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  //  If you do not agree to this license, do not download, install,
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  // are permitted provided that the following conditions are met:
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  //   * Redistribution's of source code must retain the above copyright notice,
  //     this list of conditions and the following disclaimer.
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  // indirect, incidental, special, exemplary, or consequential damages
  // (including, but not limited to, procurement of substitute goods or services;
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  #include "precomp.hpp"
  #include <ctype.h>
  
  using namespace cv;
  
  static const float ord_nan = FLT_MAX*0.5f;
  static const int min_block_size = 1 << 16;
  static const int block_size_delta = 1 << 10;
  
  CvDTreeTrainData::CvDTreeTrainData()
  {
      var_idx = var_type = cat_count = cat_ofs = cat_map =
          priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
      tree_storage = temp_storage = 0;
  
      clear();
  }
  
  
  CvDTreeTrainData::CvDTreeTrainData( const CvMat* _train_data, int _tflag,
                        const CvMat* _responses, const CvMat* _var_idx,
                        const CvMat* _sample_idx, const CvMat* _var_type,
                        const CvMat* _missing_mask, const CvDTreeParams& _params,
                        bool _shared, bool _add_labels )
  {
      var_idx = var_type = cat_count = cat_ofs = cat_map =
          priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
  
      tree_storage = temp_storage = 0;
  
      set_data( _train_data, _tflag, _responses, _var_idx, _sample_idx,
                _var_type, _missing_mask, _params, _shared, _add_labels );
  }
  
  
  CvDTreeTrainData::~CvDTreeTrainData()
  {
      clear();
  }
  
  
  bool CvDTreeTrainData::set_params( const CvDTreeParams& _params )
  {
      bool ok = false;
  
      CV_FUNCNAME( "CvDTreeTrainData::set_params" );
  
      __BEGIN__;
  
      // set parameters
      params = _params;
  
      if( params.max_categories < 2 )
          CV_ERROR( CV_StsOutOfRange, "params.max_categories should be >= 2" );
      params.max_categories = MIN( params.max_categories, 15 );
  
      if( params.max_depth < 0 )
          CV_ERROR( CV_StsOutOfRange, "params.max_depth should be >= 0" );
      params.max_depth = MIN( params.max_depth, 25 );
  
      params.min_sample_count = MAX(params.min_sample_count,1);
  
      if( params.cv_folds < 0 )
          CV_ERROR( CV_StsOutOfRange,
          "params.cv_folds should be =0 (the tree is not pruned) "
          "or n>0 (tree is pruned using n-fold cross-validation)" );
  
      if( params.cv_folds == 1 )
          params.cv_folds = 0;
  
      if( params.regression_accuracy < 0 )
          CV_ERROR( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
  
      ok = true;
  
      __END__;
  
      return ok;
  }
  
  #define CV_CMP_NUM_PTR(a,b) (*(a) < *(b))
  static CV_IMPLEMENT_QSORT_EX( icvSortIntPtr, int*, CV_CMP_NUM_PTR, int )
  static CV_IMPLEMENT_QSORT_EX( icvSortDblPtr, double*, CV_CMP_NUM_PTR, int )
  
  #define CV_CMP_NUM_IDX(i,j) (aux[i] < aux[j])
  static CV_IMPLEMENT_QSORT_EX( icvSortIntAux, int, CV_CMP_NUM_IDX, const float* )
  static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, const float* )
  
  #define CV_CMP_PAIRS(a,b) (*((a).i) < *((b).i))
  static CV_IMPLEMENT_QSORT_EX( icvSortPairs, CvPair16u32s, CV_CMP_PAIRS, int )
  
  void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
      const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx,
      const CvMat* _var_type, const CvMat* _missing_mask, const CvDTreeParams& _params,
      bool _shared, bool _add_labels, bool _update_data )
  {
      CvMat* sample_indices = 0;
      CvMat* var_type0 = 0;
      CvMat* tmp_map = 0;
      int** int_ptr = 0;
      CvPair16u32s* pair16u32s_ptr = 0;
      CvDTreeTrainData* data = 0;
      float *_fdst = 0;
      int *_idst = 0;
      unsigned short* udst = 0;
      int* idst = 0;
  
      CV_FUNCNAME( "CvDTreeTrainData::set_data" );
  
      __BEGIN__;
  
      int sample_all = 0, r_type, cv_n;
      int total_c_count = 0;
      int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0;
      int ds_step, dv_step, ms_step = 0, mv_step = 0; // {data|mask}{sample|var}_step
      int vi, i, size;
      char err[100];
      const int *sidx = 0, *vidx = 0;
      
      if( _update_data && data_root )
      {
          data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
              _sample_idx, _var_type, _missing_mask, _params, _shared, _add_labels );
  
          // compare new and old train data
          if( !(data->var_count == var_count &&
              cvNorm( data->var_type, var_type, CV_C ) < FLT_EPSILON &&
              cvNorm( data->cat_count, cat_count, CV_C ) < FLT_EPSILON &&
              cvNorm( data->cat_map, cat_map, CV_C ) < FLT_EPSILON) )
              CV_ERROR( CV_StsBadArg,
              "The new training data must have the same types and the input and output variables "
              "and the same categories for categorical variables" );
  
          cvReleaseMat( &priors );
          cvReleaseMat( &priors_mult );
          cvReleaseMat( &buf );
          cvReleaseMat( &direction );
          cvReleaseMat( &split_buf );
          cvReleaseMemStorage( &temp_storage );
  
          priors = data->priors; data->priors = 0;
          priors_mult = data->priors_mult; data->priors_mult = 0;
          buf = data->buf; data->buf = 0;
          buf_count = data->buf_count; buf_size = data->buf_size;
          sample_count = data->sample_count;
  
          direction = data->direction; data->direction = 0;
          split_buf = data->split_buf; data->split_buf = 0;
          temp_storage = data->temp_storage; data->temp_storage = 0;
          nv_heap = data->nv_heap; cv_heap = data->cv_heap;
  
          data_root = new_node( 0, sample_count, 0, 0 );
          EXIT;
      }
  
      clear();
  
      var_all = 0;
      rng = &cv::theRNG();
  
      CV_CALL( set_params( _params ));
  
      // check parameter types and sizes
      CV_CALL( cvCheckTrainData( _train_data, _tflag, _missing_mask, &var_all, &sample_all ));
  
      train_data = _train_data;
      responses = _responses;
  
      if( _tflag == CV_ROW_SAMPLE )
      {
          ds_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
          dv_step = 1;
          if( _missing_mask )
              ms_step = _missing_mask->step, mv_step = 1;
      }
      else
      {
          dv_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
          ds_step = 1;
          if( _missing_mask )
              mv_step = _missing_mask->step, ms_step = 1;
      }
      tflag = _tflag;
  
      sample_count = sample_all;
      var_count = var_all;
      
      if( _sample_idx )
      {
          CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all ));
          sidx = sample_indices->data.i;
          sample_count = sample_indices->rows + sample_indices->cols - 1;
      }
  
      if( _var_idx )
      {
          CV_CALL( var_idx = cvPreprocessIndexArray( _var_idx, var_all ));
          vidx = var_idx->data.i;
          var_count = var_idx->rows + var_idx->cols - 1;
      }
  
      is_buf_16u = false;     
      if ( sample_count < 65536 ) 
          is_buf_16u = true;                                
      
      if( !CV_IS_MAT(_responses) ||
          (CV_MAT_TYPE(_responses->type) != CV_32SC1 &&
           CV_MAT_TYPE(_responses->type) != CV_32FC1) ||
          (_responses->rows != 1 && _responses->cols != 1) ||
          _responses->rows + _responses->cols - 1 != sample_all )
          CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
                    "floating-point vector containing as many elements as "
                    "the total number of samples in the training data matrix" );
     
      r_type = CV_VAR_CATEGORICAL;
      if( _var_type )
          CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type ));
  
      CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 ));
      
      cat_var_count = 0;
      ord_var_count = -1;
  
      is_classifier = r_type == CV_VAR_CATEGORICAL;
  
      // step 0. calc the number of categorical vars
      for( vi = 0; vi < var_count; vi++ )
      {
          char vt = var_type0 ? var_type0->data.ptr[vi] : CV_VAR_ORDERED;
          var_type->data.i[vi] = vt == CV_VAR_CATEGORICAL ? cat_var_count++ : ord_var_count--;
      }
  
      ord_var_count = ~ord_var_count;
      cv_n = params.cv_folds;
      // set the two last elements of var_type array to be able
      // to locate responses and cross-validation labels using
      // the corresponding get_* functions.
      var_type->data.i[var_count] = cat_var_count;
      var_type->data.i[var_count+1] = cat_var_count+1;
  
      // in case of single ordered predictor we need dummy cv_labels
      // for safe split_node_data() operation
      have_labels = cv_n > 0 || (ord_var_count == 1 && cat_var_count == 0) || _add_labels;
  
      work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
                                 + (have_labels ? 1 : 0); // for cv_labels
                                 
      buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count;
      shared = _shared;
      buf_count = shared ? 2 : 1;
      
      if ( is_buf_16u )
      {
          CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
          CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
      }
      else
      {
          CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
          CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
      }    
  
      size = is_classifier ? (cat_var_count+1) : cat_var_count;
      size = !size ? 1 : size;
      CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 ));
      CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 ));
          
      size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories;
      size = !size ? 1 : size;
      CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 ));
  
      // now calculate the maximum size of split,
      // create memory storage that will keep nodes and splits of the decision tree
      // allocate root node and the buffer for the whole training data
      max_split_size = cvAlign(sizeof(CvDTreeSplit) +
          (MAX(0,sample_count - 33)/32)*sizeof(int),sizeof(void*));
      tree_block_size = MAX((int)sizeof(CvDTreeNode)*8, max_split_size);
      tree_block_size = MAX(tree_block_size + block_size_delta, min_block_size);
      CV_CALL( tree_storage = cvCreateMemStorage( tree_block_size ));
      CV_CALL( node_heap = cvCreateSet( 0, sizeof(*node_heap), sizeof(CvDTreeNode), tree_storage ));
  
      nv_size = var_count*sizeof(int);
      nv_size = cvAlign(MAX( nv_size, (int)sizeof(CvSetElem) ), sizeof(void*));
  
      temp_block_size = nv_size;
  
      if( cv_n )
      {
          if( sample_count < cv_n*MAX(params.min_sample_count,10) )
              CV_ERROR( CV_StsOutOfRange,
                  "The many folds in cross-validation for such a small dataset" );
  
          cv_size = cvAlign( cv_n*(sizeof(int) + sizeof(double)*2), sizeof(double) );
          temp_block_size = MAX(temp_block_size, cv_size);
      }
  
      temp_block_size = MAX( temp_block_size + block_size_delta, min_block_size );
      CV_CALL( temp_storage = cvCreateMemStorage( temp_block_size ));
      CV_CALL( nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nv_size, temp_storage ));
      if( cv_size )
          CV_CALL( cv_heap = cvCreateSet( 0, sizeof(*cv_heap), cv_size, temp_storage ));
  
      CV_CALL( data_root = new_node( 0, sample_count, 0, 0 ));
  
      max_c_count = 1;
  
      _fdst = 0;
      _idst = 0;
      if (ord_var_count)
          _fdst = (float*)cvAlloc(sample_count*sizeof(_fdst[0]));
      if (is_buf_16u && (cat_var_count || is_classifier))
          _idst = (int*)cvAlloc(sample_count*sizeof(_idst[0]));
  
      // transform the training data to convenient representation
      for( vi = 0; vi <= var_count; vi++ )
      {
          int ci;
          const uchar* mask = 0;
          int m_step = 0, step;
          const int* idata = 0;
          const float* fdata = 0;
          int num_valid = 0;
  
          if( vi < var_count ) // analyze i-th input variable
          {
              int vi0 = vidx ? vidx[vi] : vi;
              ci = get_var_type(vi);
              step = ds_step; m_step = ms_step;
              if( CV_MAT_TYPE(_train_data->type) == CV_32SC1 )
                  idata = _train_data->data.i + vi0*dv_step;
              else
                  fdata = _train_data->data.fl + vi0*dv_step;
              if( _missing_mask )
                  mask = _missing_mask->data.ptr + vi0*mv_step;
          }
          else // analyze _responses
          {
              ci = cat_var_count;
              step = CV_IS_MAT_CONT(_responses->type) ?
                  1 : _responses->step / CV_ELEM_SIZE(_responses->type);
              if( CV_MAT_TYPE(_responses->type) == CV_32SC1 )
                  idata = _responses->data.i;
              else
                  fdata = _responses->data.fl;
          }
  
          if( (vi < var_count && ci>=0) ||
              (vi == var_count && is_classifier) ) // process categorical variable or response
          {
              int c_count, prev_label;
              int* c_map;
              
              if (is_buf_16u)
                  udst = (unsigned short*)(buf->data.s + vi*sample_count);
              else
                  idst = buf->data.i + vi*sample_count;
              
              // copy data
              for( i = 0; i < sample_count; i++ )
              {
                  int val = INT_MAX, si = sidx ? sidx[i] : i;
                  if( !mask || !mask[si*m_step] )
                  {
                      if( idata )
                          val = idata[si*step];
                      else
                      {
                          float t = fdata[si*step];
                          val = cvRound(t);
                          if( fabs(t - val) > FLT_EPSILON )
                          {
                              sprintf( err, "%d-th value of %d-th (categorical) "
                                  "variable is not an integer", i, vi );
                              CV_ERROR( CV_StsBadArg, err );
                          }
                      }
  
                      if( val == INT_MAX )
                      {
                          sprintf( err, "%d-th value of %d-th (categorical) "
                              "variable is too large", i, vi );
                          CV_ERROR( CV_StsBadArg, err );
                      }
                      num_valid++;
                  }
                  if (is_buf_16u)
                  {
                      _idst[i] = val;
                      pair16u32s_ptr[i].u = udst + i;
                      pair16u32s_ptr[i].i = _idst + i;
                  }   
                  else
                  {
                      idst[i] = val;
                      int_ptr[i] = idst + i;
                  }
              }
  
              c_count = num_valid > 0;
              if (is_buf_16u)
              {
                  icvSortPairs( pair16u32s_ptr, sample_count, 0 );
                  // count the categories
                  for( i = 1; i < num_valid; i++ )
                      if (*pair16u32s_ptr[i].i != *pair16u32s_ptr[i-1].i)
                          c_count ++ ;
              }
              else
              {
                  icvSortIntPtr( int_ptr, sample_count, 0 );
                  // count the categories
                  for( i = 1; i < num_valid; i++ )
                      c_count += *int_ptr[i] != *int_ptr[i-1];
              }
  
              if( vi > 0 )
                  max_c_count = MAX( max_c_count, c_count );
              cat_count->data.i[ci] = c_count;
              cat_ofs->data.i[ci] = total_c_count;
  
