code: package edu.cmu.tetrad.algcomparison.simulation;
  
  import edu.cmu.tetrad.algcomparison.graph.RandomGraph;
  import edu.cmu.tetrad.bayes.BayesIm;
  import edu.cmu.tetrad.bayes.BayesPm;
  import edu.cmu.tetrad.bayes.MlBayesIm;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.*;
  import edu.cmu.tetrad.sem.*;
  import edu.cmu.tetrad.util.*;
  import java.util.*;
  import org.apache.commons.lang3.RandomUtils;
  
  /**
   * A simulation method based on the conditional Gaussian assumption.
   *
   * @author jdramsey
   */
  public class ConditionalGaussianSimulation implements Simulation {
  
      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;
      private double varLow = 1;
      private double varHigh = 3;
      private double coefLow = 0.05;
      private double coefHigh = 1.5;
      private boolean coefSymmetric = true;
      private double meanLow = -1;
      private double meanHigh = 1;
  
      public ConditionalGaussianSimulation(RandomGraph graph) {
          this.randomGraph = graph;
      }
  
      @Override
      public void createData(Parameters parameters) {
          setVarLow(parameters.getDouble("varLow"));
          setVarHigh(parameters.getDouble("varHigh"));
          setCoefLow(parameters.getDouble("coefLow"));
          setCoefHigh(parameters.getDouble("coefHigh"));
          setCoefSymmetric(parameters.getBoolean("coefSymmetric"));
          setMeanLow(parameters.getDouble("meanLow"));
          setMeanHigh(parameters.getDouble("meanHigh"));
  
          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 "Conditional Gaussian simulation using " + randomGraph.getDescription();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = randomGraph.getParameters();
          parameters.add("minCategories");
          parameters.add("maxCategories");
          parameters.add("percentDiscrete");
          parameters.add("numRuns");
          parameters.add("differentGraphs");
          parameters.add("sampleSize");
          parameters.add("varLow");
          parameters.add("varHigh");
          parameters.add("coefLow");
          parameters.add("coefHigh");
          parameters.add("coefSymmetric");
          parameters.add("meanLow");
          parameters.add("meanHigh");
          parameters.add("saveLatentVars");
  
          return parameters;
      }
  
      @Override
      public int getNumDataModels() {
          return dataSets.size();
      }
  
      @Override
      public DataType getDataType() {
          return dataType;
      }
  
      private DataSet simulate(Graph G, Parameters parameters) {
          HashMap<String, Integer> nd = new HashMap<>();
  
          List<Node> nodes = G.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("minCategories");
                  final int maxNumCategories = parameters.getInt("maxCategories");
                  final int value = pickNumCategories(minNumCategories, maxNumCategories);
                  nd.put(shuffledOrder.get(i).getName(), value);
              } else {
                  nd.put(shuffledOrder.get(i).getName(), 0);
              }
          }
  
          G = makeMixedGraph(G, nd);
          nodes = G.getNodes();
  
          DataSet mixedData = new BoxDataSet(new MixedDataBox(nodes, parameters.getInt("sampleSize")), nodes);
  
          List<Node> X = new ArrayList<>();
          List<Node> A = new ArrayList<>();
  
          for (Node node : G.getNodes()) {
              if (node instanceof ContinuousVariable) {
                  X.add(node);
              } else {
                  A.add(node);
              }
          }
  
          Graph AG = G.subgraph(A);
          Graph XG = G.subgraph(X);
  
          Map<ContinuousVariable, DiscreteVariable> erstatzNodes = new HashMap<>();
          Map<String, ContinuousVariable> erstatzNodesReverse = new HashMap<>();
  
          for (Node y : A) {
              for (Node x : G.getParents(y)) {
                  if (x instanceof ContinuousVariable) {
                      DiscreteVariable ersatz = erstatzNodes.get(x);
  
                      if (ersatz == null) {
                          ersatz = new DiscreteVariable("Ersatz_" + x.getName(), RandomUtil.getInstance().nextInt(3) + 2);
                          erstatzNodes.put((ContinuousVariable) x, ersatz);
                          erstatzNodesReverse.put(ersatz.getName(), (ContinuousVariable) x);
                          AG.addNode(ersatz);
                      }
  
                      AG.addDirectedEdge(ersatz, y);
                  }
              }
          }
  
          BayesPm bayesPm = new BayesPm(AG);
          BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
  
          SemPm semPm = new SemPm(XG);
  
          Map<Combination, Double> paramValues = new HashMap<>();
  
