content: /*
   *
   *
   *    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 eu.amidst.core.learning;
  
  import eu.amidst.core.Main;
  import eu.amidst.core.datastream.DataInstance;
  import eu.amidst.core.datastream.DataStream;
  import eu.amidst.core.distribution.ConditionalLinearGaussian;
  import eu.amidst.core.distribution.Multinomial;
  import eu.amidst.core.distribution.Multinomial_MultinomialParents;
  import eu.amidst.core.distribution.Normal;
  import eu.amidst.core.inference.messagepassing.VMP;
  import eu.amidst.core.io.BayesianNetworkLoader;
  import eu.amidst.core.learning.parametric.ParallelMaximumLikelihood;
  import eu.amidst.core.learning.parametric.bayesian.SVB;
  import eu.amidst.core.models.BayesianNetwork;
  import eu.amidst.core.models.DAG;
  import eu.amidst.core.utils.BayesianNetworkSampler;
  import eu.amidst.core.variables.Variable;
  import eu.amidst.core.variables.Variables;
  import junit.framework.TestCase;
  
  import java.io.IOException;
  import java.util.Random;
  
  /**
   * Created by ana@cs.aau.dk on 04/03/15.
   */
  public class SVBTest extends TestCase {
  
      public static void testMultinomials1() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newMultinomialVariable("A", 2);
  
          DAG dag = new DAG(variables);
  
  
          BayesianNetwork bn = new BayesianNetwork(dag);
  
          Multinomial distA = bn.getConditionalDistribution(varA);
  
          distA.setProbabilities(new double[]{0.6, 0.4});
  
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
          sampler.setSeed(2);
          DataStream<DataInstance> data = sampler.sampleToDataStream(1000);
  
  
          ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
          parallelMaximumLikelihood.setWindowsSize(1000);
          parallelMaximumLikelihood.setParallelMode(true);
          parallelMaximumLikelihood.setLaplace(false);
  
          parallelMaximumLikelihood.setDAG(bn.getDAG());
          parallelMaximumLikelihood.initLearning();
          parallelMaximumLikelihood.updateModel(data);
          BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
          if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
  
          SVB svb = new SVB();
          svb.setWindowsSize(1000);
          svb.setSeed(5);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.0001);
          
  
          svb.setDAG(bn.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
          if (Main.VERBOSE) System.out.println(bn.toString());
          if (Main.VERBOSE) System.out.println(learnBN.toString());
          assertTrue(bn.equalBNs(learnBN, 0.05));
      }
  
      public static void testMultinomials2() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newMultinomialVariable("A", 2);
          Variable varB = variables.newMultinomialVariable("B", 2);
  
          DAG dag = new DAG(variables);
  
          dag.getParentSet(varB).addParent(varA);
  
          BayesianNetwork bn = new BayesianNetwork(dag);
  
          Multinomial distA = bn.getConditionalDistribution(varA);
          Multinomial_MultinomialParents distB = bn.getConditionalDistribution(varB);
  
          distA.setProbabilities(new double[]{0.6, 0.4});
          distB.getMultinomial(0).setProbabilities(new double[]{0.75, 0.25});
          distB.getMultinomial(1).setProbabilities(new double[]{0.25, 0.75});
  
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
          sampler.setSeed(2);
          DataStream<DataInstance> data = sampler.sampleToDataStream(1000);
  
  
          ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
          parallelMaximumLikelihood.setWindowsSize(1000);
          parallelMaximumLikelihood.setParallelMode(true);
          parallelMaximumLikelihood.setLaplace(false);
  
          parallelMaximumLikelihood.setDAG(bn.getDAG());
          parallelMaximumLikelihood.initLearning();
          parallelMaximumLikelihood.updateModel(data);
          BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
          SVB svb = new SVB();
          svb.setWindowsSize(1000);
          svb.setSeed(5);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.0001);
  
          svb.setDAG(bn.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
          if (Main.VERBOSE) System.out.println(bn.toString());
          if (Main.VERBOSE) System.out.println(learnBN.toString());
          assertTrue(bn.equalBNs(learnBN, 0.05));
      }
  
       public static void testMultinomials5() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newMultinomialVariable("A", 5);
          Variable varB = variables.newMultinomialVariable("B", 5);
          Variable varC = variables.newMultinomialVariable("C", 5);
  
          DAG dag = new DAG(variables);
  
          dag.getParentSet(varC).addParent(varB);
          dag.getParentSet(varB).addParent(varA);
  
  
  
          BayesianNetwork bn = new BayesianNetwork(dag);
  
          bn.randomInitialization(new Random(5));
  
