### Maximum Entropy

Unsolved###### Active Learning

##### Problem reported in interviews at

Maximum entropy is a common metric for active learning. In each test case for this problem, you will be given a list of numpy arrays representing probability distributions. Your job is to calculate the *index* of the probability distribution with the *maximum entropy*. (If the first numpy array represents the distribution with the maximum entropy, then return 0; if the second numpy array represents the distribution with the maximum entropy, then return 1; etc).

##### Sample Input:

`probability_distributions: [array([0.1 , 0.5 , 0.1 , 0.15, 0.15]), array([0.2, 0.2, 0.2, 0.2, 0.2])]`

##### Expected Output:

`1`

Input Test Case

Please enter only one test case at a timenumpy has been already imported as np (import numpy as np)

Output