Examples with FLD smile.classification.FLD used on opensource projects

Example 1 with FLD

use of smile.classification.FLD in project smile by haifengl.

the class FLDDemo method learn.

@Override
public double[][] learn(double[] x, double[] y) {
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
    FLD fisher = new FLD(data, label);
    for (int i = 0; i < label.length; i++) {
        label[i] = fisher.predict(data[i]);
    }
    double trainError = error(label, label);
    System.out.format("training error = %.2f%%\n", 100 * trainError);
    double[][] z = new double[y.length][x.length];
    for (int i = 0; i < y.length; i++) {
        for (int j = 0; j < x.length; j++) {
            double[] p = { x[j], y[i] };
            z[i][j] = fisher.predict(p);
        }
    }
    return z;
}

Example 2 with FLD

use of smile.classification.FLD in project smile by haifengl.

the class LDADemo method learn.

/**
     * Execute the projection algorithm and return a swing JComponent representing
     * the clusters.
     */
public JComponent learn() {
    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;
    }
    int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
    int min = Math.min(label);
    for (int i = 0; i < label.length; i++) {
        label[i] -= min;
    }
    long clock = System.currentTimeMillis();
    FLD lda = new FLD(data, label, Math.unique(label).length > 3 ? 3 : 2);
    System.out.format("Learn LDA from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
    double[][] y = lda.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("Linear Discriminant Analysis");
    return plot;
}