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

Example 31 with PlotCanvas

use of smile.plot.PlotCanvas 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;
}

Example 32 with PlotCanvas

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

the class DENCLUEDemo method learn.

@Override
public JComponent learn() {
    try {
        k = Integer.parseInt(kField.getText().trim());
        if (k < 1) {
            JOptionPane.showMessageDialog(this, "Invalid K: " + k, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid K: " + kField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        sigma = Double.parseDouble(sigmaField.getText().trim());
        if (sigma <= 0) {
            JOptionPane.showMessageDialog(this, "Invalid Sigma: " + sigma, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid Sigma: " + sigmaField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    DENCLUE denclue = new DENCLUE(dataset[datasetIndex], sigma, k);
    System.out.format("DENCLUE 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 l = 0; l < denclue.getNumClusters(); l++) {
        double[][] cluster = new double[denclue.getClusterSize()[l]][];
        for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
            if (denclue.getClusterLabel()[i] == l) {
                cluster[j++] = dataset[datasetIndex][i];
            }
        }
        plot.points(cluster, pointLegend, Palette.COLORS[l % Palette.COLORS.length]);
    }
    plot.points(denclue.getDensityAttractors(), '@');
    pane.add(plot);
    return pane;
}

Example 33 with PlotCanvas

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

the class KPCADemo method learn.

@Override
public JComponent learn() {
    JPanel pane = new JPanel(new GridLayout(2, 2));
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    String[] names = dataset[datasetIndex].toArray(new String[dataset[datasetIndex].size()]);
    if (names[0] == null) {
        names = null;
    }
    if (gamma[datasetIndex] == 0.0) {
        int n = 0;
        for (int i = 0; i < data.length; i++) {
            for (int j = 0; j < i; j++, n++) {
                gamma[datasetIndex] += Math.squaredDistance(data[i], data[j]);
            }
        }
        gamma[datasetIndex] = Math.sqrt(gamma[datasetIndex] / n) / 4;
    } else {
        try {
            gamma[datasetIndex] = Double.parseDouble(gammaNumberField.getText().trim());
            if (gamma[datasetIndex] <= 0) {
                JOptionPane.showMessageDialog(this, "Invalid parameter: " + gamma[datasetIndex], "Error", JOptionPane.ERROR_MESSAGE);
                return null;
            }
        } catch (Exception e) {
            JOptionPane.showMessageDialog(this, "Invalid parameter: " + gammaNumberField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    }
    gammaNumberField.setText(String.format("%.4f", gamma[datasetIndex]));
    long clock = System.currentTimeMillis();
    PCA pca = new PCA(data, true);
    System.out.format("Learn PCA from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    pca.setProjection(2);
    double[][] y = pca.project(data);
    PlotCanvas plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
    if (names != null) {
        plot.points(y, names);
    } else if (dataset[datasetIndex].response() != null) {
        int[] labels = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        for (int i = 0; i < y.length; i++) {
            plot.point(pointLegend, Palette.COLORS[labels[i]], y[i]);
        }
    } else {
        plot.points(y, pointLegend);
    }
    plot.setTitle("PCA");
    pane.add(plot);
    pca.setProjection(3);
    y = pca.project(data);
    plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
    if (names != null) {
        plot.points(y, names);
    } else if (dataset[datasetIndex].response() != null) {
        int[] labels = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        for (int i = 0; i < y.length; i++) {
            plot.point(pointLegend, Palette.COLORS[labels[i]], y[i]);
        }
    } else {
        plot.points(y, pointLegend);
    }
    plot.setTitle("PCA");
    pane.add(plot);
    KPCA<double[]> kpca = new KPCA<>(data, new GaussianKernel(gamma[datasetIndex]), 2);
    y = kpca.getCoordinates();
    plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
    if (names != null) {
        plot.points(y, names);
    } else if (dataset[datasetIndex].response() != null) {
        int[] labels = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        for (int i = 0; i < y.length; i++) {
            plot.point(pointLegend, Palette.COLORS[labels[i]], y[i]);
        }
    } else {
        plot.points(y, pointLegend);
    }
    plot.setTitle("KPCA");
    pane.add(plot);
    clock = System.currentTimeMillis();
    kpca = new KPCA<>(data, new GaussianKernel(gamma[datasetIndex]), 3);
    System.out.format("Learn KPCA from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    y = kpca.getCoordinates();
    plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
    if (names != null) {
        plot.points(y, names);
    } else if (dataset[datasetIndex].response() != null) {
        int[] labels = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        for (int i = 0; i < y.length; i++) {
            plot.point(pointLegend, Palette.COLORS[labels[i]], y[i]);
        }
    } else {
        plot.points(y, pointLegend);
    }
    plot.setTitle("KPCA");
    pane.add(plot);
    return pane;
}

Example 34 with PlotCanvas

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

the class SpectralClusteringDemo method learn.

@Override
public JComponent learn() {
    try {
        gaussianWidth = Double.parseDouble(gaussianWidthField.getText().trim());
        if (gaussianWidth <= 0) {
            JOptionPane.showMessageDialog(this, "Invalid Gaussian Width: " + gaussianWidth, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid Gaussian Width: " + gaussianWidthField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    SpectralClustering spectral = new SpectralClustering(dataset[datasetIndex], clusterNumber, gaussianWidth);
    System.out.format("Spectral Clustering clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], pointLegend);
    for (int k = 0; k < spectral.getNumClusters(); k++) {
        double[][] cluster = new double[spectral.getClusterSize()[k]][];
        for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
            if (spectral.getClusterLabel()[i] == k) {
                cluster[j++] = dataset[datasetIndex][i];
            }
        }
        plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
    }
    return plot;
}

Example 35 with PlotCanvas

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

the class XMeansDemo method learn.

@Override
public JComponent learn() {
    try {
        maxClusterNumber = Integer.parseInt(maxClusterNumberField.getText().trim());
        if (maxClusterNumber < 2) {
            JOptionPane.showMessageDialog(this, "Invalid Max K: " + maxClusterNumber, "Error", JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid Max K: " + maxClusterNumberField.getText(), "Error", JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    XMeans xmeans = new XMeans(dataset[datasetIndex], maxClusterNumber);
    System.out.format("X-Means clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(xmeans.centroids(), '@');
    for (int k = 0; k < xmeans.getNumClusters(); k++) {
        if (xmeans.getClusterSize()[k] > 0) {
            double[][] cluster = new double[xmeans.getClusterSize()[k]][];
            for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
                if (xmeans.getClusterLabel()[i] == k) {
                    cluster[j++] = dataset[datasetIndex][i];
                }
            }
            plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
        }
    }
    plot.points(xmeans.centroids(), '@');
    return plot;
}