<>(dataset[datasetIndex], new EuclideanDistance(), minPts, range);
    System.out.format("DBSCAN clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    JPanel pane = new JPanel(new GridLayout(1, 2));
    PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], pointLegend);
    for (int k = 0; k < dbscan.getNumClusters(); k++) {
        double[][] cluster = new double[dbscan.getClusterSize()[k]][];
        for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
            if (dbscan.getClusterLabel()[i] == k) {
                cluster[j++] = dataset[datasetIndex][i];
            }
        }
        plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
    }
    pane.add(plot);
    return pane;
}