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.annotation.AlgType;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.search.SemBicScoreImages;
  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.Collections;
  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
   */
  @edu.cmu.tetrad.annotation.Algorithm(
          name = "IMaGES Continuous",
          command = "imgs_cont",
          algoType = AlgType.forbid_latent_common_causes
  )
  public class ImagesSemBic implements MultiDataSetAlgorithm, HasKnowledge {
  
      static final long serialVersionUID = 23L;
      private IKnowledge knowledge = new Knowledge2();
  
      public ImagesSemBic() {
      }
  
      @Override
      public Graph search(List<DataModel> dataSets, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
              final SemBicScoreImages score = new SemBicScoreImages(dataSets);
              score.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
              edu.cmu.tetrad.search.Fges search = new edu.cmu.tetrad.search.Fges(score);
              search.setKnowledge(knowledge);
              return search.search();
          } else {
              ImagesSemBic imagesSemBic = new ImagesSemBic();
  
              List<DataSet> datasets = new ArrayList<>();
  
              for (DataModel dataModel : dataSets) {
                  datasets.add((DataSet) dataModel);
              }
              GeneralResamplingTest search = new GeneralResamplingTest(datasets, imagesSemBic, 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 {
              ImagesSemBic imagesSemBic = new ImagesSemBic();
  
              List<DataSet> dataSets = Collections.singletonList(DataUtils.getContinuousDataSet(dataSet));
              GeneralResamplingTest search = new GeneralResamplingTest(dataSets, imagesSemBic, 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);
  //        return SearchGraphUtils.patternForDag(graph);
  //        return new TsDagToPag(new EdgeListGraph(graph)).convert();
      }
  
      @Override
      public String getDescription() {
          return "IMaGES 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("numRuns");
          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;
      }
  }
  