Examples with LOOCV smile.validation.LOOCV used on opensource projects

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Example 1 with LOOCV

use of smile.validation.LOOCV in project smile by haifengl.

the class RegressionTreeTest method testLongley.

/**
     * Test of predict method, of class RegressionTree.
     */
@Test
public void testLongley() {
    System.out.println("longley");
    double[][] longley = { { 234.289, 235.6, 159.0, 107.608, 1947, 60.323 }, { 259.426, 232.5, 145.6, 108.632, 1948, 61.122 }, { 258.054, 368.2, 161.6, 109.773, 1949, 60.171 }, { 284.599, 335.1, 165.0, 110.929, 1950, 61.187 }, { 328.975, 209.9, 309.9, 112.075, 1951, 63.221 }, { 346.999, 193.2, 359.4, 113.270, 1952, 63.639 }, { 365.385, 187.0, 354.7, 115.094, 1953, 64.989 }, { 363.112, 357.8, 335.0, 116.219, 1954, 63.761 }, { 397.469, 290.4, 304.8, 117.388, 1955, 66.019 }, { 419.180, 282.2, 285.7, 118.734, 1956, 67.857 }, { 442.769, 293.6, 279.8, 120.445, 1957, 68.169 }, { 444.546, 468.1, 263.7, 121.950, 1958, 66.513 }, { 482.704, 381.3, 255.2, 123.366, 1959, 68.655 }, { 502.601, 393.1, 251.4, 125.368, 1960, 69.564 }, { 518.173, 480.6, 257.2, 127.852, 1961, 69.331 }, { 554.894, 400.7, 282.7, 130.081, 1962, 70.551 } };
    double[] y = { 83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9 };
    int n = longley.length;
    LOOCV loocv = new LOOCV(n);
    double rss = 0.0;
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(longley, loocv.train[i]);
        double[] trainy = Math.slice(y, loocv.train[i]);
        RegressionTree tree = new RegressionTree(trainx, trainy, 3);
        double r = y[loocv.test[i]] - tree.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
    assertEquals(41.933087445771115, rss / n, 1E-4);
}
Also used : LOOCV(smile.validation.LOOCV) Test(org.junit.Test)

Example 2 with LOOCV

use of smile.validation.LOOCV 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);
    }
}
Also used : ArffParser(smile.data.parser.ArffParser) AttributeDataset(smile.data.AttributeDataset) LOOCV(smile.validation.LOOCV) Test(org.junit.Test)

Example 3 with LOOCV

use of smile.validation.LOOCV in project smile by haifengl.

the class GaussianProcessRegressionTest method testLearn.

/**
     * Test of learn method, of class RKHSRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    double[][] longley = { { 234.289, 235.6, 159.0, 107.608, 1947, 60.323 }, { 259.426, 232.5, 145.6, 108.632, 1948, 61.122 }, { 258.054, 368.2, 161.6, 109.773, 1949, 60.171 }, { 284.599, 335.1, 165.0, 110.929, 1950, 61.187 }, { 328.975, 209.9, 309.9, 112.075, 1951, 63.221 }, { 346.999, 193.2, 359.4, 113.270, 1952, 63.639 }, { 365.385, 187.0, 354.7, 115.094, 1953, 64.989 }, { 363.112, 357.8, 335.0, 116.219, 1954, 63.761 }, { 397.469, 290.4, 304.8, 117.388, 1955, 66.019 }, { 419.180, 282.2, 285.7, 118.734, 1956, 67.857 }, { 442.769, 293.6, 279.8, 120.445, 1957, 68.169 }, { 444.546, 468.1, 263.7, 121.950, 1958, 66.513 }, { 482.704, 381.3, 255.2, 123.366, 1959, 68.655 }, { 502.601, 393.1, 251.4, 125.368, 1960, 69.564 }, { 518.173, 480.6, 257.2, 127.852, 1961, 69.331 }, { 554.894, 400.7, 282.7, 130.081, 1962, 70.551 } };
    double[] y = { 83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9 };
    Math.standardize(longley);
    int n = longley.length;
    LOOCV loocv = new LOOCV(n);
    double rss = 0.0;
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(longley, loocv.train[i]);
        double[] trainy = Math.slice(y, loocv.train[i]);
        GaussianProcessRegression<double[]> rkhs = new GaussianProcessRegression<>(trainx, trainy, new GaussianKernel(8.0), 0.2);
        double r = y[loocv.test[i]] - rkhs.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
}
Also used : LOOCV(smile.validation.LOOCV) GaussianKernel(smile.math.kernel.GaussianKernel) Test(org.junit.Test)

