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

Example 21 with PlotCanvas

use of smile.plot.PlotCanvas 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 22 with PlotCanvas

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

the class KNNDemo method run.

@Override
public void run() {
    startButton.setEnabled(false);
    logNSlider.setEnabled(false);
    dimensionSlider.setEnabled(false);
    knnField.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 1000 searches...");
    double radius = 0.0;
    List<Neighbor<double[], double[]>[]> answers = new ArrayList<>(1000);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        answers.add(naive.knn(data[perm[i]], knn));
        for (int j = 0; j < answers.get(i).length; j++) {
            radius += answers.get(i)[j].distance;
        }
    }
    int naiveSearch = (int) (System.currentTimeMillis() - time);
    radius /= 1000 * knn;
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        kdtree.knn(data[perm[i]], knn);
    }
    int kdtreeSearch = (int) (System.currentTimeMillis() - time);
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        cover.knn(data[perm[i]], knn);
    }
    int coverSearch = (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, 2, (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, 1.5 * radius);
    int mplshBuild = (int) (System.currentTimeMillis() - time);
    double lshRecall = 0.0;
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        Neighbor<double[], double[]>[] neighbors = lsh.knn(data[perm[i]], knn);
        int hit = 0;
        for (int p = 0; p < knn && answers.get(i)[p] != null; p++) {
            for (int q = 0; q < knn && neighbors[q] != null; q++) {
                if (answers.get(i)[p].index == neighbors[q].index) {
                    hit++;
                    break;
                }
            }
        }
        lshRecall += 1.0 * hit / knn;
    }
    int lshSearch = (int) (System.currentTimeMillis() - time);
    lshRecall /= 1000;
    System.out.format("The recall of LSH is %.1f%%\n", lshRecall * 100);
    double mplshRecall = 0.0;
    time = System.currentTimeMillis();
    for (int i = 0; i < 1000; i++) {
        Neighbor<double[], double[]>[] neighbors = mplsh.knn(data[perm[i]], knn, 0.95, 10);
        int hit = 0;
        for (int p = 0; p < knn && answers.get(i)[p] != null; p++) {
            for (int q = 0; q < knn && neighbors[q] != null; q++) {
                if (answers.get(i)[p].index == neighbors[q].index) {
                    hit++;
                    break;
                }
            }
        }
        mplshRecall += 1.0 * hit / knn;
    }
    int mplshSearch = (int) (System.currentTimeMillis() - time);
    mplshRecall /= 1000;
    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);
    knnField.setEnabled(true);
}

Example 23 with PlotCanvas

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

the class GMeansDemo 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();
    GMeans gmeans = new GMeans(dataset[datasetIndex], maxClusterNumber);
    System.out.format("G-Means clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(gmeans.centroids(), '@');
    for (int k = 0; k < gmeans.getNumClusters(); k++) {
        if (gmeans.getClusterSize()[k] > 0) {
            double[][] cluster = new double[gmeans.getClusterSize()[k]][];
            for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
                if (gmeans.getClusterLabel()[i] == k) {
                    cluster[j++] = dataset[datasetIndex][i];
                }
            }
            plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
        }
    }
    plot.points(gmeans.centroids(), '@');
    return plot;
}

Example 24 with PlotCanvas

use of smile.plot.PlotCanvas 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 25 with PlotCanvas

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

the class KMeansDemo method learn.

@Override
public JComponent learn() {
    long clock = System.currentTimeMillis();
    KMeans kmeans = new KMeans(dataset[datasetIndex], clusterNumber, 100, 4);
    System.out.format("K-Means clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], kmeans.getClusterLabel(), pointLegend, Palette.COLORS);
    plot.points(kmeans.centroids(), '@');
    return plot;
}