I'm having a hard time on doing this. I have two m x n matrices (A and B) and I need to multiply every column of A by the rows in B, to generate a m x (n*n) matrix. I guess I wasn't very clear in the explanation so I'll post an example:
A =
[1 2
3 4]
B =
[5 6
7 8]
I wish to have:
[[5 6] [10 12]
[21 24] [28 32]]
I was able to do it using for loops but I want to avoid for as much as possible. Also using numpy to all this and all data is stored as np.array.
Maybe:
>>> A = np.array([[1,2],[3,4]])
>>> B = np.array([[5,6],[7,8]])
>>> (A * B[None, :].T).T
array([[[ 5, 6],
[21, 24]],
[[10, 12],
[28, 32]]])
where we use None to add an extra dimension to B, and a few transpositions to get the alignment right.
None to increase the rank of the array?
np.newaxis as the name, but np.newaxis is simply another name for None. In principle though they could change it to another sentinel.newaxis in the docs, but I never knew it was the same as None.If I understand you right, you want basic ( m * n ) multiplication right? Use numpy.dot():
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
