Simon Burton wrote:
numpy.dot.__doc__
matrixproduct(a,b) Returns the dot product of a and b for arrays of floating point types. Like the generic numpy equivalent the product sum is over the last dimension of a and the second-to-last dimension of b. NB: The first argument is not conjugated.
Does numpy support summing over arbitrary dimensions, as in tensor calculus ?
I could cook up something that uses transpose and dot, but it's reasonably tricky i think :)
I've just added tensordot to NumPy (adapted and enhanced from numarray). It allows you to sum over an arbitrary number of axes. It uses a 2-d dot-product internally as that is optimized if you have a fast blas installed. Example: If a.shape is (3,4,5) and b.shape is (4,3,2) Then tensordot(a, b, axes=([1,0],[0,1])) returns a (5,2) array which is equivalent to the code: c = zeros((5,2)) for i in range(5): for j in range(2): for k in range(3): for l in range(4): c[i,j] += a[k,l,i]*b[l,k,j] -Travis