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.DataSet;
  import edu.cmu.tetrad.data.DiscreteVariable;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.ProbUtils;
  
  import java.util.List;
  
  /**
   * Calculates the BDe score.
   */
  public class BDeScore implements LocalDiscreteScore {
      private DataSet dataSet;
  
      public BDeScore(DataSet dataSet) {
          if (dataSet == null) {
              throw new NullPointerException();
          }
  
          if (!dataSet.isDiscrete()) {
              throw new IllegalArgumentException("Need a discrete data set.");
          }
  
          this.dataSet = dataSet;
      }
  
      public double localScore(int i, int parents[]) {
  
          // Number of categories for i.
          int r = numCategories(i);
  
          // Numbers of categories of parents.
          int dims[] = new int[parents.length];
  
          for (int p = 0; p < parents.length; p++) {
              dims[p] = numCategories(parents[p]);
          }
  
          // Number of parent states.
          int q = 1;
          for (int p = 0; p < parents.length; p++) {
              q *= dims[p];
          }
  
          // Conditional cell coefs of data for i given parents(i).
          int n_ijk[][] = new int[q][r];
          int n_ij[] = new int[q];
  
          int values[] = new int[parents.length];
  
          for (int n = 0; n < sampleSize(); n++) {
              for (int p = 0; p < parents.length; p++) {
                  int parentValue = dataSet().getInt(n, parents[p]);
  
                  if (parentValue == -99) {
                      throw new IllegalStateException("Please remove or impute " +
                              "missing values.");
                  }
  
                  values[p] = parentValue;
              }
  
              int childValue = dataSet().getInt(n, i);
  
              if (childValue == -99) {
                  throw new IllegalStateException("Please remove or impute missing " +
                          "values (record " + n + " column " + i + ")");
  
              }
  
              int rowIndex = getRowIndex(dims, values);
  
  //            for (int m = 0; m < dataSet().getMultiplier(n); m++) {
              n_ijk[rowIndex][childValue]++;
  //            }
          }
  
          // Row sums.
          for (int j = 0; j < q; j++) {
              for (int k = 0; k < r; k++) {
                  n_ij[j] += n_ijk[j][k];
              }
          }
  
          //Finally, compute the score
          double score = 0;
  
          for (int j = 0; j < q; j++) {
              for (int k = 0; k < r; k++) {
                  double nPrimeijk = 1. / (r * q);
                  score += ProbUtils.lngamma(n_ijk[j][k] + nPrimeijk);
                  score -= ProbUtils.lngamma(nPrimeijk);
              }
  
              double nPrimeij = 1. / q;
  
              score += ProbUtils.lngamma(nPrimeij);
              score -= ProbUtils.lngamma(n_ij[j] + nPrimeij);
          }
  
          return score;
      }
  
      @Override
      public double localScoreDiff(int x, int y, int[] z) {
          return localScore(y, append(z, x)) - localScore(y, z);
      }
  
      @Override
      public double localScoreDiff(int x, int y) {
          return localScore(y, x) - localScore(y);
      }
  
      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;
      }
  
      @Override
      public double localScore(int i, int parent) {
          return localScore(i, new int[]{parent});
      }
  
      @Override
      public double localScore(int i) {
          return localScore(i, new int[0]);
      }
  
      public DataSet getDataSet() {
          return dataSet;
      }
  
      private int getRowIndex(int[] dim, int[] values) {
          int rowIndex = 0;
          for (int i = 0; i < dim.length; i++) {
              rowIndex *= dim[i];
              rowIndex += values[i];
          }
          return rowIndex;
      }
  
      private int sampleSize() {
          return dataSet().getNumRows();
      }
  
      private int numCategories(int i) {
          return ((DiscreteVariable) dataSet().getVariable(i)).getNumCategories();
      }
  
      private DataSet dataSet() {
          return dataSet;
      }
  
      public void setStructurePrior(double structurePrior) {
      }
  
      public void setSamplePrior(double samplePrior) {
      }
  
      @Override
      public List<Node> getVariables() {
          return dataSet.getVariables();
      }
  
      public int getSampleSize() {
          return dataSet.getNumRows();
      }
  
      @Override
      public boolean isEffectEdge(double bump) {
          return bump > -20;
      }
  
      @Override
      public Node getVariable(String targetName) {
          for (Node node : dataSet.getVariables()) {
              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;
      }
  }
  
  
  