          List<Node> tierOrdering = G.getCausalOrdering();
  
          int[] tiers = new int[tierOrdering.size()];
  
          for (int t = 0; t < tierOrdering.size(); t++) {
              tiers[t] = nodes.indexOf(tierOrdering.get(t));
          }
  
          Map<Integer, double[]> breakpointsMap = new HashMap<>();
  
          for (int mixedIndex : tiers) {
              for (int i = 0; i < parameters.getInt("sampleSize"); i++) {
                  if (nodes.get(mixedIndex) instanceof DiscreteVariable) {
                      int bayesIndex = bayesIm.getNodeIndex(nodes.get(mixedIndex));
  
                      int[] bayesParents = bayesIm.getParents(bayesIndex);
                      int[] parentValues = new int[bayesParents.length];
  
                      for (int k = 0; k < parentValues.length; k++) {
                          int bayesParentColumn = bayesParents[k];
  
                          Node bayesParent = bayesIm.getVariables().get(bayesParentColumn);
                          DiscreteVariable _parent = (DiscreteVariable) bayesParent;
                          int value;
  
                          ContinuousVariable orig = erstatzNodesReverse.get(_parent.getName());
  
                          if (orig != null) {
                              int mixedParentColumn = mixedData.getColumn(orig);
                              double d = mixedData.getDouble(i, mixedParentColumn);
                              double[] breakpoints = breakpointsMap.get(mixedParentColumn);
  
                              if (breakpoints == null) {
                                  breakpoints = getBreakpoints(mixedData, _parent, mixedParentColumn);
                                  breakpointsMap.put(mixedParentColumn, breakpoints);
                              }
  
                              value = breakpoints.length;
  
                              for (int j = 0; j < breakpoints.length; j++) {
                                  if (d < breakpoints[j]) {
                                      value = j;
                                      break;
                                  }
                              }
                          } else {
                              int mixedColumn = mixedData.getColumn(bayesParent);
                              value = mixedData.getInt(i, mixedColumn);
                          }
  
                          parentValues[k] = value;
                      }
  
                      int rowIndex = bayesIm.getRowIndex(bayesIndex, parentValues);
                      double sum = 0.0;
  
                      double r = RandomUtil.getInstance().nextDouble();
                      mixedData.setInt(i, mixedIndex, 0);
  
                      for (int k = 0; k < bayesIm.getNumColumns(bayesIndex); k++) {
                          double probability = bayesIm.getProbability(bayesIndex, rowIndex, k);
                          sum += probability;
  
                          if (sum >= r) {
                              mixedData.setInt(i, mixedIndex, k);
                              break;
                          }
                      }
                  } else {
                      Node y = nodes.get(mixedIndex);
  
                      Set<DiscreteVariable> discreteParents = new HashSet<>();
                      Set<ContinuousVariable> continuousParents = new HashSet<>();
  
                      for (Node node : G.getParents(y)) {
                          if (node instanceof DiscreteVariable) {
                              discreteParents.add((DiscreteVariable) node);
                          } else {
                              continuousParents.add((ContinuousVariable) node);
                          }
                      }
  
                      Parameter varParam = semPm.getParameter(y, y);
                      Parameter muParam = semPm.getMeanParameter(y);
  
                      Combination varComb = new Combination(varParam);
                      Combination muComb = new Combination(muParam);
  
                      for (DiscreteVariable v : discreteParents) {
                          varComb.addParamValue(v, mixedData.getInt(i, mixedData.getColumn(v)));
                          muComb.addParamValue(v, mixedData.getInt(i, mixedData.getColumn(v)));
                      }
  
                      double value = RandomUtil.getInstance().nextNormal(0, getParamValue(varComb, paramValues));
  
                      for (Node x : continuousParents) {
                          Parameter coefParam = semPm.getParameter(x, y);
                          Combination coefComb = new Combination(coefParam);
  
                          for (DiscreteVariable v : discreteParents) {
                              coefComb.addParamValue(v, mixedData.getInt(i, mixedData.getColumn(v)));
                          }
  
