Examples with EuclideanDistance smile.math.distance.EuclideanDistance used on opensource projects

Example 1 with EuclideanDistance

use of smile.math.distance.EuclideanDistance in project smile by haifengl.

the class CLARANSDemo method learn.

@Override
public JComponent learn() {
    try {
        numLocal = Integer.parseInt(numLocalField.getText().trim());
        if (numLocal < 5) {
            JOptionPane.showMessageDialog(this, "Toll smal NumLocal: " + numLocal, ERROR, JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid NumLocal: " + numLocalField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
        return null;
    }
    try {
        maxNeighbor = Integer.parseInt(maxNeighborField.getText().trim());
        if (maxNeighbor < 5) {
            JOptionPane.showMessageDialog(this, "Too small MaxNeighbor: " + maxNeighbor, ERROR, JOptionPane.ERROR_MESSAGE);
            return null;
        }
    } catch (Exception e) {
        JOptionPane.showMessageDialog(this, "Invalid MaxNeighbor: " + maxNeighborField.getText(), ERROR, JOptionPane.ERROR_MESSAGE);
        return null;
    }
    long clock = System.currentTimeMillis();
    CLARANS<double[]> clarans = new CLARANS<>(dataset[datasetIndex], new EuclideanDistance(), clusterNumber, maxNeighbor, numLocal);
    System.out.format("CLARANS clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis() - clock);
    PlotCanvas plot = ScatterPlot.plot(clarans.medoids(), '@');
    for (int k = 0; k < clusterNumber; k++) {
        if (clarans.getClusterSize()[k] > 0) {
            double[][] cluster = new double[clarans.getClusterSize()[k]][];
            for (int i = 0, j = 0; i < dataset[datasetIndex].length; i++) {
                if (clarans.getClusterLabel()[i] == k) {
                    cluster[j++] = dataset[datasetIndex][i];
                }
            }
            plot.points(cluster, pointLegend, Palette.COLORS[k % Palette.COLORS.length]);
        }
    }
    plot.points(clarans.medoids(), '@');
    return plot;
}

Example 2 with EuclideanDistance

use of smile.math.distance.EuclideanDistance 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);
    }
}

Example 3 with EuclideanDistance

use of smile.math.distance.EuclideanDistance in project smile by haifengl.

the class ValidationTest method testLoocv_4args_2.

/**
     * Test of loocv method, of class Validation.
     */
@Test
public void testLoocv_4args_2() {
    System.out.println("loocv");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        RBFNetwork.Trainer<double[]> trainer = new RBFNetwork.Trainer<>(new EuclideanDistance());
        trainer.setNumCenters(20);
        RegressionMeasure[] measures = { new RMSE(), new AbsoluteDeviation() };
        double[] results = Validation.loocv(trainer, x, y, measures);
        System.out.println("RMSE = " + results[0]);
        System.out.println("Absolute Deviation = " + results[1]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 4 with EuclideanDistance

use of smile.math.distance.EuclideanDistance in project smile by haifengl.

the class ValidationTest method testLoocv_3args_2.

/**
     * Test of loocv method, of class Validation.
     */
@Test
public void testLoocv_3args_2() {
    System.out.println("loocv");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        RBFNetwork.Trainer<double[]> trainer = new RBFNetwork.Trainer<>(new EuclideanDistance());
        trainer.setNumCenters(20);
        double rmse = Validation.loocv(trainer, x, y);
        System.out.println("RMSE = " + rmse);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}

Example 5 with EuclideanDistance

use of smile.math.distance.EuclideanDistance in project smile by haifengl.

the class ValidationTest method testCv_5args_2.

/**
     * Test of cv method, of class Validation.
     */
@Test
public void testCv_5args_2() {
    System.out.println("cv");
    ArffParser parser = new ArffParser();
    parser.setResponseIndex(6);
    try {
        AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/cpu.arff"));
        double[] y = data.toArray(new double[data.size()]);
        double[][] x = data.toArray(new double[data.size()][]);
        Math.standardize(x);
        RBFNetwork.Trainer<double[]> trainer = new RBFNetwork.Trainer<>(new EuclideanDistance());
        trainer.setNumCenters(20);
        RegressionMeasure[] measures = { new RMSE(), new AbsoluteDeviation() };
        double[] results = Validation.cv(10, trainer, x, y, measures);
        System.out.println("RMSE = " + results[0]);
        System.out.println("Absolute Deviation = " + results[1]);
    } catch (Exception ex) {
        System.err.println(ex);
    }
}