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gh-108322: Optimize statistics.NormalDist.samples() (gh-108324)

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Raymond Hettinger 2023-08-27 08:59:40 -05:00 • committed by GitHub
parent 09343dba44
commit 042aa88bcc
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3 changed files with 14 additions and 5 deletions
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Lib/statistics.py
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@ -1135,7 +1135,7 @@ def linear_regression(x, y, /, *, proportional=False):
>>> noise = NormalDist().samples(5, seed=42)
>>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
>>> linear_regression(x, y) #doctest: +ELLIPSIS
LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)
LinearRegression(slope=3.17495..., intercept=1.00925...)
If *proportional* is true, the independent variable *x* and the
dependent variable *y* are assumed to be directly proportional.
@ -1148,7 +1148,7 @@ def linear_regression(x, y, /, *, proportional=False):
>>> y = [3 * x[i] + noise[i] for i in range(5)]
>>> linear_regression(x, y, proportional=True) #doctest: +ELLIPSIS
LinearRegression(slope=3.02447542484..., intercept=0.0)
LinearRegression(slope=2.90475..., intercept=0.0)
"""
n = len(x)
@ -1279,9 +1279,11 @@ def from_samples(cls, data):
def samples(self, n, *, seed=None):
"Generate *n* samples for a given mean and standard deviation."
gauss = random.gauss if seed is None else random.Random(seed).gauss
mu, sigma = self._mu, self._sigma
return [gauss(mu, sigma) for _ in repeat(None, n)]
rnd = random.random if seed is None else random.Random(seed).random
inv_cdf = _normal_dist_inv_cdf
mu = self._mu
sigma = self._sigma
return [inv_cdf(rnd(), mu, sigma) for _ in repeat(None, n)]
def pdf(self, x):
"Probability density function. P(x <= X < x+dx) / dx"