/** Test of learn method, of class RDA. */
@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()]);
RDA rda = new RDA(x, y, 0.7);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (rda.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
assertEquals(235, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/** Test of learn method, of class MEC. */
@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()]);
AdjustedRandIndex ari = new AdjustedRandIndex();
RandIndex rand = new RandIndex();
MEC<double[]> mec = new MEC<double[]>(x, new EuclideanDistance(), 10, 8.0);
double r = rand.measure(y, mec.getClusterLabel());
double r2 = ari.measure(y, mec.getClusterLabel());
System.out.format(
"Training rand index = %.2f%%\tadjusted rand index = %.2f%%\n", 100.0 * r, 100.0 * r2);
assertTrue(r > 0.85);
assertTrue(r2 > 0.35);
int[] p = new int[testx.length];
for (int i = 0; i < testx.length; i++) {
p[i] = mec.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 > 0.35);
} catch (Exception ex) {
System.err.println(ex);
}
}
public CoverTreeSpeedTest() {
long start = System.currentTimeMillis();
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"));
x = train.toArray(new double[train.size()][]);
testx = test.toArray(new double[test.size()][]);
} catch (Exception ex) {
System.err.println(ex);
}
double time = (System.currentTimeMillis() - start) / 1000.0;
System.out.format("Loading data: %.2fs\n", time);
start = System.currentTimeMillis();
coverTree = new CoverTree<double[]>(x, new EuclideanDistance());
time = (System.currentTimeMillis() - start) / 1000.0;
System.out.format("Building cover tree: %.2fs\n", time);
}