/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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 org.apache.mahout.classifier.naivebayes;
import org.apache.mahout.math.DenseVector;
import org.junit.Before;
import org.junit.Test;
public final class ComplementaryNaiveBayesClassifierTest extends NaiveBayesTestBase {
private ComplementaryNaiveBayesClassifier classifier;
@Override
@Before
public void setUp() throws Exception {
super.setUp();
NaiveBayesModel model = createComplementaryNaiveBayesModel();
classifier = new ComplementaryNaiveBayesClassifier(model);
}
@Test
public void testNaiveBayes() throws Exception {
assertEquals(4, classifier.numCategories());
assertEquals(0, maxIndex(classifier.classify(new DenseVector(new double[] { 1.0, 0.0, 0.0, 0.0 }))));
assertEquals(1, maxIndex(classifier.classify(new DenseVector(new double[] { 0.0, 1.0, 0.0, 0.0 }))));
assertEquals(2, maxIndex(classifier.classify(new DenseVector(new double[] { 0.0, 0.0, 1.0, 0.0 }))));
assertEquals(3, maxIndex(classifier.classify(new DenseVector(new double[] { 0.0, 0.0, 0.0, 1.0 }))));
}
}