code: package edu.cmu.tetrad.algcomparison.algorithm.other;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.*;
  import edu.cmu.tetrad.util.*;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  
  import java.text.NumberFormat;
  import java.util.ArrayList;
  import java.util.List;
  import java.util.Vector;
  
  /**
   * @author jdramsey
   */
  public class FactorAnalysis implements Algorithm {
      static final long serialVersionUID = 23L;
  
      public Graph search(DataModel ds, Parameters parameters) {
      	if (parameters.getInt("numberResampling") < 1) {
  
              DataSet selectedModel = (DataSet) ds;
  
              if (selectedModel == null) {
                  throw new NullPointerException("Data not specified.");
              }
  
              edu.cmu.tetrad.search.FactorAnalysis analysis = new edu.cmu.tetrad.search.FactorAnalysis(selectedModel);
              analysis.setThreshold(parameters.getDouble("convergenceThreshold"));
              analysis.setNumFactors(parameters.getInt("numFactors"));
  
              double threshold = parameters.getDouble("fa_threshold");
  
              TetradMatrix unrotatedSolution = analysis.successiveResidual();
              TetradMatrix rotatedSolution = analysis.successiveFactorVarimax(unrotatedSolution);
  
              SemGraph graph = new SemGraph();
  
              Vector<Node> observedVariables = new Vector<>();
  
              for (Node a : selectedModel.getVariables()) {
                  graph.addNode(a);
                  observedVariables.add(a);
              }
  
              Vector<Node> factors = new Vector<>();
  
              if (parameters.getBoolean("useVarimax")) {
                  for (int i = 0; i < rotatedSolution.columns(); i++) {
                      ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
                      factor.setNodeType(NodeType.LATENT);
                      graph.addNode(factor);
                      factors.add(factor);
                  }
  
                  for (int i = 0; i < rotatedSolution.rows(); i++) {
                      for (int j = 0; j < rotatedSolution.columns(); j++) {
                          if (Math.abs(rotatedSolution.get(i, j)) > threshold) {
                              graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
                          }
                      }
                  }
              } else {
                  for (int i = 0; i < unrotatedSolution.columns(); i++) {
                      ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
                      factor.setNodeType(NodeType.LATENT);
                      graph.addNode(factor);
                      factors.add(factor);
                  }
  
                  for (int i = 0; i < unrotatedSolution.rows(); i++) {
                      for (int j = 0; j < unrotatedSolution.columns(); j++) {
                          if (Math.abs(unrotatedSolution.get(i, j)) > threshold) {
                              graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
                          }
                      }
                  }
              }
  
              if (parameters.getBoolean("verbose")) {
                  NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
  
                  String output = "Unrotated Factor Loading Matrix:\n";
  
                  output += tableString(unrotatedSolution, nf, Double.POSITIVE_INFINITY);
  
                  if (unrotatedSolution.columns() != 1) {
                      output += "\n\nRotated Matrix (using sequential varimax):\n";
                      output += tableString(rotatedSolution, nf, threshold);
                  }
  
                  System.out.println(output);
                  TetradLogger.getInstance().forceLogMessage(output);
              }
  
              return graph;
      	}else{
      		FactorAnalysis algorithm = new FactorAnalysis();
      		
  			DataSet data = (DataSet) ds;
  			GeneralResamplingTest search = new GeneralResamplingTest(data, algorithm, parameters.getInt("numberResampling"));
  
  			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();
      	}
      }
  
      private String tableString(TetradMatrix matrix, NumberFormat nf, double threshold) {
          TextTable table = new TextTable(matrix.rows() + 1, matrix.columns() + 1);
  
          for (int i = 0; i < matrix.rows() + 1; i++) {
              for (int j = 0; j < matrix.columns() + 1; j++) {
                  if (i > 0 && j == 0) {
                      table.setToken(i, j, "X" + i);
                  } else if (i == 0 && j > 0) {
                      table.setToken(i, j, "Factor " + j);
                  } else if (i > 0 && j > 0) {
                      double coefficient = matrix.get(i - 1, j - 1);
                      String token = !Double.isNaN(coefficient) ? nf.format(coefficient) : "Undefined";
                      token += Math.abs(coefficient) > threshold ? "*" : " ";
                      table.setToken(i, j, token);
                  }
              }
          }
  
          return "\n" + table.toString();
  
      }
  
      public Graph getComparisonGraph(Graph graph) {
          return GraphUtils.undirectedGraph(graph);
      }
  
      public String getDescription() {
          return "GLASSO (Graphical LASSO)";
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Mixed;
      }
  
      @Override
      public List<String> getParameters() {
          List<String> params = new ArrayList<>();
          params.add("fa_threshold");
          params.add("numFactors");
          params.add("useVarimax");
          params.add("convergenceThreshold");
          params.add("verbose");
          // Resampling
          params.add("numberResampling");
          params.add("percentResampleSize");
          params.add("resamplingWithReplacement");
          params.add("resamplingEnsemble");
          params.add("addOriginalDataset");
          return params;
      }
  }