parameters: (self, example)
return_statement: return res, float(votes[res]) / float(totVotes)
docstring: classifies the given example using the entire forest
  
        **returns** a result and a measure of confidence in it.
  
        **FIX:** statistics sucks... I'm not seeing an obvious way to get
             the confidence intervals.  For that matter, I'm not seeing
             an unobvious way.
  
             For now, this is just treated as a voting problem with the confidence
             measure being the percent of trees which voted for the winning result.
docstring_summary: classifies the given example using the entire forest
docstring_tokens: classifies the given example using the entire forest
function: def ClassifyExample(self, example):
      """ classifies the given example using the entire forest
  
        **returns** a result and a measure of confidence in it.
  
        **FIX:** statistics sucks... I'm not seeing an obvious way to get
             the confidence intervals.  For that matter, I'm not seeing
             an unobvious way.
  
             For now, this is just treated as a voting problem with the confidence
             measure being the percent of trees which voted for the winning result.
      """
      self.treeVotes = self.CollectVotes(example)
      votes = [0] * len(self._nPossible)
      for i in range(len(self.treeList)):
        res = self.treeVotes[i]
        votes[res] = votes[res] + self.countList[i]
  
      totVotes = sum(votes)
      res = numpy.argmax(votes)
      # print 'v:',res,votes,totVotes
      return res, float(votes[res]) / float(totVotes)
function_tokens: def ClassifyExample ( self , example ) : self . treeVotes = self . CollectVotes ( example ) votes = [ 0 ] * len ( self . _nPossible ) for i in range ( len ( self . treeList ) ) : res = self . treeVotes [ i ] votes [ res ] = votes [ res ] + self . countList [ i ] totVotes = sum ( votes ) res = numpy . argmax ( votes ) # print 'v:',res,votes,totVotes return res , float ( votes [ res ] ) / float ( totVotes )