code: package edu.cmu.tetrad.algcomparison.simulation;
  
  import edu.cmu.tetrad.algcomparison.graph.RandomGraph;
  import edu.cmu.tetrad.algcomparison.graph.SingleGraph;
  import edu.cmu.tetrad.bayes.BayesIm;
  import edu.cmu.tetrad.bayes.BayesPm;
  import edu.cmu.tetrad.bayes.MlBayesIm;
  import edu.cmu.tetrad.data.DataModel;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DataType;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.util.Parameters;
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * @author jdramsey
   */
  public class BayesNetSimulation implements Simulation {
  
      static final long serialVersionUID = 23L;
      private RandomGraph randomGraph;
      private BayesPm pm;
      private BayesIm im;
      private List<DataSet> dataSets = new ArrayList<>();
      private List<Graph> graphs = new ArrayList<>();
      private List<BayesIm> ims = new ArrayList<>();
  
      public BayesNetSimulation(RandomGraph graph) {
          this.randomGraph = graph;
      }
  
      public BayesNetSimulation(BayesPm pm) {
          this.randomGraph = new SingleGraph(pm.getDag());
          this.pm = pm;
      }
  
      public BayesNetSimulation(BayesIm im) {
          this.randomGraph = new SingleGraph(im.getDag());
          this.im = im;
          this.pm = im.getBayesPm();
      }
  
      @Override
      public void createData(Parameters parameters) {
          Graph graph = randomGraph.createGraph(parameters);
  
          dataSets = new ArrayList<>();
          graphs = new ArrayList<>();
  
          for (int i = 0; i < parameters.getInt("numRuns"); i++) {
              System.out.println("Simulating dataset #" + (i + 1));
  
              if (parameters.getBoolean("differentGraphs") && i > 0) {
                  graph = randomGraph.createGraph(parameters);
              }
  
              graphs.add(graph);
  
              DataSet dataSet = simulate(graph, parameters);
              dataSet.setName("" + (i + 1));
              dataSets.add(dataSet);
          }
      }
  
      @Override
      public DataModel getDataModel(int index) {
          return dataSets.get(index);
      }
  
      @Override
      public Graph getTrueGraph(int index) {
          if (graphs.isEmpty()) {
              return new EdgeListGraph();
          } else {
              return graphs.get(index);
          }
      }
  
      @Override
      public String getDescription() {
          return "Bayes net simulation using " + randomGraph.getDescription();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = new ArrayList<>();
  
          if (!(randomGraph instanceof SingleGraph)) {
              parameters.addAll(randomGraph.getParameters());
          }
  
          if (pm == null) {
              parameters.addAll(BayesPm.getParameterNames());
          }
  
          if (im == null) {
              parameters.addAll(MlBayesIm.getParameterNames());
          }
  
          parameters.add("numRuns");
          parameters.add("differentGraphs");
          parameters.add("sampleSize");
          parameters.add("saveLatentVars");
  
          return parameters;
      }
  
      @Override
      public int getNumDataModels() {
          return dataSets.size();
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Discrete;
      }
  
      private DataSet simulate(Graph graph, Parameters parameters) {
          boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
  
          try {
              BayesIm im = this.im;
  
              if (im == null) {
                  BayesPm pm = this.pm;
  
                  if (pm == null) {
                      int minCategories = parameters.getInt("minCategories");
                      int maxCategories = parameters.getInt("maxCategories");
                      pm = new BayesPm(graph, minCategories, maxCategories);
                      im = new MlBayesIm(pm, MlBayesIm.RANDOM);
                      ims.add(im);
                      return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
                  } else {
                      im = new MlBayesIm(pm, MlBayesIm.RANDOM);
                      this.im = im;
                      ims.add(im);
                      return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
                  }
              } else {
                  ims = new ArrayList<>();
                  ims.add(im);
                  return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
              }
          } catch (Exception e) {
              e.printStackTrace();
              throw new IllegalArgumentException("Sorry, I couldn't simulate from that Bayes IM; perhaps not all of\n"
                      + "the parameters have been specified.");
          }
      }
  
      public List<BayesIm> getBayesIms() {
          return ims;
      }
  
  }
  