code: package edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.IndTestScore;
  import edu.cmu.tetrad.search.SearchGraphUtils;
  import edu.cmu.tetrad.search.SemBicScoreDeterministic;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * PC.
   *
   * @author jdramsey
   */
  public class Pcd implements Algorithm, HasKnowledge {
      static final long serialVersionUID = 23L;
      private IKnowledge knowledge = new Knowledge2();
  
      public Pcd() {
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              IndTestScore test;
  
              if (dataSet instanceof ICovarianceMatrix) {
                  SemBicScoreDeterministic score = new SemBicScoreDeterministic((ICovarianceMatrix) dataSet);
                  score.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
                  score.setDeterminismThreshold(parameters.getDouble("determinismThreshold"));
                  test = new IndTestScore(score);
              } else if (dataSet instanceof DataSet) {
                  SemBicScoreDeterministic score = new SemBicScoreDeterministic(new CovarianceMatrix((DataSet) dataSet));
                  score.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
                  score.setDeterminismThreshold(parameters.getDouble("determinismThreshold"));
                  test = new IndTestScore(score);
              } else {
                  throw new IllegalArgumentException("Expecting a dataset or a covariance matrix.");
              }
  
              edu.cmu.tetrad.search.Pcd search = new edu.cmu.tetrad.search.Pcd(test);
              search.setDepth(parameters.getInt("depth"));
              search.setKnowledge(knowledge);
              search.setVerbose(parameters.getBoolean("verbose"));
              return search.search();
      	}else{
      		Pcd algorithm = new Pcd();
     		
   			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 SearchGraphUtils.patternForDag(graph);
      }
  
      @Override
      public String getDescription() {
          return "PC (\"Peter and Clark\") Deternimistic";
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Continuous;
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = new ArrayList<>();
          parameters.add("penaltyDiscount");
          parameters.add("depth");
          parameters.add("determinismThreshold");
          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;
      }
  }
  