              // resize cat_map, if need
              if( cat_map->cols < total_c_count + c_count )
              {
                  tmp_map = cat_map;
                  CV_CALL( cat_map = cvCreateMat( 1,
                      MAX(cat_map->cols*3/2,total_c_count+c_count), CV_32SC1 ));
                  for( i = 0; i < total_c_count; i++ )
                      cat_map->data.i[i] = tmp_map->data.i[i];
                  cvReleaseMat( &tmp_map );
              }
  
              c_map = cat_map->data.i + total_c_count;
              total_c_count += c_count;
  
              c_count = -1;
              if (is_buf_16u)
              {
                  // compact the class indices and build the map
                  prev_label = ~*pair16u32s_ptr[0].i;
                  for( i = 0; i < num_valid; i++ )
                  {
                      int cur_label = *pair16u32s_ptr[i].i;
                      if( cur_label != prev_label )
                          c_map[++c_count] = prev_label = cur_label;
                      *pair16u32s_ptr[i].u = (unsigned short)c_count;
                  }
                  // replace labels for missing values with -1
                  for( ; i < sample_count; i++ )
                      *pair16u32s_ptr[i].u = 65535;
              }
              else
              {
                  // compact the class indices and build the map
                  prev_label = ~*int_ptr[0];
                  for( i = 0; i < num_valid; i++ )
                  {
                      int cur_label = *int_ptr[i];
                      if( cur_label != prev_label )
                          c_map[++c_count] = prev_label = cur_label;
                      *int_ptr[i] = c_count;
                  }
                  // replace labels for missing values with -1
                  for( ; i < sample_count; i++ )
                      *int_ptr[i] = -1;
              }           
          }
          else if( ci < 0 ) // process ordered variable
          {
              if (is_buf_16u)
                  udst = (unsigned short*)(buf->data.s + vi*sample_count);
              else
                  idst = buf->data.i + vi*sample_count;
  
              for( i = 0; i < sample_count; i++ )
              {
                  float val = ord_nan;
                  int si = sidx ? sidx[i] : i;
                  if( !mask || !mask[si*m_step] )
                  {
                      if( idata )
                          val = (float)idata[si*step];
                      else
                          val = fdata[si*step];
  
                      if( fabs(val) >= ord_nan )
                      {
                          sprintf( err, "%d-th value of %d-th (ordered) "
                              "variable (=%g) is too large", i, vi, val );
                          CV_ERROR( CV_StsBadArg, err );
                      }
                      num_valid++;
                  }
  
                  if (is_buf_16u)
                      udst[i] = (unsigned short)i;
                  else
                      idst[i] = i;
                  _fdst[i] = val;
                  
              }
              if (is_buf_16u)
                  icvSortUShAux( udst, sample_count, _fdst);
              else
                  icvSortIntAux( idst, sample_count, _fdst );
          }
         
          if( vi < var_count )
              data_root->set_num_valid(vi, num_valid);
      }
  
      // set sample labels
      if (is_buf_16u)
          udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
      else
          idst = buf->data.i + work_var_count*sample_count;
  
      for (i = 0; i < sample_count; i++)
      {
          if (udst)
              udst[i] = sidx ? (unsigned short)sidx[i] : (unsigned short)i;
          else
              idst[i] = sidx ? sidx[i] : i;
      }
  
      if( cv_n )
      {
          unsigned short* udst = 0;
          int* idst = 0;
  
          if (is_buf_16u)
          {
              udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
              for( i = vi = 0; i < sample_count; i++ )
              {
                  udst[i] = (unsigned short)vi++;
                  vi &= vi < cv_n ? -1 : 0;
              }
  
              for( i = 0; i < sample_count; i++ )
              {
                  int a = (*rng)(sample_count);
                  int b = (*rng)(sample_count);
                  unsigned short unsh = (unsigned short)vi;
                  CV_SWAP( udst[a], udst[b], unsh );
              }
          }
          else
          {
              idst = buf->data.i + (get_work_var_count()-1)*sample_count;
              for( i = vi = 0; i < sample_count; i++ )
              {
                  idst[i] = vi++;
                  vi &= vi < cv_n ? -1 : 0;
              }
  
              for( i = 0; i < sample_count; i++ )
              {
                  int a = (*rng)(sample_count);
                  int b = (*rng)(sample_count);
                  CV_SWAP( idst[a], idst[b], vi );
              }
          }
      }
  
      if ( cat_map ) 
          cat_map->cols = MAX( total_c_count, 1 );
  
      max_split_size = cvAlign(sizeof(CvDTreeSplit) +
          (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
      CV_CALL( split_heap = cvCreateSet( 0, sizeof(*split_heap), max_split_size, tree_storage ));
  
      have_priors = is_classifier && params.priors;
      if( is_classifier )
      {
          int m = get_num_classes();
          double sum = 0;
          CV_CALL( priors = cvCreateMat( 1, m, CV_64F ));
          for( i = 0; i < m; i++ )
          {
              double val = have_priors ? params.priors[i] : 1.;
              if( val <= 0 )
                  CV_ERROR( CV_StsOutOfRange, "Every class weight should be positive" );
              priors->data.db[i] = val;
              sum += val;
          }
  
          // normalize weights
          if( have_priors )
              cvScale( priors, priors, 1./sum );
  
          CV_CALL( priors_mult = cvCloneMat( priors ));
          CV_CALL( counts = cvCreateMat( 1, m, CV_32SC1 ));
      }
  
  
      CV_CALL( direction = cvCreateMat( 1, sample_count, CV_8UC1 ));
      CV_CALL( split_buf = cvCreateMat( 1, sample_count, CV_32SC1 ));
  
      __END__;
  
      if( data )
          delete data;
  
      if (_fdst)
          cvFree( &_fdst );
      if (_idst)
          cvFree( &_idst );
      cvFree( &int_ptr );
      cvFree( &pair16u32s_ptr);
      cvReleaseMat( &var_type0 );
      cvReleaseMat( &sample_indices );
      cvReleaseMat( &tmp_map );
  }
  
  void CvDTreeTrainData::do_responses_copy()
  {
      responses_copy = cvCreateMat( responses->rows, responses->cols, responses->type );
      cvCopy( responses, responses_copy);
      responses = responses_copy;
  }
  
  CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
  {
      CvDTreeNode* root = 0;
      CvMat* isubsample_idx = 0;
      CvMat* subsample_co = 0;
  
      bool isMakeRootCopy = true;
  
      CV_FUNCNAME( "CvDTreeTrainData::subsample_data" );
  
      __BEGIN__;
  
      if( !data_root )
          CV_ERROR( CV_StsError, "No training data has been set" );
  
      if( _subsample_idx )
      {
          CV_CALL( isubsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ));
  
          if( isubsample_idx->cols + isubsample_idx->rows - 1 == sample_count )
          {
              const int* sidx = isubsample_idx->data.i;
              for( int i = 0; i < sample_count; i++ )
              {
                  if( sidx[i] != i )
                  {
                      isMakeRootCopy = false;
                      break;
                  }
              }
          }
          else
              isMakeRootCopy = false;
      }
  
      if( isMakeRootCopy )
      {
          // make a copy of the root node
          CvDTreeNode temp;
          int i;
          root = new_node( 0, 1, 0, 0 );
          temp = *root;
          *root = *data_root;
          root->num_valid = temp.num_valid;
          if( root->num_valid )
          {
              for( i = 0; i < var_count; i++ )
                  root->num_valid[i] = data_root->num_valid[i];
          }
          root->cv_Tn = temp.cv_Tn;
          root->cv_node_risk = temp.cv_node_risk;
          root->cv_node_error = temp.cv_node_error;
      }
      else
      {
          int* sidx = isubsample_idx->data.i;
          // co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
          int* co, cur_ofs = 0;
          int vi, i;
          int work_var_count = get_work_var_count();
          int count = isubsample_idx->rows + isubsample_idx->cols - 1;
  
          root = new_node( 0, count, 1, 0 );
  
          CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
          cvZero( subsample_co );
          co = subsample_co->data.i;
          for( i = 0; i < count; i++ )
              co[sidx[i]*2]++;
          for( i = 0; i < sample_count; i++ )
          {
              if( co[i*2] )
              {
                  co[i*2+1] = cur_ofs;
                  cur_ofs += co[i*2];
              }
              else
                  co[i*2+1] = -1;
          }
  
          cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
          for( vi = 0; vi < work_var_count; vi++ )
          {
              int ci = get_var_type(vi);
  
              if( ci >= 0 || vi >= var_count )
              {
                  int num_valid = 0;
                  const int* src = get_cat_var_data( data_root, vi, (int*)(uchar*)inn_buf );
  
                  if (is_buf_16u)
                  {
                      unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + 
                          vi*sample_count + root->offset);
                      for( i = 0; i < count; i++ )
                      {
                          int val = src[sidx[i]];
                          udst[i] = (unsigned short)val;
                          num_valid += val >= 0;
                      }
                  }
                  else
                  {
                      int* idst = buf->data.i + root->buf_idx*buf->cols + 
                          vi*sample_count + root->offset;
                      for( i = 0; i < count; i++ )
                      {
                          int val = src[sidx[i]];
                          idst[i] = val;
                          num_valid += val >= 0;
                      }
                  }
  
                  if( vi < var_count )
                      root->set_num_valid(vi, num_valid);
              }
              else
              {
                  int *src_idx_buf = (int*)(uchar*)inn_buf;
                  float *src_val_buf = (float*)(src_idx_buf + sample_count);
                  int* sample_indices_buf = (int*)(src_val_buf + sample_count);
                  const int* src_idx = 0;
                  const float* src_val = 0;
                  get_ord_var_data( data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf );
                  int j = 0, idx, count_i;
                  int num_valid = data_root->get_num_valid(vi);
  
                  if (is_buf_16u)
                  {
                      unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + 
                          vi*sample_count + data_root->offset);
                      for( i = 0; i < num_valid; i++ )
                      {
                          idx = src_idx[i];
                          count_i = co[idx*2];
                          if( count_i )
                              for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                  udst_idx[j] = (unsigned short)cur_ofs;
                      }
  
                      root->set_num_valid(vi, j);
  
                      for( ; i < sample_count; i++ )
                      {
                          idx = src_idx[i];
                          count_i = co[idx*2];
                          if( count_i )
                              for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                  udst_idx[j] = (unsigned short)cur_ofs;
                      }
                  }
                  else
                  {
                      int* idst_idx = buf->data.i + root->buf_idx*buf->cols + 
                          vi*sample_count + root->offset;
                      for( i = 0; i < num_valid; i++ )
                      {
                          idx = src_idx[i];
                          count_i = co[idx*2];
                          if( count_i )
                              for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                  idst_idx[j] = cur_ofs;
                      }
  
                      root->set_num_valid(vi, j);
  
                      for( ; i < sample_count; i++ )
                      {
                          idx = src_idx[i];
                          count_i = co[idx*2];
                          if( count_i )
                              for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                  idst_idx[j] = cur_ofs;
                      }
                  }
              }
          }
          // sample indices subsampling
          const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
          if (is_buf_16u)
          {
              unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + 
                  get_work_var_count()*sample_count + root->offset);            
              for (i = 0; i < count; i++)
                  sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
          }
          else
          {
              int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols + 
                  get_work_var_count()*sample_count + root->offset;            
              for (i = 0; i < count; i++)
                  sample_idx_dst[i] = sample_idx_src[sidx[i]];
          }
      }
  
      __END__;
  
      cvReleaseMat( &isubsample_idx );
      cvReleaseMat( &subsample_co );
  
      return root;
  }
  
  
  void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
                                      float* values, uchar* missing,
                                      float* responses, bool get_class_idx )
  {
      CvMat* subsample_idx = 0;
      CvMat* subsample_co = 0;
  
      CV_FUNCNAME( "CvDTreeTrainData::get_vectors" );
  
      __BEGIN__;
  
      int i, vi, total = sample_count, count = total, cur_ofs = 0;
      int* sidx = 0;
      int* co = 0;
  
      cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
      if( _subsample_idx )
      {
          CV_CALL( subsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ));
          sidx = subsample_idx->data.i;
          CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
          co = subsample_co->data.i;
          cvZero( subsample_co );
          count = subsample_idx->cols + subsample_idx->rows - 1;
          for( i = 0; i < count; i++ )
              co[sidx[i]*2]++;
          for( i = 0; i < total; i++ )
          {
              int count_i = co[i*2];
              if( count_i )
              {
                  co[i*2+1] = cur_ofs*var_count;
                  cur_ofs += count_i;
              }
          }
      }
  
      if( missing )
          memset( missing, 1, count*var_count );
  
      for( vi = 0; vi < var_count; vi++ )
      {
          int ci = get_var_type(vi);
          if( ci >= 0 ) // categorical
          {
              float* dst = values + vi;
              uchar* m = missing ? missing + vi : 0;
              const int* src = get_cat_var_data(data_root, vi, (int*)(uchar*)inn_buf);
  
              for( i = 0; i < count; i++, dst += var_count )
              {
                  int idx = sidx ? sidx[i] : i;
                  int val = src[idx];
                  *dst = (float)val;
                  if( m )
                  {
                      *m = (!is_buf_16u && val < 0) || (is_buf_16u && (val == 65535));
                      m += var_count;
                  }
              }
          }
          else // ordered
          {
              float* dst = values + vi;
              uchar* m = missing ? missing + vi : 0;
              int count1 = data_root->get_num_valid(vi);
              float *src_val_buf = (float*)(uchar*)inn_buf;
              int* src_idx_buf = (int*)(src_val_buf + sample_count);
              int* sample_indices_buf = src_idx_buf + sample_count;
              const float *src_val = 0;
              const int* src_idx = 0;
              get_ord_var_data(data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf);
  
              for( i = 0; i < count1; i++ )
              {
                  int idx = src_idx[i];
                  int count_i = 1;
                  if( co )
                  {
                      count_i = co[idx*2];
                      cur_ofs = co[idx*2+1];
                  }
                  else
                      cur_ofs = idx*var_count;
                  if( count_i )
                  {
                      float val = src_val[i];
                      for( ; count_i > 0; count_i--, cur_ofs += var_count )
                      {
                          dst[cur_ofs] = val;
                          if( m )
                              m[cur_ofs] = 0;
                      }
                  }
              }
          }
      }
  
      // copy responses
      if( responses )
      {
          if( is_classifier )
          {
              const int* src = get_class_labels(data_root, (int*)(uchar*)inn_buf);
              for( i = 0; i < count; i++ )
              {
                  int idx = sidx ? sidx[i] : i;
                  int val = get_class_idx ? src[idx] :
                      cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]];
                  responses[i] = (float)val;
              }
          }
          else
          {
              float* val_buf = (float*)(uchar*)inn_buf;
              int* sample_idx_buf = (int*)(val_buf + sample_count);
              const float* _values = get_ord_responses(data_root, val_buf, sample_idx_buf);
              for( i = 0; i < count; i++ )
              {
                  int idx = sidx ? sidx[i] : i;
                  responses[i] = _values[idx];
              }
          }
      }
  
