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

Example 6 with PlotCanvas

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

the class PPCADemo 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;
    }
    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);
    PPCA ppca = new PPCA(data, 2);
    y = ppca.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("PPCA");
    pane.add(plot);
    clock = System.currentTimeMillis();
    ppca = new PPCA(data, 3);
    System.out.format("Learn PPCA from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    y = ppca.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("PPCA");
    pane.add(plot);
    return pane;
}

Example 7 with PlotCanvas

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

the class RandomProjectionDemo 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;
    }
    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);
    RandomProjection rp = new RandomProjection(data[0].length, 2, sparseBox.isSelected());
    System.out.format("%d x %d Random Projection:\n", data[0].length, 3);
    double[][] projection = rp.getProjection();
    for (int i = 0; i < projection.length; i++) {
        for (int j = 0; j < projection[i].length; j++) {
            System.out.format("% .4f ", projection[i][j]);
        }
        System.out.println();
    }
    y = rp.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("Random Projection");
    pane.add(plot);
    rp = new RandomProjection(data[0].length, 3, sparseBox.isSelected());
    System.out.format("%d x %d Random Projection:\n", data[0].length, 3);
    projection = rp.getProjection();
    for (int i = 0; i < projection.length; i++) {
        for (int j = 0; j < projection[i].length; j++) {
            System.out.format("% .4f ", projection[i][j]);
        }
        System.out.println();
    }
    y = rp.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("Random Projection");
    pane.add(plot);
    return pane;
}

Example 8 with PlotCanvas

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

the class NeuralMapDemo method learn.

@Override
public JComponent learn() {
    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;
    }
    long clock = System.currentTimeMillis();
    NeuralMap cortex = new NeuralMap(2, T, 0.05, 0.0006, 5, 3);
    for (int i = 0; i < 5; i++) {
        for (double[] x : dataset[datasetIndex]) {
            cortex.update(x);
        }
    }
    cortex.purge(16);
    cortex.partition(clusterNumber, 16);
    System.out.format("Cortex clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    int[] y = new int[dataset[datasetIndex].length];
    int[] clusterSize = new int[clusterNumber];
    for (int i = 0; i < dataset[datasetIndex].length; i++) {
        y[i] = cortex.predict(dataset[datasetIndex][i]);
        if (y[i] != Clustering.OUTLIER) {
            clusterSize[y[i]]++;
        }
    }
    List<NeuralMap.Neuron> nodes = cortex.neurons();
    double[][] x = new double[nodes.size()][];
    for (int i = 0; i < x.length; i++) {
        x[i] = nodes.get(i).w;
    }
    PlotCanvas plot = ScatterPlot.plot(x, '@');
    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 (y[i] == k) {
                    cluster[j++] = dataset[datasetIndex][i];
                }
            }
            plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
        }
    }
    for (int i = 0; i < nodes.size(); i++) {
        NeuralMap.Neuron neuron = nodes.get(i);
        for (NeuralMap.Neuron neighbor : neuron.neighbors) {
            plot.line(neuron.w, neighbor.w);
        }
    }
    plot.points(x, '@');
    return plot;
}

Example 9 with PlotCanvas

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

the class RNNSearchDemo method run.

@Override
public void run() {
    startButton.setEnabled(false);
    logNSlider.setEnabled(false);
    dimensionSlider.setEnabled(false);
    radiusField.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);
    time = System.currentTimeMillis();
    LSH<double[]> lsh = new LSH<>(dimension, 5, (int) 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);
    time = System.currentTimeMillis();
    MPLSH<double[]> mplsh = new MPLSH<>(dimension, 3, (int) 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, radius);
    int mplshBuild = (int) (System.currentTimeMillis() - time);
    System.out.println("Perform 1000 searches...");
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        ArrayList<Neighbor<double[], double[]>> neighbors = new ArrayList<>();
        naive.range(data[perm[i]], radius, neighbors);
    }
    int naiveSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        ArrayList<Neighbor<double[], double[]>> neighbors = new ArrayList<>();
        kdtree.range(data[perm[i]], radius, neighbors);
    }
    int kdtreeSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        ArrayList<Neighbor<double[], double[]>> neighbors = new ArrayList<>();
        cover.range(data[perm[i]], radius, neighbors);
    }
    int coverSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        ArrayList<Neighbor<double[], double[]>> neighbors = new ArrayList<>();
        lsh.range(data[perm[i]], radius, neighbors);
    }
    int lshSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        ArrayList<Neighbor<double[], double[]>> neighbors = new ArrayList<>();
        mplsh.range(data[perm[i]], radius, neighbors, 0.95, 10);
    }
    int mplshSearch = (int) (System.currentTimeMillis() - time);
    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);
    radiusField.setEnabled(false);
}

Example 10 with PlotCanvas

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

the class HexmapDemo method main.

public static void main(String[] args) {
    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 m = 20;
        int n = 20;
        SOM som = new SOM(x, m, n);
        String[][] labels = new String[m][n];
        int[] neurons = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            neurons[i] = som.predict(x[i]);
        }
        int[] count = new int[10];
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n; j++) {
                Arrays.fill(count, 0);
                for (int k = 0; k < neurons.length; k++) {
                    if (neurons[k] == i * n + j) {
                        count[y[k]]++;
                    }
                }
                int sum = Math.sum(count);
                if (sum == 0.0) {
                    labels[i][j] = "no samples";
                } else {
                    labels[i][j] = String.format("<table border=\"1\"><tr><td>Total</td><td align=\"right\">%d</td></tr>", sum);
                    for (int l = 0; l < count.length; l++) {
                        if (count[l] > 0) {
                            labels[i][j] += String.format("<tr><td>class %d</td><td align=\"right\">%.1f%%</td></tr>", l, 100.0 * count[l] / sum);
                        }
                    }
                    labels[i][j] += "</table>";
                }
            }
        }
        double[][] umatrix = som.umatrix();
        double[][][] map = som.map();
        double[][] proximity = new double[m * n][m * n];
        for (int i = 0; i < m * n; i++) {
            for (int j = 0; j < m * n; j++) {
                proximity[i][j] = Math.distance(map[i / n][i % n], map[j / n][j % n]);
            }
        }
        MDS mds = new MDS(proximity, 3);
        double[][] coords = mds.getCoordinates();
        double[][][] mdsgrid = new double[m][n][];
        for (int i = 0; i < m * n; i++) {
            mdsgrid[i / n][i % n] = mds.getCoordinates()[i];
        }
        SammonMapping sammon = new SammonMapping(proximity, coords);
        double[][][] sammongrid = new double[m][n][];
        for (int i = 0; i < m * n; i++) {
            sammongrid[i / n][i % n] = sammon.getCoordinates()[i];
        }
        IsotonicMDS isomds = new IsotonicMDS(proximity, coords);
        double[][][] isomdsgrid = new double[m][n][];
        for (int i = 0; i < m * n; i++) {
            isomdsgrid[i / n][i % n] = isomds.getCoordinates()[i];
        }
        JFrame frame = new JFrame("Hexmap");
        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        frame.setLocationRelativeTo(null);
        frame.add(Hexmap.plot(labels, umatrix));
        PlotCanvas canvas = Surface.plot(mdsgrid);
        canvas.setTitle("MDS");
        frame.add(canvas);
        canvas = Surface.plot(isomdsgrid);
        canvas.setTitle("Isotonic MDS");
        frame.add(canvas);
        canvas = Surface.plot(sammongrid);
        canvas.setTitle("Sammon Mapping");
        frame.add(canvas);
        frame.setVisible(true);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}