>
> >>> x = MatrixSymbol('x', 3, 1) #Acts as a vector
>> >>> expr = x[1,1] + sin(x[2,1]) + cos(x[3,1])
>> >>> func = autowrap(expr)
>> >>> inp = np.array([1, 2, 3])
>> >>> func(inp)
>> 0.9193049302252362
>
>
> Do you mean it should be func(*inp)?
>
No, I mean func(inp), where inp is a numpy/contig
On Mon, Aug 4, 2014 at 5:44 PM, Tim Lahey wrote:
> I've answered your questions below.
>
> On 4 Aug 2014, at 18:27, James Crist wrote:
>
>>
>> *1. Sympy Matrices are always 2 dimensional, should this be true of the
>> generated code as well?*
>>
>
> I think the generated code should reflect the or
Yes, the Theano bridge solves this problem in some sense, except that
Theano is weak in speeding up the long scalar expression computations.
Jason
moorepants.info
+01 530-601-9791
On Tue, Aug 5, 2014 at 7:48 AM, Matthew Rocklin wrote:
> @Matthew:
>>
>> Thanks, I think a lot of people could re
>
> @Matthew:
>
> Thanks, I think a lot of people could really use this. What my main goal
> is right now is to get the basework down for *evaluating* single matrices
> with expressions as elements. After that I plan on working though
> correlating MatrixExpr with blas/lapack functionality so that