      __END__;
  
      cvReleaseMat( &subsample_idx );
      cvReleaseMat( &subsample_co );
  }
  
  
  CvDTreeNode* CvDTreeTrainData::new_node( CvDTreeNode* parent, int count,
                                           int storage_idx, int offset )
  {
      CvDTreeNode* node = (CvDTreeNode*)cvSetNew( node_heap );
  
      node->sample_count = count;
      node->depth = parent ? parent->depth + 1 : 0;
      node->parent = parent;
      node->left = node->right = 0;
      node->split = 0;
      node->value = 0;
      node->class_idx = 0;
      node->maxlr = 0.;
  
      node->buf_idx = storage_idx;
      node->offset = offset;
      if( nv_heap )
          node->num_valid = (int*)cvSetNew( nv_heap );
      else
          node->num_valid = 0;
      node->alpha = node->node_risk = node->tree_risk = node->tree_error = 0.;
      node->complexity = 0;
  
      if( params.cv_folds > 0 && cv_heap )
      {
          int cv_n = params.cv_folds;
          node->Tn = INT_MAX;
          node->cv_Tn = (int*)cvSetNew( cv_heap );
          node->cv_node_risk = (double*)cvAlignPtr(node->cv_Tn + cv_n, sizeof(double));
          node->cv_node_error = node->cv_node_risk + cv_n;
      }
      else
      {
          node->Tn = 0;
          node->cv_Tn = 0;
          node->cv_node_risk = 0;
          node->cv_node_error = 0;
      }
  
      return node;
  }
  
  
  CvDTreeSplit* CvDTreeTrainData::new_split_ord( int vi, float cmp_val,
                  int split_point, int inversed, float quality )
  {
      CvDTreeSplit* split = (CvDTreeSplit*)cvSetNew( split_heap );
      split->var_idx = vi;
      split->condensed_idx = INT_MIN;
      split->ord.c = cmp_val;
      split->ord.split_point = split_point;
      split->inversed = inversed;
      split->quality = quality;
      split->next = 0;
  
      return split;
  }
  
  
  CvDTreeSplit* CvDTreeTrainData::new_split_cat( int vi, float quality )
  {
      CvDTreeSplit* split = (CvDTreeSplit*)cvSetNew( split_heap );
      int i, n = (max_c_count + 31)/32;
  
      split->var_idx = vi;
      split->condensed_idx = INT_MIN;
      split->inversed = 0;
      split->quality = quality;
      for( i = 0; i < n; i++ )
          split->subset[i] = 0;
      split->next = 0;
  
      return split;
  }
  
  
  void CvDTreeTrainData::free_node( CvDTreeNode* node )
  {
      CvDTreeSplit* split = node->split;
      free_node_data( node );
      while( split )
      {
          CvDTreeSplit* next = split->next;
          cvSetRemoveByPtr( split_heap, split );
          split = next;
      }
      node->split = 0;
      cvSetRemoveByPtr( node_heap, node );
  }
  
  
  void CvDTreeTrainData::free_node_data( CvDTreeNode* node )
  {
      if( node->num_valid )
      {
          cvSetRemoveByPtr( nv_heap, node->num_valid );
          node->num_valid = 0;
      }
      // do not free cv_* fields, as all the cross-validation related data is released at once.
  }
  
  
  void CvDTreeTrainData::free_train_data()
  {
      cvReleaseMat( &counts );
      cvReleaseMat( &buf );
      cvReleaseMat( &direction );
      cvReleaseMat( &split_buf );
      cvReleaseMemStorage( &temp_storage );
      cvReleaseMat( &responses_copy );
      cv_heap = nv_heap = 0;
  }
  
  
  void CvDTreeTrainData::clear()
  {
      free_train_data();
  
      cvReleaseMemStorage( &tree_storage );
  
      cvReleaseMat( &var_idx );
      cvReleaseMat( &var_type );
      cvReleaseMat( &cat_count );
      cvReleaseMat( &cat_ofs );
      cvReleaseMat( &cat_map );
      cvReleaseMat( &priors );
      cvReleaseMat( &priors_mult );
      
      node_heap = split_heap = 0;
  
      sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0;
      have_labels = have_priors = is_classifier = false;
  
      buf_count = buf_size = 0;
      shared = false;
      
      data_root = 0;
  
      rng = &cv::theRNG();
  }
  
  
  int CvDTreeTrainData::get_num_classes() const
  {
      return is_classifier ? cat_count->data.i[cat_var_count] : 0;
  }
  
  
  int CvDTreeTrainData::get_var_type(int vi) const
  {
      return var_type->data.i[vi];
  }
  
  void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* sorted_indices_buf,
                                           const float** ord_values, const int** sorted_indices, int* sample_indices_buf )
  {
      int vidx = var_idx ? var_idx->data.i[vi] : vi;
      int node_sample_count = n->sample_count; 
      int td_step = train_data->step/CV_ELEM_SIZE(train_data->type);
  
      const int* sample_indices = get_sample_indices(n, sample_indices_buf);
  
      if( !is_buf_16u )
          *sorted_indices = buf->data.i + n->buf_idx*buf->cols +
          vi*sample_count + n->offset;
      else {
          const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + 
              vi*sample_count + n->offset );
          for( int i = 0; i < node_sample_count; i++ )
              sorted_indices_buf[i] = short_indices[i];
          *sorted_indices = sorted_indices_buf;
      }
      
      if( tflag == CV_ROW_SAMPLE )
      {
          for( int i = 0; i < node_sample_count && 
              ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
          {
              int idx = (*sorted_indices)[i];
              idx = sample_indices[idx];
              ord_values_buf[i] = *(train_data->data.fl + idx * td_step + vidx);
          }
      }
      else
          for( int i = 0; i < node_sample_count && 
              ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
          {
              int idx = (*sorted_indices)[i];
              idx = sample_indices[idx];
              ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx);
          }
      
      *ord_values = ord_values_buf;
  }
  
  
  const int* CvDTreeTrainData::get_class_labels( CvDTreeNode* n, int* labels_buf )
  {
      if (is_classifier)
          return get_cat_var_data( n, var_count, labels_buf);
      return 0;
  }
  
  const int* CvDTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_buf )
  {
      return get_cat_var_data( n, get_work_var_count(), indices_buf );
  }
  
  const float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n, float* values_buf, int*sample_indices_buf )
  {
      int sample_count = n->sample_count;
      int r_step = CV_IS_MAT_CONT(responses->type) ? 1 : responses->step/CV_ELEM_SIZE(responses->type);
      const int* indices = get_sample_indices(n, sample_indices_buf);
  
      for( int i = 0; i < sample_count && 
          (((indices[i] >= 0) && !is_buf_16u) || ((indices[i] != 65535) && is_buf_16u)); i++ )
      {
          int idx = indices[i];
          values_buf[i] = *(responses->data.fl + idx * r_step);
      }
      
      return values_buf;
  }
  
  
  const int* CvDTreeTrainData::get_cv_labels( CvDTreeNode* n, int* labels_buf )
  {
      if (have_labels)
          return get_cat_var_data( n, get_work_var_count()- 1, labels_buf);
      return 0;
  }
  
  
  const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf)
  {
      const int* cat_values = 0;
      if( !is_buf_16u )
          cat_values = buf->data.i + n->buf_idx*buf->cols +
              vi*sample_count + n->offset;
      else {
          const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + 
              vi*sample_count + n->offset);
          for( int i = 0; i < n->sample_count; i++ )
              cat_values_buf[i] = short_values[i];
          cat_values = cat_values_buf;
      }
      return cat_values;
  }
  
  
  int CvDTreeTrainData::get_child_buf_idx( CvDTreeNode* n )
  {
      int idx = n->buf_idx + 1;
      if( idx >= buf_count )
          idx = shared ? 1 : 0;
      return idx;
  }
  
  
  void CvDTreeTrainData::write_params( CvFileStorage* fs ) const
  {
      CV_FUNCNAME( "CvDTreeTrainData::write_params" );
  
      __BEGIN__;
  
      int vi, vcount = var_count;
  
      cvWriteInt( fs, "is_classifier", is_classifier ? 1 : 0 );
      cvWriteInt( fs, "var_all", var_all );
      cvWriteInt( fs, "var_count", var_count );
      cvWriteInt( fs, "ord_var_count", ord_var_count );
      cvWriteInt( fs, "cat_var_count", cat_var_count );
  
      cvStartWriteStruct( fs, "training_params", CV_NODE_MAP );
      cvWriteInt( fs, "use_surrogates", params.use_surrogates ? 1 : 0 );
  
      if( is_classifier )
      {
          cvWriteInt( fs, "max_categories", params.max_categories );
      }
      else
      {
          cvWriteReal( fs, "regression_accuracy", params.regression_accuracy );
      }
  
      cvWriteInt( fs, "max_depth", params.max_depth );
      cvWriteInt( fs, "min_sample_count", params.min_sample_count );
      cvWriteInt( fs, "cross_validation_folds", params.cv_folds );
  
      if( params.cv_folds > 1 )
      {
          cvWriteInt( fs, "use_1se_rule", params.use_1se_rule ? 1 : 0 );
          cvWriteInt( fs, "truncate_pruned_tree", params.truncate_pruned_tree ? 1 : 0 );
      }
  
      if( priors )
          cvWrite( fs, "priors", priors );
  
      cvEndWriteStruct( fs );
  
      if( var_idx )
          cvWrite( fs, "var_idx", var_idx );
  
      cvStartWriteStruct( fs, "var_type", CV_NODE_SEQ+CV_NODE_FLOW );
  
      for( vi = 0; vi < vcount; vi++ )
          cvWriteInt( fs, 0, var_type->data.i[vi] >= 0 );
  
      cvEndWriteStruct( fs );
  
      if( cat_count && (cat_var_count > 0 || is_classifier) )
      {
          CV_ASSERT( cat_count != 0 );
          cvWrite( fs, "cat_count", cat_count );
          cvWrite( fs, "cat_map", cat_map );
      }
  
      __END__;
  }
  
  
  void CvDTreeTrainData::read_params( CvFileStorage* fs, CvFileNode* node )
  {
      CV_FUNCNAME( "CvDTreeTrainData::read_params" );
  
      __BEGIN__;
  
      CvFileNode *tparams_node, *vartype_node;
      CvSeqReader reader;
      int vi, max_split_size, tree_block_size;
  
      is_classifier = (cvReadIntByName( fs, node, "is_classifier" ) != 0);
      var_all = cvReadIntByName( fs, node, "var_all" );
      var_count = cvReadIntByName( fs, node, "var_count", var_all );
      cat_var_count = cvReadIntByName( fs, node, "cat_var_count" );
      ord_var_count = cvReadIntByName( fs, node, "ord_var_count" );
  
      tparams_node = cvGetFileNodeByName( fs, node, "training_params" );
  
      if( tparams_node ) // training parameters are not necessary
      {
          params.use_surrogates = cvReadIntByName( fs, tparams_node, "use_surrogates", 1 ) != 0;
  
          if( is_classifier )
          {
              params.max_categories = cvReadIntByName( fs, tparams_node, "max_categories" );
          }
          else
          {
              params.regression_accuracy =
                  (float)cvReadRealByName( fs, tparams_node, "regression_accuracy" );
          }
  
          params.max_depth = cvReadIntByName( fs, tparams_node, "max_depth" );
          params.min_sample_count = cvReadIntByName( fs, tparams_node, "min_sample_count" );
          params.cv_folds = cvReadIntByName( fs, tparams_node, "cross_validation_folds" );
  
          if( params.cv_folds > 1 )
          {
              params.use_1se_rule = cvReadIntByName( fs, tparams_node, "use_1se_rule" ) != 0;
              params.truncate_pruned_tree =
                  cvReadIntByName( fs, tparams_node, "truncate_pruned_tree" ) != 0;
          }
  
          priors = (CvMat*)cvReadByName( fs, tparams_node, "priors" );
          if( priors )
          {
              if( !CV_IS_MAT(priors) )
                  CV_ERROR( CV_StsParseError, "priors must stored as a matrix" );
              priors_mult = cvCloneMat( priors );
          }
      }
  
      CV_CALL( var_idx = (CvMat*)cvReadByName( fs, node, "var_idx" ));
      if( var_idx )
      {
          if( !CV_IS_MAT(var_idx) ||
              (var_idx->cols != 1 && var_idx->rows != 1) ||
              var_idx->cols + var_idx->rows - 1 != var_count ||
              CV_MAT_TYPE(var_idx->type) != CV_32SC1 )
              CV_ERROR( CV_StsParseError,
                  "var_idx (if exist) must be valid 1d integer vector containing <var_count> elements" );
  
          for( vi = 0; vi < var_count; vi++ )
              if( (unsigned)var_idx->data.i[vi] >= (unsigned)var_all )
                  CV_ERROR( CV_StsOutOfRange, "some of var_idx elements are out of range" );
      }
  
      ////// read var type
      CV_CALL( var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 ));
  
      cat_var_count = 0;
      ord_var_count = -1;
      vartype_node = cvGetFileNodeByName( fs, node, "var_type" );
  
      if( vartype_node && CV_NODE_TYPE(vartype_node->tag) == CV_NODE_INT && var_count == 1 )
          var_type->data.i[0] = vartype_node->data.i ? cat_var_count++ : ord_var_count--;
      else
      {
          if( !vartype_node || CV_NODE_TYPE(vartype_node->tag) != CV_NODE_SEQ ||
              vartype_node->data.seq->total != var_count )
              CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
  
          cvStartReadSeq( vartype_node->data.seq, &reader );
  
          for( vi = 0; vi < var_count; vi++ )
          {
              CvFileNode* n = (CvFileNode*)reader.ptr;
              if( CV_NODE_TYPE(n->tag) != CV_NODE_INT || (n->data.i & ~1) )
                  CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
              var_type->data.i[vi] = n->data.i ? cat_var_count++ : ord_var_count--;
              CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
          }
      }
      var_type->data.i[var_count] = cat_var_count;
  
      ord_var_count = ~ord_var_count;
      if( cat_var_count != cat_var_count || ord_var_count != ord_var_count )
          CV_ERROR( CV_StsParseError, "var_type is inconsistent with cat_var_count and ord_var_count" );
      //////
  
      if( cat_var_count > 0 || is_classifier )
      {
          int ccount, total_c_count = 0;
          CV_CALL( cat_count = (CvMat*)cvReadByName( fs, node, "cat_count" ));
          CV_CALL( cat_map = (CvMat*)cvReadByName( fs, node, "cat_map" ));
  
          if( !CV_IS_MAT(cat_count) || !CV_IS_MAT(cat_map) ||
              (cat_count->cols != 1 && cat_count->rows != 1) ||
              CV_MAT_TYPE(cat_count->type) != CV_32SC1 ||
              cat_count->cols + cat_count->rows - 1 != cat_var_count + is_classifier ||
              (cat_map->cols != 1 && cat_map->rows != 1) ||
              CV_MAT_TYPE(cat_map->type) != CV_32SC1 )
              CV_ERROR( CV_StsParseError,
              "Both cat_count and cat_map must exist and be valid 1d integer vectors of an appropriate size" );
  
          ccount = cat_var_count + is_classifier;
  
          CV_CALL( cat_ofs = cvCreateMat( 1, ccount + 1, CV_32SC1 ));
          cat_ofs->data.i[0] = 0;
          max_c_count = 1;
  
          for( vi = 0; vi < ccount; vi++ )
          {
              int val = cat_count->data.i[vi];
              if( val <= 0 )
                  CV_ERROR( CV_StsOutOfRange, "some of cat_count elements are out of range" );
              max_c_count = MAX( max_c_count, val );
              cat_ofs->data.i[vi+1] = total_c_count += val;
          }
  
          if( cat_map->cols + cat_map->rows - 1 != total_c_count )
              CV_ERROR( CV_StsBadSize,
              "cat_map vector length is not equal to the total number of categories in all categorical vars" );
      }
  
      max_split_size = cvAlign(sizeof(CvDTreeSplit) +
          (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
  
      tree_block_size = MAX((int)sizeof(CvDTreeNode)*8, max_split_size);
      tree_block_size = MAX(tree_block_size + block_size_delta, min_block_size);
      CV_CALL( tree_storage = cvCreateMemStorage( tree_block_size ));
      CV_CALL( node_heap = cvCreateSet( 0, sizeof(node_heap[0]),
              sizeof(CvDTreeNode), tree_storage ));
      CV_CALL( split_heap = cvCreateSet( 0, sizeof(split_heap[0]),
              max_split_size, tree_storage ));
  
      __END__;
  }
  
  /////////////////////// Decision Tree /////////////////////////
  
  CvDTree::CvDTree()
  {
      data = 0;
      var_importance = 0;
      default_model_name = "my_tree";
  
      clear();
  }
  
  
  void CvDTree::clear()
  {
      cvReleaseMat( &var_importance );
      if( data )
      {
          if( !data->shared )
              delete data;
          else
              free_tree();
          data = 0;
      }
      root = 0;
      pruned_tree_idx = -1;
  }
  
