code: package edu.cmu.tetrad.algcomparison.algorithm.multi;
  
  import edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm;
  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.*;
  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
   */
  public class ImagesCcd implements MultiDataSetAlgorithm, HasKnowledge {
  
      static final long serialVersionUID = 23L;
      private IKnowledge knowledge = new Knowledge2();
  
      public ImagesCcd() {
      }
  
      @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);
              }
  
              SemBicScoreImages score = new SemBicScoreImages(dataModels);
              score.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
              IndependenceTest test = new IndTestScore(score);
              edu.cmu.tetrad.search.CcdMax search = new edu.cmu.tetrad.search.CcdMax(test);
              search.setCollapseTiers(parameters.getBoolean("collapseTiers"));
              search.setOrientConcurrentFeedbackLoops(parameters.getBoolean("orientVisibleFeedbackLoops"));
              search.setDoColliderOrientations(parameters.getBoolean("doColliderOrientation"));
              search.setUseHeuristic(parameters.getBoolean("useMaxPOrientationHeuristic"));
              search.setMaxPathLength(parameters.getInt("maxPOrientationMaxPathLength"));
              search.setDepth(parameters.getInt("depth"));
              search.setApplyOrientAwayFromCollider(parameters.getBoolean("applyR1"));
              search.setUseOrientTowardDConnections(parameters.getBoolean("orientTowardDConnections"));
              search.setKnowledge(knowledge);
              search.setDepth(parameters.getInt("depth"));
              return search.search();
          } else {
              ImagesCcd imagesCcd = new ImagesCcd();
  
              List<DataSet> datasets = new ArrayList<>();
  
              for (DataModel dataModel : dataModels) {
                  datasets.add((DataSet) dataModel);
              }
              GeneralResamplingTest search = new GeneralResamplingTest(datasets, imagesCcd, 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 {
              ImagesCcd imagesCcd = new ImagesCcd();
  
              List<DataSet> dataSets = Collections.singletonList(DataUtils.getContinuousDataSet(dataSet));
              GeneralResamplingTest search = new GeneralResamplingTest(dataSets, imagesCcd, 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 "CCD-Max using the IMaGEs score for continuous variables";
      }
  
      @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("orientVisibleFeedbackLoops");
          parameters.add("doColliderOrientation");
          parameters.add("useMaxPOrientationHeuristic");
          parameters.add("maxPOrientationMaxPathLength");
          parameters.add("applyR1");
          parameters.add("orientTowardDConnections");
          parameters.add("assumeIID");
          parameters.add("collapseTiers");
          // 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;
      }
  }
  