code: ///////////////////////////////////////////////////////////////////////////////
  // For information as to what this class does, see the Javadoc, below.       //
  // Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,       //
  // 2007, 2008, 2009, 2010, 2014, 2015 by Peter Spirtes, Richard Scheines, Joseph   //
  // Ramsey, and Clark Glymour.                                                //
  //                                                                           //
  // This program is free software; you can redistribute it and/or modify      //
  // it under the terms of the GNU General Public License as published by      //
  // the Free Software Foundation; either version 2 of the License, or         //
  // (at your option) any later version.                                       //
  //                                                                           //
  // This program is distributed in the hope that it will be useful,           //
  // but WITHOUT ANY WARRANTY; without even the implied warranty of            //
  // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the             //
  // GNU General Public License for more details.                              //
  //                                                                           //
  // You should have received a copy of the GNU General Public License         //
  // along with this program; if not, write to the Free Software               //
  // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA //
  ///////////////////////////////////////////////////////////////////////////////
  
  package edu.cmu.tetrad.search;
  
  import edu.cmu.tetrad.data.DataModel;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.ICovarianceMatrix;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.DepthChoiceGenerator;
  import edu.cmu.tetrad.util.TetradMatrix;
  import edu.cmu.tetrad.util.TetradVector;
  
  import java.io.PrintStream;
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * Implements the continuous BIC score for FGES.
   *
   * @author Joseph Ramsey
   */
  public class BdeuScoreImages implements IBDeuScore {
  
      // The covariance matrix.
      private List<BDeuScore> scores;
  
      // The variables of the covariance matrix.
      private List<Node> variables;
  
      // The sample size of the covariance matrix.
      private int sampleSize;
  
      // The printstream output should be sent to.
      private PrintStream out = System.out;
  
      // True if verbose output should be sent to out.
      private boolean verbose = false;
  
      private double samplePrior = 1.0;
  
      private double structurePrior = 1.0;
  
      /**
       * Constructs the score using a covariance matrix.
       */
      public BdeuScoreImages(List<DataModel> dataModels) {
          if (dataModels == null) {
              throw new NullPointerException();
          }
  
          List<BDeuScore> scores = new ArrayList<>();
  
          for (DataModel model : dataModels) {
              if (model instanceof DataSet) {
                  DataSet dataSet = (DataSet) model;
  
                  if (!dataSet.isDiscrete()) {
                      throw new IllegalArgumentException("Datasets must be discrete.");
                  }
  
                  scores.add(new BDeuScore(dataSet));
              } else {
                  throw new IllegalArgumentException("Only continuous data sets and covariance matrices may be used as input.");
              }
          }
  
          List<Node> variables = scores.get(0).getVariables();
  
          for (int i = 2; i < scores.size(); i++) {
              scores.get(i).setVariables(variables);
          }
  
          this.scores = scores;
          this.variables = variables;
      }
  
  
      public double localScoreDiff(int x, int y, int[] z) {
          double sum = 0.0;
  
          for (BDeuScore score : scores) {
              sum += score.localScoreDiff(x, y, z);
          }
  
          return sum / scores.size();
      }
  
      @Override
      public double localScoreDiff(int x, int y) {
          return localScoreDiff(x, y, new int[0]);
      }
  
      /**
       * Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model
       */
      public double localScore(int i, int[] parents) {
          double sum = 0.0;
  
          for (BDeuScore score : scores) {
              sum += score.localScore(i, parents);
          }
  
          return sum / scores.size();
      }
  
      public double localScore(int i, int[] parents, int index) {
          return localScoreOneDataSet(i, parents, index);
      }
  
      private double localScoreOneDataSet(int i, int[] parents, int index) {
          return scores.get(index).localScore(i, parents);
      }
  
  
      int[] append(int[] parents, int extra) {
          int[] all = new int[parents.length + 1];
          System.arraycopy(parents, 0, all, 0, parents.length);
          all[parents.length] = extra;
          return all;
      }
  
      /**
       * Specialized scoring method for a single parent. Used to speed up the effect edges search.
       */
      public double localScore(int i, int parent) {
          double sum = 0.0;
  
          for (BDeuScore score : scores) {
              sum += score.localScore(i, parent);
          }
  
          return sum / scores.size();
      }
  
      /**
       * Specialized scoring method for no parents. Used to speed up the effect edges search.
       */
      public double localScore(int i) {
          double sum = 0.0;
  
          for (BDeuScore score : scores) {
              sum += score.localScore(i);
          }
  
          return sum / scores.size();
      }
  
      public void setOut(PrintStream out) {
          this.out = out;
      }
  
      @Override
      public boolean isEffectEdge(double bump) {
          return false;
      }
  
      public DataSet getDataSet() {
          throw new UnsupportedOperationException();
      }
  
      public boolean isVerbose() {
          return verbose;
      }
  
      public void setVerbose(boolean verbose) {
          this.verbose = verbose;
      }
  
      @Override
      public List<Node> getVariables() {
          return variables;
      }
  
      @Override
      public int getSampleSize() {
          return scores.get(0).getSampleSize();
      }
  
      // Calculates the BIC score.
      private double score(double residualVariance, int n, int p, double c) {
          return -n * Math.log(residualVariance) - c * (p + 1) * Math.log(n);
      }
  
      private TetradMatrix getSelection1(ICovarianceMatrix cov, int[] rows) {
          return cov.getSelection(rows, rows);
      }
  
      private TetradVector getSelection2(ICovarianceMatrix cov, int[] rows, int k) {
          return cov.getSelection(rows, new int[]{k}).getColumn(0);
      }
  
      // Prints a smallest subset of parents that causes a singular matrix exception.
      private void printMinimalLinearlyDependentSet(int[] parents, ICovarianceMatrix cov) {
          List<Node> _parents = new ArrayList<>();
          for (int p : parents) _parents.add(variables.get(p));
  
          DepthChoiceGenerator gen = new DepthChoiceGenerator(_parents.size(), _parents.size());
          int[] choice;
  
          while ((choice = gen.next()) != null) {
              int[] sel = new int[choice.length];
              List<Node> _sel = new ArrayList<>();
              for (int m = 0; m < choice.length; m++) {
                  sel[m] = parents[m];
                  _sel.add(variables.get(sel[m]));
              }
  
              TetradMatrix m = cov.getSelection(sel, sel);
  
              try {
                  m.inverse();
              } catch (Exception e2) {
                  out.println("### Linear dependence among variables: " + _sel);
              }
          }
      }
  
      public double getSamplePrior() {
          return samplePrior;
      }
  
      public void setSamplePrior(double samplePrior) {
          for (BDeuScore score : scores) {
              score.setSamplePrior(samplePrior);
          }
          this.samplePrior = samplePrior;
      }
  
      public double getStructurePrior() {
          return structurePrior;
      }
  
      public void setStructurePrior(double structurePrior) {
          for (BDeuScore score : scores) {
              score.setStructurePrior(structurePrior);
          }
          this.structurePrior = structurePrior;
      }
  
      @Override
      public Node getVariable(String targetName) {
          for (Node node : variables) {
              if (node.getName().equals(targetName)) {
                  return node;
              }
          }
  
          return null;
      }
  
      @Override
      public int getMaxDegree() {
          return 1000;
      }
  
      @Override
      public boolean determines(List<Node> z, Node y) {
          return false;
      }
  }
  
  
  
  