  
  CvDTree::~CvDTree()
  {
      clear();
  }
  
  
  const CvDTreeNode* CvDTree::get_root() const
  {
      return root;
  }
  
  
  int CvDTree::get_pruned_tree_idx() const
  {
      return pruned_tree_idx;
  }
  
  
  CvDTreeTrainData* CvDTree::get_data()
  {
      return data;
  }
  
  
  bool CvDTree::train( const CvMat* _train_data, int _tflag,
                       const CvMat* _responses, const CvMat* _var_idx,
                       const CvMat* _sample_idx, const CvMat* _var_type,
                       const CvMat* _missing_mask, CvDTreeParams _params )
  {
      bool result = false;
  
      CV_FUNCNAME( "CvDTree::train" );
  
      __BEGIN__;
  
      clear();
      data = new CvDTreeTrainData( _train_data, _tflag, _responses,
                                   _var_idx, _sample_idx, _var_type,
                                   _missing_mask, _params, false );
      CV_CALL( result = do_train(0) );
  
      __END__;
  
      return result;
  }
  
  bool CvDTree::train( const Mat& _train_data, int _tflag,
                      const Mat& _responses, const Mat& _var_idx,
                      const Mat& _sample_idx, const Mat& _var_type,
                      const Mat& _missing_mask, CvDTreeParams _params )
  {
      CvMat tdata = _train_data, responses = _responses, vidx=_var_idx,
          sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask; 
      return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0,
                   vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params);
  }
  
  
  bool CvDTree::train( CvMLData* _data, CvDTreeParams _params )
  {
     bool result = false;
  
      CV_FUNCNAME( "CvDTree::train" );
  
      __BEGIN__;
  
      const CvMat* values = _data->get_values();
      const CvMat* response = _data->get_responses();
      const CvMat* missing = _data->get_missing();
      const CvMat* var_types = _data->get_var_types();
      const CvMat* train_sidx = _data->get_train_sample_idx();
      const CvMat* var_idx = _data->get_var_idx();
  
      CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx,
          train_sidx, var_types, missing, _params ) );
  
      __END__;
  
      return result;
  }
  
  bool CvDTree::train( CvDTreeTrainData* _data, const CvMat* _subsample_idx )
  {
      bool result = false;
  
      CV_FUNCNAME( "CvDTree::train" );
  
      __BEGIN__;
  
      clear();
      data = _data;
      data->shared = true;
      CV_CALL( result = do_train(_subsample_idx));
  
      __END__;
  
      return result;
  }
  
  
  bool CvDTree::do_train( const CvMat* _subsample_idx )
  {
      bool result = false;
  
      CV_FUNCNAME( "CvDTree::do_train" );
  
      __BEGIN__;
  
      root = data->subsample_data( _subsample_idx );
  
      CV_CALL( try_split_node(root));
  
      if( data->params.cv_folds > 0 )
          CV_CALL( prune_cv() );
  
      if( !data->shared )
          data->free_train_data();
  
      result = true;
  
      __END__;
  
      return result;
  }
  
  
  void CvDTree::try_split_node( CvDTreeNode* node )
  {
      CvDTreeSplit* best_split = 0;
      int i, n = node->sample_count, vi;
      bool can_split = true;
      double quality_scale;
  
      calc_node_value( node );
  
      if( node->sample_count <= data->params.min_sample_count ||
          node->depth >= data->params.max_depth )
          can_split = false;
  
      if( can_split && data->is_classifier )
      {
          // check if we have a "pure" node,
          // we assume that cls_count is filled by calc_node_value()
          int* cls_count = data->counts->data.i;
          int nz = 0, m = data->get_num_classes();
          for( i = 0; i < m; i++ )
              nz += cls_count[i] != 0;
          if( nz == 1 ) // there is only one class
              can_split = false;
      }
      else if( can_split )
      {
          if( sqrt(node->node_risk)/n < data->params.regression_accuracy )
              can_split = false;
      }
  
      if( can_split )
      {
          best_split = find_best_split(node);
          // TODO: check the split quality ...
          node->split = best_split;
      }
      if( !can_split || !best_split )
      {
          data->free_node_data(node);
          return;
      }
  
      quality_scale = calc_node_dir( node );
      if( data->params.use_surrogates )
      {
          // find all the surrogate splits
          // and sort them by their similarity to the primary one
          for( vi = 0; vi < data->var_count; vi++ )
          {
              CvDTreeSplit* split;
              int ci = data->get_var_type(vi);
  
              if( vi == best_split->var_idx )
                  continue;
  
              if( ci >= 0 )
                  split = find_surrogate_split_cat( node, vi );
              else
                  split = find_surrogate_split_ord( node, vi );
  
              if( split )
              {
                  // insert the split
                  CvDTreeSplit* prev_split = node->split;
                  split->quality = (float)(split->quality*quality_scale);
  
                  while( prev_split->next &&
                         prev_split->next->quality > split->quality )
                      prev_split = prev_split->next;
                  split->next = prev_split->next;
                  prev_split->next = split;
              }
          }
      }
      split_node_data( node );
      try_split_node( node->left );
      try_split_node( node->right );
  }
  
  
  // calculate direction (left(-1),right(1),missing(0))
  // for each sample using the best split
  // the function returns scale coefficients for surrogate split quality factors.
  // the scale is applied to normalize surrogate split quality relatively to the
  // best (primary) split quality. That is, if a surrogate split is absolutely
  // identical to the primary split, its quality will be set to the maximum value =
  // quality of the primary split; otherwise, it will be lower.
  // besides, the function compute node->maxlr,
  // minimum possible quality (w/o considering the above mentioned scale)
  // for a surrogate split. Surrogate splits with quality less than node->maxlr
  // are not discarded.
  double CvDTree::calc_node_dir( CvDTreeNode* node )
  {
      char* dir = (char*)data->direction->data.ptr;
      int i, n = node->sample_count, vi = node->split->var_idx;
      double L, R;
  
      assert( !node->split->inversed );
  
      if( data->get_var_type(vi) >= 0 ) // split on categorical var
      {
          cv::AutoBuffer<int> inn_buf(n*(!data->have_priors ? 1 : 2));
          int* labels_buf = (int*)inn_buf;
          const int* labels = data->get_cat_var_data( node, vi, labels_buf );
          const int* subset = node->split->subset;
          if( !data->have_priors )
          {
              int sum = 0, sum_abs = 0;
  
              for( i = 0; i < n; i++ )
              {
                  int idx = labels[i];
                  int d = ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) ) ?
                      CV_DTREE_CAT_DIR(idx,subset) : 0;
                  sum += d; sum_abs += d & 1;
                  dir[i] = (char)d;
              }
  
              R = (sum_abs + sum) >> 1;
              L = (sum_abs - sum) >> 1;
          }
          else
          {
              const double* priors = data->priors_mult->data.db;
              double sum = 0, sum_abs = 0;
              int* responses_buf = labels_buf + n;
              const int* responses = data->get_class_labels(node, responses_buf);
  
              for( i = 0; i < n; i++ )
              {
                  int idx = labels[i];
                  double w = priors[responses[i]];
                  int d = idx >= 0 ? CV_DTREE_CAT_DIR(idx,subset) : 0;
                  sum += d*w; sum_abs += (d & 1)*w;
                  dir[i] = (char)d;
              }
  
              R = (sum_abs + sum) * 0.5;
              L = (sum_abs - sum) * 0.5;
          }
      }
      else // split on ordered var
      {
          int split_point = node->split->ord.split_point;
          int n1 = node->get_num_valid(vi);
          cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)));
          float* val_buf = (float*)(uchar*)inn_buf;
          int* sorted_buf = (int*)(val_buf + n);
          int* sample_idx_buf = sorted_buf + n;
          const float* val = 0;
          const int* sorted = 0;
          data->get_ord_var_data( node, vi, val_buf, sorted_buf, &val, &sorted, sample_idx_buf);
          
          assert( 0 <= split_point && split_point < n1-1 );
  
          if( !data->have_priors )
          {
              for( i = 0; i <= split_point; i++ )
                  dir[sorted[i]] = (char)-1;
              for( ; i < n1; i++ )
                  dir[sorted[i]] = (char)1;
              for( ; i < n; i++ )
                  dir[sorted[i]] = (char)0;
  
              L = split_point-1;
              R = n1 - split_point + 1;
          }
          else
          {
              const double* priors = data->priors_mult->data.db;
              int* responses_buf = sample_idx_buf + n;
              const int* responses = data->get_class_labels(node, responses_buf);
              L = R = 0;
  
              for( i = 0; i <= split_point; i++ )
              {
                  int idx = sorted[i];
                  double w = priors[responses[idx]];
                  dir[idx] = (char)-1;
                  L += w;
              }
  
              for( ; i < n1; i++ )
              {
                  int idx = sorted[i];
                  double w = priors[responses[idx]];
                  dir[idx] = (char)1;
                  R += w;
              }
  
              for( ; i < n; i++ )
                  dir[sorted[i]] = (char)0;
          }
      }
      node->maxlr = MAX( L, R );
      return node->split->quality/(L + R);
  }
  
  
  namespace cv
  {
  
  template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj()
  {
      fastFree(obj);
  }
      
  DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node)
  {
      tree = _tree;
      node = _node;
      splitSize = tree->get_data()->split_heap->elem_size;
  
      bestSplit = (CvDTreeSplit*)fastMalloc(splitSize);
      memset((CvDTreeSplit*)bestSplit, 0, splitSize);
      bestSplit->quality = -1;
      bestSplit->condensed_idx = INT_MIN;
      split = (CvDTreeSplit*)fastMalloc(splitSize);
      memset((CvDTreeSplit*)split, 0, splitSize);
      //haveSplit = false;
  }
  
  DTreeBestSplitFinder::DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split )
  {
      tree = finder.tree;
      node = finder.node;
      splitSize = tree->get_data()->split_heap->elem_size;
  
      bestSplit = (CvDTreeSplit*)fastMalloc(splitSize);
      memcpy((CvDTreeSplit*)(bestSplit), (const CvDTreeSplit*)finder.bestSplit, splitSize);
      split = (CvDTreeSplit*)fastMalloc(splitSize);
      memset((CvDTreeSplit*)split, 0, splitSize);
  }
  
  void DTreeBestSplitFinder::operator()(const BlockedRange& range)
  {
      int vi, vi1 = range.begin(), vi2 = range.end();
      int n = node->sample_count;
      CvDTreeTrainData* data = tree->get_data();
      AutoBuffer<uchar> inn_buf(2*n*(sizeof(int) + sizeof(float)));
  
      for( vi = vi1; vi < vi2; vi++ )
      {
          CvDTreeSplit *res;
          int ci = data->get_var_type(vi);
          if( node->get_num_valid(vi) <= 1 )
              continue;
  
          if( data->is_classifier )
          {
              if( ci >= 0 )
                  res = tree->find_split_cat_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
              else
                  res = tree->find_split_ord_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
          }
          else
          {
              if( ci >= 0 )
                  res = tree->find_split_cat_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
              else
                  res = tree->find_split_ord_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
          }
  
          if( res && bestSplit->quality < split->quality )
                  memcpy( (CvDTreeSplit*)bestSplit, (CvDTreeSplit*)split, splitSize );
      }
  }
  
  void DTreeBestSplitFinder::join( DTreeBestSplitFinder& rhs )
  {
      if( bestSplit->quality < rhs.bestSplit->quality )
          memcpy( (CvDTreeSplit*)bestSplit, (CvDTreeSplit*)rhs.bestSplit, splitSize );
  }
  }
  
  
  CvDTreeSplit* CvDTree::find_best_split( CvDTreeNode* node )
  {
      DTreeBestSplitFinder finder( this, node );
  
      cv::parallel_reduce(cv::BlockedRange(0, data->var_count), finder);
  
      CvDTreeSplit *bestSplit = 0;
      if( finder.bestSplit->quality > 0 )
      {
          bestSplit = data->new_split_cat( 0, -1.0f );
          memcpy( bestSplit, finder.bestSplit, finder.splitSize );
      }
  
      return bestSplit;
  }
  
  CvDTreeSplit* CvDTree::find_split_ord_class( CvDTreeNode* node, int vi,
                                               float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
  {
      const float epsilon = FLT_EPSILON*2;
      int n = node->sample_count;
      int n1 = node->get_num_valid(vi);
      int m = data->get_num_classes();
  
      int base_size = 2*m*sizeof(int);
      cv::AutoBuffer<uchar> inn_buf(base_size);
      if( !_ext_buf )
        inn_buf.allocate(base_size + n*(3*sizeof(int)+sizeof(float)));
      uchar* base_buf = (uchar*)inn_buf;
      uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
      float* values_buf = (float*)ext_buf;
      int* sorted_indices_buf = (int*)(values_buf + n);
      int* sample_indices_buf = sorted_indices_buf + n;
      const float* values = 0;
      const int* sorted_indices = 0;
      data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values,
                              &sorted_indices, sample_indices_buf );
      int* responses_buf =  sample_indices_buf + n;
      const int* responses = data->get_class_labels( node, responses_buf );
  
      const int* rc0 = data->counts->data.i;
      int* lc = (int*)base_buf;
      int* rc = lc + m;
      int i, best_i = -1;
      double lsum2 = 0, rsum2 = 0, best_val = init_quality;
      const double* priors = data->have_priors ? data->priors_mult->data.db : 0;
  
      // init arrays of class instance counters on both sides of the split
      for( i = 0; i < m; i++ )
      {
          lc[i] = 0;
          rc[i] = rc0[i];
      }
  
      // compensate for missing values
      for( i = n1; i < n; i++ )
      {
          rc[responses[sorted_indices[i]]]--;
      }
  
      if( !priors )
      {
          int L = 0, R = n1;
  
          for( i = 0; i < m; i++ )
              rsum2 += (double)rc[i]*rc[i];
  
          for( i = 0; i < n1 - 1; i++ )
          {
              int idx = responses[sorted_indices[i]];
              int lv, rv;
              L++; R--;
              lv = lc[idx]; rv = rc[idx];
              lsum2 += lv*2 + 1;
              rsum2 -= rv*2 - 1;
              lc[idx] = lv + 1; rc[idx] = rv - 1;
  
              if( values[i] + epsilon < values[i+1] )
              {
                  double val = (lsum2*R + rsum2*L)/((double)L*R);
                  if( best_val < val )
                  {
                      best_val = val;
                      best_i = i;
                  }
              }
          }
      }
      else
      {
          double L = 0, R = 0;
          for( i = 0; i < m; i++ )
          {
              double wv = rc[i]*priors[i];
              R += wv;
              rsum2 += wv*wv;
          }
  
          for( i = 0; i < n1 - 1; i++ )
          {
              int idx = responses[sorted_indices[i]];
              int lv, rv;
              double p = priors[idx], p2 = p*p;
              L += p; R -= p;
              lv = lc[idx]; rv = rc[idx];
              lsum2 += p2*(lv*2 + 1);
              rsum2 -= p2*(rv*2 - 1);
              lc[idx] = lv + 1; rc[idx] = rv - 1;
  
              if( values[i] + epsilon < values[i+1] )
              {
                  double val = (lsum2*R + rsum2*L)/((double)L*R);
                  if( best_val < val )
                  {
                      best_val = val;
                      best_i = i;
                  }
              }
          }
      }
  
