parameters: (self, data, p)
return_statement: return maxvote, numpy.min(numpy.absolute(r[maxvote]))
docstring: Suppose that x is classified to class c, then the margin is
          defined as the minimum margin found against the k-1 other classes
docstring_summary: Suppose that x is classified to class c, then the margin is
          defined as the minimum margin found against the k-1 other classes
docstring_tokens: Suppose that x is classified to class c then the margin is defined as the minimum margin found against the k - 1 other classes
function: def classify(self, data, p):
          '''Suppose that x is classified to class c, then the margin is
          defined as the minimum margin found against the k-1 other classes
          '''
          
          numClasses = self.labels.numClasses
          r = numpy.zeros((numClasses, numClasses),numpy.float_)
          vote = numpy.zeros(numClasses)
          for i in range(numClasses - 1) :
              for j in range(i+1, numClasses) :
                  r[i][j] = self.classifiers[i][j].decisionFunc(data, p)
                  # afterwards I take the minimum or r, so assign:
                  r[j][i] = r[i][j]  
                  if r[i][j] > 0 :
                      vote[j] += 1
                  else:
                      vote[i] += 1
                      
          maxvote = numpy.argmax(vote)
          return maxvote, numpy.min(numpy.absolute(r[maxvote]))
function_tokens: def classify ( self , data , p ) : numClasses = self . labels . numClasses r = numpy . zeros ( ( numClasses , numClasses ) , numpy . float_ ) vote = numpy . zeros ( numClasses ) for i in range ( numClasses - 1 ) : for j in range ( i + 1 , numClasses ) : r [ i ] [ j ] = self . classifiers [ i ] [ j ] . decisionFunc ( data , p ) # afterwards I take the minimum or r, so assign: r [ j ] [ i ] = r [ i ] [ j ] if r [ i ] [ j ] > 0 : vote [ j ] += 1 else : vote [ i ] += 1 maxvote = numpy . argmax ( vote ) return maxvote , numpy . min ( numpy . absolute ( r [ maxvote ] ) )