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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
   *
   *            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.
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  package eu.amidst.core.conceptdrift;
  
  import eu.amidst.core.Main;
  import eu.amidst.core.datastream.DataInstance;
  import eu.amidst.core.datastream.DataStream;
  import eu.amidst.core.io.BayesianNetworkLoader;
  import eu.amidst.core.models.BayesianNetwork;
  import eu.amidst.core.utils.BayesianNetworkSampler;
  import eu.amidst.core.variables.Variable;
  import org.junit.Assert;
  import org.junit.Test;
  
  import java.io.IOException;
  
  /**
   * Created by Hanen on 27/01/15.
   */
  public class MaximumLikelihoodFadingTest {
  
          @Test
          public void testNoFading() throws  IOException, ClassNotFoundException {
  
              // load the true WasteIncinerator hugin Bayesian network containing 3 Multinomial and 6 Gaussian variables
  
              BayesianNetwork trueBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/WasteIncinerator.bn");
  
              if (Main.VERBOSE) System.out.println("\nWasteIncinerator network \n ");
              if (Main.VERBOSE) System.out.println(trueBN.getDAG().toString());
              if (Main.VERBOSE) System.out.println(trueBN.toString());
  
              //Sampling from trueBN
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(trueBN);
              sampler.setSeed(0);
  
              //Load the sampled data
              DataStream<DataInstance> data = sampler.sampleToDataStream(100000);
  
              //Parameter Learning
              MaximumLikelihoodFading maximumLikelihoodFading = new MaximumLikelihoodFading();
              maximumLikelihoodFading.setLaplace(false);
              maximumLikelihoodFading.setFadingFactor(1.0);
              maximumLikelihoodFading.setWindowsSize(1000);
              maximumLikelihoodFading.setDAG(trueBN.getDAG());
              maximumLikelihoodFading.setDataStream(data);
  
              maximumLikelihoodFading.runLearning();
              BayesianNetwork bnet = maximumLikelihoodFading.getLearntBayesianNetwork();
  
              //Check if the probability distributions of each node
              for (Variable var : trueBN.getVariables()) {
                  if (Main.VERBOSE) System.out.println("\n------ Variable " + var.getName() + " ------");
                  if (Main.VERBOSE) System.out.println("\nTrue distribution:\n"+ trueBN.getConditionalDistribution(var));
                  if (Main.VERBOSE) System.out.println("\nLearned distribution:\n"+ bnet.getConditionalDistribution(var));
                  Assert.assertTrue(bnet.getConditionalDistribution(var).equalDist(trueBN.getConditionalDistribution(var), 0.05));
              }
  
              //Or check directly if the true and learned networks are equals
              Assert.assertTrue(bnet.equalBNs(trueBN, 0.05));
          }
  
      @Test
      public void testFading() throws  IOException, ClassNotFoundException {
  
          // load the true WasteIncinerator hugin Bayesian network containing 3 Multinomial and 6 Gaussian variables
  
          BayesianNetwork trueBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/WasteIncinerator.bn");
  
          if (Main.VERBOSE) System.out.println("\nWasteIncinerator network \n ");
          if (Main.VERBOSE) System.out.println(trueBN.getDAG().toString());
          if (Main.VERBOSE) System.out.println(trueBN.toString());
  
          //Sampling from trueBN
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(trueBN);
          sampler.setSeed(0);
  
          //Load the sampled data
          DataStream<DataInstance> data = sampler.sampleToDataStream(100000);
  
          //Parameter Learning
          MaximumLikelihoodFading maximumLikelihoodFading = new MaximumLikelihoodFading();
          maximumLikelihoodFading.setFadingFactor(0.9);
          maximumLikelihoodFading.setWindowsSize(1000);
          maximumLikelihoodFading.setDAG(trueBN.getDAG());
          maximumLikelihoodFading.setDataStream(data);
  
          maximumLikelihoodFading.runLearning();
          BayesianNetwork bnet = maximumLikelihoodFading.getLearntBayesianNetwork();
  
          //Check if the probability distributions of each node
          for (Variable var : trueBN.getVariables()) {
              if (Main.VERBOSE) System.out.println("\n------ Variable " + var.getName() + " ------");
              if (Main.VERBOSE) System.out.println("\nTrue distribution:\n"+ trueBN.getConditionalDistribution(var));
              if (Main.VERBOSE) System.out.println("\nLearned distribution:\n"+ bnet.getConditionalDistribution(var));
              Assert.assertTrue(bnet.getConditionalDistribution(var).equalDist(trueBN.getConditionalDistribution(var), 0.05));
          }
  
          //Or check directly if the true and learned networks are equals
          Assert.assertTrue(bnet.equalBNs(trueBN, 0.05));
      }
  }
  
  