      CvDTreeSplit* split = 0;
      if( best_i >= 0 )
      {
          split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
          split->var_idx = vi;
          split->ord.c = (values[best_i] + values[best_i+1])*0.5f;
          split->ord.split_point = best_i;
          split->inversed = 0;
          split->quality = (float)best_val;
      }
      return split;
  }
  
  
  void CvDTree::cluster_categories( const int* vectors, int n, int m,
                                  int* csums, int k, int* labels )
  {
      // TODO: consider adding priors (class weights) and sample weights to the clustering algorithm
      int iters = 0, max_iters = 100;
      int i, j, idx;
      cv::AutoBuffer<double> buf(n + k);
      double *v_weights = buf, *c_weights = buf + n;
      bool modified = true;
      RNG* r = data->rng;
  
      // assign labels randomly
      for( i = 0; i < n; i++ )
      {
          int sum = 0;
          const int* v = vectors + i*m;
          labels[i] = i < k ? i : (*r)(k);
  
          // compute weight of each vector
          for( j = 0; j < m; j++ )
              sum += v[j];
          v_weights[i] = sum ? 1./sum : 0.;
      }
  
      for( i = 0; i < n; i++ )
      {
          int i1 = (*r)(n);
          int i2 = (*r)(n);
          CV_SWAP( labels[i1], labels[i2], j );
      }
  
      for( iters = 0; iters <= max_iters; iters++ )
      {
          // calculate csums
          for( i = 0; i < k; i++ )
          {
              for( j = 0; j < m; j++ )
                  csums[i*m + j] = 0;
          }
  
          for( i = 0; i < n; i++ )
          {
              const int* v = vectors + i*m;
              int* s = csums + labels[i]*m;
              for( j = 0; j < m; j++ )
                  s[j] += v[j];
          }
  
          // exit the loop here, when we have up-to-date csums
          if( iters == max_iters || !modified )
              break;
  
          modified = false;
  
          // calculate weight of each cluster
          for( i = 0; i < k; i++ )
          {
              const int* s = csums + i*m;
              int sum = 0;
              for( j = 0; j < m; j++ )
                  sum += s[j];
              c_weights[i] = sum ? 1./sum : 0;
          }
  
          // now for each vector determine the closest cluster
          for( i = 0; i < n; i++ )
          {
              const int* v = vectors + i*m;
              double alpha = v_weights[i];
              double min_dist2 = DBL_MAX;
              int min_idx = -1;
  
              for( idx = 0; idx < k; idx++ )
              {
                  const int* s = csums + idx*m;
                  double dist2 = 0., beta = c_weights[idx];
                  for( j = 0; j < m; j++ )
                  {
                      double t = v[j]*alpha - s[j]*beta;
                      dist2 += t*t;
                  }
                  if( min_dist2 > dist2 )
                  {
                      min_dist2 = dist2;
                      min_idx = idx;
                  }
              }
  
              if( min_idx != labels[i] )
                  modified = true;
              labels[i] = min_idx;
          }
      }
  }
  
  
  CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality,
                                               CvDTreeSplit* _split, uchar* _ext_buf )
  {
      int ci = data->get_var_type(vi);
      int n = node->sample_count;
      int m = data->get_num_classes();
      int _mi = data->cat_count->data.i[ci], mi = _mi;
  
      int base_size = m*(3 + mi)*sizeof(int) + (mi+1)*sizeof(double);
      if( m > 2 && mi > data->params.max_categories )
          base_size += (m*min(data->params.max_categories, n) + mi)*sizeof(int);
      else
          base_size += mi*sizeof(int*);
      cv::AutoBuffer<uchar> inn_buf(base_size);
      if( !_ext_buf )
          inn_buf.allocate(base_size + 2*n*sizeof(int));
      uchar* base_buf = (uchar*)inn_buf;
      uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
  
      int* lc = (int*)base_buf;
      int* rc = lc + m;
      int* _cjk = rc + m*2, *cjk = _cjk;
      double* c_weights = (double*)alignPtr(cjk + m*mi, sizeof(double));
  
      int* labels_buf = (int*)ext_buf;
      const int* labels = data->get_cat_var_data(node, vi, labels_buf);
      int* responses_buf = labels_buf + n;
      const int* responses = data->get_class_labels(node, responses_buf);
  
      int* cluster_labels = 0;
      int** int_ptr = 0;
      int i, j, k, idx;
      double L = 0, R = 0;
      double best_val = init_quality;
      int prevcode = 0, best_subset = -1, subset_i, subset_n, subtract = 0;
      const double* priors = data->priors_mult->data.db;
  
      // init array of counters:
      // c_{jk} - number of samples that have vi-th input variable = j and response = k.
      for( j = -1; j < mi; j++ )
          for( k = 0; k < m; k++ )
              cjk[j*m + k] = 0;
  
      for( i = 0; i < n; i++ )
      {
         j = ( labels[i] == 65535 && data->is_buf_16u) ? -1 : labels[i];
         k = responses[i];
         cjk[j*m + k]++;
      }
  
      if( m > 2 )
      {
          if( mi > data->params.max_categories )
          {
              mi = MIN(data->params.max_categories, n);
              cjk = (int*)(c_weights + _mi);
              cluster_labels = cjk + m*mi;
              cluster_categories( _cjk, _mi, m, cjk, mi, cluster_labels );
          }
          subset_i = 1;
          subset_n = 1 << mi;
      }
      else
      {
          assert( m == 2 );
          int_ptr = (int**)(c_weights + _mi);
          for( j = 0; j < mi; j++ )
              int_ptr[j] = cjk + j*2 + 1;
          icvSortIntPtr( int_ptr, mi, 0 );
          subset_i = 0;
          subset_n = mi;
      }
  
      for( k = 0; k < m; k++ )
      {
          int sum = 0;
          for( j = 0; j < mi; j++ )
              sum += cjk[j*m + k];
          rc[k] = sum;
          lc[k] = 0;
      }
  
      for( j = 0; j < mi; j++ )
      {
          double sum = 0;
          for( k = 0; k < m; k++ )
              sum += cjk[j*m + k]*priors[k];
          c_weights[j] = sum;
          R += c_weights[j];
      }
  
      for( ; subset_i < subset_n; subset_i++ )
      {
          double weight;
          int* crow;
          double lsum2 = 0, rsum2 = 0;
  
          if( m == 2 )
              idx = (int)(int_ptr[subset_i] - cjk)/2;
          else
          {
              int graycode = (subset_i>>1)^subset_i;
              int diff = graycode ^ prevcode;
  
              // determine index of the changed bit.
              Cv32suf u;
              idx = diff >= (1 << 16) ? 16 : 0;
              u.f = (float)(((diff >> 16) | diff) & 65535);
              idx += (u.i >> 23) - 127;
              subtract = graycode < prevcode;
              prevcode = graycode;
          }
  
          crow = cjk + idx*m;
          weight = c_weights[idx];
          if( weight < FLT_EPSILON )
              continue;
  
          if( !subtract )
          {
              for( k = 0; k < m; k++ )
              {
                  int t = crow[k];
                  int lval = lc[k] + t;
                  int rval = rc[k] - t;
                  double p = priors[k], p2 = p*p;
                  lsum2 += p2*lval*lval;
                  rsum2 += p2*rval*rval;
                  lc[k] = lval; rc[k] = rval;
              }
              L += weight;
              R -= weight;
          }
          else
          {
              for( k = 0; k < m; k++ )
              {
                  int t = crow[k];
                  int lval = lc[k] - t;
                  int rval = rc[k] + t;
                  double p = priors[k], p2 = p*p;
                  lsum2 += p2*lval*lval;
                  rsum2 += p2*rval*rval;
                  lc[k] = lval; rc[k] = rval;
              }
              L -= weight;
              R += weight;
          }
  
          if( L > FLT_EPSILON && R > FLT_EPSILON )
          {
              double val = (lsum2*R + rsum2*L)/((double)L*R);
              if( best_val < val )
              {
                  best_val = val;
                  best_subset = subset_i;
              }
          }
      }
  
      CvDTreeSplit* split = 0;
      if( best_subset >= 0 ) 
      {
          split = _split ? _split : data->new_split_cat( 0, -1.0f );
          split->var_idx = vi;
          split->quality = (float)best_val;
          memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));
          if( m == 2 )
          {
              for( i = 0; i <= best_subset; i++ )
              {
                  idx = (int)(int_ptr[i] - cjk) >> 1;
                  split->subset[idx >> 5] |= 1 << (idx & 31);
              }
          }
          else
          {
              for( i = 0; i < _mi; i++ )
              {
                  idx = cluster_labels ? cluster_labels[i] : i;
                  if( best_subset & (1 << idx) )
                      split->subset[i >> 5] |= 1 << (i & 31);
              }
          }
      }
      return split;
  }
  
  
  CvDTreeSplit* CvDTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
  {
      const float epsilon = FLT_EPSILON*2;
      int n = node->sample_count;
      int n1 = node->get_num_valid(vi);
  
      cv::AutoBuffer<uchar> inn_buf;
      if( !_ext_buf )
          inn_buf.allocate(2*n*(sizeof(int) + sizeof(float)));
      uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
      float* values_buf = (float*)ext_buf;
      int* sorted_indices_buf = (int*)(values_buf + n);
      int* sample_indices_buf = sorted_indices_buf + n;
      const float* values = 0;
      const int* sorted_indices = 0;
      data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
      float* responses_buf =  (float*)(sample_indices_buf + n);
      const float* responses = data->get_ord_responses( node, responses_buf, sample_indices_buf );
  
      int i, best_i = -1;
      double best_val = init_quality, lsum = 0, rsum = node->value*n;
      int L = 0, R = n1;
  
      // compensate for missing values
      for( i = n1; i < n; i++ )
          rsum -= responses[sorted_indices[i]];
  
      // find the optimal split
      for( i = 0; i < n1 - 1; i++ )
      {
          float t = responses[sorted_indices[i]];
          L++; R--;
          lsum += t;
          rsum -= t;
  
          if( values[i] + epsilon < values[i+1] )
          {
              double val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R);
              if( best_val < val )
              {
                  best_val = val;
                  best_i = i;
              }
          }
      }
  
      CvDTreeSplit* split = 0;
      if( best_i >= 0 )
      {
          split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
          split->var_idx = vi;
          split->ord.c = (values[best_i] + values[best_i+1])*0.5f;
          split->ord.split_point = best_i;
          split->inversed = 0;
          split->quality = (float)best_val;
      }
      return split;
  }
  
  CvDTreeSplit* CvDTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
  {
      int ci = data->get_var_type(vi);
      int n = node->sample_count;
      int mi = data->cat_count->data.i[ci];
  
      int base_size = (mi+2)*sizeof(double) + (mi+1)*(sizeof(int) + sizeof(double*));
      cv::AutoBuffer<uchar> inn_buf(base_size);
      if( !_ext_buf )
          inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
      uchar* base_buf = (uchar*)inn_buf;
      uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
      int* labels_buf = (int*)ext_buf;
      const int* labels = data->get_cat_var_data(node, vi, labels_buf);
      float* responses_buf = (float*)(labels_buf + n);
      int* sample_indices_buf = (int*)(responses_buf + n);
      const float* responses = data->get_ord_responses(node, responses_buf, sample_indices_buf);
  
      double* sum = (double*)cv::alignPtr(base_buf,sizeof(double)) + 1;
      int* counts = (int*)(sum + mi) + 1;
      double** sum_ptr = (double**)(counts + mi);
      int i, L = 0, R = 0;
      double best_val = init_quality, lsum = 0, rsum = 0;
      int best_subset = -1, subset_i;
  
      for( i = -1; i < mi; i++ )
          sum[i] = counts[i] = 0;
  
      // calculate sum response and weight of each category of the input var
      for( i = 0; i < n; i++ )
      {
          int idx = ( (labels[i] == 65535) && data->is_buf_16u ) ? -1 : labels[i];
          double s = sum[idx] + responses[i];
          int nc = counts[idx] + 1;
          sum[idx] = s;
          counts[idx] = nc;
      }
  
      // calculate average response in each category
      for( i = 0; i < mi; i++ )
      {
          R += counts[i];
          rsum += sum[i];
          sum[i] /= MAX(counts[i],1);
          sum_ptr[i] = sum + i;
      }
  
      icvSortDblPtr( sum_ptr, mi, 0 );
  
      // revert back to unnormalized sums
      // (there should be a very little loss of accuracy)
      for( i = 0; i < mi; i++ )
          sum[i] *= counts[i];
  
      for( subset_i = 0; subset_i < mi-1; subset_i++ )
      {
          int idx = (int)(sum_ptr[subset_i] - sum);
          int ni = counts[idx];
  
          if( ni )
          {
              double s = sum[idx];
              lsum += s; L += ni;
              rsum -= s; R -= ni;
  
              if( L && R )
              {
                  double val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R);
                  if( best_val < val )
                  {
                      best_val = val;
                      best_subset = subset_i;
                  }
              }
          }
      }
  
      CvDTreeSplit* split = 0;
      if( best_subset >= 0 )
      {
          split = _split ? _split : data->new_split_cat( 0, -1.0f);
          split->var_idx = vi;
          split->quality = (float)best_val;
          memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));
          for( i = 0; i <= best_subset; i++ )
          {
              int idx = (int)(sum_ptr[i] - sum);
              split->subset[idx >> 5] |= 1 << (idx & 31);
          }
      }
      return split;
  }
  
  CvDTreeSplit* CvDTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, uchar* _ext_buf )
  {
      const float epsilon = FLT_EPSILON*2;
      const char* dir = (char*)data->direction->data.ptr;
      int n = node->sample_count, n1 = node->get_num_valid(vi);
      cv::AutoBuffer<uchar> inn_buf;
      if( !_ext_buf )
          inn_buf.allocate( n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)) );
      uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
      float* values_buf = (float*)ext_buf;
      int* sorted_indices_buf = (int*)(values_buf + n);
      int* sample_indices_buf = sorted_indices_buf + n;
      const float* values = 0;
      const int* sorted_indices = 0;
      data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
      // LL - number of samples that both the primary and the surrogate splits send to the left
      // LR - ... primary split sends to the left and the surrogate split sends to the right
      // RL - ... primary split sends to the right and the surrogate split sends to the left
      // RR - ... both send to the right
      int i, best_i = -1, best_inversed = 0;
      double best_val;
  
      if( !data->have_priors )
      {
          int LL = 0, RL = 0, LR, RR;
          int worst_val = cvFloor(node->maxlr), _best_val = worst_val;
          int sum = 0, sum_abs = 0;
  
          for( i = 0; i < n1; i++ )
          {
              int d = dir[sorted_indices[i]];
              sum += d; sum_abs += d & 1;
          }
  
          // sum_abs = R + L; sum = R - L
          RR = (sum_abs + sum) >> 1;
          LR = (sum_abs - sum) >> 1;
  
          // initially all the samples are sent to the right by the surrogate split,
          // LR of them are sent to the left by primary split, and RR - to the right.
          // now iteratively compute LL, LR, RL and RR for every possible surrogate split value.
          for( i = 0; i < n1 - 1; i++ )
          {
              int d = dir[sorted_indices[i]];
  
              if( d < 0 )
              {
                  LL++; LR--;
                  if( LL + RR > _best_val && values[i] + epsilon < values[i+1] )
                  {
                      best_val = LL + RR;
                      best_i = i; best_inversed = 0;
                  }
              }
              else if( d > 0 )
              {
                  RL++; RR--;
                  if( RL + LR > _best_val && values[i] + epsilon < values[i+1] )
                  {
                      best_val = RL + LR;
                      best_i = i; best_inversed = 1;
                  }
              }
          }
          best_val = _best_val;
      }
      else
      {
          double LL = 0, RL = 0, LR, RR;
          double worst_val = node->maxlr;
          double sum = 0, sum_abs = 0;
          const double* priors = data->priors_mult->data.db;
          int* responses_buf = sample_indices_buf + n;
          const int* responses = data->get_class_labels(node, responses_buf);
          best_val = worst_val;
  
          for( i = 0; i < n1; i++ )
          {
              int idx = sorted_indices[i];
              double w = priors[responses[idx]];
              int d = dir[idx];
              sum += d*w; sum_abs += (d & 1)*w;
          }
  
