On Tue, Jul 19, 2016 at 3:53 PM, Ecem sogancıoglu <ecemsogancio...@gmail.com > 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? > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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