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   *
   *    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
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   *            http://www.apache.org/licenses/LICENSE-2.0
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   *    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.
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  package eu.amidst.huginlink.inference;
  
  import com.google.common.primitives.Doubles;
  import eu.amidst.core.datastream.DataStream;
  import eu.amidst.core.distribution.UnivariateDistribution;
  import eu.amidst.core.utils.Utils;
  import eu.amidst.core.variables.Variable;
  import eu.amidst.dynamic.datastream.DynamicDataInstance;
  import eu.amidst.dynamic.inference.DynamicVMP;
  import eu.amidst.dynamic.inference.InferenceEngineForDBN;
  import eu.amidst.dynamic.io.DynamicBayesianNetworkLoader;
  import eu.amidst.dynamic.io.DynamicBayesianNetworkWriter;
  import eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier;
  import eu.amidst.dynamic.models.DynamicBayesianNetwork;
  import eu.amidst.dynamic.utils.DataSetGenerator;
  import org.junit.Before;
  import org.junit.Test;
  
  import java.io.IOException;
  import java.util.ArrayList;
  
  import static org.junit.Assert.assertArrayEquals;
  
  /**
   * Created by afa on 1/3/15.
   */
  public class HuginInferenceForDBNTest {
  
      ArrayList<Double> huginProbabilities;
      ArrayList<Double> amidstProbabilities;
      DynamicBayesianNetwork amidstDBN;
      UnivariateDistribution posterior;
      Variable defaultVar;
      DataStream<DynamicDataInstance> dataPredict;
  
      @Before
      public void setUp() throws IOException, ClassNotFoundException {
  
          //**************************************************************************************************************
          // LEARN A DYNAMIC BAYESIAN NETWORK
          //**************************************************************************************************************
  
          //String file = "./datasets/bank_data_train.arff";
          //DataStream<DynamicDataInstance> data = DynamicDataStreamLoader.loadFromFile(file);
          DataStream<DynamicDataInstance> data = DataSetGenerator.generate(234,100,10,0);
          DynamicNaiveBayesClassifier model = new DynamicNaiveBayesClassifier();
          //model.setClassVarID(data.getAttributes().getNumberOfAttributes() - 3);//We set -3 to account for time id and seq_id
          model.setClassVarID(0);
          model.setParallelMode(true);
          model.learn(data);
          amidstDBN = model.getDynamicBNModel();
          DynamicBayesianNetworkWriter.save(amidstDBN, "../networks/bnaic2015/BCC/CajamarDBN.dbn");
  
          amidstDBN = DynamicBayesianNetworkLoader.loadFromFile("../networks/bnaic2015/BCC/CajamarDBN.dbn");
          //defaultVar = amidstDBN.getDynamicVariables().getVariableByName("DEFAULT");
          defaultVar = amidstDBN.getDynamicVariables().getVariableByName("DiscreteVar0");
  
          //**************************************************************************************************************
          // DATA TO PREDICT
          //**************************************************************************************************************
  
          //String filePredict = "./datasets/bank_data_predict.arff";
          //dataPredict = DynamicDataStreamLoader.loadFromFile(filePredict);
          dataPredict = DataSetGenerator.generate(123,50,10,0);
      }
  
      @Test
      public void testFilteredPosterior() throws IOException, ClassNotFoundException {
  
          //**************************************************************************************************************
          // HUGIN - FILTERED POSTERIOR
          //**************************************************************************************************************
          posterior = null;
          huginProbabilities = new ArrayList();
          InferenceEngineForDBN.setInferenceAlgorithmForDBN(new HuginInferenceForDBN());
          InferenceEngineForDBN.setModel(amidstDBN);
  
          for (DynamicDataInstance instance : dataPredict) {
              if (instance.getTimeID()==0 && posterior != null) {
                  huginProbabilities.add(posterior.getProbability(0.0));
                  InferenceEngineForDBN.reset();
              }
              instance.setValue(defaultVar, Utils.missingValue());
              InferenceEngineForDBN.addDynamicEvidence(instance);
              InferenceEngineForDBN.runInference();
              posterior = InferenceEngineForDBN.getFilteredPosterior(defaultVar);
          }
  
          //**************************************************************************************************************
          // AMIDST - FILTERED POSTERIOR
          //**************************************************************************************************************
  
          posterior = null;
          amidstProbabilities = new ArrayList();
          InferenceEngineForDBN.setInferenceAlgorithmForDBN(new DynamicVMP());
          InferenceEngineForDBN.setModel(amidstDBN);
  
          for (DynamicDataInstance instance : dataPredict) {
              if (instance.getTimeID()==0 && posterior != null) {
                  amidstProbabilities.add(posterior.getProbability(0.0));
                  InferenceEngineForDBN.reset();
              }
              instance.setValue(defaultVar, Utils.missingValue());
              InferenceEngineForDBN.addDynamicEvidence(instance);
              InferenceEngineForDBN.runInference();
              posterior = InferenceEngineForDBN.getFilteredPosterior(defaultVar);
          }
  
          //**************************************************************************************************************
          //TODO the test passes only using a delta > 0.025 in the assert !!!!!
          assertArrayEquals(Doubles.toArray(huginProbabilities), Doubles.toArray(amidstProbabilities), 0.02);
          //**************************************************************************************************************
      }
  
      @Test
      public void testPredictivePosterior() throws IOException, ClassNotFoundException {
  
          //**************************************************************************************************************
          // HUGIN - PREDICTIVE POSTERIOR
          //**************************************************************************************************************
  
          posterior = null;
          huginProbabilities = new ArrayList();
          InferenceEngineForDBN.setInferenceAlgorithmForDBN(new HuginInferenceForDBN());
          InferenceEngineForDBN.setModel(amidstDBN);
  
          for (DynamicDataInstance instance : dataPredict) {
              if (instance.getTimeID()==0 && posterior != null) {
                  huginProbabilities.add(posterior.getProbability(0.0));
                  //System.out.println(posterior.getProbability(0.0));
                  InferenceEngineForDBN.reset();
              }
              instance.setValue(defaultVar, Utils.missingValue());
              InferenceEngineForDBN.addDynamicEvidence(instance);
              InferenceEngineForDBN.runInference();
              posterior = InferenceEngineForDBN.getPredictivePosterior(defaultVar,2);
          }
  
          System.out.println("\n");
  
          //**************************************************************************************************************
          // AMIDST - PREDICTIVE POSTERIOR
          //**************************************************************************************************************
  
          posterior = null;
          amidstProbabilities = new ArrayList();
          InferenceEngineForDBN.setInferenceAlgorithmForDBN(new DynamicVMP());
          InferenceEngineForDBN.setModel(amidstDBN);
  
          for (DynamicDataInstance instance : dataPredict) {
              if (instance.getTimeID()==0 && posterior != null) {
                  amidstProbabilities.add(posterior.getProbability(0.0));
                  //System.out.println(posterior.getProbability(0.0));
                  InferenceEngineForDBN.reset();
              }
              instance.setValue(defaultVar, Utils.missingValue());
              InferenceEngineForDBN.addDynamicEvidence(instance);
              InferenceEngineForDBN.runInference();
              posterior = InferenceEngineForDBN.getPredictivePosterior(defaultVar,2);
          }
  
          //**************************************************************************************************************
          //TODO the test passes only using a delta > 0.025 in the assert !!!!!
          assertArrayEquals(Doubles.toArray(huginProbabilities), Doubles.toArray(amidstProbabilities), 0.025);
          //**************************************************************************************************************
      }
  }