On Fri, 13 Jan 2006, "Paulo J. S. Silva" wrote:
as dot already makes a scalar out the multiplication of two rank-1 arrays (in which case it computes the inner product), I thought that this behavior could be extended to matrix objects.
t matrix([[2]]) z matrix([[0, 0, 0], [0, 1, 2], [0, 2, 4]]) t+z matrix([[2, 2, 2], [2, 3, 4], [2, 4, 6]]) t*z Traceback (most recent call last): File "<stdin>", line 1, in ? File "C:\Python24\Lib\site-packages\numpy\core\defmatrix.py", line 128, in __m
Well, 'dot' for matrices is just a synonym for 'matrixmultiply', which it cannot be (in the same sense) for arrays. But I grant that I find it odd to enforce conformability for multiplication and not for addition. (See below.) I will also grant that GAUSS and Matlab behave as you wish, which might reflect a natural convenience and might reflect their impoverished types. Finally, I grant that I have not been able to quickly think up a use case where I want a 1x1 matrix for anything except error checking. Just to be clear, you do not want to get rid of 1x1 matrices, you just want to get rid of them as the result of *multiplication*, right. So [[1]]*[[2]]=2 but [[1]]+[[2]]=[[3]]. Right? And you would, I presume, find a 'scalar' function to be too clumsy. Cheers, Alan Isaac PS Conformability details: ul__ return N.dot(self, other) ValueError: objects are not aligned
t.A*z.A array([[0, 0, 0], [0, 2, 4], [0, 4, 8]])