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.ContinuousVariable;
  import edu.cmu.tetrad.data.DataFilter;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DiscreteVariable;
  import edu.cmu.tetrad.graph.Node;
  
  import java.util.Arrays;
  
  /**
   * Creates a data set in which missing values in each column are filled using
   * the mode of that column.
   *
   * @author Joseph Ramsey
   */
  public final class ModeInterpolator implements DataFilter {
  
  
      public DataSet filter(DataSet dataSet) {
          DataSet newDataSet = dataSet.copy();
  
          for (int j = 0; j < dataSet.getNumColumns(); j++) {
              Node var = dataSet.getVariable(j);
              if (var instanceof DiscreteVariable) {
                  DiscreteVariable variable = (DiscreteVariable) var;
                  int numCategories = variable.getNumCategories();
                  int[] categoryCounts = new int[numCategories];
  
                  for (int i = 0; i < dataSet.getNumRows(); i++) {
                      if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                          continue;
                      }
  
                      categoryCounts[dataSet.getInt(i, j)]++;
                  }
  
                  int mode = -1;
                  int max = -1;
  
                  for (int i = 0; i < numCategories; i++) {
                      if (categoryCounts[i] > max) {
                          max = categoryCounts[i];
                          mode = i;
                      }
                  }
  
                  for (int i = 0; i < dataSet.getNumRows(); i++) {
                      if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                          newDataSet.setInt(i, j, mode);
                      }
  //                    else {
  //                        newDataSet.setInt(i, j, dataSet.getInt(i, j));
  //                    }
                  }
              } else if (dataSet.getVariable(j) instanceof ContinuousVariable) {
                  double[] data = new double[dataSet.getNumRows()];
                  int k = -1;
  
                  for (int i = 0; i < dataSet.getNumRows(); i++) {
                      if (!Double.isNaN(dataSet.getDouble(i, j))) {
                          data[++k] = dataSet.getDouble(i, j);
                      }
                  }
  
                  Arrays.sort(data);
  
                  double mode = Double.NaN;
  
                  if (k >= 0) {
                      mode = (data[(k + 1) / 2] + data[k / 2]) / 2.d;
                  }
  
                  for (int i = 0; i < dataSet.getNumRows(); i++) {
                      if (Double.isNaN(dataSet.getDouble(i, j))) {
                          newDataSet.setDouble(i, j, mode);
                      }
  //                    else {
  //                        newDataSet.setDouble(i, j, dataSet.getDouble(i, j));
  //                    }
                  }
              }
          }
  
          return newDataSet;
      }
  }
  
  
  
  
  
  