Statistics inv_cdf sync with corresponding random module normal distributions#95265
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rhettinger merged 2 commits intopython:mainfrom Jul 26, 2022
Merged
Statistics inv_cdf sync with corresponding random module normal distributions#95265rhettinger merged 2 commits intopython:mainfrom
rhettinger merged 2 commits intopython:mainfrom
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jackoconnordev
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See python/cpython#95265. To quote: > Restores alignment with random.gauss(mu, sigma) and random.normalvariate(mu, sigma) both. of which are equivalent to sampling from NormalDist(mu, sigma).inv_cdf(random()). The two functions in the random module happy accept sigma=0 and give a well-defined result. > This also lets the function gently handle a sigma getting smaller, eventually becoming zero. As sigma decrease, NormalDist(mu, sigma).inv_cdf(p) forms a tighter and tighter internal around mu and becoming exactly mu in the limit. For example, NormalDist(100, 1E-300).inv_cdf(0.3) cleanly evaluates to 100.0but withsigma=1e-500`` the function previously would raised an unexpected error.
youknowone
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* Copy CPython 3.13 statistics module into RustPython * Adjust CPython "magic constants" in KDE tests ## test_kde I'm not too sure why but this one takes a few seconds to run the second for loop which calculates the cumulative distribution and does a rough calculation of the area under the curve. ## test_kde_random I have a lower bound for RustPython to sort a random list of 1_000_000 numbers on my laptop of > 1 hour. By dropping n to 30_000 sort will not take an egregious amount of time to run. It is then necessary to lower the tolerance for the math.isclose check, or the computed values may **randomly** fail due to the higher variance caused by the smaller sample size. * Reintroduce expected failure in test_statistics.TestNormalDict.test_slots * Sync Rust `normal_dist_inv_cdf` with Python equivalent See python/cpython#95265. To quote: > Restores alignment with random.gauss(mu, sigma) and random.normalvariate(mu, sigma) both. of which are equivalent to sampling from NormalDist(mu, sigma).inv_cdf(random()). The two functions in the random module happy accept sigma=0 and give a well-defined result. > This also lets the function gently handle a sigma getting smaller, eventually becoming zero. As sigma decrease, NormalDist(mu, sigma).inv_cdf(p) forms a tighter and tighter internal around mu and becoming exactly mu in the limit. For example, NormalDist(100, 1E-300).inv_cdf(0.3) cleanly evaluates to 100.0but withsigma=1e-500`` the function previously would raised an unexpected error.
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Remove the inconsistent restriction that inv_cdf() is undefined when sigma is zero.
Aligns the C
_normal_dist_inv_cdf()function with its pure python equivalent.Restores the invariant that
NormalDist(mu, sigma).inv_cdf(p)is equivalent to `NormalDist(0.0, 1.0)inv_cdf(p) * sigma + mi``. In general, operations on NormalDist should always match the rescaled and reentered results for the same operation on the standard normal distribution.Restores alignment with
random.gauss(mu, sigma)andrandom.normalvariate(mu, sigma)both. of which are equivalent to sampling fromNormalDist(mu, sigma).inv_cdf(random()). The two functions in the random module happy accept sigma=0 and give a well-defined result.This also lets the function gently handle a sigma getting smaller, eventually becoming zero. As sigma decrease,
NormalDist(mu, sigma).inv_cdf(p)forms a tighter and tighter internal around mu and becoming exactly mu in the limit. For example,NormalDist(100, 1E-300).inv_cdf(0.3)cleanly evaluates to 100.0but withsigma=1e-500`` the function previously would raised an unexpected error.The only opposing idea is that
inv_cdf()means to give the inverse ofcdf(), but the cdf isn't defined with sigma=0. This is fine though. all supported inputs tocdf()are still invertible. There is just a relaxation of a restriction beyond the supported range which makesinv_cdf()obey other invariants and handle edge cases cleanly.The edit also gives a very small performance improvement.