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.graph.Node;
  
  import java.util.List;
  
  /**
   * Implements a conditional Gaussian BIC score for FGS.
   *
   * @author Joseph Ramsey
   */
  public class DiscreteMixedScore implements Score {
  
      private DataSet dataSet;
  
      // The variables of the continuousData set.
      private List<Node> variables;
  
      // Likelihood function
      private DiscreteMixedLikelihood likelihood;
  
      private double penaltyDiscount = 1;
      private int numCategoriesToDiscretize = 3;
      private double sp;
  
      /**
       * Constructs the score using a covariance matrix.
       */
      public DiscreteMixedScore(DataSet dataSet, double sp) {
          if (dataSet == null) {
              throw new NullPointerException();
          }
  
          this.dataSet = dataSet;
          this.variables = dataSet.getVariables();
          this.sp = sp;
  
          this.likelihood = new DiscreteMixedLikelihood(dataSet);
      }
  
      /**
       * Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model
       */
      public double localScore(int i, int... parents) {
          likelihood.setNumCategoriesToDiscretize(numCategoriesToDiscretize);
          likelihood.setPenaltyDiscount(penaltyDiscount);
  
          DiscreteMixedLikelihood.Ret ret = likelihood.getLikelihood(i, parents);
  
          int N = dataSet.getNumRows();
          double lik = ret.getLik();
          int k = ret.getDof();
  
          double strucPrior = getStructurePrior(parents);
          if (strucPrior > 0) {
              strucPrior = -2 * k * strucPrior;
          }
  
          return 2.0 * lik - /*getPenaltyDiscount() **/ k * Math.log(N) + strucPrior;
      }
  
      private double getStructurePrior(int[] parents) {
          if (sp < 0) { return getEBICprior(); }
          else if (sp == 0) { return 0; }
          else {
              int i = parents.length;
              int c = dataSet.getNumColumns() - 1;
              double p = sp / (double) c;
              return i * Math.log(p) + (c - i) * Math.log(1.0 - p);
          }
      }
  
      private double getEBICprior() {
  
          double n = dataSet.getNumColumns();
          double gamma = -sp;
          return gamma * Math.log(n);
  
      }
  
      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);
      }
  
      private 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) {
          return localScore(i, new int[]{parent});
      }
  
      /**
       * Specialized scoring method for no parents. Used to speed up the effect edges search.
       */
      public double localScore(int i) {
          return localScore(i, new int[0]);
      }
  
      public int getSampleSize() {
          return dataSet.getNumRows();
      }
  
      @Override
      public boolean isEffectEdge(double bump) {
          return bump > 0;
      }
  
      @Override
      public List<Node> getVariables() {
          return variables;
      }
  
      @Override
      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(dataSet.getNumRows()));
      }
  
      @Override
      public boolean determines(List<Node> z, Node y) {
          return false;
      }
  
      public double getPenaltyDiscount() {
          return penaltyDiscount;
      }
  
      public void setPenaltyDiscount(double penaltyDiscount) {
          this.penaltyDiscount = penaltyDiscount;
      }
  
      public void setNumCategoriesToDiscretize(int numCategoriesToDiscretize) {
          this.numCategoriesToDiscretize = numCategoriesToDiscretize;
      }
  }
  
  
  
  