In fact, there is already an inner1d implemented in numpy.core.umath_tests.inner1d
from numpy.core.umath_tests import inner1d
It should do the trick :)
On Thu, Mar 14, 2013 at 12:54 PM, Jaakko Luttinen firstname.lastname@example.org wrote:
Answering to myself, this pull request seems to implement an inner product with broadcasting (inner1d) and many other useful functions: https://github.com/numpy/numpy/pull/2954/ -J
On 03/13/2013 04:21 PM, Jaakko Luttinen wrote:
How can I compute dot product (or similar multiply&sum operations) efficiently so that broadcasting is utilized? For multi-dimensional arrays, NumPy's inner and dot functions do not match the leading axes and use broadcasting, but instead the result has first the leading axes of the first input array and then the leading axes of the second input array.
For instance, I would like to compute the following inner-product: np.sum(A*B, axis=-1)
But numpy.inner gives: A = np.random.randn(2,3,4) B = np.random.randn(3,4) np.inner(A,B).shape # -> (2, 3, 3) instead of (2, 3)
Similarly for dot product, I would like to compute for instance: np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2)
But numpy.dot gives: In : A = np.random.randn(2,3,4); B = np.random.randn(2,4,5) In : np.dot(A,B).shape # -> (2, 3, 2, 5) instead of (2, 3, 5)
I could use einsum for these operations, but I'm not sure whether that's as efficient as using some BLAS-supported(?) dot products.
I couldn't find any function which could perform this kind of operations. NumPy's functions seem to either flatten the input arrays (vdot, outer) or just use the axes of the input arrays separately (dot, inner, tensordot).
Best regards, Jaakko _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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