code: package edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern;
  
  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.TakesInitialGraph;
  import edu.cmu.tetrad.data.*;
  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.util.List;
  
  /**
   * CPC.
   *
   * @author jdramsey
   */
  public class Jcpc implements Algorithm, TakesInitialGraph, HasKnowledge {
  
      static final long serialVersionUID = 23L;
      private IndependenceWrapper test;
      private ScoreWrapper score;
      private Algorithm algorithm = null;
      private Graph initialGraph = null;
      private IKnowledge knowledge = new Knowledge2();
  
      public Jcpc(IndependenceWrapper type, ScoreWrapper score) {
          this.test = type;
          this.score = score;
      }
  
      public Jcpc(IndependenceWrapper type, Algorithm algorithm) {
          this.test = type;
          this.algorithm = algorithm;
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              DataSet continuousDataSet = DataUtils.getContinuousDataSet(dataSet);
              edu.cmu.tetrad.search.Jcpc search = new edu.cmu.tetrad.search.Jcpc(
                      test.getTest(continuousDataSet, parameters),
                      score.getScore(continuousDataSet, parameters));
              search.setKnowledge(knowledge);
  
              return search.search();
      	}else{
      		Jcpc jcpc = new Jcpc(test, score);
      		
  			if (initialGraph != null) {
  				jcpc.setInitialGraph(initialGraph);
  			}
  			DataSet data = (DataSet) dataSet;
  			GeneralResamplingTest search = new GeneralResamplingTest(data, jcpc, 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 SearchGraphUtils.patternForDag(new EdgeListGraph(graph));
      }
  
      @Override
      public String getDescription() {
          return "JCPC (Joe's CPC) using " + test.getDescription() + (algorithm != null ? " with initial graph from " +
          		algorithm.getDescription() : "");
      }
  
      @Override
      public DataType getDataType() {
          return test.getDataType();
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = test.getParameters();
          parameters.add("depth");
          // 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 Graph getInitialGraph() {
  		return initialGraph;
  	}
  
  	@Override
  	public void setInitialGraph(Graph initialGraph) {
  		this.initialGraph = initialGraph;
  	}
  
      @Override
      public void setInitialGraph(Algorithm algorithm) {
          this.algorithm = algorithm;
      }
  
  }
  