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

Example 6 with LOOCV

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

the class RandomForestTest method testIris.

/**
     * Test of learn method, of class RandomForest.
     */
@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]);
            RandomForest forest = new RandomForest(iris.attributes(), trainx, trainy, 100);
            if (y[loocv.test[i]] != forest.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Random Forest error = " + error);
        assertTrue(error <= 9);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 7 with LOOCV

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

the class LASSOTest method testLongley.

/**
     * Test of learn method, of class RidgeRegression.
     */
@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 };
    double rss = 0.0;
    int n = longley.length;
    LOOCV loocv = new LOOCV(n);
    for (int i = 0; i < n; i++) {
        double[][] trainx = Math.slice(longley, loocv.train[i]);
        double[] trainy = Math.slice(y, loocv.train[i]);
        LASSO lasso = new LASSO(trainx, trainy, 0.1);
        double r = y[loocv.test[i]] - lasso.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("LOOCV MSE = " + rss / n);
    assertEquals(2.0012529348358212, rss / n, 1E-4);
}

Example 8 with LOOCV

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

the class OLSTest method testLearn.

/**
     * Test of learn method, of class LinearRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    OLS model = new OLS(longley, y);
    System.out.println(model);
    assertEquals(12.8440, model.RSS(), 1E-4);
    assertEquals(1.1946, model.error(), 1E-4);
    assertEquals(9, model.df());
    assertEquals(0.9926, model.RSquared(), 1E-4);
    assertEquals(0.9877, model.adjustedRSquared(), 1E-4);
    assertEquals(202.5094, model.ftest(), 1E-4);
    assertEquals(4.42579E-9, model.pvalue(), 1E-14);
    for (int i = 0; i < w.length; i++) {
        for (int j = 0; j < 4; j++) {
            assertEquals(w[i][j], model.ttest()[i][j], 1E-3);
        }
    }
    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]);
        OLS linear = new OLS(trainx, trainy);
        double r = y[loocv.test[i]] - linear.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
    assertEquals(2.2148948268123756, rss / n, 1E-4);
}

Example 9 with LOOCV

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

the class RBFNetworkTest method testLearn.

/**
     * Test of learn method, of class RKHSRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    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]);
        double[][] centers = new double[10][];
        RadialBasisFunction[] basis = SmileUtils.learnGaussianRadialBasis(trainx, centers, 5.0);
        RBFNetwork<double[]> rbf = new RBFNetwork<>(trainx, trainy, new EuclideanDistance(), basis, centers);
        double r = y[loocv.test[i]] - rbf.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("MSE = " + rss / n);
}

Example 10 with LOOCV

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

the class RandomForestTest method testPredict.

/**
     * Test of predict method, of class RandomForest.
     */
@Test
public void testPredict() {
    System.out.println("predict");
    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]);
        try {
            RandomForest forest = new RandomForest(trainx, trainy, 300, n, 3, 2);
            double r = y[loocv.test[i]] - forest.predict(longley[loocv.test[i]]);
            rss += r * r;
        } catch (Exception ex) {
            System.err.println(ex);
        }
    }
    System.out.println("MSE = " + rss / n);
}