code: package edu.cmu.tetrad.algcomparison.algorithm.multi;
  
  import edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm;
  import edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges;
  import edu.cmu.tetrad.algcomparison.score.SemBicScore;
  import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.search.IndTestScore;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.SearchGraphUtils;
  import edu.cmu.tetrad.search.SemBicScoreImages;
  
  import java.util.ArrayList;
  import java.util.Collections;
  import java.util.List;
  
  /**
   * Wraps the tsIMaGES 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
   * @author dmalinsky
   */
  public class TsImagesSemBic implements MultiDataSetAlgorithm, HasKnowledge {
      static final long serialVersionUID = 23L;
      private IKnowledge knowledge = new Knowledge2();
  
      public TsImagesSemBic() {
      }
  
      @Override
      public Graph search(List<DataModel> dataModels, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              List<DataSet> dataSets = new ArrayList<>();
  
              for (DataModel dataModel : dataModels) {
                  dataSets.add((DataSet) dataModel);
              }
  
              edu.cmu.tetrad.search.TsGFci search = new edu.cmu.tetrad.search.TsGFci(new IndTestScore(
                      new SemBicScoreImages(dataModels)), new SemBicScoreImages(dataModels));
              search.setFaithfulnessAssumed(true);
              search.setKnowledge(knowledge);
  
              return search.search();
      	}else{
      		TsImagesSemBic tsImagesSemBic = new TsImagesSemBic();
      		
      		List<DataSet> datasets = new ArrayList<>();
  
  			for (DataModel dataModel : dataModels) {
  				datasets.add((DataSet) dataModel);
  			}
  			GeneralResamplingTest search = new GeneralResamplingTest(datasets, tsImagesSemBic, 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 search(DataModel dataSet, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              return search(Collections.singletonList((DataModel) DataUtils.getContinuousDataSet(dataSet)), parameters);
      	}else{
      		TsImagesSemBic tsImagesSemBic = new TsImagesSemBic();
      		
      		List<DataSet> dataSets = Collections.singletonList(DataUtils.getContinuousDataSet(dataSet));
      		GeneralResamplingTest search = new GeneralResamplingTest(dataSets, tsImagesSemBic, 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 "tsIMaGES for continuous variables (using the SEM BIC score)";
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Continuous;
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = new Fges(new SemBicScore(), false).getParameters();
          parameters.add("randomSelectionSize");
          // 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;
      }
  }
  