#include "header.h"

NEGENTROPY negentropy ( REAL **data, UINT n_samples, NODE *local, UINT target)
{
   /* Calculates the entropy of classification of an attribute, given a table 
    * of attributes already used, the attribute on which splitting is to be
    * taken, and the target attribute. Entropy is calculated in bits, so logs
    * are taken to base 2 by dividing by LN_2.
    * The returned value always lies in the (closed) range [0, 1].
    */
   NEGENTROPY ret_val;
   NODE *_node, *_parent;
   UINT on_ctr, off_ctr, p1, p2, i, _match;
   REAL p_on, p_off, negentropy_on, negentropy_off;
   on_ctr = off_ctr = p1 = p2 = 0;
   /* Scan through all supplied data samples */

   for (i = 0; i < n_samples; i++) 
   {
      /* If pattern matches the current position in the decision tree, then use
       * this vector. The match is determined by passing up the decision tree
       * and checking whether 'data[idx] >= threshold' matches at each step, 
       * where idx and threshold are taken from each node in turn.
       */
      _match = 1;
      _node = local;
      while (_node->parent != NULL) { /* If at root node, all entries match */
         _parent = _node->parent;
         if (_node == _parent->on) { /* if parent node is ON */
            if (data[i][_parent->idx] < _parent->threshold)
               _match = 0;
     }
         else
         if (_node == _parent->off) { /* if parent node is OFF */
            if (data[i][_parent->idx] >= _parent->threshold)
              _match = 0;
     }
      _node = _parent;
      }
      if (_match) {
         if (data[i][local->idx] >= local->threshold) {
            on_ctr++;
              if (data[i][target] >= 0.5)
               p1++;
         }
         else {
            off_ctr++;
            if (data[i][target] >= 0.5)
               p2++;
         }
      }
   }   /* for (i=0; i<n_samples; i++) */

   /* 1: Entropy of subtable with activation ON */
   /* We now have the numbers of samples that match this part of the decision
    * tree, & the number of samples for which the supplied condition are true.
    * From these quantities we can find the negentropy of this partition.
    */
   if (on_ctr > 0)
   {
      p_on  = (REAL) p1 / (REAL) on_ctr;
      p_off = 1 - p_on;
      negentropy_on = -entropy (p_on) - entropy (p_off);
   }
   else
      negentropy_on = 0.0;
   /* 2: Entropy of subtable with activation OFF */
   if (off_ctr > 0)
   {
      p_on  = (REAL) p2 / (REAL) off_ctr;
      p_off = 1 - p_on;
      negentropy_off = -entropy (p_on) - entropy (p_off);
   }
   else
      negentropy_off = 0.0;
   ret_val.ne = (negentropy_on * on_ctr + negentropy_off * off_ctr);
   ret_val.ne /= (on_ctr + off_ctr);
   /* If all values examined were the same, set 'ret_val.status' to
    * the target value since this will be an end-of-branch node
    */
   if ((p1 == on_ctr) && (p2 == off_ctr))
      ret_val.status = ON;

   else if ((p1 == 0) && (p2 == 0))
      ret_val.status = OFF;
   else
      ret_val.status = INACTIVE;
   return ret_val;
}