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

Example 86 with AttributeDataset

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

the class SVMTest method testSegment.

/**
     * Test of learn method, of class SVM.
     */
@Test
public void testSegment() {
    System.out.println("Segment");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(19);
    try {
        AttributeDataset train = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
        AttributeDataset test = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
        System.out.println(train.size() + " " + test.size());
        double[][] x = train.toArray(new double[0][]);
        int[] y = train.toArray(new int[0]);
        double[][] testx = test.toArray(new double[0][]);
        int[] testy = test.toArray(new int[0]);
        SVM<double[]> svm = new SVM<>(new GaussianKernel(8.0), 5.0, Math.max(y) + 1, SVM.Multiclass.ONE_VS_ALL);
        svm.learn(x, y);
        svm.finish();
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (svm.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.format("Segment error rate = %.2f%%%n", 100.0 * error / testx.length);
        assertTrue(error < 70);
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

Example 87 with AttributeDataset

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

the class AdaBoostTest method testWeather.

/**
     * Test of learn method, of class AdaBoost.
     */
@Test
public void testWeather() {
    System.out.println("Weather");
    ArffParser arffParser = new ArffParser();
    arffParser.setResponseIndex(4);
    try {
        AttributeDataset weather = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/weather.nominal.arff"));
        double[][] x = weather.toArray(new double[weather.size()][]);
        int[] y = weather.toArray(new int[weather.size()]);
        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(weather.attributes(), trainx, trainy, 200, 4);
            if (y[loocv.test[i]] != forest.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("AdaBoost error = " + error);
        assertEquals(3, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 88 with AttributeDataset

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

the class AdaBoostTest method testUSPS10.

/**
     * Test of learn method, of class AdaBoost.
     */
@Test
public void testUSPS10() {
    System.out.println("USPS 10 classes");
    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"));
        AttributeDataset test = parser.parse("USPS Test", smile.data.parser.IOUtils.getTestDataFile("usps/zip.test"));
        double[][] x = train.toArray(new double[train.size()][]);
        int[] y = train.toArray(new int[train.size()]);
        double[][] testx = test.toArray(new double[test.size()][]);
        int[] testy = test.toArray(new int[test.size()]);
        AdaBoost forest = new AdaBoost(x, y, 100, 64);
        int error = 0;
        for (int i = 0; i < testx.length; i++) {
            if (forest.predict(testx[i]) != testy[i]) {
                error++;
            }
        }
        System.out.println("AdaBoost error = " + error);
        System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
        double[] accuracy = forest.test(testx, testy);
        for (int i = 1; i <= accuracy.length; i++) {
            System.out.format("%d trees accuracy = %.2f%%%n", i, 100.0 * accuracy[i - 1]);
        }
        double[] importance = forest.importance();
        int[] index = QuickSort.sort(importance);
        for (int i = importance.length; i-- > 0; ) {
            System.out.format("%s importance is %.4f%n", train.attributes()[index[i]], importance[i]);
        }
        assertTrue(error <= 170);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 89 with AttributeDataset

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

the class LogisticRegressionTest method testIris2.

/**
     * Test of learn method, of class LogisticRegression.
     */
@Test
public void testIris2() {
    System.out.println("Iris binary");
    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] == 2) {
                y[i] = 1;
            } else {
                y[i] = 0;
            }
        }
        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]);
            LogisticRegression logit = new LogisticRegression(trainx, trainy);
            if (y[loocv.test[i]] != logit.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Logistic Regression error = " + error);
        assertEquals(3, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 90 with AttributeDataset

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

the class LogisticRegressionTest method testIris.

/**
     * Test of learn method, of class LogisticRegression.
     */
@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()]);
        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]);
            LogisticRegression logit = new LogisticRegression(trainx, trainy);
            if (y[loocv.test[i]] != logit.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Logistic Regression error = " + error);
        assertEquals(3, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}