Effective Sample Size

Problem

Effective sample size of a weight vector after normalizing:

ESS = 1 / sum(w_i^2).

Return 0.0 for an empty vector or one with zero total weight.

Hint

ESS is maximal for uniform weights and drops toward 1 for degenerate ones.

case 1

in: [[0.25,0.25,0.25,0.25]]

expected: 4

case 2

in: [[1]]

expected: 1

case 3

in: [[0.5,0.5]]

expected: 2

case 4

in: [[0.9,0.1]]

expected: 1.2195121951219512

case 5

in: [[0,0]]

expected: 0

Starter codePython

def effective_sample_size(weights):
    # Your code here
    pass

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