code: package edu.cmu.tetrad.algcomparison.algorithm.oracle.pag;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper;
  import edu.cmu.tetrad.algcomparison.score.ScoreWrapper;
  import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
  import edu.cmu.tetrad.algcomparison.utils.TakesIndependenceWrapper;
  import edu.cmu.tetrad.algcomparison.utils.UsesScoreWrapper;
  import edu.cmu.tetrad.annotation.AlgType;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.DagToPag;
  import edu.cmu.tetrad.search.GFci;
  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;
  
  /**
   * GFCI.
   *
   * @author jdramsey
   */
  @edu.cmu.tetrad.annotation.Algorithm(
          name = "GFCI",
          command = "gfci",
          algoType = AlgType.allow_latent_common_causes
  )
  public class Gfci implements Algorithm, HasKnowledge, UsesScoreWrapper, TakesIndependenceWrapper {
  
      static final long serialVersionUID = 23L;
      private IndependenceWrapper test;
      private ScoreWrapper score;
      private IKnowledge knowledge = new Knowledge2();
  
      public Gfci() {
      }
  
      public Gfci(IndependenceWrapper test, ScoreWrapper score) {
          this.test = test;
          this.score = score;
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              GFci search = new GFci(test.getTest(dataSet, parameters), score.getScore(dataSet, parameters));  
              search.setMaxDegree(parameters.getInt("maxDegree"));
              search.setKnowledge(knowledge);
              search.setVerbose(parameters.getBoolean("verbose"));
              search.setFaithfulnessAssumed(parameters.getBoolean("faithfulnessAssumed"));
              search.setMaxPathLength(parameters.getInt("maxPathLength"));
              search.setCompleteRuleSetUsed(parameters.getBoolean("completeRuleSetUsed"));
  
              Object obj = parameters.get("printStream");
  
              if (obj instanceof PrintStream) {
                  search.setOut((PrintStream) obj);
              }
  
              return search.search();
          } else {
              Gfci algorithm = new Gfci(test, score);
  
              //algorithm.setKnowledge(knowledge);
  //          if (initialGraph != null) {
  //      		algorithm.setInitialGraph(initialGraph);
  //  		}
              DataSet data = (DataSet) dataSet;
              GeneralResamplingTest search = new GeneralResamplingTest(data, algorithm, 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) {
          return new DagToPag(graph).convert();
      }
  
      @Override
      public String getDescription() {
          return "GFCI (Greedy Fast Causal Inference) using " + test.getDescription()
                  + " and " + score.getDescription();
      }
  
      @Override
      public DataType getDataType() {
          return test.getDataType();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = test.getParameters();
          parameters.addAll(score.getParameters());
          parameters.add("faithfulnessAssumed");
          parameters.add("maxDegree");
  //        parameters.add("printStream");
          parameters.add("maxPathLength");
          parameters.add("completeRuleSetUsed");
          // Resampling
          parameters.add("numberResampling");
          parameters.add("percentResampleSize");
          parameters.add("resamplingWithReplacement");
          parameters.add("resamplingEnsemble");
          parameters.add("addOriginalDataset");
          parameters.add("verbose");
          return parameters;
      }
  
      @Override
      public IKnowledge getKnowledge() {
          return knowledge;
      }
  
      @Override
      public void setKnowledge(IKnowledge knowledge) {
          this.knowledge = knowledge;
      }
  
      @Override
      public void setScoreWrapper(ScoreWrapper score) {
          this.score = score;
      }
  
      @Override
      public void setIndependenceWrapper(IndependenceWrapper test) {
          this.test = test;
      }
  
      @Override
      public IndependenceWrapper getIndependenceWrapper() {
          return test;
      }
  
      @Override
      public ScoreWrapper getScoreWarpper() {
          return score;
      }
  
  }
  