Examples with PlotCanvas smile.plot.PlotCanvas used on opensource projects

Example 26 with PlotCanvas

use of smile.plot.PlotCanvas in project smile by haifengl.

the class MECDemo method learn.

@Override
public JComponent learn() {
    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();
    MEC<double[]> mec = new MEC<>(dataset[datasetIndex], new EuclideanDistance(), clusterNumber, range);
    System.out.format("MEC clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], pointLegend);
    for (int k = 0; k < mec.getNumClusters(); k++) {
        double[][] cluster = new double[mec.getClusterSize()[k]][];
        for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
            if (mec.getClusterLabel()[i] == k) {
                cluster[j++] = dataset[datasetIndex][i];
            }
        }
        plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
    }
    return plot;
}

Example 27 with PlotCanvas

use of smile.plot.PlotCanvas in project smile by haifengl.

the class NeuralGasDemo method learn.

@Override
public JComponent learn() {
    long clock = System.currentTimeMillis();
    NeuralGas gas = new NeuralGas(dataset[datasetIndex], clusterNumber);
    System.out.format("Neural Gas clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(gas.centroids(), '@');
    for (int k = 0; k < clusterNumber; k++) {
        if (gas.getClusterSize()[k] > 0) {
            double[][] cluster = new double[gas.getClusterSize()[k]][];
            for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
                if (gas.getClusterLabel()[i] == k) {
                    cluster[j++] = dataset[datasetIndex][i];
                }
            }
            plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
        }
    }
    plot.points(gas.centroids(), '@');
    return plot;
}

Example 28 with PlotCanvas

use of smile.plot.PlotCanvas in project smile by haifengl.

the class ExponentialFamilyMixtureDemo method main.

public static void main(String[] args) {
    // Mixture of Gaussian, Exponential, and Gamma.
    double[] data = new double[2000];
    GaussianDistribution gaussian = new GaussianDistribution(-2.0, 1.0);
    for (int i = 0; i < 500; i++) data[i] = gaussian.rand();
    ExponentialDistribution exp = new ExponentialDistribution(0.8);
    for (int i = 500; i < 1000; i++) data[i] = exp.rand();
    GammaDistribution gamma = new GammaDistribution(2.0, 3.0);
    for (int i = 1000; i < 2000; i++) data[i] = gamma.rand();
    List<Mixture.Component> m = new ArrayList<>();
    Mixture.Component c = new Mixture.Component();
    c.priori = 0.25;
    c.distribution = new GaussianDistribution(0.0, 1.0);
    m.add(c);
    c = new Mixture.Component();
    c.priori = 0.25;
    c.distribution = new ExponentialDistribution(1.0);
    m.add(c);
    c = new Mixture.Component();
    c.priori = 0.25;
    c.distribution = new GammaDistribution(1.0, 2.0);
    m.add(c);
    ExponentialFamilyMixture mixture = new ExponentialFamilyMixture(m, data);
    System.out.println(mixture);
    JFrame frame = new JFrame("Mixture of Exponential Family Distributions");
    PlotCanvas canvas = Histogram.plot(data, 50);
    frame.add(canvas);
    double width = (Math.max(data) - Math.min(data)) / 50;
    double[][] p = new double[400][2];
    for (int i = 0; i < p.length; i++) {
        p[i][0] = -10 + i * 0.1;
        p[i][1] = mixture.p(p[i][0]) * width;
    }
    canvas.line(p, Color.RED);
    frame.add(QQPlot.plot(data, mixture));
    frame.setVisible(true);
}

Example 29 with PlotCanvas

use of smile.plot.PlotCanvas in project smile by haifengl.

the class NearestNeighborDemo method run.

