I'm new to sympy (and *very *impressed with what I can do with it), but
have been unable to figure something out about converting certain matrix
expressions into their "lambda function" form for fast evaluation.
I have a sympy matrix containing symbols x, y, and z. If I invoke lambdify
on this matrix for those symbols, I get a function that returns a numpy
matrix (great!). Is it also possible to make it return a numpy array
(rather than a matrix)? The reason I need this is because my matrix is
actually a rank-3 tensor when the arguments x,y,z are supplied (which are
vectors). In other words, I want to supply 3 equally sized vectors to my
matrix to create an m-by-n-by-k numpy array where my original [sympy]
matrix was m-by-n.
For example, I could do:
>>> fn = lambda x,y: np.array( [ [x+y, x-y], [x-y, y-x] ] )
>>> fn(1,2)
array([[ 3, -1],
[-1, 1]]) # Gives back original array shape (2x2)
>>>
>>> arr1 = np.array([1,2,3])
>>> arr2 = np.array([4,5,6])
>>> fn(arr1, arr2)
array([[[ 5, 7, 9],
[-3, -3, -3]],
[[-3, -3, -3],
[ 3, 3, 3]]])
>>> fn(arr1, arr2).shape # (2x2x3)
(2L, 2L, 3L)
I basically want to do the above with a sympy matrix rather than explicitly
typing the lambda function.
Thanks!
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