Shuffle fairness tester

shuffle_simulation.py

def fact(n):
    return 1 if n == 1 else n * fact(n-1)

def test_shufller(shuffler, items, n=100000):
    counts = {}
    for _ in range(n):
        array = list(items)
        shuffler(array)
        p = ''.join(array)
        counts[p] = counts.get(p, 0) + 1
    # exptected counts
    expected = n / fact(len(deck))
    ok = all([0.99 <= val/expected <= 1.1 for val in counts.values()])
    if ok:
        print('fairly random: yep')
    else:
        print('fairly random: nope')
    print('shuffler: ', shuffler.__name__)
    print('input: ', array)
    print('permutations\'s distribution', [(key,val*100/n) for key, val in counts.items()])
    print('------------------------------------')
 
test_input = 'abc'
test_shufller(naive_shuffler, test_input)
test_shufller(knuth_shuffler, test_input)