I would certainly use einsum. It is almost perfect for these use cases,
e.g.,
np.einsum('ki,kij,kj->k', A, inv(B), A)On Thu, Oct 26, 2017 at 12:38 PM Charles R Harris <charlesr.har...@gmail.com> wrote: > On Thu, Oct 26, 2017 at 12:11 PM, Daniele Nicolodi <dani...@grinta.net> > wrote: > >> Hello, >> >> is there a better way to write the dot product between a stack of >> matrices? In my case I need to compute >> >> y = A.T @ inv(B) @ A >> >> with A a 3x1 matrix and B a 3x3 matrix, N times, with N in the few >> hundred thousands range. I thus "vectorize" the thing using stack of >> matrices, so that A is a Nx3x1 matrix and B is Nx3x3 and I can write: >> >> y = np.matmul(np.transpose(A, (0, 2, 1)), np.matmul(inv(B), A)) >> >> which I guess could be also written (in Python 3.6 and later): >> >> y = np.transpose(A, (0, 2, 1)) @ inv(B) @ A >> >> and I obtain a Nx1x1 y matrix which I can collapse to the vector I need >> with np.squeeze(). >> >> However, the need for the second argument of np.transpose() seems odd to >> me, because all other functions handle transparently the matrix stacking. >> >> Am I missing something? Is there a more natural matrix arrangement that >> I could use to obtain the same results more naturally? > > > There has been discussion of adding a operator for transposing the > matrices in a stack, but no resolution at this point. However, if you have > a stack of vectors (not matrices) you can turn then into transposed > matrices like `A[..., None, :]`, so `A[..., None, :] @ inv(B) @ A[..., > None]` and then squeeze. > > Another option is to use einsum. > > Chuck > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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