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

Example 11 with LOOCV

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

the class NaiveBayesTest method testPredict.

/**
     * Test of predict method, of class NaiveBayes.
     */
@Test
public void testPredict() {
    System.out.println("predict");
    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 l = 0; l < n; l++) {
            double[][] trainx = Math.slice(x, loocv.train[l]);
            int[] trainy = Math.slice(y, loocv.train[l]);
            int p = trainx[0].length;
            int k = Math.max(trainy) + 1;
            double[] priori = new double[k];
            Distribution[][] condprob = new Distribution[k][p];
            for (int i = 0; i < k; i++) {
                priori[i] = 1.0 / k;
                for (int j = 0; j < p; j++) {
                    ArrayList<Double> axi = new ArrayList<>();
                    for (int m = 0; m < trainx.length; m++) {
                        if (trainy[m] == i) {
                            axi.add(trainx[m][j]);
                        }
                    }
                    double[] xi = new double[axi.size()];
                    for (int m = 0; m < xi.length; m++) {
                        xi[m] = axi.get(m);
                    }
                    condprob[i][j] = new GaussianMixture(xi, 3);
                }
            }
            NaiveBayes bayes = new NaiveBayes(priori, condprob);
            if (y[loocv.test[l]] != bayes.predict(x[loocv.test[l]]))
                error++;
        }
        System.out.format("Iris error rate = %.2f%%%n", 100.0 * error / x.length);
        assertEquals(5, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 12 with LOOCV

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

the class NeuralNetworkTest method testIris2.

/**
     * Test of learn method, of class NeuralNetwork.
     */
@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;
        int p = x[0].length;
        double[] mu = Math.colMean(x);
        double[] sd = Math.colSd(x);
        for (int i = 0; i < n; i++) {
            for (int j = 0; j < p; j++) {
                x[i][j] = (x[i][j] - mu[j]) / sd[j];
            }
        }
        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]);
            NeuralNetwork net = new NeuralNetwork(NeuralNetwork.ErrorFunction.CROSS_ENTROPY, NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, x[0].length, 10, 1);
            for (int j = 0; j < 30; j++) {
                net.learn(trainx, trainy);
            }
            if (y[loocv.test[i]] != net.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Neural network error = " + error);
        assertTrue(error <= 8);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 13 with LOOCV

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

the class DecisionTreeTest method testIris.

/**
     * Test of learn method, of class DecisionTree.
     */
@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]);
            DecisionTree tree = new DecisionTree(iris.attributes(), trainx, trainy, 4);
            if (y[loocv.test[i]] != tree.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("Decision Tree error = " + error);
        assertEquals(7, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 14 with LOOCV

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

the class FLDTest method testPredict.

/**
     * Test of predict method, of class FDA.
     */
@Test
public void testPredict() {
    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]);
            FLD fisher = new FLD(trainx, trainy);
            if (y[loocv.test[i]] != fisher.predict(x[loocv.test[i]]))
                error++;
        }
        System.out.println("FLD error = " + error);
        assertEquals(5, error);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 15 with LOOCV

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

the class RidgeRegressionTest method testLearn.

/**
     * Test of learn method, of class RidgeRegression.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    RidgeRegression model = new RidgeRegression(longley, y, 0.0);
    double rss = 0.0;
    int n = longley.length;
    for (int i = 0; i < n; i++) {
        double r = y[i] - model.predict(longley[i]);
        assertEquals(residuals[i], r, 1E-7);
        rss += r * r;
    }
    System.out.println("Training MSE = " + rss / n);
    model = new RidgeRegression(longley, y, 0.1);
    assertEquals(-1.354007e+03, model.intercept(), 1E-3);
    assertEquals(5.457700e-02, model.coefficients()[0], 1E-7);
    assertEquals(1.198440e-02, model.coefficients()[1], 1E-7);
    assertEquals(1.261978e-02, model.coefficients()[2], 1E-7);
    assertEquals(-1.856041e-01, model.coefficients()[3], 1E-7);
    assertEquals(7.218054e-01, model.coefficients()[4], 1E-7);
    assertEquals(5.884884e-01, model.coefficients()[5], 1E-7);
    LOOCV loocv = new LOOCV(n);
    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]);
        RidgeRegression ridge = new RidgeRegression(trainx, trainy, 0.1);
        double r = y[loocv.test[i]] - ridge.predict(longley[loocv.test[i]]);
        rss += r * r;
    }
    System.out.println("LOOCV MSE = " + rss / n);
}