Particle Filter Weight Update

Problem

Update particle weights with a likelihood: return the normalized product weights:

w_i_new = w_i * l_i / sum over j of w_j * l_j.

Return an empty list for empty, mismatched or all-zero inputs.

Hint

Multiply then normalize so the weights sum to 1.

case 1

in: [[0.5,0.5],[1,2]]

expected: [0.3333333333333333,0.6666666666666666]

case 2

in: [[0.2,0.3,0.5],[0.1,0.2,0.7]]

expected: [0.04651162790697675,0.13953488372093023,0.813953488372093]

case 3

in: [[0.5,0.5],[0,0]]

expected: []

case 4

in: [[1],[3]]

expected: [1]

Starter codePython

def particle_filter_weight_update(weights, likelihoods):
    # Your code here
    pass

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