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

Example 56 with ArffParser

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

the class ValidationTest method testBootstrap_4args_2.

/**
     * Test of bootstrap method, of class Validation.
     */
@Test
public void testBootstrap_4args_2() {
    System.out.println("bootstrap");
    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.bootstrap(100, trainer, x, y);
        System.out.println("100-fold bootstrap RMSE average = " + Math.mean(rmse));
        System.out.println("100-fold bootstrap RMSE std.dev = " + Math.sd(rmse));
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 57 with ArffParser

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

the class DecisionTreeTest method testWeather.

/**
     * Test of learn method, of class DecisionTree.
     */
@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]);
            DecisionTree tree = new DecisionTree(weather.attributes(), trainx, trainy, 3);
            if (y[loocv.test[i]] != tree.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Decision Tree error = " + error);
        assertEquals(5, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 58 with ArffParser

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

the class GradientTreeBoostTest method testIris2.

/**
     * Test of predict method, of class GradientTreeBoost.
     */
@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]);
            GradientTreeBoost boost = new GradientTreeBoost(iris.attributes(), trainx, trainy, 100);
            if (y[loocv.test[i]] != boost.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Gradient Tree Boost error = " + error);
    //assertEquals(6, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 59 with ArffParser

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

the class GradientTreeBoostTest method testIris.

/**
     * Test of learn method, of class GradientTreeBoost.
     */
@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]);
            GradientTreeBoost boost = new GradientTreeBoost(iris.attributes(), trainx, trainy, 100);
            if (y[loocv.test[i]] != boost.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Gradient Tree Boost error = " + error);
    //assertEquals(6, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 60 with ArffParser

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

the class KNNTest method testLearn_3args.

/**
     * Test of learn method, of class KNN.
     */
@Test
public void testLearn_3args() {
    System.out.println("learn");
    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[0][]);
        int[] y = iris.toArray(new int[0]);
        KNN<double[]> knn = KNN.learn(x, y, 1);
        int error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("1-nn error = " + error);
        assertEquals(6, error);
        knn = KNN.learn(x, y, 3);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("3-nn error = " + error);
        assertEquals(6, error);
        knn = KNN.learn(x, y, 5);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("5-nn error = " + error);
        assertEquals(5, error);
        knn = KNN.learn(x, y, 7);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("7-nn error = " + error);
        assertEquals(5, error);
        knn = KNN.learn(x, y, 9);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("9-nn error = " + error);
        assertEquals(5, error);
        knn = KNN.learn(x, y, 11);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("11-nn error = " + error);
        assertEquals(4, error);
        knn = KNN.learn(x, y, 13);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("13-nn error = " + error);
        assertEquals(5, error);
        knn = KNN.learn(x, y, 15);
        error = 0;
        for (int i = 0; i < x.length; i++) {
            if (knn.predict(x[i]) != y[i]) {
                error++;
            }
        }
        System.out.println("15-nn error = " + error);
        assertEquals(4, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}