/*
* 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 opennlp.maxent;
import java.io.IOException;
import opennlp.model.FileEventStream;
import opennlp.model.OnePassRealValueDataIndexer;
import opennlp.model.RealValueFileEventStream;
import junit.framework.TestCase;
public class RealValueModelTest extends TestCase {
public void testRealValuedWeightsVsRepeatWeighting() throws IOException {
GISModel realModel;
RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
try {
realModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes1,1));
} finally {
rvfes1.close();
}
GISModel repeatModel;
FileEventStream rvfes2 = new FileEventStream("src/test/resources/data/opennlp/maxent/repeat-weighting-training-data.txt");
try {
repeatModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes2,1));
} finally {
rvfes2.close();
}
String[] features2Classify = new String[] {"feature2","feature5"};
double[] realResults = realModel.eval(features2Classify);
double[] repeatResults = repeatModel.eval(features2Classify);
assertEquals(realResults.length, repeatResults.length);
for(int i=0; i<realResults.length; i++) {
System.out.println(String.format("classifiy with realModel: %1$s = %2$f", realModel.getOutcome(i), realResults[i]));
System.out.println(String.format("classifiy with repeatModel: %1$s = %2$f", repeatModel.getOutcome(i), repeatResults[i]));
assertEquals(realResults[i], repeatResults[i], 0.01f);
}
features2Classify = new String[] {"feature1","feature2","feature3","feature4","feature5"};
realResults = realModel.eval(features2Classify, new float[] {5.5f, 6.1f, 9.1f, 4.0f, 1.8f});
repeatResults = repeatModel.eval(features2Classify, new float[] {5.5f, 6.1f, 9.1f, 4.0f, 1.8f});
System.out.println();
assertEquals(realResults.length, repeatResults.length);
for(int i=0; i<realResults.length; i++) {
System.out.println(String.format("classifiy with realModel: %1$s = %2$f", realModel.getOutcome(i), realResults[i]));
System.out.println(String.format("classifiy with repeatModel: %1$s = %2$f", repeatModel.getOutcome(i), repeatResults[i]));
assertEquals(realResults[i], repeatResults[i], 0.01f);
}
}
}