On Wed, Dec 24, 2014 at 10:30 AM, Julian Taylor <
[email protected]> 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?
e.g.
http://stackoverflow.com/questions/14758283/is-there-a-numpy-scipy-dot-product-calculating-only-the-diagonal-entries-of-the
I didn't pay a lot of attention to the einsum changes, since I haven't
really used it yet.
Josef
X V X.T but vectorized
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