Factor Analysis Loading Norm

Problem

In factor analysis the loading matrix maps latent factors to observed features. Return the Euclidean norm of column factor of loadings, that is the square root of the sum of squared loadings of that factor. Return 0.0 for an empty matrix or an out-of-range factor index.

Hint

The column norm measures how strongly a factor loads on the observed features.

Test cases

case 1

in: [[[0.5,0.8],[0.3,0.6]],0]

expected: 0.58309518948453

case 2

in: [[[0.5,0.8],[0.3,0.6]],1]

expected: 1

case 3

in: [[[1],[2]],0]

expected: 2.23606797749979

case 4

in: [[[1,2],[3,4]],5]

expected: 0

case 5

in: [[],0]

expected: 0

Starter codePython

import math


def factor_analysis_loading_norm(loadings, factor):
    # Your code here
    pass

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