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.utils.HasKnowledge;
  import edu.cmu.tetrad.algcomparison.utils.TakesIndependenceWrapper;
  import edu.cmu.tetrad.algcomparison.utils.TakesInitialGraph;
  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.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.TsDagToPag;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  import java.util.List;
  
  /**
   * tsFCI.
   *
   * @author jdramsey
   * @author dmalinsky
   */
  @edu.cmu.tetrad.annotation.Algorithm(
          name = "TsFCI",
          command = "ts-fci",
          algoType = AlgType.allow_latent_common_causes
  )
  public class TsFci implements Algorithm, TakesInitialGraph, HasKnowledge, TakesIndependenceWrapper {
  
      static final long serialVersionUID = 23L;
      private IndependenceWrapper test;
      private Algorithm algorithm = null;
      private Graph initialGraph = null;
      private IKnowledge knowledge = null;
  
      public TsFci() {
      }
  
      public TsFci(IndependenceWrapper test) {
          this.test = test;
      }
  
      public TsFci(IndependenceWrapper type, Algorithm algorithm) {
          this.test = type;
          this.algorithm = algorithm;
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
  //    	if (!(dataSet instanceof TimeSeriesData)) {
  //            throw new IllegalArgumentException("You need a (labeled) time series data set to run TsFCI.");
  //        }
          
          if (parameters.getInt("numberResampling") < 1) {
          	if(knowledge != null) {
          		dataSet.setKnowledge(knowledge);
          	}
              edu.cmu.tetrad.search.TsFci search = new edu.cmu.tetrad.search.TsFci(test.getTest(dataSet, parameters));
              search.setDepth(parameters.getInt("depth"));
              search.setKnowledge(dataSet.getKnowledge());
              search.setVerbose(parameters.getBoolean("verbose"));
              
              return search.search();
          } else {
              TsFci tsFci = new TsFci(test, algorithm);
  
              DataSet data = (DataSet) dataSet;
              GeneralResamplingTest search = new GeneralResamplingTest(data, tsFci, 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 TsDagToPag(new EdgeListGraph(graph)).convert();
      }
  
      public String getDescription() {
          return "tsFCI (Time Series Fast Causal Inference) 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();
          // 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 this.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;
      }
  
      @Override
      public void setIndependenceWrapper(IndependenceWrapper test) {
          this.test = test;
      }
  
      @Override
      public IndependenceWrapper getIndependenceWrapper() {
          return test;
      }
  }
  