/**
* Copyright 2013-2015 Pierre Merienne
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.github.pmerienne.trident.ml.classification;
import static org.junit.Assert.assertTrue;
import java.util.List;
import org.junit.Test;
import com.github.pmerienne.trident.ml.classification.PerceptronClassifier;
import com.github.pmerienne.trident.ml.core.Instance;
import com.github.pmerienne.trident.ml.testing.data.Datasets;
public class PerceptronTest extends ClassifierTest {
@Test
public void testWithNand() {
List<Instance<Boolean>> samples = Datasets.generatedNandInstances(100);
double error = this.eval(new PerceptronClassifier(), samples);
assertTrue("Error " + error + " is to big!", error < 0.05);
}
@Test
public void testWithGaussianData() {
double error = this.eval(new PerceptronClassifier(), Datasets.generateDataForClassification(100, 10));
assertTrue("Error " + error + " is to big!", error < 0.05);
}
@Test
public void testWithSPAMData() {
double error = this.eval(new PerceptronClassifier(), Datasets.getSpamSamples());
assertTrue("Error " + error + " is to big!", error < 0.05);
}
}