          // sum_abs = R + L; sum = R - L
          RR = (sum_abs + sum)*0.5;
          LR = (sum_abs - sum)*0.5;
  
          // initially all the samples are sent to the right by the surrogate split,
          // LR of them are sent to the left by primary split, and RR - to the right.
          // now iteratively compute LL, LR, RL and RR for every possible surrogate split value.
          for( i = 0; i < n1 - 1; i++ )
          {
              int idx = sorted_indices[i];
              double w = priors[responses[idx]];
              int d = dir[idx];
  
              if( d < 0 )
              {
                  LL += w; LR -= w;
                  if( LL + RR > best_val && values[i] + epsilon < values[i+1] )
                  {
                      best_val = LL + RR;
                      best_i = i; best_inversed = 0;
                  }
              }
              else if( d > 0 )
              {
                  RL += w; RR -= w;
                  if( RL + LR > best_val && values[i] + epsilon < values[i+1] )
                  {
                      best_val = RL + LR;
                      best_i = i; best_inversed = 1;
                  }
              }
          }
      }
      return best_i >= 0 && best_val > node->maxlr ? data->new_split_ord( vi,
          (values[best_i] + values[best_i+1])*0.5f, best_i, best_inversed, (float)best_val ) : 0;
  }
  
  
  CvDTreeSplit* CvDTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, uchar* _ext_buf )
  {
      const char* dir = (char*)data->direction->data.ptr;
      int n = node->sample_count;
      int i, mi = data->cat_count->data.i[data->get_var_type(vi)], l_win = 0;
  
      int base_size = (2*(mi+1)+1)*sizeof(double) + (!data->have_priors ? 2*(mi+1)*sizeof(int) : 0);
      cv::AutoBuffer<uchar> inn_buf(base_size);
      if( !_ext_buf )
          inn_buf.allocate(base_size + n*(sizeof(int) + (data->have_priors ? sizeof(int) : 0)));
      uchar* base_buf = (uchar*)inn_buf;
      uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
  
      int* labels_buf = (int*)ext_buf;
      const int* labels = data->get_cat_var_data(node, vi, labels_buf);
      // LL - number of samples that both the primary and the surrogate splits send to the left
      // LR - ... primary split sends to the left and the surrogate split sends to the right
      // RL - ... primary split sends to the right and the surrogate split sends to the left
      // RR - ... both send to the right
      CvDTreeSplit* split = data->new_split_cat( vi, 0 );
      double best_val = 0;
      double* lc = (double*)cv::alignPtr(base_buf,sizeof(double)) + 1;
      double* rc = lc + mi + 1;
  
      for( i = -1; i < mi; i++ )
          lc[i] = rc[i] = 0;
  
      // for each category calculate the weight of samples
      // sent to the left (lc) and to the right (rc) by the primary split
      if( !data->have_priors )
      {
          int* _lc = (int*)rc + 1;
          int* _rc = _lc + mi + 1;
  
          for( i = -1; i < mi; i++ )
              _lc[i] = _rc[i] = 0;
  
          for( i = 0; i < n; i++ )
          {
              int idx = ( (labels[i] == 65535) && (data->is_buf_16u) ) ? -1 : labels[i];
              int d = dir[i];
              int sum = _lc[idx] + d;
              int sum_abs = _rc[idx] + (d & 1);
              _lc[idx] = sum; _rc[idx] = sum_abs;
          }
  
          for( i = 0; i < mi; i++ )
          {
              int sum = _lc[i];
              int sum_abs = _rc[i];
              lc[i] = (sum_abs - sum) >> 1;
              rc[i] = (sum_abs + sum) >> 1;
          }
      }
      else
      {
          const double* priors = data->priors_mult->data.db;
          int* responses_buf = labels_buf + n;
          const int* responses = data->get_class_labels(node, responses_buf);
  
          for( i = 0; i < n; i++ )
          {
              int idx = ( (labels[i] == 65535) && (data->is_buf_16u) ) ? -1 : labels[i];
              double w = priors[responses[i]];
              int d = dir[i];
              double sum = lc[idx] + d*w;
              double sum_abs = rc[idx] + (d & 1)*w;
              lc[idx] = sum; rc[idx] = sum_abs;
          }
  
          for( i = 0; i < mi; i++ )
          {
              double sum = lc[i];
              double sum_abs = rc[i];
              lc[i] = (sum_abs - sum) * 0.5;
              rc[i] = (sum_abs + sum) * 0.5;
          }
      }
  
      // 2. now form the split.
      // in each category send all the samples to the same direction as majority
      for( i = 0; i < mi; i++ )
      {
          double lval = lc[i], rval = rc[i];
          if( lval > rval )
          {
              split->subset[i >> 5] |= 1 << (i & 31);
              best_val += lval;
              l_win++;
          }
          else
              best_val += rval;
      }
  
      split->quality = (float)best_val;
      if( split->quality <= node->maxlr || l_win == 0 || l_win == mi )
          cvSetRemoveByPtr( data->split_heap, split ), split = 0;
  
      return split;
  }
  
  
  void CvDTree::calc_node_value( CvDTreeNode* node )
  {
      int i, j, k, n = node->sample_count, cv_n = data->params.cv_folds;
      int m = data->get_num_classes();
  
      int base_size = data->is_classifier ? m*cv_n*sizeof(int) : 2*cv_n*sizeof(double)+cv_n*sizeof(int);
      int ext_size = n*(sizeof(int) + (data->is_classifier ? sizeof(int) : sizeof(int)+sizeof(float)));
      cv::AutoBuffer<uchar> inn_buf(base_size + ext_size);
      uchar* base_buf = (uchar*)inn_buf;
      uchar* ext_buf = base_buf + base_size;
  
      int* cv_labels_buf = (int*)ext_buf;
      const int* cv_labels = data->get_cv_labels(node, cv_labels_buf);
  
      if( data->is_classifier )
      {
          // in case of classification tree:
          //  * node value is the label of the class that has the largest weight in the node.
          //  * node risk is the weighted number of misclassified samples,
          //  * j-th cross-validation fold value and risk are calculated as above,
          //    but using the samples with cv_labels(*)!=j.
          //  * j-th cross-validation fold error is calculated as the weighted number of
          //    misclassified samples with cv_labels(*)==j.
  
          // compute the number of instances of each class
          int* cls_count = data->counts->data.i;
          int* responses_buf = cv_labels_buf + n;
          const int* responses = data->get_class_labels(node, responses_buf);
          int* cv_cls_count = (int*)base_buf;
          double max_val = -1, total_weight = 0;
          int max_k = -1;
          double* priors = data->priors_mult->data.db;
  
          for( k = 0; k < m; k++ )
              cls_count[k] = 0;
  
          if( cv_n == 0 )
          {
              for( i = 0; i < n; i++ )
                  cls_count[responses[i]]++;
          }
          else
          {
              for( j = 0; j < cv_n; j++ )
                  for( k = 0; k < m; k++ )
                      cv_cls_count[j*m + k] = 0;
  
              for( i = 0; i < n; i++ )
              {
                  j = cv_labels[i]; k = responses[i];
                  cv_cls_count[j*m + k]++;
              }
  
              for( j = 0; j < cv_n; j++ )
                  for( k = 0; k < m; k++ )
                      cls_count[k] += cv_cls_count[j*m + k];
          }
  
          if( data->have_priors && node->parent == 0 )
          {
              // compute priors_mult from priors, take the sample ratio into account.
              double sum = 0;
              for( k = 0; k < m; k++ )
              {
                  int n_k = cls_count[k];
                  priors[k] = data->priors->data.db[k]*(n_k ? 1./n_k : 0.);
                  sum += priors[k];
              }
              sum = 1./sum;
              for( k = 0; k < m; k++ )
                  priors[k] *= sum;
          }
  
          for( k = 0; k < m; k++ )
          {
              double val = cls_count[k]*priors[k];
              total_weight += val;
              if( max_val < val )
              {
                  max_val = val;
                  max_k = k;
              }
          }
  
          node->class_idx = max_k;
          node->value = data->cat_map->data.i[
              data->cat_ofs->data.i[data->cat_var_count] + max_k];
          node->node_risk = total_weight - max_val;
  
          for( j = 0; j < cv_n; j++ )
          {
              double sum_k = 0, sum = 0, max_val_k = 0;
              max_val = -1; max_k = -1;
  
              for( k = 0; k < m; k++ )
              {
                  double w = priors[k];
                  double val_k = cv_cls_count[j*m + k]*w;
                  double val = cls_count[k]*w - val_k;
                  sum_k += val_k;
                  sum += val;
                  if( max_val < val )
                  {
                      max_val = val;
                      max_val_k = val_k;
                      max_k = k;
                  }
              }
  
              node->cv_Tn[j] = INT_MAX;
              node->cv_node_risk[j] = sum - max_val;
              node->cv_node_error[j] = sum_k - max_val_k;
          }
      }
      else
      {
          // in case of regression tree:
          //  * node value is 1/n*sum_i(Y_i), where Y_i is i-th response,
          //    n is the number of samples in the node.
          //  * node risk is the sum of squared errors: sum_i((Y_i - <node_value>)^2)
          //  * j-th cross-validation fold value and risk are calculated as above,
          //    but using the samples with cv_labels(*)!=j.
          //  * j-th cross-validation fold error is calculated
          //    using samples with cv_labels(*)==j as the test subset:
          //    error_j = sum_(i,cv_labels(i)==j)((Y_i - <node_value_j>)^2),
          //    where node_value_j is the node value calculated
          //    as described in the previous bullet, and summation is done
          //    over the samples with cv_labels(*)==j.
  
          double sum = 0, sum2 = 0;
          float* values_buf = (float*)(cv_labels_buf + n);
          int* sample_indices_buf = (int*)(values_buf + n);
          const float* values = data->get_ord_responses(node, values_buf, sample_indices_buf);
          double *cv_sum = 0, *cv_sum2 = 0;
          int* cv_count = 0;
  
          if( cv_n == 0 )
          {
              for( i = 0; i < n; i++ )
              {
                  double t = values[i];
                  sum += t;
                  sum2 += t*t;
              }
          }
          else
          {
              cv_sum = (double*)base_buf;
              cv_sum2 = cv_sum + cv_n;
              cv_count = (int*)(cv_sum2 + cv_n);
  
              for( j = 0; j < cv_n; j++ )
              {
                  cv_sum[j] = cv_sum2[j] = 0.;
                  cv_count[j] = 0;
              }
  
              for( i = 0; i < n; i++ )
              {
                  j = cv_labels[i];
                  double t = values[i];
                  double s = cv_sum[j] + t;
                  double s2 = cv_sum2[j] + t*t;
                  int nc = cv_count[j] + 1;
                  cv_sum[j] = s;
                  cv_sum2[j] = s2;
                  cv_count[j] = nc;
              }
  
              for( j = 0; j < cv_n; j++ )
              {
                  sum += cv_sum[j];
                  sum2 += cv_sum2[j];
              }
          }
  
          node->node_risk = sum2 - (sum/n)*sum;
          node->value = sum/n;
  
          for( j = 0; j < cv_n; j++ )
          {
              double s = cv_sum[j], si = sum - s;
              double s2 = cv_sum2[j], s2i = sum2 - s2;
              int c = cv_count[j], ci = n - c;
              double r = si/MAX(ci,1);
              node->cv_node_risk[j] = s2i - r*r*ci;
              node->cv_node_error[j] = s2 - 2*r*s + c*r*r;
              node->cv_Tn[j] = INT_MAX;
          }
      }
  }
  
  
  void CvDTree::complete_node_dir( CvDTreeNode* node )
  {
      int vi, i, n = node->sample_count, nl, nr, d0 = 0, d1 = -1;
      int nz = n - node->get_num_valid(node->split->var_idx);
      char* dir = (char*)data->direction->data.ptr;
  
      // try to complete direction using surrogate splits
      if( nz && data->params.use_surrogates )
      {
          cv::AutoBuffer<uchar> inn_buf(n*(2*sizeof(int)+sizeof(float)));
          CvDTreeSplit* split = node->split->next;
          for( ; split != 0 && nz; split = split->next )
          {
              int inversed_mask = split->inversed ? -1 : 0;
              vi = split->var_idx;
  
              if( data->get_var_type(vi) >= 0 ) // split on categorical var
              {
                  int* labels_buf = (int*)(uchar*)inn_buf;
                  const int* labels = data->get_cat_var_data(node, vi, labels_buf);
                  const int* subset = split->subset;
  
                  for( i = 0; i < n; i++ )
                  {
                      int idx = labels[i];
                      if( !dir[i] && ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) ))
                          
                      {
                          int d = CV_DTREE_CAT_DIR(idx,subset);
                          dir[i] = (char)((d ^ inversed_mask) - inversed_mask);
                          if( --nz )
                              break;
                      }
                  }
              }
              else // split on ordered var
              {
                  float* values_buf = (float*)(uchar*)inn_buf;
                  int* sorted_indices_buf = (int*)(values_buf + n);
                  int* sample_indices_buf = sorted_indices_buf + n;
                  const float* values = 0;
                  const int* sorted_indices = 0;
                  data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
                  int split_point = split->ord.split_point;
                  int n1 = node->get_num_valid(vi);
  
                  assert( 0 <= split_point && split_point < n-1 );
  
                  for( i = 0; i < n1; i++ )
                  {
                      int idx = sorted_indices[i];
                      if( !dir[idx] )
                      {
                          int d = i <= split_point ? -1 : 1;
                          dir[idx] = (char)((d ^ inversed_mask) - inversed_mask);
                          if( --nz )
                              break;
                      }
                  }
              }
          }
      }
  
      // find the default direction for the rest
      if( nz )
      {
          for( i = nr = 0; i < n; i++ )
              nr += dir[i] > 0;
          nl = n - nr - nz;
          d0 = nl > nr ? -1 : nr > nl;
      }
  
      // make sure that every sample is directed either to the left or to the right
      for( i = 0; i < n; i++ )
      {
          int d = dir[i];
          if( !d )
          {
              d = d0;
              if( !d )
                  d = d1, d1 = -d1;
          }
          d = d > 0;
          dir[i] = (char)d; // remap (-1,1) to (0,1)
      }
  }
  
  
  void CvDTree::split_node_data( CvDTreeNode* node )
  {
      int vi, i, n = node->sample_count, nl, nr, scount = data->sample_count;
      char* dir = (char*)data->direction->data.ptr;
      CvDTreeNode *left = 0, *right = 0;
      int* new_idx = data->split_buf->data.i;
      int new_buf_idx = data->get_child_buf_idx( node );
      int work_var_count = data->get_work_var_count();
      CvMat* buf = data->buf;
      cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int) + sizeof(float)));
      int* temp_buf = (int*)(uchar*)inn_buf;
  
      complete_node_dir(node);
  
      for( i = nl = nr = 0; i < n; i++ )
      {
          int d = dir[i];
          // initialize new indices for splitting ordered variables
          new_idx[i] = (nl & (d-1)) | (nr & -d); // d ? ri : li
          nr += d;
          nl += d^1;
      }
  
      bool split_input_data;
      node->left = left = data->new_node( node, nl, new_buf_idx, node->offset );
      node->right = right = data->new_node( node, nr, new_buf_idx, node->offset + nl );
  
      split_input_data = node->depth + 1 < data->params.max_depth &&
          (node->left->sample_count > data->params.min_sample_count ||
          node->right->sample_count > data->params.min_sample_count);
  
