https://github.com/python/cpython/commit/9a6b99ee8b750c8891b488a6bd60696bc164c6fa
commit: 9a6b99ee8b750c8891b488a6bd60696bc164c6fa
branch: 3.12
author: Miss Islington (bot) <[email protected]>
committer: rhettinger <[email protected]>
date: 2024-01-09T13:24:55-06:00
summary:
[3.12] Simplify binomial approximation example with random.binomialvariate()
(gh-113871) (gh-113872)
files:
M Doc/library/statistics.rst
diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
index 318e5d74611426..36f47b92ee3df5 100644
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -1016,19 +1016,16 @@ probability that the Python room will stay within its
capacity limits?
>>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
0.8402
- >>> # Solution using the cumulative binomial distribution
+ >>> # Exact solution using the cumulative binomial distribution
>>> from math import comb, fsum
>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4)
0.8402
>>> # Approximation using a simulation
- >>> from random import seed, choices
+ >>> from random import seed, binomialvariate
>>> seed(8675309)
- >>> def trial():
- ... return choices(('Python', 'Ruby'), (p, q), k=n).count('Python')
- ...
- >>> mean(trial() <= k for i in range(10_000))
- 0.8398
+ >>> mean(binomialvariate(n, p) <= k for i in range(10_000))
+ 0.8406
Naive bayesian classifier
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