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

Example 6 with ArffParser

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

the class ValidationTest method testLoocv_3args_2.

/**
     * Test of loocv method, of class Validation.
     */
@Test
public void testLoocv_3args_2() {
    System.out.println("loocv");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        RBFNetwork.Trainer<double[]> trainer = new RBFNetwork.Trainer<>(new EuclideanDistance());
        trainer.setNumCenters(20);
        double rmse = Validation.loocv(trainer, x, y);
        System.out.println("RMSE = " + rmse);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 7 with ArffParser

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

the class ValidationTest method testLoocv_3args_1.

/**
     * Test of loocv method, of class Validation.
     */
@Test
public void testLoocv_3args_1() {
    System.out.println("loocv");
    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()]);
        ClassifierTrainer<double[]> trainer = new LDA.Trainer();
        double accuracy = Validation.loocv(trainer, x, y);
        System.out.println("LOOCV accuracy = " + accuracy);
        assertEquals(0.8533, accuracy, 1E-4);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 8 with ArffParser

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

the class ValidationTest method testCv_5args_2.

/**
     * Test of cv method, of class Validation.
     */
@Test
public void testCv_5args_2() {
    System.out.println("cv");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        RBFNetwork.Trainer<double[]> trainer = new RBFNetwork.Trainer<>(new EuclideanDistance());
        trainer.setNumCenters(20);
        RegressionMeasure[] measures = { new RMSE(), new AbsoluteDeviation() };
        double[] results = Validation.cv(10, trainer, x, y, measures);
        System.out.println("RMSE = " + results[0]);
        System.out.println("Absolute Deviation = " + results[1]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 9 with ArffParser

use of smile.data.parser.ArffParser 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 10 with ArffParser

use of smile.data.parser.ArffParser 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);
    }
}