/**
This file is part of JkernelMachines.
JkernelMachines is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
JkernelMachines is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with JkernelMachines. If not, see <http://www.gnu.org/licenses/>.
Copyright David Picard - 2013
*/
package fr.lip6.jkernelmachines.test.classifier;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import java.util.List;
import org.junit.Before;
import org.junit.Test;
import fr.lip6.jkernelmachines.classifier.DoubleSGD;
import fr.lip6.jkernelmachines.type.TrainingSample;
import fr.lip6.jkernelmachines.util.generators.GaussianGenerator;
/**
* @author picard
*
*/
public class DoubleSGDTest {
List<TrainingSample<double[]>> train;
DoubleSGD svm;
/**
* @throws java.lang.Exception
*/
@Before
public void setUp() throws Exception {
GaussianGenerator g = new GaussianGenerator(10, 5.0f, 1.0);
train = g.generateList(10);
svm = new DoubleSGD();
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#train(java.util.List)}.
*/
@Test
public final void testTrainListOfTrainingSampleOfdouble() {
svm.train(train);
for(TrainingSample<double[]> t : train) {
double v = t.label * svm.valueOf(t.sample);
assertTrue(v > 0);
}
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#trainOnce(java.util.List)}.
*/
@Test
public final void testTrainOnce() {
svm.train(train);
svm.trainOnce(train);
for(TrainingSample<double[]> t : train) {
double v = t.label * svm.valueOf(t.sample);
assertTrue(v > 0);
}
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#setLoss(int)}.
*/
@Test
public final void testSetLoss() {
svm.setLoss(DoubleSGD.HINGELOSS);
assertEquals(DoubleSGD.HINGELOSS, svm.getLoss());
svm.setLoss(DoubleSGD.LOGLOSS);
assertEquals(DoubleSGD.LOGLOSS, svm.getLoss());
svm.setLoss(DoubleSGD.LOGLOSSMARGIN);
assertEquals(DoubleSGD.LOGLOSSMARGIN, svm.getLoss());
svm.setLoss(DoubleSGD.SMOOTHHINGELOSS);
assertEquals(DoubleSGD.SMOOTHHINGELOSS, svm.getLoss());
svm.setLoss(DoubleSGD.SQUAREDHINGELOSS);
assertEquals(DoubleSGD.SQUAREDHINGELOSS, svm.getLoss());
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#setLambda(double)}.
*/
@Test
public final void testSetLambda() {
svm.setLambda(1e-3);
assertEquals(1e-3, svm.getLambda(), 1e-15);
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#setHasBias(boolean)}.
*/
@Test
public final void testSetHasBias() {
svm.setHasBias(true);
assertTrue(svm.isHasBias());
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#setEpochs(int)}.
*/
@Test
public final void testSetEpochs() {
svm.setEpochs(10);
assertEquals(10, svm.getEpochs());
}
/**
* Test method for {@link fr.lip6.jkernelmachines.classifier.DoubleSGD#setShuffle(boolean)}.
*/
@Test
public final void testSetShuffle() {
svm.setShuffle(true);
assertTrue(svm.isShuffle());
}
}