code: package edu.cmu.tetrad.algcomparison.algorithm.multi;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.Lofs2;
  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;
  
  /**
   * Wraps the IMaGES algorithm for continuous variables.
   * </p>
   * Requires that the parameter 'randomSelectionSize' be set to indicate how many
   * datasets should be taken at a time (randomly). This cannot given multiple values.
   *
   * @author jdramsey
   */
  public class FasLofs implements Algorithm, HasKnowledge {
      static final long serialVersionUID = 23L;
      private final Lofs2.Rule rule;
      private IKnowledge knowledge = new Knowledge2();
  
      public FasLofs(Lofs2.Rule rule) {
          this.rule = rule;
      }
  
      private Graph getGraph(edu.cmu.tetrad.search.FasLofs search) {
          return search.search();
      }
  
      @Override
      public Graph search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              edu.cmu.tetrad.search.FasLofs search = new edu.cmu.tetrad.search.FasLofs((DataSet) dataSet, rule);
              search.setDepth(parameters.getInt("depth"));
              search.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
              search.setKnowledge(knowledge);
              return getGraph(search);
      	}else{
      		FasLofs fasLofs = new FasLofs(rule);
      		//fasLofs.setKnowledge(knowledge);
      		
      		DataSet data = (DataSet) dataSet;
      		GeneralResamplingTest search = new GeneralResamplingTest(data, fasLofs, 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 EdgeListGraph(graph);
      }
  
      @Override
      public String getDescription() {
          return "FAS followed by " + rule;
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Continuous;
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = new ArrayList<>();
          parameters.add("depth");
          parameters.add("penaltyDiscount");
          // 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;
      }
  }
  