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
          sampler.setSeed(5);
          sampler.setHiddenVar(varB);
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
          SVB svb = new SVB();
          svb.setWindowsSize(1000);
          svb.setSeed(5);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.0001);
  
          svb.setDAG(bn.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
          if (Main.VERBOSE) System.out.println(bn.toString());
          if (Main.VERBOSE) System.out.println(learnBN.toString());
          //assertTrue(bn.equalBNs(learnBN, 0.1));
  
      }
  
      public static void testMultinomial6() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varB = variables.newMultinomialVariable("B",4);
  
          DAG dag = new DAG(variables);
  
  
          for (int i = 0; i < 10; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(0));
  
  
              bn.randomInitialization(new Random(0));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i+299);
              sampler.setHiddenVar(varB);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              //assertTrue(bn.equalBNs(learnBN, 0.5));
          }
      }
  
      public static void testAsia() throws IOException, ClassNotFoundException{
  
          BayesianNetwork asianet = BayesianNetworkLoader.loadFromFile("../networks/dataWeka/asia.bn");
          asianet.randomInitialization(new Random(0));
          if (Main.VERBOSE) System.out.println("\nAsia network \n ");
  
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(asianet);
          sampler.setSeed(0);
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
          SVB svb = new SVB();
          svb.setWindowsSize(1000);
          svb.setSeed(5);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.0001);
  
          svb.setDAG(asianet.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          if (Main.VERBOSE) System.out.println(svb.getLogMarginalProbability());
  
          BayesianNetwork learnAsianet = svb.getLearntBayesianNetwork();
  
          if (Main.VERBOSE) System.out.println(asianet.toString());
          if (Main.VERBOSE) System.out.println(learnAsianet.toString());
          assertTrue(asianet.equalBNs(learnAsianet, 0.05));
  
      }
  
      public static void testAsia2() throws IOException, ClassNotFoundException{
  
          BayesianNetwork asianet = BayesianNetworkLoader.loadFromFile("../networks/dataWeka/asia.bn");
          if (Main.VERBOSE) System.out.println(asianet.toString());
          asianet.randomInitialization(new Random(0));
          if (Main.VERBOSE) System.out.println("\nAsia network \n ");
  
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(asianet);
          sampler.setHiddenVar(asianet.getVariables().getVariableByName("E"));
          sampler.setHiddenVar(asianet.getVariables().getVariableByName("L"));
  
          //sampler.setMARVar(asianet.getVariables().getVariableByName("E"),0.5);
          //sampler.setMARVar(asianet.getVariables().getVariableByName("L"),0.5);
  
          sampler.setSeed(0);
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
          SVB svb = new SVB();
          svb.setWindowsSize(100);
          svb.setSeed(5);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.0001);
  
          svb.setDAG(asianet.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          if (Main.VERBOSE) System.out.println(svb.getLogMarginalProbability());
  
          BayesianNetwork learnAsianet = svb.getLearntBayesianNetwork();
  
          //if (Main.VERBOSE) System.out.println(asianet.outputString());
          //if (Main.VERBOSE) System.out.println(learnAsianet.outputString());
          //assertTrue(asianet.equalBNs(learnAsianet,0.05));
  
      }
  
      public static void testGaussian0() throws IOException, ClassNotFoundException{
  
          BayesianNetwork oneNormalVarBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/Normal.bn");
  
          for (int i = 0; i < 1; i++) {
  
              Variable varA = oneNormalVarBN.getVariables().getVariableByName("A");
              Normal dist = oneNormalVarBN.getConditionalDistribution(varA);
  
              dist.setMean(2000);
              dist.setVariance(30);
  
              oneNormalVarBN.randomInitialization(new Random(i));
  
              if (Main.VERBOSE) System.out.println("\nOne normal variable network \n ");
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(oneNormalVarBN);
              sampler.setSeed(0);
  
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(oneNormalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
  
              SVB svb = new SVB();
              svb.setWindowsSize(1);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setOutput(false);
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(oneNormalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println(svb.getLogMarginalProbability());
  
              BayesianNetwork learntOneNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(oneNormalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntOneNormalVarBN.toString());
              assertTrue(oneNormalVarBN.equalBNs(learntOneNormalVarBN, 0.1));
          }
      }
  
      public static void testWasteIncinerator() throws IOException, ClassNotFoundException{
  
