Examples with AttributeDataset smile.data.AttributeDataset used on opensource projects

Example 21 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class RidgeRegressionTest method testCPU.

/**
     * Test of learn method, of class LinearRegression.
     */
@Test
public void testCPU() {
    System.out.println("CPU");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[][] datax = data.toArray(new double[data.size()][]);
        double[] datay = data.toArray(new double[data.size()]);
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 0.0;
        for (int i = 0; i < k; i++) {
            double[][] trainx = Math.slice(datax, cv.train[i]);
            double[] trainy = Math.slice(datay, cv.train[i]);
            double[][] testx = Math.slice(datax, cv.test[i]);
            double[] testy = Math.slice(datay, cv.test[i]);
            RidgeRegression ridge = new RidgeRegression(trainx, trainy, 10.0);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - ridge.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 22 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class SVRTest method testCPU.

/**
     * Test of learn method, of class SVR.
     */
@Test
public void testCPU() {
    System.out.println("CPU");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] datay = data.toArray(new double[data.size()]);
        double[][] datax = data.toArray(new double[data.size()][]);
        Math.standardize(datax);
        int n = datax.length;
        int k = 10;
        CrossValidation cv = new CrossValidation(n, k);
        double rss = 0.0;
        for (int i = 0; i < k; i++) {
            double[][] trainx = Math.slice(datax, cv.train[i]);
            double[] trainy = Math.slice(datay, cv.train[i]);
            double[][] testx = Math.slice(datax, cv.test[i]);
            double[] testy = Math.slice(datay, cv.test[i]);
            SVR<double[]> svr = new SVR<>(trainx, trainy, new PolynomialKernel(3, 1.0, 1.0), 0.1, 1.0);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - svr.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV RMSE = " + Math.sqrt(rss / n));
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 23 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class FeatureSet method f.

/**
     * Returns an attribute dataset with generated features.
     * @param data input dataset.
     * @return an attribute dataset with generated features 
     */
public AttributeDataset f(Dataset<T> data) {
    AttributeDataset dataset = new AttributeDataset(data.getName(), attributes(), data.response());
    dataset.setDescription(data.getDescription());
    for (int i = 0; i < data.size(); i++) {
        Datum<T> datum = data.get(i);
        Datum<double[]> x = new Datum<>(f(datum.x), datum.y, datum.weight);
        x.name = datum.name;
        x.description = datum.description;
        x.timestamp = datum.timestamp;
        dataset.add(x);
    }
    return dataset;
}

Example 24 with AttributeDataset

use of smile.data.AttributeDataset 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);
    }
}

Example 25 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class AdaBoostTest method testIris.

/**
     * Test of learn method, of class AdaBoost.
     */
@Test
public void testIris() {
    System.out.println("Iris");
    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    try {
        AttributeDataset iris = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/iris.arff"));
        double[][] x = iris.toArray(new double[iris.size()][]);
        int[] y = iris.toArray(new int[iris.size()]);
        for (int i = 0; i < y.length; i++) {
            if (y[i] != 0)
                y[i] = 1;
        }
        int n = x.length;
        LOOCV loocv = new LOOCV(n);
        int error = 0;
        for (int i = 0; i < n; i++) {
            double[][] trainx = Math.slice(x, loocv.train[i]);
            int[] trainy = Math.slice(y, loocv.train[i]);
            AdaBoost forest = new AdaBoost(iris.attributes(), trainx, trainy, 200);
            if (y[loocv.test[i]] != forest.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("AdaBoost error = " + error);
        assertEquals(0, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}