Min-Entropy

Problem

Return the min-entropy in bits of a probability distribution:

H_min = -log2(max(p))

The min-entropy is governed by the single most likely outcome rather than by the whole distribution.

Hint

Min-entropy is never larger than Shannon entropy.

case 1

in: [[0.5,0.5]]

expected: 1

case 2

in: [[0.25,0.75]]

expected: 0.4150374992788438

case 3

in: [[1]]

expected: 0

case 4

in: [[0.1,0.2,0.7]]

expected: 0.5145731728297583

Starter codePython

import math
def min_entropy(probs):
    # Your code here
    pass

4 test cases · run your code against them in the editor