      // split ordered variables, keep both halves sorted.
      for( vi = 0; vi < data->var_count; vi++ )
      {
          int ci = data->get_var_type(vi);
  
          if( ci >= 0 || !split_input_data )
              continue;
  
          int n1 = node->get_num_valid(vi);
          float* src_val_buf = (float*)(uchar*)(temp_buf + n);
          int* src_sorted_idx_buf = (int*)(src_val_buf + n);
          int* src_sample_idx_buf = src_sorted_idx_buf + n;
          const float* src_val = 0;
          const int* src_sorted_idx = 0;
          data->get_ord_var_data(node, vi, src_val_buf, src_sorted_idx_buf, &src_val, &src_sorted_idx, src_sample_idx_buf);
  
          for(i = 0; i < n; i++)
              temp_buf[i] = src_sorted_idx[i];
  
          if (data->is_buf_16u)
          {
              unsigned short *ldst, *rdst, *ldst0, *rdst0;
              //unsigned short tl, tr;
              ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + 
                  vi*scount + left->offset);
              rdst0 = rdst = (unsigned short*)(ldst + nl);
  
              // split sorted
              for( i = 0; i < n1; i++ )
              {
                  int idx = temp_buf[i];
                  int d = dir[idx];
                  idx = new_idx[idx];
                  if (d)
                  {
                      *rdst = (unsigned short)idx;
                      rdst++;
                  }
                  else
                  {
                      *ldst = (unsigned short)idx;
                      ldst++;
                  }
              }
  
              left->set_num_valid(vi, (int)(ldst - ldst0));
              right->set_num_valid(vi, (int)(rdst - rdst0));
  
              // split missing
              for( ; i < n; i++ )
              {
                  int idx = temp_buf[i];
                  int d = dir[idx];
                  idx = new_idx[idx];
                  if (d)
                  {
                      *rdst = (unsigned short)idx;
                      rdst++;
                  }
                  else
                  {
                      *ldst = (unsigned short)idx;
                      ldst++;
                  }
              }
          }
          else
          {
              int *ldst0, *ldst, *rdst0, *rdst;
              ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols + 
                  vi*scount + left->offset;
              rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols + 
                  vi*scount + right->offset;
  
              // split sorted
              for( i = 0; i < n1; i++ )
              {
                  int idx = temp_buf[i];
                  int d = dir[idx];
                  idx = new_idx[idx];
                  if (d)
                  {
                      *rdst = idx;
                      rdst++;
                  }
                  else
                  {
                      *ldst = idx;
                      ldst++;
                  }
              }
  
              left->set_num_valid(vi, (int)(ldst - ldst0));
              right->set_num_valid(vi, (int)(rdst - rdst0));
  
              // split missing
              for( ; i < n; i++ )
              {
                  int idx = temp_buf[i];
                  int d = dir[idx];
                  idx = new_idx[idx];
                  if (d)
                  {
                      *rdst = idx;
                      rdst++;
                  }
                  else
                  {
                      *ldst = idx;
                      ldst++;
                  }
              }
          }
      }
  
      // split categorical vars, responses and cv_labels using new_idx relocation table
      for( vi = 0; vi < work_var_count; vi++ )
      {
          int ci = data->get_var_type(vi);
          int n1 = node->get_num_valid(vi), nr1 = 0;
          
          if( ci < 0 || (vi < data->var_count && !split_input_data) )
              continue;
  
          int *src_lbls_buf = temp_buf + n;
          const int* src_lbls = data->get_cat_var_data(node, vi, src_lbls_buf);
  
          for(i = 0; i < n; i++)
              temp_buf[i] = src_lbls[i];
  
          if (data->is_buf_16u)
          {
              unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + 
                  vi*scount + left->offset);
              unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + 
                  vi*scount + right->offset);
              
              for( i = 0; i < n; i++ )
              {
                  int d = dir[i];
                  int idx = temp_buf[i];
                  if (d)
                  {
                      *rdst = (unsigned short)idx;
                      rdst++;
                      nr1 += (idx != 65535 )&d;
                  }
                  else
                  {
                      *ldst = (unsigned short)idx;
                      ldst++;
                  }
              }
  
              if( vi < data->var_count )
              {
                  left->set_num_valid(vi, n1 - nr1);
                  right->set_num_valid(vi, nr1);
              }
          }
          else
          {
              int *ldst = buf->data.i + left->buf_idx*buf->cols + 
                  vi*scount + left->offset;
              int *rdst = buf->data.i + right->buf_idx*buf->cols + 
                  vi*scount + right->offset;
              
              for( i = 0; i < n; i++ )
              {
                  int d = dir[i];
                  int idx = temp_buf[i];
                  if (d)
                  {
                      *rdst = idx;
                      rdst++;
                      nr1 += (idx >= 0)&d;
                  }
                  else
                  {
                      *ldst = idx;
                      ldst++;
                  }
                  
              }
  
              if( vi < data->var_count )
              {
                  left->set_num_valid(vi, n1 - nr1);
                  right->set_num_valid(vi, nr1);
              }
          }        
      }
  
  
      // split sample indices
      int *sample_idx_src_buf = temp_buf + n;
      const int* sample_idx_src = data->get_sample_indices(node, sample_idx_src_buf);
  
      for(i = 0; i < n; i++)
          temp_buf[i] = sample_idx_src[i];
  
      int pos = data->get_work_var_count();
      if (data->is_buf_16u)
      {
          unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + 
              pos*scount + left->offset);
          unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + 
              pos*scount + right->offset);
          for (i = 0; i < n; i++)
          {
              int d = dir[i];
              unsigned short idx = (unsigned short)temp_buf[i];
              if (d)
              {
                  *rdst = idx;
                  rdst++;
              }
              else
              {
                  *ldst = idx;
                  ldst++;
              }
          }
      }
      else
      {
          int* ldst = buf->data.i + left->buf_idx*buf->cols + 
              pos*scount + left->offset;
          int* rdst = buf->data.i + right->buf_idx*buf->cols + 
              pos*scount + right->offset;
          for (i = 0; i < n; i++)
          {
              int d = dir[i];
              int idx = temp_buf[i];
              if (d)
              {
                  *rdst = idx;
                  rdst++;
              }
              else
              {
                  *ldst = idx;
                  ldst++;
              }
          }
      }
      
      // deallocate the parent node data that is not needed anymore
      data->free_node_data(node);    
  }
  
  float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
  {
      float err = 0;
      const CvMat* values = _data->get_values();
      const CvMat* response = _data->get_responses();
      const CvMat* missing = _data->get_missing();
      const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
      const CvMat* var_types = _data->get_var_types();
      int* sidx = sample_idx ? sample_idx->data.i : 0;
      int r_step = CV_IS_MAT_CONT(response->type) ?
                  1 : response->step / CV_ELEM_SIZE(response->type);
      bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
      int sample_count = sample_idx ? sample_idx->cols : 0;
      sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? values->rows : sample_count;
      float* pred_resp = 0;
      if( resp && (sample_count > 0) )
      {
          resp->resize( sample_count );
          pred_resp = &((*resp)[0]);
      }
  
      if ( is_classifier )
      {
          for( int i = 0; i < sample_count; i++ )
          {
              CvMat sample, miss;
              int si = sidx ? sidx[i] : i;
              cvGetRow( values, &sample, si ); 
              if( missing ) 
                  cvGetRow( missing, &miss, si );             
              float r = (float)predict( &sample, missing ? &miss : 0 )->value;
              if( pred_resp )
                  pred_resp[i] = r;
              int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
              err += d;
          }
          err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
      }
      else
      {
          for( int i = 0; i < sample_count; i++ )
          {
              CvMat sample, miss;
              int si = sidx ? sidx[i] : i;
              cvGetRow( values, &sample, si ); 
              if( missing ) 
                  cvGetRow( missing, &miss, si );             
              float r = (float)predict( &sample, missing ? &miss : 0 )->value;
              if( pred_resp )
                  pred_resp[i] = r;
              float d = r - response->data.fl[si*r_step];
              err += d*d;
          }
          err = sample_count ? err / (float)sample_count : -FLT_MAX;    
      }
      return err;
  }
  
  void CvDTree::prune_cv()
  {
      CvMat* ab = 0;
      CvMat* temp = 0;
      CvMat* err_jk = 0;
  
      // 1. build tree sequence for each cv fold, calculate error_{Tj,beta_k}.
      // 2. choose the best tree index (if need, apply 1SE rule).
      // 3. store the best index and cut the branches.
  
      CV_FUNCNAME( "CvDTree::prune_cv" );
  
      __BEGIN__;
  
      int ti, j, tree_count = 0, cv_n = data->params.cv_folds, n = root->sample_count;
      // currently, 1SE for regression is not implemented
      bool use_1se = data->params.use_1se_rule != 0 && data->is_classifier;
      double* err;
      double min_err = 0, min_err_se = 0;
      int min_idx = -1;
  
      CV_CALL( ab = cvCreateMat( 1, 256, CV_64F ));
  
      // build the main tree sequence, calculate alpha's
      for(;;tree_count++)
      {
          double min_alpha = update_tree_rnc(tree_count, -1);
          if( cut_tree(tree_count, -1, min_alpha) )
              break;
  
          if( ab->cols <= tree_count )
          {
              CV_CALL( temp = cvCreateMat( 1, ab->cols*3/2, CV_64F ));
              for( ti = 0; ti < ab->cols; ti++ )
                  temp->data.db[ti] = ab->data.db[ti];
              cvReleaseMat( &ab );
              ab = temp;
              temp = 0;
          }
  
          ab->data.db[tree_count] = min_alpha;
      }
  
      ab->data.db[0] = 0.;
  
      if( tree_count > 0 )
      {
          for( ti = 1; ti < tree_count-1; ti++ )
              ab->data.db[ti] = sqrt(ab->data.db[ti]*ab->data.db[ti+1]);
          ab->data.db[tree_count-1] = DBL_MAX*0.5;
  
          CV_CALL( err_jk = cvCreateMat( cv_n, tree_count, CV_64F ));
          err = err_jk->data.db;
  
          for( j = 0; j < cv_n; j++ )
          {
              int tj = 0, tk = 0;
              for( ; tk < tree_count; tj++ )
              {
                  double min_alpha = update_tree_rnc(tj, j);
                  if( cut_tree(tj, j, min_alpha) )
                      min_alpha = DBL_MAX;
  
                  for( ; tk < tree_count; tk++ )
                  {
                      if( ab->data.db[tk] > min_alpha )
                          break;
                      err[j*tree_count + tk] = root->tree_error;
                  }
              }
          }
  
          for( ti = 0; ti < tree_count; ti++ )
          {
              double sum_err = 0;
              for( j = 0; j < cv_n; j++ )
                  sum_err += err[j*tree_count + ti];
              if( ti == 0 || sum_err < min_err )
              {
                  min_err = sum_err;
                  min_idx = ti;
                  if( use_1se )
                      min_err_se = sqrt( sum_err*(n - sum_err) );
              }
              else if( sum_err < min_err + min_err_se )
                  min_idx = ti;
          }
      }
  
      pruned_tree_idx = min_idx;
      free_prune_data(data->params.truncate_pruned_tree != 0);
  
      __END__;
  
      cvReleaseMat( &err_jk );
      cvReleaseMat( &ab );
      cvReleaseMat( &temp );
  }
  
  
  double CvDTree::update_tree_rnc( int T, int fold )
  {
      CvDTreeNode* node = root;
      double min_alpha = DBL_MAX;
  
      for(;;)
      {
          CvDTreeNode* parent;
          for(;;)
          {
              int t = fold >= 0 ? node->cv_Tn[fold] : node->Tn;
              if( t <= T || !node->left )
              {
                  node->complexity = 1;
                  node->tree_risk = node->node_risk;
                  node->tree_error = 0.;
                  if( fold >= 0 )
                  {
                      node->tree_risk = node->cv_node_risk[fold];
                      node->tree_error = node->cv_node_error[fold];
                  }
                  break;
              }
              node = node->left;
          }
  
          for( parent = node->parent; parent && parent->right == node;
              node = parent, parent = parent->parent )
          {
              parent->complexity += node->complexity;
              parent->tree_risk += node->tree_risk;
              parent->tree_error += node->tree_error;
  
              parent->alpha = ((fold >= 0 ? parent->cv_node_risk[fold] : parent->node_risk)
                  - parent->tree_risk)/(parent->complexity - 1);
              min_alpha = MIN( min_alpha, parent->alpha );
          }
  
          if( !parent )
              break;
  
          parent->complexity = node->complexity;
          parent->tree_risk = node->tree_risk;
          parent->tree_error = node->tree_error;
          node = parent->right;
      }
  
      return min_alpha;
  }
  
  
  int CvDTree::cut_tree( int T, int fold, double min_alpha )
  {
      CvDTreeNode* node = root;
      if( !node->left )
          return 1;
  
      for(;;)
      {
          CvDTreeNode* parent;
          for(;;)
          {
              int t = fold >= 0 ? node->cv_Tn[fold] : node->Tn;
              if( t <= T || !node->left )
                  break;
              if( node->alpha <= min_alpha + FLT_EPSILON )
              {
                  if( fold >= 0 )
                      node->cv_Tn[fold] = T;
                  else
                      node->Tn = T;
                  if( node == root )
                      return 1;
                  break;
              }
              node = node->left;
          }
  
          for( parent = node->parent; parent && parent->right == node;
              node = parent, parent = parent->parent )
              ;
  
          if( !parent )
              break;
  
          node = parent->right;
      }
  
      return 0;
  }
  
  
  void CvDTree::free_prune_data(bool cut_tree)
  {
      CvDTreeNode* node = root;
  
      for(;;)
      {
          CvDTreeNode* parent;
          for(;;)
          {
              // do not call cvSetRemoveByPtr( cv_heap, node->cv_Tn )
              // as we will clear the whole cross-validation heap at the end
              node->cv_Tn = 0;
              node->cv_node_error = node->cv_node_risk = 0;
              if( !node->left )
                  break;
              node = node->left;
          }
  
          for( parent = node->parent; parent && parent->right == node;
              node = parent, parent = parent->parent )
          {
              if( cut_tree && parent->Tn <= pruned_tree_idx )
              {
                  data->free_node( parent->left );
                  data->free_node( parent->right );
                  parent->left = parent->right = 0;
              }
          }
  
          if( !parent )
              break;
  
          node = parent->right;
      }
  
      if( data->cv_heap )
          cvClearSet( data->cv_heap );
  }
  
  
  void CvDTree::free_tree()
  {
      if( root && data && data->shared )
      {
          pruned_tree_idx = INT_MIN;
          free_prune_data(true);
          data->free_node(root);
          root = 0;
      }
  }
  
