/*
 * 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.opennlp.ml.maxent;

import java.io.IOException;

import org.apache.opennlp.ml.model.FileEventStream;
import org.apache.opennlp.ml.model.OnePassRealValueDataIndexer;
import org.apache.opennlp.ml.model.RealValueFileEventStream;


import junit.framework.TestCase;

public class RealValueModelTest extends TestCase {

  public void testRealValuedWeightsVsRepeatWeighting() throws IOException {
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
    GISModel realModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes1,1));

    FileEventStream rvfes2 = new FileEventStream("src/test/resources/data/opennlp/maxent/repeat-weighting-training-data.txt");
    GISModel repeatModel = GIS.trainModel(100,new OnePassRealValueDataIndexer(rvfes2,1));

    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);      
    }

  }
}