code: package edu.cmu.tetrad.algcomparison.simulation;
  
  import edu.cmu.tetrad.algcomparison.graph.RandomGraph;
  import edu.cmu.tetrad.algcomparison.utils.HasParameters;
  import edu.cmu.tetrad.data.DataModel;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DataType;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.sem.GeneralizedSemIm;
  import edu.cmu.tetrad.sem.GeneralizedSemPm;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.csb.mgm.MixedUtils;
  import java.util.*;
  import org.apache.commons.lang3.RandomUtils;
  
  /**
   * A version of the Lee & Hastic simulation which is guaranteed ot generate a
   * discrete data set.
   *
   * @author jdramsey
   */
  public class LeeHastieSimulation implements Simulation, HasParameters {
  
      static final long serialVersionUID = 23L;
      private RandomGraph randomGraph;
      private List<DataSet> dataSets = new ArrayList<>();
      private List<Graph> graphs = new ArrayList<>();
      private DataType dataType;
      private List<Node> shuffledOrder;
  
      public LeeHastieSimulation(RandomGraph graph) {
          this.randomGraph = graph;
      }
  
      @Override
      public void createData(Parameters parameters) {
          double percentDiscrete = parameters.getDouble("percentDiscrete");
  
          boolean discrete = parameters.getString("dataType").equals("discrete");
          boolean continuous = parameters.getString("dataType").equals("continuous");
  
          if (discrete && percentDiscrete != 100.0) {
              throw new IllegalArgumentException("To simulate discrete data, 'percentDiscrete' must be set to 0.0.");
          } else if (continuous && percentDiscrete != 0.0) {
              throw new IllegalArgumentException("To simulate continuoue data, 'percentDiscrete' must be set to 100.0.");
          }
  
          if (discrete) {
              this.dataType = DataType.Discrete;
          }
          if (continuous) {
              this.dataType = DataType.Continuous;
          }
  
          this.shuffledOrder = null;
  
          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 Graph getTrueGraph(int index) {
          return graphs.get(index);
      }
  
      @Override
      public DataModel getDataModel(int index) {
          return dataSets.get(index);
      }
  
      @Override
      public String getDescription() {
          return "Lee & Hastie simulation using " + randomGraph.getDescription();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = randomGraph.getParameters();
          parameters.add("numCategories");
          parameters.add("percentDiscrete");
          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;
      }
  
      private DataSet simulate(Graph dag, Parameters parameters) {
          HashMap<String, Integer> nd = new HashMap<>();
  
          List<Node> nodes = dag.getNodes();
  
          Collections.shuffle(nodes);
  
          if (this.shuffledOrder == null) {
              List<Node> shuffledNodes = new ArrayList<>(nodes);
              Collections.shuffle(shuffledNodes);
              this.shuffledOrder = shuffledNodes;
          }
  
          for (int i = 0; i < nodes.size(); i++) {
              if (i < nodes.size() * parameters.getDouble("percentDiscrete") * 0.01) {
                  final int minNumCategories = parameters.getInt("numCategories");
                  final int maxNumCategories = parameters.getInt("numCategories");
                  final int value = pickNumCategories(minNumCategories, maxNumCategories);
                  nd.put(shuffledOrder.get(i).getName(), value);
              } else {
                  nd.put(shuffledOrder.get(i).getName(), 0);
              }
          }
  
          Graph graph = MixedUtils.makeMixedGraph(dag, nd);
  
          GeneralizedSemPm pm = MixedUtils.GaussianCategoricalPm(graph, "Split(-1.5,-.5,.5,1.5)");
          GeneralizedSemIm im = MixedUtils.GaussianCategoricalIm(pm);
  
          boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
          DataSet ds = im.simulateDataAvoidInfinity(parameters.getInt("sampleSize"), saveLatentVars);
  
          return MixedUtils.makeMixedData(ds, nd);
      }
  
      private int pickNumCategories(int min, int max) {
          return RandomUtils.nextInt(min, max + 1);
      }
  }
  