[Numpy-discussion] loop vectorization
qubax@g...
qubax@g...
Fri Mar 11 13:07:49 CST 2011
we have had that discussion about ... two days ago. please look up
'How to sum weighted matrices' with at least two efficient solutions.
On Fri, Mar 11, 2011 at 02:00:09PM -0500, Josh Hykes wrote:
> I think you can use tensordot here. Maybe something like the
> following:
>
> from numpy.random import random
> import numpy as np
> ni, nj, nk = 4, 5, 6
> bipData = random((ni,nj,nk))
> data1 = np.zeros((nk,nk))
> # loop
> for i in range(nj):
> data1 += np.dot(np.transpose(bipData[:,i,:]), bipData[:,i,:])
> # tensordot
> axes_list = [(1,2), (1,0)]
> data2 = np.tensordot(bipData.T, bipData, axes=axes_list)
> diff = np.max((data1 - data2)/data1)
> print diff
> I hope this helps.
> -Josh
> On Fri, Mar 11, 2011 at 1:13 PM, Thomas K Gamble <[1]tkg@lanl.gov>
> wrote:
>
> I have the followin loop in my code:
>
> for i in range(0, nFrames):
>
> data += dot(transpose(bipData[:,i,:]), bipData[:,i,:])
>
> bipData is a 1024x258x256 double precision float array.
>
> The loop takes all of 15 seconds to run on my computer and, with
> several hundred files to process...
>
> Is there a way to do something like:
>
> data = sum(dot(transpose(bipData), bipData))
>
> with dot done on the desired axis of bipData?
>
> This might give a fair speed increase. Or perhaps a different approach
> I'm not seeing?
>
> --
>
> Thomas K. Gamble
>
> Research Technologist, System/Network Administrator
>
> Chemical Diagnostics and Engineering (C-CDE)
>
> Los Alamos National Laboratory
>
> MS-E543,p:[2]505-665-4323 f:[3]505-665-4267
>
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> --
> Josh Hykes
> NCSU grad student
> (717) 742-0264
> [6]jmhykes@ncsu.edu or [7]jhykes@gmail.com
>
> References
>
> 1. mailto:tkg@lanl.gov
> 2. tel:505-665-4323
> 3. tel:505-665-4267
> 4. mailto:NumPy-Discussion@scipy.org
> 5. http://mail.scipy.org/mailman/listinfo/numpy-discussion
> 6. mailto:jmhykes@ncsu.edu
> 7. mailto:jhykes@gmail.com
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