code: package edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.algcomparison.score.ScoreWrapper;
  import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
  import edu.cmu.tetrad.algcomparison.utils.TakesInitialGraph;
  import edu.cmu.tetrad.algcomparison.utils.UsesScoreWrapper;
  import edu.cmu.tetrad.annotation.AlgType;
  import edu.cmu.tetrad.data.DataModel;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DataType;
  import edu.cmu.tetrad.data.IKnowledge;
  import edu.cmu.tetrad.data.Knowledge2;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.SearchGraphUtils;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  import java.io.PrintStream;
  import java.util.List;
  
  /**
   * FGES (the heuristic version).
   *
   * @author jdramsey
   */
  @edu.cmu.tetrad.annotation.Algorithm(
          name = "FGES",
          command = "fges",
          algoType = AlgType.forbid_latent_common_causes
  )
  public class Fges implements Algorithm, TakesInitialGraph, HasKnowledge, UsesScoreWrapper {
  
      static final long serialVersionUID = 23L;
  
      private boolean compareToTrue = false;
      private ScoreWrapper score;
      private Algorithm algorithm = null;
      private Graph initialGraph = null;
      private IKnowledge knowledge = new Knowledge2();
  
      public Fges() {
  
      }
  
      public Fges(ScoreWrapper score) {
          this.score = score;
          this.compareToTrue = false;
      }
  
      public Fges(ScoreWrapper score, boolean compareToTrueGraph) {
          this.score = score;
          this.compareToTrue = compareToTrueGraph;
      }
  
      public Fges(ScoreWrapper score, Algorithm algorithm) {
          this.score = score;
          this.algorithm = algorithm;
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              if (algorithm != null) {
  //                initialGraph = algorithm.search(dataSet, parameters);
              }
  
              edu.cmu.tetrad.search.Fges search
                      = new edu.cmu.tetrad.search.Fges(score.getScore(dataSet, parameters));
              search.setFaithfulnessAssumed(parameters.getBoolean("faithfulnessAssumed"));
              search.setKnowledge(knowledge);
              search.setVerbose(parameters.getBoolean("verbose"));
              search.setMaxDegree(parameters.getInt("maxDegree"));
              search.setSymmetricFirstStep(parameters.getBoolean("symmetricFirstStep"));
  
              Object obj = parameters.get("printStream");
              if (obj instanceof PrintStream) {
                  search.setOut((PrintStream) obj);
              }
  
              if (initialGraph != null) {
                  search.setInitialGraph(initialGraph);
              }
  
              return search.search();
          } else {
              Fges fges = new Fges(score, algorithm);
  
              //fges.setKnowledge(knowledge);
              DataSet data = (DataSet) dataSet;
              GeneralResamplingTest search = new GeneralResamplingTest(data, fges, parameters.getInt("numberResampling"));
              search.setKnowledge(knowledge);
              
              search.setPercentResampleSize(parameters.getDouble("percentResampleSize"));
              search.setResamplingWithReplacement(parameters.getBoolean("resamplingWithReplacement"));
              
              ResamplingEdgeEnsemble edgeEnsemble = ResamplingEdgeEnsemble.Highest;
              switch (parameters.getInt("resamplingEnsemble", 1)) {
                  case 0:
                      edgeEnsemble = ResamplingEdgeEnsemble.Preserved;
                      break;
                  case 1:
                      edgeEnsemble = ResamplingEdgeEnsemble.Highest;
                      break;
                  case 2:
                      edgeEnsemble = ResamplingEdgeEnsemble.Majority;
              }
              search.setEdgeEnsemble(edgeEnsemble);
              search.setAddOriginalDataset(parameters.getBoolean("addOriginalDataset"));
              
              search.setParameters(parameters);
              search.setVerbose(parameters.getBoolean("verbose"));
              return search.search();
          }
  
      }
  
      @Override
      public Graph getComparisonGraph(Graph graph) {
          if (compareToTrue) {
              return new EdgeListGraph(graph);
          } else {
              return SearchGraphUtils.patternForDag(new EdgeListGraph(graph));
          }
      }
  
      @Override
      public String getDescription() {
          return "FGES (Fast Greedy Equivalence Search) using " + score.getDescription();
      }
  
      @Override
      public DataType getDataType() {
          return score.getDataType();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = score.getParameters();
          parameters.add("faithfulnessAssumed");
          parameters.add("symmetricFirstStep");
          parameters.add("maxDegree");
          parameters.add("verbose");
          // Resampling
          parameters.add("numberResampling");
          parameters.add("percentResampleSize");
          parameters.add("resamplingWithReplacement");
          parameters.add("resamplingEnsemble");
          parameters.add("addOriginalDataset");
          return parameters;
      }
  
      @Override
      public IKnowledge getKnowledge() {
          return knowledge;
      }
  
      @Override
      public void setKnowledge(IKnowledge knowledge) {
          this.knowledge = knowledge;
      }
  
      public void setCompareToTrue(boolean compareToTrue) {
          this.compareToTrue = compareToTrue;
      }
  
      @Override
      public Graph getInitialGraph() {
          return initialGraph;
      }
  
      @Override
      public void setInitialGraph(Graph initialGraph) {
          this.initialGraph = initialGraph;
      }
  
      @Override
      public void setInitialGraph(Algorithm algorithm) {
          this.algorithm = algorithm;
  
      }
  
      @Override
      public void setScoreWrapper(ScoreWrapper score) {
          this.score = score;
      }
      
      @Override
      public ScoreWrapper getScoreWarpper() {
          return score;
      }
  
  }
  