Examples with ArffParser smile.data.parser.ArffParser used on opensource projects

Example 66 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class LASSOTest 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]);
            LASSO lasso = new LASSO(trainx, trainy, 50.0);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - lasso.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 67 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class OLSTest 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]);
            OLS linear = new OLS(trainx, trainy);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - linear.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 68 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class RBFNetworkTest method testCPU.

/**
     * Test of learn method, of class RBFNetwork.
     */
@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]);
            double[][] centers = new double[20][];
            RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
            RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rbf.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 69 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class RBFNetworkTest method test2DPlanes.

/**
     * Test of learn method, of class RBFNetwork.
     */
@Test
public void test2DPlanes() {
    System.out.println("2dplanes");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(10);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/regression/2dplanes.arff"));
        double[] datay = data.toArray(new double[data.size()]);
        double[][] datax = data.toArray(new double[data.size()][]);
        //Math.normalize(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]);
            double[][] centers = new double[20][];
            RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
            RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
            for (int j = 0; j < testx.length; j++) {
                double r = testy[j] - rbf.predict(testx[j]);
                rss += r * r;
            }
        }
        System.out.println("10-CV MSE = " + rss / n);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 70 with ArffParser

use of smile.data.parser.ArffParser in project smile by haifengl.

the class RandomForestTest method testCPU.

/**
     * Test of learn method, of class RandomForest.
     */
@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()][]);
        int n = datax.length;
        int m = 3 * n / 4;
        int[] index = Math.permutate(n);
        double[][] trainx = new double[m][];
        double[] trainy = new double[m];
        for (int i = 0; i < m; i++) {
            trainx[i] = datax[index[i]];
            trainy[i] = datay[index[i]];
        }
        double[][] testx = new double[n - m][];
        double[] testy = new double[n - m];
        for (int i = m; i < n; i++) {
            testx[i - m] = datax[index[i]];
            testy[i - m] = datay[index[i]];
        }
        RandomForest forest = new RandomForest(data.attributes(), trainx, trainy, 100, n, 5, trainx[0].length / 3);
        System.out.format("RMSE = %.4f%n", Validation.test(forest, testx, testy));
        double[] rmse = forest.test(testx, testy);
        for (int i = 1; i <= rmse.length; i++) {
            System.out.format("%d trees RMSE = %.4f%n", i, rmse[i - 1]);
        }
        double[] importance = forest.importance();
        index = QuickSort.sort(importance);
        for (int i = importance.length; i-- > 0; ) {
            System.out.format("%s importance is %.4f%n", data.attributes()[index[i]], importance[i]);
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}