text: /*M///////////////////////////////////////////////////////////////////////////////////////
  //
  //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
  //
  //  By downloading, copying, installing or using the software you agree to this license.
  //  If you do not agree to this license, do not download, install,
  //  copy or use the software.
  //
  // This file originates from the openFABMAP project:
  // [http://code.google.com/p/openfabmap/]
  //
  // For published work which uses all or part of OpenFABMAP, please cite:
  // [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
  //
  // Original Algorithm by Mark Cummins and Paul Newman:
  // [http://ijr.sagepub.com/content/27/6/647.short]
  // [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
  // [http://ijr.sagepub.com/content/30/9/1100.abstract]
  //
  //                           License Agreement
  //
  // Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
  //                    Will Maddern [w.maddern@qut.edu.au], all rights reserved.
  //
  //
  // Redistribution and use in source and binary forms, with or without modification,
  // are permitted provided that the following conditions are met:
  //
  //   * Redistribution's of source code must retain the above copyright notice,
  //     this list of conditions and the following disclaimer.
  //
  //   * Redistribution's in binary form must reproduce the above copyright notice,
  //     this list of conditions and the following disclaimer in the documentation
  //     and/or other materials provided with the distribution.
  //
  //   * The name of the copyright holders may not be used to endorse or promote products
  //     derived from this software without specific prior written permission.
  //
  // This software is provided by the copyright holders and contributors "as is" and
  // any express or implied warranties, including, but not limited to, the implied
  // warranties of merchantability and fitness for a particular purpose are disclaimed.
  // In no event shall the Intel Corporation or contributors be liable for any direct,
  // indirect, incidental, special, exemplary, or consequential damages
  // (including, but not limited to, procurement of substitute goods or services;
  // loss of use, data, or profits; or business interruption) however caused
  // and on any theory of liability, whether in contract, strict liability,
  // or tort (including negligence or otherwise) arising in any way out of
  // the use of this software, even if advised of the possibility of such damage.
  //
  //M*/
  
  #include "precomp.hpp"
  #include "opencv2/contrib/openfabmap.hpp"
  
  namespace cv {
  
  namespace of2 {
  
  ChowLiuTree::ChowLiuTree() {
  }
  
  ChowLiuTree::~ChowLiuTree() {
  }
  
  void ChowLiuTree::add(const Mat& imgDescriptor) {
      CV_Assert(!imgDescriptor.empty());
      if (!imgDescriptors.empty()) {
          CV_Assert(imgDescriptors[0].cols == imgDescriptor.cols);
          CV_Assert(imgDescriptors[0].type() == imgDescriptor.type());
      }
  
      imgDescriptors.push_back(imgDescriptor);
  
  }
  
  void ChowLiuTree::add(const std::vector<Mat>& _imgDescriptors) {
      for (size_t i = 0; i < _imgDescriptors.size(); i++) {
          add(_imgDescriptors[i]);
      }
  }
  
  const std::vector<cv::Mat>& ChowLiuTree::getImgDescriptors() const {
      return imgDescriptors;
  }
  
  Mat ChowLiuTree::make(double infoThreshold) {
      CV_Assert(!imgDescriptors.empty());
  
      unsigned int descCount = 0;
      for (size_t i = 0; i < imgDescriptors.size(); i++)
          descCount += imgDescriptors[i].rows;
  
      mergedImgDescriptors = cv::Mat(descCount, imgDescriptors[0].cols,
          imgDescriptors[0].type());
      for (size_t i = 0, start = 0; i < imgDescriptors.size(); i++)
      {
          Mat submut = mergedImgDescriptors.rowRange((int)start,
              (int)(start + imgDescriptors[i].rows));
          imgDescriptors[i].copyTo(submut);
          start += imgDescriptors[i].rows;
      }
  
      std::list<info> edges;
      createBaseEdges(edges, infoThreshold);
  
      // TODO: if it cv_asserts here they really won't know why.
  
      CV_Assert(reduceEdgesToMinSpan(edges));
  
      return buildTree(edges.front().word1, edges);
  }
  
  double ChowLiuTree::P(int a, bool za) {
  
      if(za) {
          return (0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
              mergedImgDescriptors.rows) + 0.01;
      } else {
          return 1 - ((0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
              mergedImgDescriptors.rows) + 0.01);
      }
  
  }
  double ChowLiuTree::JP(int a, bool za, int b, bool zb) {
  
      double count = 0;
      for(int i = 0; i < mergedImgDescriptors.rows; i++) {
          if((mergedImgDescriptors.at<float>(i,a) > 0) == za &&
              (mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
                  count++;
          }
      }
      return count / mergedImgDescriptors.rows;
  