  CvDTreeNode* CvDTree::predict( const CvMat* _sample,
      const CvMat* _missing, bool preprocessed_input ) const
  {
      cv::AutoBuffer<int> catbuf;
  
      int i, mstep = 0;
      const uchar* m = 0;
      CvDTreeNode* node = root;
  
      if( !node )
          CV_Error( CV_StsError, "The tree has not been trained yet" );
  
      if( !CV_IS_MAT(_sample) || CV_MAT_TYPE(_sample->type) != CV_32FC1 ||
          (_sample->cols != 1 && _sample->rows != 1) ||
          (_sample->cols + _sample->rows - 1 != data->var_all && !preprocessed_input) ||
          (_sample->cols + _sample->rows - 1 != data->var_count && preprocessed_input) )
              CV_Error( CV_StsBadArg,
          "the input sample must be 1d floating-point vector with the same "
          "number of elements as the total number of variables used for training" );
  
      const float* sample = _sample->data.fl;
      int step = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(sample[0]);
  
      if( data->cat_count && !preprocessed_input ) // cache for categorical variables
      {
          int n = data->cat_count->cols;
          catbuf.allocate(n);
          for( i = 0; i < n; i++ )
              catbuf[i] = -1;
      }
  
      if( _missing )
      {
          if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) ||
              !CV_ARE_SIZES_EQ(_missing, _sample) )
              CV_Error( CV_StsBadArg,
          "the missing data mask must be 8-bit vector of the same size as input sample" );
          m = _missing->data.ptr;
          mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step/sizeof(m[0]);
      }
  
      const int* vtype = data->var_type->data.i;
      const int* vidx = data->var_idx && !preprocessed_input ? data->var_idx->data.i : 0;
      const int* cmap = data->cat_map ? data->cat_map->data.i : 0;
      const int* cofs = data->cat_ofs ? data->cat_ofs->data.i : 0;
  
      while( node->Tn > pruned_tree_idx && node->left )
      {
          CvDTreeSplit* split = node->split;
          int dir = 0;
          for( ; !dir && split != 0; split = split->next )
          {
              int vi = split->var_idx;
              int ci = vtype[vi];
              i = vidx ? vidx[vi] : vi;
              float val = sample[i*step];
              if( m && m[i*mstep] )
                  continue;
              if( ci < 0 ) // ordered
                  dir = val <= split->ord.c ? -1 : 1;
              else // categorical
              {
                  int c;
                  if( preprocessed_input )
                      c = cvRound(val);
                  else
                  {
                      c = catbuf[ci];
                      if( c < 0 )
                      {
                          int a = c = cofs[ci];
                          int b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1];
                          
                          int ival = cvRound(val);
                          if( ival != val )
                              CV_Error( CV_StsBadArg,
                              "one of input categorical variable is not an integer" );
                          
                          int sh = 0;
                          while( a < b )
                          {
                              sh++;
                              c = (a + b) >> 1;
                              if( ival < cmap[c] )
                                  b = c;
                              else if( ival > cmap[c] )
                                  a = c+1;
                              else
                                  break;
                          }
  
                          if( c < 0 || ival != cmap[c] )
                              continue;
  
                          catbuf[ci] = c -= cofs[ci];
                      }
                  }
                  c = ( (c == 65535) && data->is_buf_16u ) ? -1 : c;
                  dir = CV_DTREE_CAT_DIR(c, split->subset);
              }
  
              if( split->inversed )
                  dir = -dir;
          }
  
          if( !dir )
          {
              double diff = node->right->sample_count - node->left->sample_count;
              dir = diff < 0 ? -1 : 1;
          }
          node = dir < 0 ? node->left : node->right;
      }
  
      return node;
  }
  
  
  CvDTreeNode* CvDTree::predict( const Mat& _sample, const Mat& _missing, bool preprocessed_input ) const
  {
      CvMat sample = _sample, mmask = _missing;
      return predict(&sample, mmask.data.ptr ? &mmask : 0, preprocessed_input);
  }
  
  
  const CvMat* CvDTree::get_var_importance()
  {
      if( !var_importance )
      {
          CvDTreeNode* node = root;
          double* importance;
          if( !node )
              return 0;
          var_importance = cvCreateMat( 1, data->var_count, CV_64F );
          cvZero( var_importance );
          importance = var_importance->data.db;
  
          for(;;)
          {
              CvDTreeNode* parent;
              for( ;; node = node->left )
              {
                  CvDTreeSplit* split = node->split;
  
                  if( !node->left || node->Tn <= pruned_tree_idx )
                      break;
  
                  for( ; split != 0; split = split->next )
                      importance[split->var_idx] += split->quality;
              }
  
              for( parent = node->parent; parent && parent->right == node;
                  node = parent, parent = parent->parent )
                  ;
  
              if( !parent )
                  break;
  
              node = parent->right;
          }
  
          cvNormalize( var_importance, var_importance, 1., 0, CV_L1 );
      }
  
      return var_importance;
  }
  
  
  void CvDTree::write_split( CvFileStorage* fs, CvDTreeSplit* split ) const
  {
      int ci;
  
      cvStartWriteStruct( fs, 0, CV_NODE_MAP + CV_NODE_FLOW );
      cvWriteInt( fs, "var", split->var_idx );
      cvWriteReal( fs, "quality", split->quality );
  
      ci = data->get_var_type(split->var_idx);
      if( ci >= 0 ) // split on a categorical var
      {
          int i, n = data->cat_count->data.i[ci], to_right = 0, default_dir;
          for( i = 0; i < n; i++ )
              to_right += CV_DTREE_CAT_DIR(i,split->subset) > 0;
  
          // ad-hoc rule when to use inverse categorical split notation
          // to achieve more compact and clear representation
          default_dir = to_right <= 1 || to_right <= MIN(3, n/2) || to_right <= n/3 ? -1 : 1;
  
          cvStartWriteStruct( fs, default_dir*(split->inversed ? -1 : 1) > 0 ?
                              "in" : "not_in", CV_NODE_SEQ+CV_NODE_FLOW );
  
          for( i = 0; i < n; i++ )
          {
              int dir = CV_DTREE_CAT_DIR(i,split->subset);
              if( dir*default_dir < 0 )
                  cvWriteInt( fs, 0, i );
          }
          cvEndWriteStruct( fs );
      }
      else
          cvWriteReal( fs, !split->inversed ? "le" : "gt", split->ord.c );
  
      cvEndWriteStruct( fs );
  }
  
  
  void CvDTree::write_node( CvFileStorage* fs, CvDTreeNode* node ) const
  {
      CvDTreeSplit* split;
  
      cvStartWriteStruct( fs, 0, CV_NODE_MAP );
  
      cvWriteInt( fs, "depth", node->depth );
      cvWriteInt( fs, "sample_count", node->sample_count );
      cvWriteReal( fs, "value", node->value );
  
      if( data->is_classifier )
          cvWriteInt( fs, "norm_class_idx", node->class_idx );
  
      cvWriteInt( fs, "Tn", node->Tn );
      cvWriteInt( fs, "complexity", node->complexity );
      cvWriteReal( fs, "alpha", node->alpha );
      cvWriteReal( fs, "node_risk", node->node_risk );
      cvWriteReal( fs, "tree_risk", node->tree_risk );
      cvWriteReal( fs, "tree_error", node->tree_error );
  
      if( node->left )
      {
          cvStartWriteStruct( fs, "splits", CV_NODE_SEQ );
  
          for( split = node->split; split != 0; split = split->next )
              write_split( fs, split );
  
          cvEndWriteStruct( fs );
      }
  
      cvEndWriteStruct( fs );
  }
  
  
  void CvDTree::write_tree_nodes( CvFileStorage* fs ) const
  {
      //CV_FUNCNAME( "CvDTree::write_tree_nodes" );
  
      __BEGIN__;
  
      CvDTreeNode* node = root;
  
      // traverse the tree and save all the nodes in depth-first order
      for(;;)
      {
          CvDTreeNode* parent;
          for(;;)
          {
              write_node( fs, node );
              if( !node->left )
                  break;
              node = node->left;
          }
  
          for( parent = node->parent; parent && parent->right == node;
              node = parent, parent = parent->parent )
              ;
  
          if( !parent )
              break;
  
          node = parent->right;
      }
  
      __END__;
  }
  
  
  void CvDTree::write( CvFileStorage* fs, const char* name ) const
  {
      //CV_FUNCNAME( "CvDTree::write" );
  
      __BEGIN__;
  
      cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_TREE );
  
      //get_var_importance();
      data->write_params( fs );
      //if( var_importance )
      //cvWrite( fs, "var_importance", var_importance );
      write( fs );
  
      cvEndWriteStruct( fs );
  
      __END__;
  }
  
  
  void CvDTree::write( CvFileStorage* fs ) const
  {
      //CV_FUNCNAME( "CvDTree::write" );
  
      __BEGIN__;
  
      cvWriteInt( fs, "best_tree_idx", pruned_tree_idx );
  
      cvStartWriteStruct( fs, "nodes", CV_NODE_SEQ );
      write_tree_nodes( fs );
      cvEndWriteStruct( fs );
  
      __END__;
  }
  
  
  CvDTreeSplit* CvDTree::read_split( CvFileStorage* fs, CvFileNode* fnode )
  {
      CvDTreeSplit* split = 0;
  
      CV_FUNCNAME( "CvDTree::read_split" );
  
      __BEGIN__;
  
      int vi, ci;
  
      if( !fnode || CV_NODE_TYPE(fnode->tag) != CV_NODE_MAP )
          CV_ERROR( CV_StsParseError, "some of the splits are not stored properly" );
  
      vi = cvReadIntByName( fs, fnode, "var", -1 );
      if( (unsigned)vi >= (unsigned)data->var_count )
          CV_ERROR( CV_StsOutOfRange, "Split variable index is out of range" );
  
      ci = data->get_var_type(vi);
      if( ci >= 0 ) // split on categorical var
      {
          int i, n = data->cat_count->data.i[ci], inversed = 0, val;
          CvSeqReader reader;
          CvFileNode* inseq;
          split = data->new_split_cat( vi, 0 );
          inseq = cvGetFileNodeByName( fs, fnode, "in" );
          if( !inseq )
          {
              inseq = cvGetFileNodeByName( fs, fnode, "not_in" );
              inversed = 1;
          }
          if( !inseq ||
              (CV_NODE_TYPE(inseq->tag) != CV_NODE_SEQ && CV_NODE_TYPE(inseq->tag) != CV_NODE_INT))
              CV_ERROR( CV_StsParseError,
              "Either 'in' or 'not_in' tags should be inside a categorical split data" );
  
          if( CV_NODE_TYPE(inseq->tag) == CV_NODE_INT )
          {
              val = inseq->data.i;
              if( (unsigned)val >= (unsigned)n )
                  CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" );
  
              split->subset[val >> 5] |= 1 << (val & 31);
          }
          else
          {
              cvStartReadSeq( inseq->data.seq, &reader );
  
              for( i = 0; i < reader.seq->total; i++ )
              {
                  CvFileNode* inode = (CvFileNode*)reader.ptr;
                  val = inode->data.i;
                  if( CV_NODE_TYPE(inode->tag) != CV_NODE_INT || (unsigned)val >= (unsigned)n )
                      CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" );
  
                  split->subset[val >> 5] |= 1 << (val & 31);
                  CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
              }
          }
  
          // for categorical splits we do not use inversed splits,
          // instead we inverse the variable set in the split
          if( inversed )
              for( i = 0; i < (n + 31) >> 5; i++ )
                  split->subset[i] ^= -1;
      }
      else
      {
          CvFileNode* cmp_node;
          split = data->new_split_ord( vi, 0, 0, 0, 0 );
  
          cmp_node = cvGetFileNodeByName( fs, fnode, "le" );
          if( !cmp_node )
          {
              cmp_node = cvGetFileNodeByName( fs, fnode, "gt" );
              split->inversed = 1;
          }
  
          split->ord.c = (float)cvReadReal( cmp_node );
      }
  
      split->quality = (float)cvReadRealByName( fs, fnode, "quality" );
  
      __END__;
  
      return split;
  }
  
  
  CvDTreeNode* CvDTree::read_node( CvFileStorage* fs, CvFileNode* fnode, CvDTreeNode* parent )
  {
      CvDTreeNode* node = 0;
  
      CV_FUNCNAME( "CvDTree::read_node" );
  
      __BEGIN__;
  
      CvFileNode* splits;
      int i, depth;
  
      if( !fnode || CV_NODE_TYPE(fnode->tag) != CV_NODE_MAP )
          CV_ERROR( CV_StsParseError, "some of the tree elements are not stored properly" );
  
      CV_CALL( node = data->new_node( parent, 0, 0, 0 ));
      depth = cvReadIntByName( fs, fnode, "depth", -1 );
      if( depth != node->depth )
          CV_ERROR( CV_StsParseError, "incorrect node depth" );
  
      node->sample_count = cvReadIntByName( fs, fnode, "sample_count" );
      node->value = cvReadRealByName( fs, fnode, "value" );
      if( data->is_classifier )
          node->class_idx = cvReadIntByName( fs, fnode, "norm_class_idx" );
  
      node->Tn = cvReadIntByName( fs, fnode, "Tn" );
      node->complexity = cvReadIntByName( fs, fnode, "complexity" );
      node->alpha = cvReadRealByName( fs, fnode, "alpha" );
      node->node_risk = cvReadRealByName( fs, fnode, "node_risk" );
      node->tree_risk = cvReadRealByName( fs, fnode, "tree_risk" );
      node->tree_error = cvReadRealByName( fs, fnode, "tree_error" );
  
      splits = cvGetFileNodeByName( fs, fnode, "splits" );
      if( splits )
      {
          CvSeqReader reader;
          CvDTreeSplit* last_split = 0;
  
          if( CV_NODE_TYPE(splits->tag) != CV_NODE_SEQ )
              CV_ERROR( CV_StsParseError, "splits tag must stored as a sequence" );
  
          cvStartReadSeq( splits->data.seq, &reader );
          for( i = 0; i < reader.seq->total; i++ )
          {
              CvDTreeSplit* split;
              CV_CALL( split = read_split( fs, (CvFileNode*)reader.ptr ));
              if( !last_split )
                  node->split = last_split = split;
              else
                  last_split = last_split->next = split;
  
              CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
          }
      }
  
      __END__;
  
      return node;
  }
  
  
  void CvDTree::read_tree_nodes( CvFileStorage* fs, CvFileNode* fnode )
  {
      CV_FUNCNAME( "CvDTree::read_tree_nodes" );
  
      __BEGIN__;
  
      CvSeqReader reader;
      CvDTreeNode _root;
      CvDTreeNode* parent = &_root;
      int i;
      parent->left = parent->right = parent->parent = 0;
  
      cvStartReadSeq( fnode->data.seq, &reader );
  
      for( i = 0; i < reader.seq->total; i++ )
      {
          CvDTreeNode* node;
  
          CV_CALL( node = read_node( fs, (CvFileNode*)reader.ptr, parent != &_root ? parent : 0 ));
          if( !parent->left )
              parent->left = node;
          else
              parent->right = node;
          if( node->split )
              parent = node;
          else
          {
              while( parent && parent->right )
                  parent = parent->parent;
          }
  
          CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
      }
  
      root = _root.left;
  
      __END__;
  }
  
  
  void CvDTree::read( CvFileStorage* fs, CvFileNode* fnode )
  {
      CvDTreeTrainData* _data = new CvDTreeTrainData();
      _data->read_params( fs, fnode );
  
      read( fs, fnode, _data );
      get_var_importance();
  }
  
  
  // a special entry point for reading weak decision trees from the tree ensembles
  void CvDTree::read( CvFileStorage* fs, CvFileNode* node, CvDTreeTrainData* _data )
  {
      CV_FUNCNAME( "CvDTree::read" );
  
      __BEGIN__;
  
      CvFileNode* tree_nodes;
  
      clear();
      data = _data;
  
      tree_nodes = cvGetFileNodeByName( fs, node, "nodes" );
      if( !tree_nodes || CV_NODE_TYPE(tree_nodes->tag) != CV_NODE_SEQ )
          CV_ERROR( CV_StsParseError, "nodes tag is missing" );
  
      pruned_tree_idx = cvReadIntByName( fs, node, "best_tree_idx", -1 );
      read_tree_nodes( fs, tree_nodes );
  
      __END__;
  }
  
  Mat CvDTree::getVarImportance()
  {
      return Mat(get_var_importance());
  }
  
  /* End of file. */
  