Example 4 with LOOCV

use of smile.validation.LOOCV in project smile by haifengl.

the class QDATest method testLearn.

/**
     * Test of learn method, of class QDA.
     */
@Test
public void testLearn() {
    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[iris.size()][]);
        int[] y = iris.toArray(new int[iris.size()]);
        int n = x.length;
        LOOCV loocv = new LOOCV(n);
        int error = 0;
        double[] posteriori = new double[3];
        for (int i = 0; i < n; i++) {
            double[][] trainx = Math.slice(x, loocv.train[i]);
            int[] trainy = Math.slice(y, loocv.train[i]);
            QDA qda = new QDA(trainx, trainy);
            if (y[loocv.test[i]] != qda.predict(x[loocv.test[i]], posteriori))
                error++;
        //System.out.println(posteriori[0]+"\t"+posteriori[1]+"\t"+posteriori[2]);
        }
        System.out.println("QDA error = " + error);
        assertEquals(4, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}
Also used : ArffParser(smile.data.parser.ArffParser) AttributeDataset(smile.data.AttributeDataset) LOOCV(smile.validation.LOOCV) Test(org.junit.Test)

Example 5 with LOOCV

use of smile.validation.LOOCV in project smile by haifengl.

the class RDATest method testLearn.

/**
     * Test of learn method, of class RDA.
     */
@Test
public void testLearn() {
    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[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]);
            RDA rda = new RDA(trainx, trainy, 0.0);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.0) error = " + error);
        assertEquals(22, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.1);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.1) error = " + error);
        assertEquals(24, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.2);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.2) error = " + error);
        assertEquals(20, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.3);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.3) error = " + error);
        assertEquals(19, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.4);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.4) error = " + error);
        assertEquals(16, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.5);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.5) error = " + error);
        assertEquals(12, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.6);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.6) error = " + error);
        assertEquals(11, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.7);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.7) error = " + error);
        assertEquals(9, error);
        error = 0;
        double[] posteriori = new double[3];
        for (int i = 0; i < n; i++) {
            double[][] trainx = Math.slice(x, loocv.train[i]);
            int[] trainy = Math.slice(y, loocv.train[i]);
            RDA rda = new RDA(trainx, trainy, 0.8);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]], posteriori))
                error++;
        //System.out.println(posteriori[0]+"\t"+posteriori[1]+"\t"+posteriori[2]);
        }
        System.out.println("RDA (0.8) error = " + error);
        assertEquals(6, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 0.9);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (0.9) error = " + error);
        assertEquals(3, error);
        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]);
            RDA rda = new RDA(trainx, trainy, 1.0);
            if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("RDA (1.0) error = " + error);
        assertEquals(4, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}
Also used : ArffParser(smile.data.parser.ArffParser) AttributeDataset(smile.data.AttributeDataset) LOOCV(smile.validation.LOOCV) Test(org.junit.Test)

Aggregations

Test (org.junit.Test)26 LOOCV (smile.validation.LOOCV)26 AttributeDataset (smile.data.AttributeDataset)18 ArffParser (smile.data.parser.ArffParser)18 EuclideanDistance (smile.math.distance.EuclideanDistance)2 RadialBasisFunction (smile.math.rbf.RadialBasisFunction)2 IOException (java.io.IOException)1 ArrayList (java.util.ArrayList)1 GaussianKernel (smile.math.kernel.GaussianKernel)1 Distribution (smile.stat.distribution.Distribution)1 GaussianMixture (smile.stat.distribution.GaussianMixture)1
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