Examples with LDA smile.classification.LDA used on opensource projects

Example 1 with LDA

use of smile.classification.LDA in project smile by haifengl.

the class LDADemo method learn.

@Override
public double[][] learn(double[] x, double[] y) {
    double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
    int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
    LDA lda = new LDA(data, label);
    for (int i = 0; i < label.length; i++) {
        label[i] = lda.predict(data[i]);
    }
    double trainError = error(label, label);
    System.out.format("training error = %.2f%%\n", 100 * trainError);
    double[][] z = new double[y.length][x.length];
    for (int i = 0; i < y.length; i++) {
        for (int j = 0; j < x.length; j++) {
            double[] p = { x[j], y[i] };
            z[i][j] = lda.predict(p);
        }
    }
    return z;
}

Example 2 with LDA

use of smile.classification.LDA 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 LDA

use of smile.classification.LDA in project smile by haifengl.

the class ValidationTest method testTest_3args_1.

/**
     * Test of test method, of class Validation.
     */
@Test
public void testTest_3args_1() {
    System.out.println("test");
    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()]);
        LDA lda = new LDA(x, y);
        double accuracy = Validation.test(lda, testx, testy);
        System.out.println("accuracy = " + accuracy);
        assertEquals(0.8724, accuracy, 1E-4);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with LDA

use of smile.classification.LDA in project smile by haifengl.

the class ValidationTest method testTest_4args_1.

/**
     * Test of test method, of class Validation.
     */
@Test
public void testTest_4args_1() {
    System.out.println("test");
    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()]);
        LDA lda = new LDA(x, y);
        ClassificationMeasure[] measures = { new Accuracy() };
        double[] accuracy = Validation.test(lda, testx, testy, measures);
        System.out.println("accuracy = " + accuracy[0]);
        assertEquals(0.8724, accuracy[0], 1E-4);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}