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.regression;
  
  import edu.cmu.tetrad.data.ColtDataSet;
  import edu.cmu.tetrad.data.DataFilter;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.MeanInterpolator;
  import edu.cmu.tetrad.graph.Node;
  
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * Creates a data set in which missing values in each column are filled using
   * the mean of that column.
   * @author Joseph Ramsey
   */
  public final class RegressionInterpolator implements DataFilter {
      public DataSet filter(DataSet dataSet) {
          DataSet d1 = dataSet.copy();
          DataSet d2 = dataSet.copy();
          d2 = new MeanInterpolator().filter(d2);
  
          // Copy out columns and names from mean-interpolated d2 to feed to
          // the regression class.
          int numVars = d2.getNumColumns();
          int numCases = d2.getNumRows();
          double[][] columns = new double[numVars][numCases];
          String[] names = new String[numVars];
  
          for (int i = 0; i < numCases; i++) {
              for (int j = 0; j < numVars; j++) {
                  columns[j][i] = d2.getDouble(i, j);
              }
          }
  
          for (int j = 0; j < numVars; j++) {
              names[j] = d2.getVariable(j).getName();
          }
  
          // An array to keep track of visited variables. visited[i] will be
          // set to true when variable i is visited.
          int j;
  
          while ((j = columnWithMaxMissing(d1)) != -1) {
  
              // Impute missing values in d1 using regression models from d2.
              String targetName = names[j];
              Node _target = dataSet.getVariable(targetName);
  //            double[][] regressors = new double[numVars - 1][numCases];
              String[] regressorNames = new String[numVars - 1];
  
              List<Node> _regressors = new ArrayList<>();
  
              int k = -1;
  
              for (int m = 0; m < numVars; m++) {
                  if (m == j) continue;
  
                  ++k;
  //                `regressors[k] = columns[m];
                  regressorNames[k] = names[m];
  
                  _regressors.add(dataSet.getVariable(regressorNames[k]));
              }
  
              Regression regression = new RegressionDataset(dataSet);
              RegressionResult result = regression.regress(_target, _regressors);
  //            RegressionResult result = regression.regress(target, targetName);
  
  
  //            System.out.println(result);
  
              for (int i = 0; i < numCases; i++) {
                  if (!Double.isNaN(d1.getDouble(i, j))) {
                      continue;
                  }
  
                  // Build array of case values.
                  k = -1;
                  double[] values = new double[numVars - 1];
  
                  for (int m = 0; m < numVars; m++) {
                      if (m == j) continue;
  
                      ++k;
                      values[k] = d2.getDouble(i, m);
                  }
  
                  double predicted = result.getPredictedValue(values);
                  d1.setDouble(i, j, predicted);
              }
  
              // Copy column into d2.
              for (int i = 0; i < numCases; i++) {
                  d2.setDouble(i, j, d1.getDouble(i, j));
              }
          }
  
          return d1;
  
      }
  
      private int columnWithMaxMissing(DataSet d1) {
          int max = -1;
          int maxCol = -1;
  
          for (int j = 0; j < d1.getNumColumns(); j++) {
              int n = 0;
  
              for (int i = 0; i < d1.getNumRows(); i++) {
                  if (Double.isNaN(d1.getDouble(i, j))) {
                      n++;
                  }
              }
  
              if (n > 0 && n > max) {
                  max = n;
                  maxCol = j;
              }
          }
  
          return maxCol;
      }
  }
  
  
  
  