Examples with AttributeDataset smile.data.AttributeDataset used on opensource projects

Example 1 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class GCTParserTest method testParse.

/**
     * Test of parse method, of class GCTParser.
     */
@Test
public void testParse() throws Exception {
    System.out.println("parse");
    GCTParser parser = new GCTParser();
    try {
        AttributeDataset data = parser.parse("GCT", smile.data.parser.IOUtils.getTestDataFile("microarray/allaml.dataset.gct"));
        double[][] x = data.toArray(new double[data.size()][]);
        String[] id = data.toArray(new String[data.size()]);
        for (Attribute attribute : data.attributes()) {
            assertEquals(Attribute.Type.NUMERIC, attribute.getType());
            System.out.println(attribute.getName());
        }
        assertEquals(12564, data.size());
        assertEquals(48, data.attributes().length);
        assertEquals("AFFX-MurIL2_at", id[0]);
        assertEquals(-161.8, x[0][0], 1E-7);
        assertEquals(-231.0, x[0][1], 1E-7);
        assertEquals(-279.0, x[0][2], 1E-7);
        assertEquals("128_at", id[12563]);
        assertEquals(95.0, x[12563][45], 1E-7);
        assertEquals(108.0, x[12563][46], 1E-7);
        assertEquals(346.0, x[12563][47], 1E-7);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 2 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class ClusteringDemo method actionPerformed.

@Override
public void actionPerformed(ActionEvent e) {
    if ("startButton".equals(e.getActionCommand())) {
        try {
            clusterNumber = Integer.parseInt(clusterNumberField.getText().trim());
            if (clusterNumber < 2) {
                JOptionPane.showMessageDialog(this, "Invalid K: " + clusterNumber, ERROR, JOptionPane.ERROR_MESSAGE);
                return;
            }
            if (clusterNumber > dataset[datasetIndex].length / 2) {
                JOptionPane.showMessageDialog(this, "Too large K: " + clusterNumber, ERROR, JOptionPane.ERROR_MESSAGE);
                return;
            }
        } catch (Exception ex) {
            JOptionPane.showMessageDialog(this, "Invalid K: " + clusterNumberField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
            return;
        }
        Thread thread = new Thread(this);
        thread.start();
    } else if ("datasetBox".equals(e.getActionCommand())) {
        datasetIndex = datasetBox.getSelectedIndex();
        if (dataset[datasetIndex] == null) {
            DelimitedTextParser parser = new DelimitedTextParser();
            parser.setDelimiter("[\t ]+");
            try {
                AttributeDataset data = parser.parse(datasetName[datasetIndex], smile.data.parser.IOUtils.getTestDataFile(datasource[datasetIndex]));
                dataset[datasetIndex] = data.toArray(new double[data.size()][]);
            } catch (Exception ex) {
                JOptionPane.showMessageDialog(null, "Failed to load dataset.", "ERROR", JOptionPane.ERROR_MESSAGE);
                System.err.println(ex);
            }
        }
        remove(canvas);
        if (dataset[datasetIndex].length < 500) {
            pointLegend = 'o';
        } else {
            pointLegend = '.';
        }
        canvas = ScatterPlot.plot(dataset[datasetIndex], pointLegend);
        add(canvas, BorderLayout.CENTER);
        validate();
    }
}

Example 3 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class ValidationTest method testCv_5args_1.

/**
     * Test of cv method, of class Validation.
     */
@Test
public void testCv_5args_1() {
    System.out.println("cv");
    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()]);
        ClassifierTrainer<double[]> trainer = new LDA.Trainer();
        ClassificationMeasure[] measures = { new Accuracy() };
        double[] results = Validation.cv(10, trainer, x, y, measures);
        for (int i = 0; i < measures.length; i++) {
            System.out.println(measures[i] + " = " + results[i]);
        }
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class ValidationTest method testCv_4args_1.

/**
     * Test of cv method, of class Validation.
     */
@Test
public void testCv_4args_1() {
    System.out.println("cv");
    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()]);
        ClassifierTrainer<double[]> trainer = new LDA.Trainer();
        double accuracy = Validation.cv(10, trainer, x, y);
        System.out.println("10-fold CV accuracy = " + accuracy);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 5 with AttributeDataset

use of smile.data.AttributeDataset in project smile by haifengl.

the class ValidationTest method testTest_4args_2.

/**
     * Test of test method, of class Validation.
     */
@Test
public void testTest_4args_2() {
    System.out.println("test");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] datay = data.toArray(new double[data.size()]);
        double[][] datax = data.toArray(new double[data.size()][]);
        Math.standardize(datax);
        int n = datax.length;
        int m = 3 * n / 4;
        double[][] x = new double[m][];
        double[] y = new double[m];
        double[][] testx = new double[n - m][];
        double[] testy = new double[n - m];
        int[] index = Math.permutate(n);
        for (int i = 0; i < m; i++) {
            x[i] = datax[index[i]];
            y[i] = datay[index[i]];
        }
        for (int i = m; i < n; i++) {
            testx[i - m] = datax[index[i]];
            testy[i - m] = datay[index[i]];
        }
        double[][] centers = new double[20][];
        RadialBasisFunction[] rbf = SmileUtils.learnGaussianRadialBasis(x, centers, 2);
        RBFNetwork<double[]> rkhs = new RBFNetwork<>(x, y, new EuclideanDistance(), rbf, centers);
        RegressionMeasure[] measures = { new RMSE(), new AbsoluteDeviation() };
        double[] results = Validation.test(rkhs, testx, testy, measures);
        System.out.println("RMSE = " + results[0]);
        System.out.println("Absolute Deviation = " + results[1]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}