[Numpy-discussion] Tensor-like product with extra indices?
huaiyu_zhu at yahoo.com
Fri Sep 13 01:46:03 CDT 2002
Two related questions:
1. Suppose B = ravel(A) and
B[i] corresponds to A[i0,i1,...].
Are there named functions that map the indices
(i0,i0,...) -> i
and back? This is assuming A.shape = (m0,m1,...) is known.
2. Suppose the above function is called ravel_index and its
Given a list of matrices [A0, A1, ...] is there a function that
calculates a matrix B such that
B[i,j] = A0[i0,j] * A1[i1,j] * ...
where i = ravel_index(i0, i1, ...).
What about the inverse function that turns B into C such that
C[i0,i1,...,j] = A0[i0,j] * A1[i1,j] * ...
I've considered reshape, ravel, take, put, outerproct but couldn't come
up with a combination to do it without a for-loop on j and a for-loop on
The above two questions I need answers right now. But I can imagine a
need for a more general operations in the future. Given a sequence of
arrays [A0,A1,...], produce a tensor product by picking certain indices
from each array (like i0,i1 above), while keeping some other indices
fixed (like j above). For example, the above could be written as
B = tensor_like([A0,A1...], [(0,None,None,...), ...,
As another example, if len(A.shape)==3 then
transpose(A) == tensor_like([A], [(2,), (1,), (0,)])
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