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 java.util.List;
  
  /**
   * Calculates the BDeu score.
   */
  public class DirichletScore implements LocalDiscreteScore, Score {
      private List<Node> variables;
      private int[][] data;
      private int sampleSize;
  
      private double samplePrior = 1;
      private double structurePrior = 1;
  
      private int[] numCategories;
  
      private double lastBumpThreshold = 0.0;
  
      public DirichletScore(DataSet dataSet) {
          if (dataSet == null) {
              throw new NullPointerException();
          }
  
          if (dataSet instanceof BoxDataSet) {
              DataBox dataBox = ((BoxDataSet) dataSet).getDataBox();
  
              this.variables = dataSet.getVariables();
  
              if (!(((BoxDataSet) dataSet).getDataBox() instanceof VerticalIntDataBox)) {
                  throw new IllegalArgumentException();
              }
  
              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 r = 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 q = 1;
  
          for (int p = 0; p < parents.length; p++) {
              q *= dims[p];
          }
  
          // Conditional cell coefs of data for node given parents(node).
          int n_jk[][] = new int[q][r];
          int n_j[] = new int[q];
  
          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];
  
          for (int i = 0; i < sampleSize; i++) {
              for (int p = 0; p < parents.length; p++) {
                  parentValues[p] = myParents[p][i];
              }
  
              int childValue = myChild[i];
  
              if (childValue == -99) {
                  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;
  
          final double cellPrior = getSamplePrior();
          final double rowPrior = r * getSamplePrior();
  
          for (int j = 0; j < q; j++) {
              double rowSum = rowPrior + n_j[j];
              int cellCount = 0;
              double rowScore = 0;
  
              for (int k = 0; k < r; k++) {
                  double alpha = cellPrior + n_jk[j][k];
                  double pk = (alpha) / rowSum;
                  if (Double.isInfinite(pk)) continue;
                  double _score = (alpha - 1) * Math.log(pk);
                  rowScore += _score;
                  cellCount++;
              }
  
              if (rowScore == 0) continue;
              score += rowScore;
  
              score -= 2 * cellCount;
  
  //            score -= .5 * cellCount * Math.log(sampleSize);
          }
  
  
  //        double h = Math.log(1.0 / r) * sampleSize;
  
  //        System.out.println("(1/r)^N = " + h + " score = " + score);
  
          lastBumpThreshold = 0.01;//((r - 1) * q * FastMath.log(getStructurePrior()));
  
          return score;
      }
  
      private double getPriorForStructure(int numParents) {
          double e = getStructurePrior();
          double k = numParents;
          double n = data.length;
          return k * Math.log(e / n) + (n - k) * Math.log(1.0 - (e / n));
      }
  
      @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});
  
  //        // Number of categories for node.
  //        int r = numCategories[node];
  //
  //        // Numbers of categories of parents.
  //        int q = numCategories[parent];
  //
  //        // Conditional cell coefs of data for node given parents(node).
  //        int n_jk[][] = new int[q][r];
  //        int n_j[] = new int[q];
  //
  //        int[] parentData = data[parent];
  //        int[] childData = data[node];
  //
  //        for (int i = 0; i < sampleSize; i++) {
  //            int parentValue = parentData[i];
  //            int childValue = childData[i];
  //            n_jk[parentValue][childValue]++;
  //            n_j[parentValue]++;
  //        }
  //
  //        //Finally, compute the score
  //        double score = r * q * Math.log(getStructurePrior());
  //
  //        final double cellPrior = getSamplePrior() / (r * q);
  //        final double rowPrior = getSamplePrior() / q;
  //
  //        for (int j = 0; j < q; j++) {
  //            score -= Gamma.logGamma(rowPrior + n_j[j]);
  //
  //            for (int k = 0; k < r; k++) {
  //                score += Gamma.logGamma(cellPrior + n_jk[j][k]);
  //            }
  //        }
  //
  //        score += q * Gamma.logGamma(rowPrior);
  //        score -= r * q * Gamma.logGamma(cellPrior);
  //
  //        lastBumpThreshold = 0.01;//((r - 1) * q * FastMath.log(getStructurePrior()));
  //
  //        return score;
      }
  
      @Override
      public double localScore(int node) {
          return localScore(node, new int[0]);
  
  //        // Number of categories for node.
  //        int r = numCategories[node];
  //
  //        // Conditional cell coefs of data for node given parents(node).
  //        int n_jk[] = new int[numCategories[node]];
  //        int n_j = 0;
  //
  //        int[] childData = data[node];
  //
  //        for (int i = 0; i < sampleSize; i++) {
  //            int childValue = childData[i];
  //            n_jk[childValue]++;
  //            n_j++;
  //        }
  //
  //        //Finally, compute the score
  //        int q = 1;
  //        double score = r * q * Math.log(getStructurePrior());
  //
  //        final double cellPrior = getSamplePrior() / r;
  //        final double rowPrior = getSamplePrior();
  //
  //        score -= Gamma.logGamma(rowPrior + n_j);
  //
  //        for (int k = 0; k < r; k++) {
  //            score += Gamma.logGamma(cellPrior + n_jk[k]);
  //        }
  //
  //        score += Gamma.logGamma(rowPrior);
  //        score -= r * Gamma.logGamma(cellPrior);
  //
  //        lastBumpThreshold = 0.01;//((r - 1) * q * FastMath.log(getStructurePrior()));
  //
  //        return score;
      }
  
      @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 > 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;
      }
  
      public double getStructurePrior() {
          return structurePrior;
      }
  
      public double getSamplePrior() {
          return samplePrior;
      }
  
      public void setStructurePrior(double structurePrior) {
          this.structurePrior = structurePrior;
      }
  
      public void setSamplePrior(double samplePrior) {
          this.samplePrior = samplePrior;
      }
  
      @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;
      }
  }
  
  
  
  