/**
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.evaluation;
import static org.junit.Assert.assertEquals;
import java.util.List;
import org.junit.Before;
import org.junit.Test;
import fr.lip6.jkernelmachines.classifier.LaSVM;
import fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll;
import fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator;
import fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2;
import fr.lip6.jkernelmachines.type.TrainingSample;
import fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator;
/**
* @author picard
*
*/
public class MulticlassAccuracyEvaluatorTest {
List<TrainingSample<double[]>> train;
OneAgainstAll<double[]> multisvm;
/**
* @throws java.lang.Exception
*/
@Before
public void setUp() throws Exception {
MultiClassGaussianGenerator mcgg = new MultiClassGaussianGenerator(4);
mcgg.setP(10);
mcgg.setSigma(1);
train = mcgg.generateList(5);
DoubleGaussL2 k = new DoubleGaussL2();
k.setGamma(0.5);
LaSVM<double[]> svm = new LaSVM<double[]>(k);
svm.setC(10);
multisvm = new OneAgainstAll<double[]>(svm);
}
/**
* Test method for {@link fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator#evaluate()}.
*/
@Test
public final void testEvaluate() {
MulticlassAccuracyEvaluator<double[]> ae = new MulticlassAccuracyEvaluator<double[]>();
ae.setClassifier(multisvm);
ae.setTrainingSet(train);
ae.setTestingSet(train);
ae.evaluate();
assertEquals(1.0, ae.getScore(), 1e-15);
}
}