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.*;
  import edu.cmu.tetrad.graph.Node;
  import org.apache.commons.math3.special.Gamma;
  
  import java.util.List;
  
  /**
   * Calculates the BDeu score.
   */
  public class BDeuScore implements LocalDiscreteScore, IBDeuScore, Score {
      private List<Node> variables;
      private int[][] data;
      private int sampleSize;
  
      private double samplePrior = 1;
      private double structurePrior = 1;
  
      private int[] numCategories;
  
      public BDeuScore(DataSet dataSet) {
          if (dataSet == null) {
              throw new NullPointerException("Data was not provided.");
          }
  
          if (dataSet instanceof BoxDataSet) {
              DataBox dataBox = ((BoxDataSet) dataSet).getDataBox();
  
              this.variables = dataSet.getVariables();
  
              if (!(dataBox instanceof VerticalIntDataBox)) {
                  dataBox = new VerticalIntDataBox(dataBox);
              }
  
              VerticalIntDataBox box = (VerticalIntDataBox) dataBox;
  
              data = box.getVariableVectors();
              this.sampleSize = dataSet.getNumRows();
          } else {
              data = new int[dataSet.getNumColumns()][];
              this.variables = dataSet.getVariables();
  
              for (int j = 0; j < dataSet.getNumColumns(); j++) {
                  data[j] = new int[dataSet.getNumRows()];
  
                  for (int i = 0; i < dataSet.getNumRows(); i++) {
                      data[j][i] = dataSet.getInt(i, j);
                  }
              }
  
              this.sampleSize = dataSet.getNumRows();
          }
  
          final List<Node> variables = dataSet.getVariables();
          numCategories = new int[variables.size()];
          for (int i = 0; i < variables.size(); i++) {
              numCategories[i] = (getVariable(i)).getNumCategories();
          }
      }
  
      private DiscreteVariable getVariable(int i) {
          return (DiscreteVariable) variables.get(i);
      }
  
      @Override
      public double localScore(int node, int parents[]) {
  
          // Number of categories for node.
          int c = numCategories[node];
  
          // 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 r = 1;
  
          for (int p = 0; p < parents.length; p++) {
              r *= dims[p];
          }
  
          // Conditional cell coefs of data for node given parents(node).
          int n_jk[][] = new int[r][c];
          int n_j[] = new int[r];
  
          int[] parentValues = new int[parents.length];
  
          int[][] myParents = new int[parents.length][];
          for (int i = 0; i < parents.length; i++) {
              myParents[i] = data[parents[i]];
          }
  
          int[] myChild = data[node];
  
  
          ROW:
          for (int i = 0; i < sampleSize; i++) {
              for (int p = 0; p < parents.length; p++) {
                  if (myParents[p][i] == -99) continue ROW;
                  parentValues[p] = myParents[p][i];
              }
  
              int childValue = myChild[i];
  
              if (childValue == -99) {
                  continue ROW;
  //                throw new IllegalStateException("Please remove or impute missing " +
  //                        "values (record " + i + " column " + i + ")");
              }
  
              int rowIndex = getRowIndex(dims, parentValues);
  
              n_jk[rowIndex][childValue]++;
              n_j[rowIndex]++;
          }
  
          //Finally, compute the score
          double score = 0.0;
  
          score += getPriorForStructure(parents.length);
  
          final double cellPrior = getSamplePrior() / (c * r);
          final double rowPrior = getSamplePrior() / r;
  
          for (int j = 0; j < r; j++) {
              score -= Gamma.logGamma(rowPrior + n_j[j]);
  
              for (int k = 0; k < c; k++) {
                  score += Gamma.logGamma(cellPrior + n_jk[j][k]);
              }
          }
  
          score += r * Gamma.logGamma(rowPrior);
          score -= c * r * Gamma.logGamma(cellPrior);
  
          return score;
      }
  
      private double getPriorForStructure(int numParents) {
          double e = getStructurePrior();
          int vm = data.length - 1;
          return numParents * Math.log(e / (vm)) + (vm - numParents) * Math.log(1.0 - (e / (vm)));
      }
  
      @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 node, int parent) {
          return localScore(node, new int[]{parent});
      }
  
      @Override
      public double localScore(int node) {
          return localScore(node, new int[0]);
      }
  
      @Override
      public List<Node> getVariables() {
          return this.variables;
      }
  
      public int getSampleSize() {
          return sampleSize;
      }
  
      /**
       * Must be called directly after the corresponding scoring call.
       */
      public boolean isEffectEdge(double bump) {
          return bump > 0;//lastBumpThreshold;
      }
  
      @Override
      public DataSet getDataSet() {
          throw new UnsupportedOperationException();
      }
  
      private static 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;
      }
  
      @Override
      public double getStructurePrior() {
          return structurePrior;
      }
  
      @Override
      public double getSamplePrior() {
          return samplePrior;
      }
  
      @Override
      public void setStructurePrior(double structurePrior) {
          this.structurePrior = structurePrior;
      }
  
      @Override
      public void setSamplePrior(double samplePrior) {
          this.samplePrior = samplePrior;
      }
  
      public void setVariables(List<Node> variables) {
          for (int i = 0; i < variables.size(); i++) {
              if (!variables.get(i).getName().equals(this.variables.get(i).getName())) {
                  throw new IllegalArgumentException("Variable in index " + (i + 1) + " does not have the same name " +
                          "as the variable being substituted for it.");
              }
          }
  
          this.variables = variables;
      }
  
      public Node getVariable(String targetName) {
          for (Node node : variables) {
              if (node.getName().equals(targetName)) {
                  return node;
              }
          }
  
          return null;
      }
  
      @Override
      public int getMaxDegree() {
          return (int) Math.ceil(Math.log(sampleSize));
      }
  
      @Override
      public boolean determines(List<Node> z, Node y) {
          return false;
      }
  }
  
  
  
  