                          int parent = nodes.indexOf(x);
                          double parentValue = mixedData.getDouble(i, parent);
                          double parentCoef = getParamValue(coefComb, paramValues);
                          value += parentValue * parentCoef;
                      }
  
                      value += getParamValue(muComb, paramValues);
                      mixedData.setDouble(i, mixedIndex, value);
                  }
              }
          }
  
          boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
          return saveLatentVars ? mixedData : DataUtils.restrictToMeasured(mixedData);
      }
  
      private double[] getBreakpoints(DataSet mixedData, DiscreteVariable _parent, int mixedParentColumn) {
          double[] data = new double[mixedData.getNumRows()];
  
          for (int r = 0; r < mixedData.getNumRows(); r++) {
              data[r] = mixedData.getDouble(r, mixedParentColumn);
          }
  
          return Discretizer.getEqualFrequencyBreakPoints(data, _parent.getNumCategories());
      }
  
      private Double getParamValue(Combination values, Map<Combination, Double> map) {
          Double d = map.get(values);
  
          if (d == null) {
              Parameter parameter = values.getParameter();
  
              if (parameter.getType() == ParamType.VAR) {
                  d = RandomUtil.getInstance().nextUniform(varLow, varHigh);
                  map.put(values, d);
              } else if (parameter.getType() == ParamType.COEF) {
                  double min = coefLow;
                  double max = coefHigh;
                  double value = RandomUtil.getInstance().nextUniform(min, max);
                  d = RandomUtil.getInstance().nextUniform(0, 1) < 0.5 && coefSymmetric ? -value : value;
                  map.put(values, d);
              } else if (parameter.getType() == ParamType.MEAN) {
                  d = RandomUtil.getInstance().nextUniform(meanLow, meanHigh);
                  map.put(values, d);
              }
          }
  
          return d;
      }
  
      public void setVarLow(double varLow) {
          this.varLow = varLow;
      }
  
      public void setVarHigh(double varHigh) {
          this.varHigh = varHigh;
      }
  
      public void setCoefLow(double coefLow) {
          this.coefLow = coefLow;
      }
  
      public void setCoefHigh(double coefHigh) {
          this.coefHigh = coefHigh;
      }
  
      public void setCoefSymmetric(boolean coefSymmetric) {
          this.coefSymmetric = coefSymmetric;
      }
  
      public void setMeanLow(double meanLow) {
          this.meanLow = meanLow;
      }
  
      public void setMeanHigh(double meanHigh) {
          this.meanHigh = meanHigh;
      }
  
      private class Combination {
  
          private Parameter parameter;
          private Set<VariableValues> paramValues;
  
          public Combination(Parameter parameter) {
              this.parameter = parameter;
              this.paramValues = new HashSet<>();
          }
  
          public void addParamValue(DiscreteVariable variable, int value) {
              this.paramValues.add(new VariableValues(variable, value));
          }
  
          public int hashCode() {
              return parameter.hashCode() + paramValues.hashCode();
          }
  
          public boolean equals(Object o) {
              if (o == this) {
                  return true;
              }
              if (!(o instanceof Combination)) {
                  return false;
              }
              Combination v = (Combination) o;
              return v.parameter == this.parameter && v.paramValues.equals(this.paramValues);
          }
  
          public Parameter getParameter() {
              return parameter;
          }
      }
  
      private class VariableValues {
  
          private DiscreteVariable variable;
          private int value;
  
          public VariableValues(DiscreteVariable variable, int value) {
              this.variable = variable;
              this.value = value;
          }
  
          public DiscreteVariable getVariable() {
              return variable;
          }
  
          public int getValue() {
              return value;
          }
  
          public int hashCode() {
              return variable.hashCode() + value;
          }
  
          public boolean equals(Object o) {
              if (o == this) {
                  return true;
              }
              if (!(o instanceof VariableValues)) {
                  return false;
              }
              VariableValues v = (VariableValues) o;
              return v.variable.equals(this.variable) && v.value == this.value;
          }
      }
  
      private static Graph makeMixedGraph(Graph g, Map<String, Integer> m) {
          List<Node> nodes = g.getNodes();
          for (int i = 0; i < nodes.size(); i++) {
              Node n = nodes.get(i);
              int nL = m.get(n.getName());
              if (nL > 0) {
                  Node nNew = new DiscreteVariable(n.getName(), nL);
                  nNew.setNodeType(n.getNodeType());
                  nodes.set(i, nNew);
              } else {
                  Node nNew = new ContinuousVariable(n.getName());
                  nNew.setNodeType(n.getNodeType());
                  nodes.set(i, nNew);
              }
  
          }
  
          Graph outG = new EdgeListGraph(nodes);
  
          for (Edge e : g.getEdges()) {
              Node n1 = e.getNode1();
              Node n2 = e.getNode2();
              Edge eNew = new Edge(outG.getNode(n1.getName()), outG.getNode(n2.getName()), e.getEndpoint1(), e.getEndpoint2());
              outG.addEdge(eNew);
          }
  
          return outG;
      }
  
      private int pickNumCategories(int min, int max) {
          return RandomUtils.nextInt(min, max + 1);
      }
  }
  