On Mon, Jan 24, 2011 at 4:29 PM, eat <e.antero.ta...@gmail.com> wrote:
> Hi,
>
> Running on:
> In []: np.__version__
> Out[]: '1.5.1'
> In []: sys.version
> Out[]: '2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)]'
>
> For the reference:
> In []: X= randn(10, 125)
> In []: timeit dot(X.T, X)
> 10000 loops, best of 3: 170 us per loop
> In []: X= randn(10, 250)
> In []: timeit dot(X.T, X)
> 1000 loops, best of 3: 671 us per loop
> In []: X= randn(10, 500)
> In []: timeit dot(X.T, X)
> 100 loops, best of 3: 5.15 ms per loop
> In []: X= randn(10, 1000)
> In []: timeit dot(X.T, X)
> 100 loops, best of 3: 20 ms per loop
> In []: X= randn(10, 2000)
> In []: timeit dot(X.T, X)
> 10 loops, best of 3: 80.7 ms per loop
>
> Performance of triu_indices:
> In []: timeit triu_indices(125)
> 1000 loops, best of 3: 662 us per loop
> In []: timeit triu_indices(250)
> 100 loops, best of 3: 2.55 ms per loop
> In []: timeit triu_indices(500)
> 100 loops, best of 3: 15 ms per loop
> In []: timeit triu_indices(1000)
> 10 loops, best of 3: 59.8 ms per loop
> In []: timeit triu_indices(2000)
> 1 loops, best of 3: 239 ms per loop
>
> So the tri*_indices calculations seems to be unreasonable slow compared to for
> example calculations of inner products.
>
> Now, just to compare for a very naive implementation of triu indices.
> In []: def iut(n):
> ..: r= np.empty(n* (n+ 1)/ 2, dtype= int)
> ..: c= r.copy()
> ..: a= np.arange(n)
> ..: m= 0
> ..: for i in xrange(n):
> ..: ni= n- i
> ..: mni= m+ ni
> ..: r[m: mni]= i
> ..: c[m: mni]= a[i: n]
> ..: m+= ni
> ..: return (r, c)
> ..:
>
> Are we really calculating the same thing?
> In []: triu_indices(5)
> Out[]:
> (array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
> array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
> In []: iut(5)
> Out[]:
> (array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
> array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
>
> Seems so, and then its performance:
> In []: timeit iut(125)
> 1000 loops, best of 3: 992 us per loop
> In []: timeit iut(250)
> 100 loops, best of 3: 2.03 ms per loop
> In []: timeit iut(500)
> 100 loops, best of 3: 5.3 ms per loop
> In []: timeit iut(1000)
> 100 loops, best of 3: 13.9 ms per loop
> In []: timeit iut(2000)
> 10 loops, best of 3: 39.8 ms per loop
>
> Even the naive implementation is very slow, but allready outperforms
> triu_indices, when n is > 250!
>
> So finally my question is how one could substantially improve the performance
> of indices calculations?
What's the timing of this version (taken from nitime) ? it builds a
full intermediate array
m = np.ones((n,n),int)
a = np.triu(m,k)
np.where(a != 0)
>>> n=5
>>> m = np.ones((n,n),int)
>>> np.where(np.triu(m,0) != 0)
(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
or maybe variations on it that all build a full intermediate matrix
>>> np.where(1-np.tri(n,n,-1))
(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
>>> np.where(np.subtract.outer(np.arange(n), np.arange(n))<=0)
(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
Josef
>
>
> Regards,
> eat
>
>
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