[Numpy-discussion] Optimizing mean(axis=0) on a 3D array
Martin Spacek
numpy at mspacek.mm.st
Sun Aug 27 07:28:03 CDT 2006
Tim Hochberg wrote:
> Here's an approach (mean_accumulate) that avoids making any copies of
> the data. It runs almost 4x as fast as your approach (called baseline
> here) on my box. Perhaps this will be useful:
>
--snip--
> def mean_accumulate(data, indices):
> result = np.zeros([32, 32], float)
> for i in indices:
> result += data[i]
> result /= len(indices)
> return result
Great! I got a roughly 9x speed improvement using take() in combination
with this approach. Thanks Tim!
Here's what my code looks like now:
>>> def mean_accum(data):
>>> result = np.zeros(data[0].shape, np.float64)
>>> for dataslice in data:
>>> result += dataslice
>>> result /= len(data)
>>> return result
>>>
>>> # frameis are int64
>>> frames = data.take(frameis.astype(np.int32), axis=0)
>>> meanframe = mean_accum(frames)
I'm surprised that using a python for loop is faster than the built-in
mean method. I suppose mean() can't perform the same in-place operations
because in certain cases doing so would fail?
Martin
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