@Override
public void run() {
    startButton.setEnabled(false);
    logNSlider.setEnabled(false);
    dimensionSlider.setEnabled(false);
    logN = logNSlider.getValue();
    dimension = dimensionSlider.getValue();
    System.out.println("Generating dataset...");
    int n = (int) Math.pow(10, logN);
    double[][] data = new double[n][];
    for (int i = 0; i < n; i++) {
        data[i] = new double[dimension];
        for (int j = 0; j < dimension; j++) {
            data[i][j] = Math.random();
        }
    }
    int[] perm = Math.permutate(n);
    System.out.println("Building searching data structure...");
    long time = System.currentTimeMillis();
    LinearSearch<double[]> naive = new LinearSearch<>(data, new EuclideanDistance());
    int naiveBuild = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    KDTree<double[]> kdtree = new KDTree<>(data, data);
    int kdtreeBuild = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    CoverTree<double[]> cover = new CoverTree<>(data, new EuclideanDistance());
    int coverBuild = (int) (System.currentTimeMillis() - time);
    System.out.println("Perform 100 searches...");
    int[] answer = new int[100];
    double radius = 0.0;
    time = System.currentTimeMillis();
    for (int i = 0; i < 100; i++) {
        Neighbor<double[], double[]> neighbor = naive.nearest(data[perm[i]]);
        answer[i] = neighbor.index;
        radius += neighbor.distance;
    }
    int naiveSearch = (int) (System.currentTimeMillis() - time);
    radius /= 100;
    time = System.currentTimeMillis();
    for (int i = 0; i < 100; i++) {
        kdtree.nearest(data[perm[i]]);
    }
    int kdtreeSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 100; i++) {
        cover.nearest(data[perm[i]]);
    }
    int coverSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    LSH<double[]> lsh = new LSH<>(dimension, 5, (int) Math.ceil(Math.log2(dimension)), 4 * radius, 1017881);
    for (int i = 0; i < n; i++) {
        lsh.put(data[i], data[i]);
    }
    int lshBuild = (int) (System.currentTimeMillis() - time);
    double lshRecall = 0.0;
    time = System.currentTimeMillis();
    for (int i = 0; i < 100; i++) {
        if (lsh.nearest(data[perm[i]]).index == answer[i]) {
            lshRecall++;
        }
    }
    int lshSearch = (int) (System.currentTimeMillis() - time);
    lshRecall /= 100;
    System.out.format("The recall of LSH is %.1f%%\n", lshRecall * 100);
    time = System.currentTimeMillis();
    MPLSH<double[]> mplsh = new MPLSH<>(dimension, 5, (int) Math.ceil(Math.log2(n)), 4 * radius, 1017881);
    for (int i = 0; i < n; i++) {
        mplsh.put(data[i], data[i]);
    }
    double[][] train = new double[1000][];
    for (int i = 0; i < train.length; i++) {
        train[i] = data[perm[i]];
    }
    mplsh.learn(kdtree, train, 1.5 * radius);
    int mplshBuild = (int) (System.currentTimeMillis() - time);
    double mplshRecall = 0.0;
    time = System.currentTimeMillis();
    for (int i = 0; i < 100; i++) {
        if (mplsh.nearest(data[perm[i]], 0.95, 10).index == answer[i]) {
            mplshRecall++;
        }
    }
    int mplshSearch = (int) (System.currentTimeMillis() - time);
    mplshRecall /= 100;
    System.out.format("The recall of MPLSH is %.1f%%\n", mplshRecall * 100);
    canvas.removeAll();
    double[] buildTime = { naiveBuild, kdtreeBuild, coverBuild, lshBuild, mplshBuild };
    PlotCanvas build = BarPlot.plot(buildTime, label);
    build.setTitle("Build Time");
    canvas.add(build);
    double[] searchTime = { naiveSearch, kdtreeSearch, coverSearch, lshSearch, mplshSearch };
    PlotCanvas search = BarPlot.plot(searchTime, label);
    search.setTitle("Search Time");
    canvas.add(search);
    validate();
    startButton.setEnabled(true);
    logNSlider.setEnabled(true);
    dimensionSlider.setEnabled(true);
}

Example 30 with PlotCanvas

use of smile.plot.PlotCanvas 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;
}