          String[] bns = {"../networks/simulated/Normal.bn",
                  "../networks/simulated/Normal_1NormalParents.bn",
                  "../networks/simulated/Normal_NormalParents.bn",
                  "../networks/simulated/Normal_MultinomialParents.bn",
                  "../networks/simulated/WasteIncinerator.bn"
          };
  
  
          for (int i = 0; i < bns.length; i++) {
              if (Main.VERBOSE) System.out.println("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%");
              if (Main.VERBOSE) System.out.println("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%" + bns[i] + "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%");
              if (Main.VERBOSE) System.out.println("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%");
              BayesianNetwork normalVarBN = BayesianNetworkLoader.loadFromFile(bns[i]);
              //normalVarBN.randomInitialization(new Random(0));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(normalVarBN);
              sampler.setSeed(1);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(normalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.2));
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(5);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.001);
  
              svb.setDAG(normalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println(svb.getLogMarginalProbability());
  
              learntNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.2));
          }
      }
  
      public static void testGaussian1() throws IOException, ClassNotFoundException{
  
          BayesianNetwork normalVarBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/Normal_1NormalParents.bn");
  
          for (int i = 0; i < 1; i++) {
  
              if (Main.VERBOSE) System.out.println("\nNormal|Normal variable network \n ");
  
              normalVarBN.randomInitialization(new Random(i));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(normalVarBN);
              sampler.setSeed(2);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(normalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.2));
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setOutput(false);
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(normalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
  
  
              if (Main.VERBOSE) System.out.println(svb.getLogMarginalProbability());
              learntNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.3));
          }
  
      }
  
      public static void testGaussian2() throws IOException, ClassNotFoundException{
  
          BayesianNetwork normalVarBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/Normal_NormalParents.bn");
  
          int cont=0;
          for (int i = 0; i < 10; i++) {
  
              normalVarBN.randomInitialization(new Random(i));
              if (Main.VERBOSE) System.out.println("\nNormal|2Normal variable network \n ");
  
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(normalVarBN);
              sampler.setSeed(0);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(normalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.2));
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.000001);
  
              svb.setDAG(normalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              learntNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.25));
          }
      }
  
      public static void testGaussian3() throws IOException, ClassNotFoundException{
  
          BayesianNetwork normalVarBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/Normal_MultinomialParents.bn");
  
          for (int i = 0; i < 10; i++) {
  
  
              normalVarBN.randomInitialization(new Random(i));
              if (Main.VERBOSE) System.out.println("\nNormal|2Normal variable network \n ");
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(normalVarBN);
              sampler.setSeed(0);
              DataStream<DataInstance> data = sampler.sampleToDataStream(20000);
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(normalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.6));
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(normalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              learntNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.6));
          }
      }
  
      public static void testGaussian4() throws IOException, ClassNotFoundException{
  
          BayesianNetwork normalVarBN = BayesianNetworkLoader.loadFromFile("../networks/simulated/Normal_MultinomialNormalParents.bn");
          int contA=0;
          int contB=0;
  
          for (int i = 1; i < 2; i++) {
  
              normalVarBN.randomInitialization(new Random(i));
              if (Main.VERBOSE) System.out.println("\nNormal|2Normal variable network \n ");
  
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(normalVarBN);
              sampler.setSeed(0);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
              ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
              parallelMaximumLikelihood.setWindowsSize(1000);
              parallelMaximumLikelihood.setParallelMode(true);
              parallelMaximumLikelihood.setLaplace(false);
  
              parallelMaximumLikelihood.setDAG(normalVarBN.getDAG());
              parallelMaximumLikelihood.initLearning();
              parallelMaximumLikelihood.updateModel(data);
              BayesianNetwork learntNormalVarBN = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.3));
              if (normalVarBN.equalBNs(learntNormalVarBN, 0.3)) contA++;
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(normalVarBN.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              learntNormalVarBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(normalVarBN.toString());
              if (Main.VERBOSE) System.out.println(learntNormalVarBN.toString());
              assertTrue(normalVarBN.equalBNs(learntNormalVarBN, 0.5));
              if (normalVarBN.equalBNs(learntNormalVarBN, 0.5)) contB++;
          }
          if (Main.VERBOSE) System.out.println(contA);
          if (Main.VERBOSE) System.out.println(contB);
  
      }
  
      public static void testGaussian5() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newGaussianVariable("A");
          Variable varB = variables.newGaussianVariable("B");
          Variable varC = variables.newGaussianVariable("C");
  
          DAG dag = new DAG(variables);
  
          dag.getParentSet(varB).addParent(varA);
          dag.getParentSet(varC).addParent(varA);
  
  
          BayesianNetwork bn = new BayesianNetwork(dag);
          bn.randomInitialization(new Random(0));
  
          if (Main.VERBOSE) System.out.println(bn.toString());
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
          sampler.setSeed(2);
          sampler.setHiddenVar(varA);
          DataStream<DataInstance> data = sampler.sampleToDataStream(50);
  
  
          SVB svb = new SVB();
          svb.setWindowsSize(5); //Set to 2 and an exception is raised. Numerical instability.
          svb.setSeed(1);
          VMP vmp = svb.getPlateuStructure().getVMP();
          vmp.setTestELBO(true);
          vmp.setMaxIter(1000);
          vmp.setThreshold(0.001);
  
          svb.setDAG(bn.getDAG());
          svb.setDataStream(data);
          svb.runLearning();
  
          BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
          if (Main.VERBOSE) System.out.println(bn.toString());
          if (Main.VERBOSE) System.out.println(learnBN.toString());
          //assertTrue(bn.equalBNs(learnBN,0.1));
      }
  
      public static void testGaussian6() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newGaussianVariable("A");
          Variable varB = variables.newGaussianVariable("B");
          Variable varC = variables.newGaussianVariable("C");
  
          DAG dag = new DAG(variables);
  
          dag.getParentSet(varB).addParent(varA);
          dag.getParentSet(varC).addParent(varB);
  
          for (int i = 0; i < 1; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
  
              bn.randomInitialization(new Random(i));
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              sampler.setMARVar(varB, 0.8);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              assertTrue(bn.equalBNs(learnBN, 0.3));
          }
      }
  
      public static void testGaussian7() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varB = variables.newGaussianVariable("B");
  
          DAG dag = new DAG(variables);
  
  
          for (int i = 0; i < 10; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(0));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              sampler.setHiddenVar(varB);
              DataStream<DataInstance> data = sampler.sampleToDataStream(1000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000); //Set to 2 and an exception is raised. Numerical instability.
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.01);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              //assertTrue(bn.equalBNs(learnBN, 0.5));
          }
      }
  
      public static void testGaussian8() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varA = variables.newGaussianVariable("A");
          Variable varB = variables.newGaussianVariable("B");
          Variable varC = variables.newGaussianVariable("C");
  
          DAG dag = new DAG(variables);
  
          dag.getParentSet(varB).addParent(varA);
          dag.getParentSet(varC).addParent(varB);
  
          for (int i = 1; i < 2; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(0));
  
              bn.randomInitialization(new Random(i));
  
              //if (Main.VERBOSE) System.out.println(bn.outputString());
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              sampler.setMARVar(varB,0.7);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(10);
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.001);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              ConditionalLinearGaussian distCP = bn.getConditionalDistribution(varC);
              ConditionalLinearGaussian distCQ = learnBN.getConditionalDistribution(varC);
  
              assertEquals(distCP.getSd(), distCQ.getSd(), 0.1);
          }
      }
  
  
  
      public static void testGaussian9() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varB = variables.newMultinomialVariable("B",2);
          Variable varC = variables.newGaussianVariable("C");
  
          DAG dag = new DAG(variables);
          dag.getParentSet(varC).addParent(varB);
  
  
          for (int i = 0; i < 10; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(0));
  
  
              bn.randomInitialization(new Random(0));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              sampler.setHiddenVar(varB);
              DataStream<DataInstance> data = sampler.sampleToDataStream(1000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000); //Set to 2 and an exception is raised. Numerical instability.
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              //assertTrue(bn.equalBNs(learnBN, 0.5));
          }
      }
  
      public static void testGaussian10() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varB = variables.newMultinomialVariable("B",2);
          Variable varC = variables.newGaussianVariable("C");
  
          DAG dag = new DAG(variables);
          dag.getParentSet(varC).addParent(varB);
  
  
          for (int i = 0; i < 10; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(i));
  
  
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000); //Set to 2 and an exception is raised. Numerical instability.
              //svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.0001);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              assertTrue(bn.equalBNs(learnBN, 0.5));
          }
      }
  
      public static void testGaussian11() throws IOException, ClassNotFoundException {
          Variables variables = new Variables();
          Variable varB = variables.newGaussianVariable("B");
  
          DAG dag = new DAG(variables);
  
  
          for (int i = 0; i < 10; i++) {
  
  
              BayesianNetwork bn = new BayesianNetwork(dag);
              bn.randomInitialization(new Random(0));
  
              BayesianNetworkSampler sampler = new BayesianNetworkSampler(bn);
              sampler.setSeed(i);
              DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
  
  
              SVB svb = new SVB();
              svb.setWindowsSize(1000); //Set to 2 and an exception is raised. Numerical instability.
              svb.setSeed(i);
              VMP vmp = svb.getPlateuStructure().getVMP();
              vmp.setTestELBO(true);
              vmp.setMaxIter(1000);
              vmp.setThreshold(0.01);
  
              svb.setDAG(bn.getDAG());
              svb.setDataStream(data);
              svb.runLearning();
  
              if (Main.VERBOSE) System.out.println("Data Prob: " + svb.getLogMarginalProbability());
  
  
              BayesianNetwork learnBN = svb.getLearntBayesianNetwork();
  
              if (Main.VERBOSE) System.out.println(bn.toString());
              if (Main.VERBOSE) System.out.println(learnBN.toString());
              assertTrue(bn.equalBNs(learnBN, 0.2));
          }
      }
  
  }
  