Examining various approaches to mathematical software solutions.

fbid_3769368939762114.py

# https://www.facebook.com/photo?fbid=3769368939762114&set=gm.1306912559653197

from itertools import chain
import timeit


# Interesting Code Sample
# expected = [x for xs  in [[x]*int(expected_dist[x]) for x in range(0, N+1)] for x in xs]

# Original Appraoch
def whaaat():
    expected_dist = range(0,10)
    N = 9
    return [x for xs  in [[x]*int(expected_dist[x]) for x in range(0, N+1)] for x in xs]


# FP Approach
def flatten(x):
    return list(chain.from_iterable(x))

def ideal_matrix_fp(expected_dist = range(0,10), N = 9):
    return [[x]*int(expected_dist[x]) for x in range(0, N+1)]
    

# Imperative Approach
def ideal_matrix_imperative(expected_dist = range(0,10), N = 9):
    matrix = []
    for x in range(0, N+1):
        matrix.append([x] * int(expected_dist[x]))
    return matrix


# Output
ideal_matrix = ideal_matrix_fp
expected = flatten(ideal_matrix())
print(expected)  # [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9]


# Performance
timeit.timeit(ideal_matrix_fp)          # 3.8432489000000487 <- fastest
timeit.timeit(ideal_matrix_imperative)  # 4.300187199999982
timeit.timeit(whaaat)                   # 4.519539700000003  <- slowest