One problem with the matrix class is that it follows matlab way too much. For example:
>>> a = arange(9).reshape(3,3)
>>> A = asmatrix(a)
>>> v = arange(3)
>>> dot(a, v)
array([ 5, 14, 23])
>>> A*v
Traceback (most recent call last):
File "<pyshell#15>", line 1, in <module>
A*v
File "C:\Python25\lib\site-packages\numpy\core\defmatrix.py", line 157, in
__mul__
return N.dot(self, asmatrix(other))
ValueError: objects are not aligned
I do a lot of colour image processing. Most of the time I treat an image as a
MxNx3 array, but some time I have to do matrix/ vector operations like
colour-space conversion. In these cases the dot function becomes very handy
(better then Matlab matrix multiplication), since I can write:
new_image = dot(old_image, A)
where A is either a 3x3 matrix or a length 3 vector. The result is that my code
is cluttered with a lot of "dot"s, and the matrix class can not help much. It
is possible that my case is special and does not justify a special attention,
but if many of us do colour/spectral imaging, or other type of high-rank
tensors algebra, there could be a case to give numpy an edge.
Nadav.
-----הודעה מקורית-----
מאת: [EMAIL PROTECTED] בשם Travis E. Oliphant
נשלח: ש 22-מרץ-08 01:12
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] matrices in 1.1
Charles R Harris wrote:
>
>
> On Fri, Mar 21, 2008 at 1:57 PM, Alan G Isaac <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
> On Fri, 21 Mar 2008, Nadav Horesh apparently wrote:
> > But asmatrix returns a matrix object and any subsequent
> > operation of it returns a matrix. What I am thinking about
> > is a convenient way to apply matrix operation on an array.
>
> I suspect what you are really wanting is a way for NumPy to
> define new operators ...
>
>
> I still kinda like the idea of using the call operator for matrix
> multiplication, i.e. A(v) := dot(A,v).
Interesting idea. I kind of like that too.
-Travis
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