Verify distribution uniformity/Naive
You are encouraged to solve this task according to the task description, using any language you may know.
This task is an adjunct to Seven-dice from Five-dice.
Create a function to check that the random integers returned from a small-integer generator function have uniform distribution.
The function should take as arguments:
- The function producing random integers.
- The number of times to call the integer generator.
- A 'delta' value of some sort that indicates how close to a flat distribution is close enough.
The function should produce:
- Some indication of the distribution achieved.
- An 'error' if the distribution is not flat enough.
Show the distribution checker working when the produced distribution is flat enough and when it is not. (Use a generator from Seven-dice from Five-dice).
Python
<lang python>from collections import Counter from pprint import pprint as pp
def distcheck(fn, repeats, delta):
\
Bin the answers to fn() and check bin counts are within +/- delta %
of repeats/bincount
bin = Counter(fn() for i in range(repeats))
target = repeats // len(bin)
deltacount = int(delta / 100. * target)
assert all( abs(target - count) < deltacount
for count in bin.values() ), "Bin distribution skewed from %i +/- %i: %s" % (
target, deltacount, [ (key, target - count)
for key, count in sorted(bin.items()) ]
)
pp(dict(bin))</lang>
Sample output:
>>> distcheck(dice5, 1000000, 1)
{1: 200244, 2: 199831, 3: 199548, 4: 199853, 5: 200524}
>>> distcheck(dice5, 1000, 1)
Traceback (most recent call last):
File "<pyshell#30>", line 1, in <module>
distcheck(dice5, 1000, 1)
File "C://Paddys/rand7fromrand5.py", line 54, in distcheck
for key, count in sorted(bin.items()) ]
AssertionError: Bin distribution skewed from 200 +/- 2: [(1, 4), (2, -33), (3, 6), (4, 11), (5, 12)]