On Mon, 2005-07-25 at 17:47 -0400, Perry Greenfield wrote:
I missed this part and was reminded by Travis's message.
On Jul 23, 2005, at 12:03 PM, Soeren Sonnenburg wrote:
-- How can one obtain submatrices from full matrices:
numarray gives only elements (which is very, very rarely needed and should IMHO rather be some extra function):
i=[0,1] j=[0,2] a[i,j] array([1, 6])
vs octave:
i=[1,2]; j=[1,3]; a(i,j) ans = 1 3 4 6
Maybe for you it is rarely needed, but not for us. The situation is reversed. The latter is rarely used in our case. This is largely a reflection of whether your orientation is matrices or multidimensional arrays. In our case it is quite handy to select out a list of point in an image by giving a list of their respective indices (e.g., stars).
Hmmhh. I see that this again breaks with R/octave/matlab. One should not do so if there is no serious reason. It just makes life harder for every potential convert from any of these languages. A funktion take() would have served this purposed much better... but this is ofcourse all IMHO and I can see your point: You design it according to your (or your communities) requirements and different communities different needs... I am realizing that this must have been why cvxopt switched away from numarray/numeric. There slicing/indexing and '*' work as I would have expected: In [71]: a=matrix([1,2,3,4,5,6,7,8,9],size=(3,3)) In [72]: a Out[72]: <3x3 matrix, tc='d'> In [73]: b=matrix([1,2,3,4,5,6,7,8,9],size=(3,3)) In [74]: a*b Out[74]: <3x3 matrix, tc='d'> In [75]: print a 1.0000e+00 4.0000e+00 7.0000e+00 2.0000e+00 5.0000e+00 8.0000e+00 3.0000e+00 6.0000e+00 9.0000e+00 In [76]: print b 1.0000e+00 4.0000e+00 7.0000e+00 2.0000e+00 5.0000e+00 8.0000e+00 3.0000e+00 6.0000e+00 9.0000e+00 In [77]: print a*b 3.0000e+01 6.6000e+01 1.0200e+02 3.6000e+01 8.1000e+01 1.2600e+02 4.2000e+01 9.6000e+01 1.5000e+02 In [78]: print a[:,0] 1.0000e+00 2.0000e+00 3.0000e+00 In [79]: print a[0,1] 4.0 In [80]: print a[0,:] 1.0000e+00 4.0000e+00 7.0000e+00
True, I do see that others may need the other view, but then the question is which should get the convenience. Since Numeric/numarray are primarily oriented towards multidimensional arrays (e.g., operations are element-by-element rather than matrix) it seemed to make sense to go this way for consistency, but I understand that there is another side to this.
It now seems very difficult for me to end up with a single numeric/matrix package that makes it into core python - which is at the same time very sad. Soeren