Charles R Harris wrote:
> Sort of, it's actually (Xi.T*S).T, now that I think of it... I'll see if
> that is any faster. And if there is a neater way of doing it I'd love to
> hear about it.
>
> Xi*S[:,newaxis]
Thanks! (Obviously doesn't matter much in terms of performance, as it's
o
Bruce Southey wrote:
>> So far this is the fastest code I've got:
>>
>> import numpy as np
>>
>> nmax = 100
>>
>> def minover(Xi,S):
>> P,N = Xi.shape
>> SXi = Xi.copy()
>> for i in xrange(0,P):
>>
Francesc Alted wrote:
> A Monday 14 December 2009 17:09:13 Francesc Alted escrigué:
>> The things seems to be worst than 1.6x times slower for numpy, as matlab
>> orders arrays by column, while numpy order is by row. So, if we want to
>> compare pears with pears:
>>
>> For Python 600x200:
>>Ad
Bruce Southey wrote:
> Really I would suggest asking the list for the real problem because it
> is often amazing what solutions have been given.
So far this is the fastest code I've got:
import numpy as np
nmax = 100
def mi
Francesc Alted wrote:
> ...
> Yeah, I think taking slices here is taking quite a lot of time:
>
> In [58]: timeit E + Xi2[P/2,:]
> 10 loops, best of 3: 3.95 µs per loop
>
> In [59]: timeit E + Xi2[P/2]
> 10 loops, best of 3: 2.17 µs per loop
>
> don't know why the additional ',:' in the
Dag Sverre Seljebotn wrote:
> Jasper van de Gronde wrote:
>> I've attached a test file which shows the problem. It also tries adding
>> columns instead of rows (in case the memory layout is playing tricks),
>> but this seems to make no difference. This is the output I
(Resending without attachment as I don't think my previous message arrived.)
I just started using numpy and am very, very pleased with the
functionality and cleanness so far. However, I tried what I though would
be a simple optimization and found that the opposite was true.
Specifically, I had a l
