Entropy from Counts

Problem

Convert raw counts into a probability distribution and return its Shannon entropy in bits:

H = -sum(p_i * log2(p_i)), p_i = c_i / sum(c)

Ignore zero counts. Return 0.0 if the counts sum to zero.

Hint

Normalize the counts first, then apply the entropy formula.

case 1

in: [[1,1]]

expected: 1

case 2

in: [[3,1]]

expected: 0.8112781244591328

case 3

in: [[1,2,3,4]]

expected: 1.8464393446710154

case 4

in: [[10]]

expected: 0

case 5

in: [[0,5,5]]

expected: 1

Starter codePython

import math
def entropy_from_counts(counts):
    # Your code here
    pass

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