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.bayes;
  
  import edu.cmu.tetrad.data.*;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.graph.GraphUtils;
  import edu.cmu.tetrad.graph.Node;
  import edu.cmu.tetrad.util.ProbUtils;
  import org.apache.commons.math3.distribution.ChiSquaredDistribution;
  
  import java.util.List;
  
  /**
   * Calculates some scores for Bayes nets as a whole.
   *
   * @author Joseph Ramsey
   */
  public final class BayesProperties {
      private DataSet dataSet;
      private double chisq;
      private double dof;
      private double bic;
      private double likelihood;
      private List<Node> variables;
      private int[][] data;
      private int sampleSize;
      private int[] numCategories;
  
      public BayesProperties(DataSet dataSet) {
          if (dataSet == null) {
              throw new NullPointerException();
          }
  
          this.dataSet = dataSet;
  
          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++) {
              DiscreteVariable variable = getVariable(i);
  
              if (variable != null) {
                  numCategories[i] = variable.getNumCategories();
              }
          }
  
      }
  
      /**
       * Calculates the p-value of the graph with respect to the given data.
       */
      public final double getLikelihoodRatioP(Graph graph) {
  
          // Null hypothesis = complete graph.
          List<Node> nodes = graph.getNodes();
  
          Graph graph0 = new EdgeListGraph(nodes);
  
          for (int i = 0; i < nodes.size(); i++) {
              for (int j = i + 1; j < nodes.size(); j++)
                  graph0.addDirectedEdge(nodes.get(i), nodes.get(j));
          }
  
          Ret r0 = getLikelihood2(graph0);
          Ret r1 = getLikelihood2(graph);
  
          this.likelihood = r1.getLik();
  
          double lDiff = r0.getLik() - r1.getLik();
          System.out.println("lDiff = " + lDiff);
  
          int nDiff = r0.getDof() - r1.getDof();
          System.out.println("nDiff = " + nDiff);
  
          double chisq = 2.0 * lDiff;
          double dof = nDiff;
  
          this.chisq = chisq;
          this.dof = dof;
  
          int N = dataSet.getNumRows();
          this.bic = 2 * r1.getLik() - r1.getDof() * Math.log(N);
          System.out.println("bic = " + bic);
  
          System.out.println("chisq = " + chisq);
          System.out.println("dof = " + dof);
  
          double p = 1.0 - new ChiSquaredDistribution(dof).cumulativeProbability(chisq);
  
          System.out.println("p = " + p);
  
          return p;
      }
  
  
      /**
       * Call after calling getLikelihoodP().
       */
      public double getChisq() {
          return chisq;
      }
  
      /**
       * Call after calling getLikelihoodP().
       */
      public double getDof() {
          return dof;
      }
  
      /**
       * Call after calling getLikelihoodP().
       */
      public double getBic() {
          return bic;
      }
  
      /**
       * Call after calling getLikelihoodP().
       */
      public double getLikelihood() {
          return likelihood;
      }
  
      private int getDof(Graph graph) {
          graph = GraphUtils.replaceNodes(graph, dataSet.getVariables());
          BayesPm pm = new BayesPm(graph);
          BayesIm im = new MlBayesEstimator().estimate(pm, dataSet);
  
          int numParams = 0;
  
          for (int j = 0; j < im.getNumNodes(); j++) {
              int numColumns = im.getNumColumns(j);
              int numRows = im.getNumRows(j);
              numParams += (numColumns - 1) * numRows;
          }
  
          return numParams;
      }
  
      private double getLikelihood(Graph graph) {
          graph = GraphUtils.replaceNodes(graph, dataSet.getVariables());
          BayesPm pm = new BayesPm(graph);
          BayesIm im = new MlBayesEstimator().estimate(pm, dataSet);
          double lik = 0.0;
  
          ROW:
          for (int i = 0; i < dataSet.getNumRows(); i++) {
              double lik0 = 0.0;
  
              for (int j = 0; j < dataSet.getNumColumns(); j++) {
                  int[] parents = im.getParents(j);
                  int[] parentValues = new int[parents.length];
  
                  for (int k = 0; k < parents.length; k++) {
                      parentValues[k] = dataSet.getInt(i, parents[k]);
                  }
  
                  int dataValue = dataSet.getInt(i, j);
                  double p = im.getProbability(j, im.getRowIndex(j, parentValues), dataValue);
  
                  if (p == 0) continue ROW;
  
                  lik0 += Math.log(p);
              }
  
              lik += lik0;
          }
  
          return lik;
      }
  
      private Ret getLikelihood2(Graph graph) {
          double lik = 0.0;
          int dof = 0;
  
          for (Node node : graph.getNodes()) {
              List<Node> parents = graph.getParents(node);
  
              int i = variables.indexOf(getVariable(node.getName()));
  
              int[] z = new int[parents.size()];
  
              for (int j = 0; j < parents.size(); j++) {
                  z[j] = variables.indexOf(getVariable(parents.get(j).getName()));
              }
  
              Ret ret = getLikelihoodNode(i, z);
              lik += ret.getLik();
              dof += ret.getDof();
          }
  
          return new Ret(lik, dof);
      }
  
      private int getDof2(Graph graph) {
          int dof = 0;
  
          for (Node node : graph.getNodes()) {
              List<Node> parents = graph.getParents(node);
  
              int i = variables.indexOf(getVariable(node.getName()));
  
              int[] z = new int[parents.size()];
  
              for (int j = 0; j < parents.size(); j++) {
                  z[j] = variables.indexOf(getVariable(parents.get(j).getName()));
              }
  
              dof += getDofNode(i, z);
          }
  
          return dof;
      }
  
      private Ret getLikelihoodNode(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];
  
          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 lik = 0.0;
          int dof = 0;
  
          for (int rowIndex = 0; rowIndex < r; rowIndex++) {
              int d = 0;
  
              for (int childValue = 0; childValue < c; childValue++) {
                  int cellCount = n_jk[rowIndex][childValue];
                  int rowCount = n_j[rowIndex];
  
                  if (cellCount == 0) continue;
                  lik += cellCount * Math.log(cellCount / (double) rowCount);
                  d++;
              }
  
              if (d > 0) dof += c - 1;
          }
  
          return new Ret(lik, dof);
      }
  
      private class Ret {
          private double lik;
          private int dof;
  
          public Ret(double lik, int dof) {
              this.lik = lik;
              this.dof = dof;
          }
  
          public double getLik() {
              return lik;
          }
  
          public int getDof() {
              return dof;
          }
      }
  
      private double getDofNode(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];
          }
  
          return r * c;
      }
  
  
      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 int getSampleSize() {
          return sampleSize;
      }
  
      public Node getVariable(String targetName) {
          for (Node node : variables) {
              if (node.getName().equals(targetName)) {
                  return node;
              }
          }
  
          return null;
      }
  
      private DiscreteVariable getVariable(int i) {
          if (variables.get(i) instanceof DiscreteVariable) {
              return (DiscreteVariable) variables.get(i);
          } else {
              return null;
          }
      }
  }
  
  
  
  
  
  