prob_sum = sum(prob_values)
if prob_sum==0:
number_classes = len(self.nbclasses)
pl = []
for prob_element in probability_list:
pl.append( ((1 / number_classes), prob_element[1]))
probability_list = pl
else:
probability_list = [ (p[0] / prob_sum, p[1]) for p in probability_list]
if best_only:
return max(probability_list)
else:
return probability_list