10.02.2012 05:39, Dave Cook kirjoitti:
Why is numpy.cumsum (along axis=0) so much slower than a simple loop?
The same goes for numpy.add.accumulate
The reason is loop ordering. The reduction operator when using `cumsum`
or `add.reduce` does the summation in the inmost loop, whereas the
Why is numpy.cumsum (along axis=0) so much slower than a simple loop? The
same goes for numpy.add.accumulate
# cumsumtest.py
import numpy as np
def loopcumsum(a):
csum = np.empty_like(a)
s = 0.0
for i in range(len(a)):
csum[i] = s = s + a[i]
return csum
npcumsum =
On Thu, Feb 9, 2012 at 11:39 PM, Dave Cook dav...@gmail.com wrote:
Why is numpy.cumsum (along axis=0) so much slower than a simple loop? The
same goes for numpy.add.accumulate
# cumsumtest.py
import numpy as np
def loopcumsum(a):
csum = np.empty_like(a)
s = 0.0
for i in
On Thu, Feb 9, 2012 at 9:21 PM, josef.p...@gmail.com wrote:
strange (if I didn't make a mistake)
In [12]: timeit a.cumsum(0)
100 loops, best of 3: 7.17 ms per loop
In [13]: timeit a.T.cumsum(-1).T
1000 loops, best of 3: 1.78 ms per loop
In [14]: (a.T.cumsum(-1).T == a.cumsum(0)).all()
numpy 1.6.1, OSX, Core 2 Duo:
In [7]: timeit a.cumsum(0)
100 loops, best of 3: 6.67 ms per loop
In [8]: timeit a.T.cumsum(-1).T
100 loops, best of 3: 6.75 ms per loop
-E
On Thu, Feb 9, 2012 at 9:51 PM, Dave Cook dav...@gmail.com wrote:
On Thu, Feb 9, 2012 at 9:41 PM, Dave Cook
