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Commit042aa88

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gh-108322: Optimize statistics.NormalDist.samples() (gh-108324)
1 parent09343db commit042aa88

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+14
-5
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3 files changed

+14
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‎Doc/library/statistics.rst‎

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@@ -828,6 +828,11 @@ of applications in statistics.
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number generator. This is useful for creating reproducible results,
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even in a multi-threading context.
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..versionchanged::3.13
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Switched to a faster algorithm. To reproduce samples from previous
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versions, use:func:`random.seed` and:func:`random.gauss`.
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..method::NormalDist.pdf(x)
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Using a `probability density function (pdf)

‎Lib/statistics.py‎

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@@ -1135,7 +1135,7 @@ def linear_regression(x, y, /, *, proportional=False):
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>>> noise = NormalDist().samples(5, seed=42)
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>>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
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>>> linear_regression(x, y) #doctest: +ELLIPSIS
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LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)
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LinearRegression(slope=3.17495..., intercept=1.00925...)
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If *proportional* is true, the independent variable *x* and the
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dependent variable *y* are assumed to be directly proportional.
@@ -1148,7 +1148,7 @@ def linear_regression(x, y, /, *, proportional=False):
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>>> y = [3 * x[i] + noise[i] for i in range(5)]
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>>> linear_regression(x, y, proportional=True) #doctest: +ELLIPSIS
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LinearRegression(slope=3.02447542484..., intercept=0.0)
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LinearRegression(slope=2.90475..., intercept=0.0)
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"""
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n=len(x)
@@ -1279,9 +1279,11 @@ def from_samples(cls, data):
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defsamples(self,n,*,seed=None):
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"Generate *n* samples for a given mean and standard deviation."
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gauss=random.gaussifseedisNoneelserandom.Random(seed).gauss
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mu,sigma=self._mu,self._sigma
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return [gauss(mu,sigma)for_inrepeat(None,n)]
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rnd=random.randomifseedisNoneelserandom.Random(seed).random
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inv_cdf=_normal_dist_inv_cdf
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mu=self._mu
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sigma=self._sigma
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return [inv_cdf(rnd(),mu,sigma)for_inrepeat(None,n)]
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defpdf(self,x):
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"Probability density function. P(x <= X < x+dx) / dx"
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Speed-up NormalDist.samples() by using the inverse CDF method instead of
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calling random.gauss().

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