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

Example 1 with NominalAttribute

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

the class ClassificationDemo method actionPerformed.

@Override
public void actionPerformed(ActionEvent e) {
    if ("startButton".equals(e.getActionCommand())) {
        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 ]+");
            parser.setResponseIndex(new NominalAttribute("class"), 0);
            try {
                dataset[datasetIndex] = parser.parse(datasetName[datasetIndex], smile.data.parser.IOUtils.getTestDataFile(datasource[datasetIndex]));
            } catch (Exception ex) {
                JOptionPane.showMessageDialog(null, "Failed to load dataset.", "ERROR", JOptionPane.ERROR_MESSAGE);
                System.err.println(ex);
            }
        }
        double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);
        int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        if (data.length < 500) {
            pointLegend = 'o';
        } else {
            pointLegend = '.';
        }
        PlotCanvas canvas = ScatterPlot.plot(data, pointLegend);
        for (int i = 0; i < data.length; i++) {
            canvas.point(pointLegend, Palette.COLORS[label[i]], data[i]);
        }
        BorderLayout layout = (BorderLayout) getLayout();
        remove(layout.getLayoutComponent(BorderLayout.CENTER));
        add(canvas, BorderLayout.CENTER);
        validate();
    }
}

Example 2 with NominalAttribute

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

the class GrowingNeuralGasTest method testUSPS.

/**
     * Test of learn method, of class GrowingNeuralGas.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    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()]);
        GrowingNeuralGas gng = new GrowingNeuralGas(x[0].length);
        for (int i = 0; i < 10; i++) {
            int[] index = Math.permutate(x.length);
            for (int j = 0; j < x.length; j++) {
                gng.update(x[index[j]]);
            }
        }
        gng.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        int[] p = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            p[i] = gng.predict(x[i]);
        }
        double r = rand.measure(y, p);
        double r2 = ari.measure(y, p);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.85);
        assertTrue(r2 > 0.40);
        p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = gng.predict(testx[i]);
        }
        r = rand.measure(testy, p);
        r2 = ari.measure(testy, p);
        System.out.format("Testing rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.85);
        assertTrue(r2 > 0.40);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 3 with NominalAttribute

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

the class NeuralMapTest method testUSPS.

/**
     * Test of learn method, of class NeuralMap.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    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()]);
        NeuralMap cortex = new NeuralMap(x[0].length, 8.0, 0.05, 0.0006, 5, 3);
        for (int i = 0; i < 5; i++) {
            for (double[] xi : x) {
                cortex.update(xi);
            }
        }
        cortex.purge(16);
        cortex.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        int[] p = new int[x.length];
        for (int i = 0; i < x.length; i++) {
            p[i] = cortex.predict(x[i]);
        }
        double r = rand.measure(y, p);
        double r2 = ari.measure(y, p);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        //assertTrue(r > 0.65);
        //assertTrue(r2 > 0.18);
        p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = cortex.predict(testx[i]);
        }
        r = rand.measure(testy, p);
        r2 = ari.measure(testy, p);
        System.out.format("Testing rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
    //assertTrue(r > 0.65);
    //assertTrue(r2 > 0.18);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with NominalAttribute

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

the class SOMTest method testUSPS.

/**
     * Test of learn method, of class SOM.
     */
@Test
public void testUSPS() {
    System.out.println("USPS");
    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()]);
        SOM som = new SOM(x, 10, 10);
        int[] label = som.partition(10);
        AdjustedRandIndex ari = new AdjustedRandIndex();
        RandIndex rand = new RandIndex();
        double r = rand.measure(y, label);
        double r2 = ari.measure(y, label);
        System.out.format("Training rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.88);
        assertTrue(r2 > 0.45);
        int[] p = new int[testx.length];
        for (int i = 0; i < testx.length; i++) {
            p[i] = som.predict(testx[i]);
        }
        r = rand.measure(testy, p);
        r2 = ari.measure(testy, p);
        System.out.format("Testing rand index = %.2f%%\tadjusted rand index = %.2f%%%n", 100.0 * r, 100.0 * r2);
        assertTrue(r > 0.88);
        assertTrue(r2 > 0.45);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 5 with NominalAttribute

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

the class ArffParser method parse.

/**
     * Parse a dataset from given stream.
     */
public AttributeDataset parse(InputStream stream) throws IOException, ParseException {
    try (Reader r = new BufferedReader(new InputStreamReader(stream))) {
        StreamTokenizer tokenizer = new StreamTokenizer(r);
        initTokenizer(tokenizer);
        List<Attribute> attributes = new ArrayList<>();
        String relationName = readHeader(tokenizer, attributes);
        if (attributes.isEmpty()) {
            throw new IOException("no header information available");
        }
        Attribute response = null;
        Attribute[] attr = new Attribute[attributes.size()];
        attributes.toArray(attr);
        for (int i = 0; i < attributes.size(); i++) {
            if (responseIndex == i) {
                response = attributes.remove(i);
                break;
            }
        }
        AttributeDataset data = new AttributeDataset(relationName, attributes.toArray(new Attribute[attributes.size()]), response);
        while (true) {
            // Check if end of file reached.
            getFirstToken(tokenizer);
            if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
                break;
            }
            // Parse instance
            if (tokenizer.ttype == '{') {
                data.add(getSparseInstance(tokenizer, attr));
            } else {
                data.add(getInstance(tokenizer, attr));
            }
        }
        for (Attribute attribute : attributes) {
            if (attribute instanceof NominalAttribute) {
                NominalAttribute a = (NominalAttribute) attribute;
                a.setOpen(false);
            }
            if (attribute instanceof StringAttribute) {
                StringAttribute a = (StringAttribute) attribute;
                a.setOpen(false);
            }
        }
        return data;
    }
}