From 6084bbe075649e10f5a05d88550fe6a227dbbba2 Mon Sep 17 00:00:00 2001 From: Raymond Hettinger Date: Sun, 25 Aug 2019 00:57:26 -0700 Subject: [PATCH] bpo-37905: Improve docs for NormalDist (GH-15486) (cherry picked from commit 8371799e300475c8f9f967e900816218d3500e5d) Co-authored-by: Raymond Hettinger --- Doc/library/statistics.rst | 27 +++++++-------------------- 1 file changed, 7 insertions(+), 20 deletions(-) diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst index 1a19e374192159..cbe2b8223faf9b 100644 --- a/Doc/library/statistics.rst +++ b/Doc/library/statistics.rst @@ -667,12 +667,8 @@ of applications in statistics. .. method:: NormalDist.overlap(other) - Compute the `overlapping coefficient (OVL) - `_ - between two normal distributions, giving a measure of agreement. - Returns a value between 0.0 and 1.0 giving `the overlapping area for - the two probability density functions - `_. + Returns a value between 0.0 and 1.0 giving the overlapping area for + the two probability density functions. Instances of :class:`NormalDist` support addition, subtraction, multiplication and division by a constant. These operations @@ -734,16 +730,6 @@ Find the `quartiles `_ and `deciles >>> [round(sat.inv_cdf(p / 10)) for p in range(1, 10)] [810, 896, 958, 1011, 1060, 1109, 1162, 1224, 1310] -What percentage of men and women will have the same height in `two normally -distributed populations with known means and standard deviations -`_? - - >>> men = NormalDist(70, 4) - >>> women = NormalDist(65, 3.5) - >>> ovl = men.overlap(women) - >>> round(ovl * 100.0, 1) - 50.3 - To estimate the distribution for a model than isn't easy to solve analytically, :class:`NormalDist` can generate input samples for a `Monte Carlo simulation `_: @@ -754,11 +740,12 @@ Carlo simulation `_: ... return (3*x + 7*x*y - 5*y) / (11 * z) ... >>> n = 100_000 - >>> X = NormalDist(10, 2.5).samples(n) - >>> Y = NormalDist(15, 1.75).samples(n) - >>> Z = NormalDist(5, 1.25).samples(n) + >>> seed = 86753099035768 + >>> X = NormalDist(10, 2.5).samples(n, seed=seed) + >>> Y = NormalDist(15, 1.75).samples(n, seed=seed) + >>> Z = NormalDist(50, 1.25).samples(n, seed=seed) >>> NormalDist.from_samples(map(model, X, Y, Z)) # doctest: +SKIP - NormalDist(mu=19.640137307085507, sigma=47.03273142191088) + NormalDist(mu=1.8661894803304777, sigma=0.65238717376862) Normal distributions commonly arise in machine learning problems. pFad - Phonifier reborn

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