/** 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 LogisticRegression. */
@Test
public void testSegment() {
System.out.println("Segment");
ArffParser arffParser = new ArffParser();
arffParser.setResponseIndex(19);
try {
AttributeDataset train =
arffParser.parse(
smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
AttributeDataset test =
arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
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()]);
LogisticRegression logit = new LogisticRegression(x, y, 0.05, 1E-3, 1000);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (logit.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Segment error rate = %.2f%%\n", 100.0 * error / testx.length);
assertEquals(48, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/** Test of learn method, of class LogisticRegression. */
@Test
public void testIris() {
System.out.println("Iris");
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()]);
int n = x.length;
LOOCV loocv = new LOOCV(n);
int error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
LogisticRegression logit = new LogisticRegression(trainx, trainy);
if (y[loocv.test[i]] != logit.predict(x[loocv.test[i]])) error++;
}
System.out.println("Logistic Regression error = " + error);
assertEquals(3, 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);
}
}
/** Test of learn method, of class RDA. */
@Test
public void testLearn() {
System.out.println("learn");
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()]);
int n = x.length;
LOOCV loocv = new LOOCV(n);
int error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.0);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.0) error = " + error);
assertEquals(22, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.1);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.1) error = " + error);
assertEquals(24, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.2);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.2) error = " + error);
assertEquals(20, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.3);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.3) error = " + error);
assertEquals(19, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.4);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.4) error = " + error);
assertEquals(16, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.5);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.5) error = " + error);
assertEquals(12, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.6);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.6) error = " + error);
assertEquals(11, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.7);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.7) error = " + error);
assertEquals(9, error);
error = 0;
double[] posteriori = new double[3];
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.8);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]], posteriori)) error++;
// System.out.println(posteriori[0]+"\t"+posteriori[1]+"\t"+posteriori[2]);
}
System.out.println("RDA (0.8) error = " + error);
assertEquals(6, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 0.9);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (0.9) error = " + error);
assertEquals(3, error);
error = 0;
for (int i = 0; i < n; i++) {
double[][] trainx = Math.slice(x, loocv.train[i]);
int[] trainy = Math.slice(y, loocv.train[i]);
RDA rda = new RDA(trainx, trainy, 1.0);
if (y[loocv.test[i]] != rda.predict(x[loocv.test[i]])) error++;
}
System.out.println("RDA (1.0) error = " + error);
assertEquals(4, error);
} catch (Exception ex) {
System.err.println(ex);
}
}