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

Example 1 with Accuracy

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

the class GAFeatureSelectionTest method testLearn.

/**
     * Test of learn method, of class GAFeatureSelection.
     */
@Test
public void testLearn() {
    System.out.println("learn");
    int size = 100;
    int generation = 20;
    ClassifierTrainer<double[]> trainer = new LDA.Trainer();
    ClassificationMeasure measure = new Accuracy();
    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()]);
        GAFeatureSelection instance = new GAFeatureSelection();
        BitString[] result = instance.learn(size, generation, trainer, measure, x, y, testx, testy);
        for (BitString bits : result) {
            System.out.format("%.2f%% %d ", 100 * bits.fitness(), Math.sum(bits.bits()));
            for (int i = 0; i < x[0].length; i++) {
                System.out.print(bits.bits()[i] + " ");
            }
            System.out.println();
        }
        assertTrue(result[result.length - 1].fitness() > 0.88);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 2 with Accuracy

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

the class SumSquaresRatioTest method testLearn.

/**
     * Test of learn method, of class SumSquaresRatio.
     */
@Test
public void testLearn() {
    System.out.println("USPS");
    try {
        DelimitedTextParser parser = new DelimitedTextParser();
        parser.setResponseIndex(new NominalAttribute("class"), 0);
        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()]);
        SumSquaresRatio ssr = new SumSquaresRatio();
        double[] score = ssr.rank(x, y);
        int[] index = QuickSort.sort(score);
        int p = 135;
        int n = x.length;
        double[][] xx = new double[n][p];
        for (int j = 0; j < p; j++) {
            for (int i = 0; i < n; i++) {
                xx[i][j] = x[i][index[255 - j]];
            }
        }
        int testn = testx.length;
        double[][] testxx = new double[testn][p];
        for (int j = 0; j < p; j++) {
            for (int i = 0; i < testn; i++) {
                testxx[i][j] = testx[i][index[255 - j]];
            }
        }
        LDA lda = new LDA(xx, y);
        int[] prediction = new int[testn];
        for (int i = 0; i < testn; i++) {
            prediction[i] = lda.predict(testxx[i]);
        }
        double accuracy = new Accuracy().measure(testy, prediction);
        System.out.format("SSR %.2f%%%n", 100 * accuracy);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 3 with Accuracy

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

the class AdaBoost method test.

/**
     * Test the model on a validation dataset.
     * 
     * @param x the test data set.
     * @param y the test data response values.
     * @return accuracies with first 1, 2, ..., decision trees.
     */
public double[] test(double[][] x, int[] y) {
    int T = trees.length;
    double[] accuracy = new double[T];
    int n = x.length;
    int[] label = new int[n];
    Accuracy measure = new Accuracy();
    if (k == 2) {
        double[] prediction = new double[n];
        for (int i = 0; i < T; i++) {
            for (int j = 0; j < n; j++) {
                prediction[j] += alpha[i] * trees[i].predict(x[j]);
                label[j] = prediction[j] > 0 ? 1 : 0;
            }
            accuracy[i] = measure.measure(y, label);
        }
    } else {
        double[][] prediction = new double[n][k];
        for (int i = 0; i < T; i++) {
            for (int j = 0; j < n; j++) {
                prediction[j][trees[i].predict(x[j])] += alpha[i];
                label[j] = Math.whichMax(prediction[j]);
            }
            accuracy[i] = measure.measure(y, label);
        }
    }
    return accuracy;
}

Example 4 with Accuracy

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

the class GradientTreeBoost method test.

/**
     * Test the model on a validation dataset.
     * 
     * @param x the test data set.
     * @param y the test data response values.
     * @return accuracies with first 1, 2, ..., decision trees.
     */
public double[] test(double[][] x, int[] y) {
    double[] accuracy = new double[ntrees];
    int n = x.length;
    int[] label = new int[n];
    Accuracy measure = new Accuracy();
    if (k == 2) {
        double[] prediction = new double[n];
        Arrays.fill(prediction, b);
        for (int i = 0; i < ntrees; i++) {
            for (int j = 0; j < n; j++) {
                prediction[j] += shrinkage * trees[i].predict(x[j]);
                label[j] = prediction[j] > 0 ? 1 : 0;
            }
            accuracy[i] = measure.measure(y, label);
        }
    } else {
        double[][] prediction = new double[n][k];
        for (int i = 0; i < ntrees; i++) {
            for (int j = 0; j < n; j++) {
                for (int l = 0; l < k; l++) {
                    prediction[j][l] += shrinkage * forest[l][i].predict(x[j]);
                }
                label[j] = Math.whichMax(prediction[j]);
            }
            accuracy[i] = measure.measure(y, label);
        }
    }
    return accuracy;
}

Example 5 with Accuracy

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

the class RandomForest method test.

/**
     * Test the model on a validation dataset.
     * 
     * @param x the test data set.
     * @param y the test data response values.
     * @return accuracies with first 1, 2, ..., decision trees.
     */
public double[] test(double[][] x, int[] y) {
    int T = trees.size();
    double[] accuracy = new double[T];
    int n = x.length;
    int[] label = new int[n];
    int[][] prediction = new int[n][k];
    Accuracy measure = new Accuracy();
    for (int i = 0; i < T; i++) {
        for (int j = 0; j < n; j++) {
            prediction[j][trees.get(i).tree.predict(x[j])]++;
            label[j] = Math.whichMax(prediction[j]);
        }
        accuracy[i] = measure.measure(y, label);
    }
    return accuracy;
}