decisiontree_continuous.py 文件源码

def calculateIG(self,groups,labels):
        # current entropy
        labelfracs = self.obtain_labelfracs(labels)
        current_entropy = self.calculate_entropy(labelfracs)
        # entropy of each grouping
        group_entropy = []
        for group in groups:
            labelfracs = self.obtain_labelfracs(group)
            group_entropy.append((len(group)/len(labels)) * self.calculate_entropy(labelfracs))
        infogain = current_entropy - sum(group_entropy)
        return infogain