  }
  double ChowLiuTree::CP(int a, bool za, int b, bool zb){
  
      int count = 0, total = 0;
      for(int i = 0; i < mergedImgDescriptors.rows; i++) {
          if((mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
              total++;
              if((mergedImgDescriptors.at<float>(i,a) > 0) == za) {
                  count++;
              }
          }
      }
      if(total) {
          return (double)(0.98 * count)/total + 0.01;
      } else {
          return (za) ? 0.01 : 0.99;
      }
  }
  
  cv::Mat ChowLiuTree::buildTree(int root_word, std::list<info> &edges) {
  
      int q = root_word;
      cv::Mat cltree(4, (int)edges.size()+1, CV_64F);
  
      cltree.at<double>(0, q) = q;
      cltree.at<double>(1, q) = P(q, true);
      cltree.at<double>(2, q) = P(q, true);
      cltree.at<double>(3, q) = P(q, true);
      //setting P(zq|zpq) to P(zq) gives the root node of the chow-liu
      //independence from a parent node.
  
      //find all children and do the same
      std::vector<int> nextqs = extractChildren(edges, q);
  
      int pq = q;
      std::vector<int>::iterator nextq;
      for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
          recAddToTree(cltree, *nextq, pq, edges);
      }
  
      return cltree;
  
  
  }
  
  void ChowLiuTree::recAddToTree(cv::Mat &cltree, int q, int pq,
                                 std::list<info>& remaining_edges) {
  
      cltree.at<double>(0, q) = pq;
      cltree.at<double>(1, q) = P(q, true);
      cltree.at<double>(2, q) = CP(q, true, pq, true);
      cltree.at<double>(3, q) = CP(q, true, pq, false);
  
      //find all children and do the same
      std::vector<int> nextqs = extractChildren(remaining_edges, q);
  
      pq = q;
      std::vector<int>::iterator nextq;
      for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
          recAddToTree(cltree, *nextq, pq, remaining_edges);
      }
  }
  
  std::vector<int> ChowLiuTree::extractChildren(std::list<info> &remaining_edges, int q) {
  
      std::vector<int> children;
      std::list<info>::iterator edge = remaining_edges.begin();
  
      while(edge != remaining_edges.end()) {
          if(edge->word1 == q) {
              children.push_back(edge->word2);
              edge = remaining_edges.erase(edge);
              continue;
          }
          if(edge->word2 == q) {
              children.push_back(edge->word1);
              edge = remaining_edges.erase(edge);
              continue;
          }
          edge++;
      }
  
      return children;
  }
  
  bool ChowLiuTree::sortInfoScores(const info& first, const info& second) {
      return first.score > second.score;
  }
  
  double ChowLiuTree::calcMutInfo(int word1, int word2) {
      double accumulation = 0;
  
      double P00 = JP(word1, false, word2, false);
      if(P00) accumulation += P00 * std::log(P00 / (P(word1, false)*P(word2, false)));
  
      double P01 = JP(word1, false, word2, true);
      if(P01) accumulation += P01 * std::log(P01 / (P(word1, false)*P(word2, true)));
  
      double P10 = JP(word1, true, word2, false);
      if(P10) accumulation += P10 * std::log(P10 / (P(word1, true)*P(word2, false)));
  
      double P11 = JP(word1, true, word2, true);
      if(P11) accumulation += P11 * std::log(P11 / (P(word1, true)*P(word2, true)));
  
      return accumulation;
  }
  
  void ChowLiuTree::createBaseEdges(std::list<info>& edges, double infoThreshold) {
  
      int nWords = imgDescriptors[0].cols;
      info mutInfo;
  
      for(int word1 = 0; word1 < nWords; word1++) {
          for(int word2 = word1 + 1; word2 < nWords; word2++) {
              mutInfo.word1 = (short)word1;
              mutInfo.word2 = (short)word2;
              mutInfo.score = (float)calcMutInfo(word1, word2);
              if(mutInfo.score >= infoThreshold)
              edges.push_back(mutInfo);
          }
      }
      edges.sort(sortInfoScores);
  }
  
  bool ChowLiuTree::reduceEdgesToMinSpan(std::list<info>& edges) {
  
      std::map<int, int> groups;
      std::map<int, int>::iterator groupIt;
      for(int i = 0; i < imgDescriptors[0].cols; i++) groups[i] = i;
      int group1, group2;
  
      std::list<info>::iterator edge = edges.begin();
      while(edge != edges.end()) {
          if(groups[edge->word1] != groups[edge->word2]) {
              group1 = groups[edge->word1];
              group2 = groups[edge->word2];
              for(groupIt = groups.begin(); groupIt != groups.end(); groupIt++)
              if(groupIt->second == group2) groupIt->second = group1;
              edge++;
          } else {
              edge = edges.erase(edge);
          }
      }
  
      if(edges.size() != (unsigned int)imgDescriptors[0].cols - 1) {
          return false;
      } else {
          return true;
      }
  
  }
  
  }
  
  }
  