[Numpy-discussion] simple reduction question
Charles R Harris
charlesr.harris at gmail.com
Wed Dec 24 14:21:40 EST 2014
On Wed, Dec 24, 2014 at 10:29 AM, <josef.pktd at gmail.com> wrote:
> On Wed, Dec 24, 2014 at 10:30 AM, Julian Taylor <
> jtaylor.debian at googlemail.com> wrote:
>> On 24.12.2014 16:25, Neal Becker wrote:
>> > What would be the most efficient way to compute:
>> > c[j] = \sum_i (a[i] * b[i,j])
>> > where a[i] is a 1-d vector, b[i,j] is a 2-d array?
>> > This seems to be one way:
>> > import numpy as np
>> > a = np.arange (3)
>> > b = np.arange (12).reshape (3,4)
>> > c = np.dot (a, b).sum()
>> > but np.dot returns a vector, which then needs further reduction. Don't
>> know if
>> > there's a better way.
>> the formula maps nice to einsum:
>> np.einsum("i,ij->", a, b)
>> should also be reasonably efficient, but that probably depends on your
>> BLAS library and the sizes of the arrays.
> hijacking a bit since I was just trying to replicate various
> multidimensional dot products with einsum
> Are the older timings for einsum still a useful guide?
> I didn't pay a lot of attention to the einsum changes, since I haven't
> really used it yet.
It is quite a bit slower for dot products, but very convenient for stacked
arrays, vectors, and other such things that are complicated to do with dot
products. I find the extra execution time negligible in relation to the
savings in programming effort, but the tradeoff might be different for a
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