Performance issue in covariance function in Numpy 1.9 and later
Hello All, there seems to be a performance issue with the covariance function in numpy 1.9 and later. Code example: *np.cov(np.random.randn(700,37000))* In numpy 1.8, this line of code requires 4.5755 seconds. In numpy 1.9 and later, the same line of code requires more than 30.3709 s execution time. Has anyone else observed this problem and is there a known bugfix?
On Tue, Jul 19, 2016 at 3:53 PM, Ecem sogancıoglu wrote: Hello All, there seems to be a performance issue with the covariance function in
numpy 1.9 and later. Code example:
*np.cov(np.random.randn(700,37000))* In numpy 1.8, this line of code requires 4.5755 seconds.
In numpy 1.9 and later, the same line of code requires more than 30.3709 s
execution time. Hi Ecem, can you make sure to use the exact same random array as input to
np.cov when testing this? Also timing just the function call you're
interested in would be good; the creating of your 2-D array takes longer
than the np.cov call:
In [5]: np.random.seed(1234)
In [6]: x = np.random.randn(700,37000)
In [7]: %timeit np.cov(x)
1 loops, best of 3: 572 ms per loop
In [8]: %timeit np.random.randn(700, 37000)
1 loops, best of 3: 1.26 s per loop
Cheers,
Ralf Has anyone else observed this problem and is there a known bugfix? _______________________________________________
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participants (2)
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Ecem sogancıoglu
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Ralf Gommers