On 11/23/06, Carsten Rostgaard <[EMAIL PROTECTED]> wrote:
Hi!
I am trying to use the "dot" method on multi-(more than 2)-dimensional
arrays.
Specifically I do
>> y = dot(a, b)
where a is a 2D array and b is a 3D array.
using numpy I get the the help:
"
dot(...)
dot(a,v) returns matrix-multiplication between a and b.
The product-sum is over the last dimension of a and the
second-to-last dimension of b.
"
I then expect that
>> y[i, j, k] = sum(a[i, :] * b[j, :, k])
which is actually what I get.
The question is then:
1) Is there any way to change the axis for which the product-sum is
performed. This can of course be done by a swapaxis before and after the
operation, but this makes the array non-contiguous, in which case the
dot operation often makes bugs (at least in Numeric).
2) For complicated reasons we still use Numeric in our software package,
and in this, "dot" behaves very strangely.
According to the Numeric help:
In Numpy
tensordot(a, b, axes=2)
tensordot returns the product for any (ndim >= 1) arrays.
r_{xxx, yyy} = \sum_k a_{xxx,k} b_{k,yyy} where
the axes to be summed over are given by the axes argument.
the first element of the sequence determines the axis or axes
in arr1 to sum over, and the second element in axes argument sequence
determines the axis or axes in arr2 to sum over.
When there is more than one axis to sum over, the corresponding
arguments to axes should be sequences of the same length with the first
axis to sum over given first in both sequences, the second axis second,
and so forth.
If the axes argument is an integer, N, then the last N dimensions of a
and first N dimensions of b are summed over.
I don't know about numeric.
Chuck
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