Bill Baxter wrote: > On 3/26/07, Colin J. Williams <[EMAIL PROTECTED]> wrote: >> Bill Baxter wrote: >>> This may sound silly, but I really think seeing all those brackets is >>> what makes it feel wrong. Matlab's output doesn't put it in your >>> face that your 4 is really a matrix([[4]]), even though that's what it >>> is to Matlab. But I don't see a good way to change that behavior. >>> >>> The other thing I find problematic about matrices is the inability to >>> go higher than 2d. To me that means that it's impossible to go "pure >>> matrix" in my code because I'll have to switch back to arrays any time >>> I want more than 2d (or use a mixed solution like a list of matrices). >>> Matlab allows allows >2D. >>> >>> --bb >> "pure matrix" seems to me an area of exploration, does it have any >> application in numerical computation at this time? > > I'm not sure what you thought I meant, but all I meant by going "pure > matrix" was having my Numpy code use the 'matrix' type exclusively > instead of some mix of 'matrix' and the base 'ndarray' type. It was a term I had not come across before but I assumed that you were referring to something like this link - beyond my comprehension. http://72.14.203.104/search?q=cache:Yu9gbUQEfWkJ:math.ca/Events/winter05/abs/pdf/ma-df.pdf+pure+matrix&hl=en&ct=clnk&cd=4&gl=ca&lr=lang_en Things > become messy when you mix and match them because you don't know any > more if an expression like A[1] is going to give you a 1-D thing or a > 2-D thing, and you can't be sure what A * B will do without always > coercing A and B.
Yes, to my mind it's best to consider the multi-dimensional array and the matrix to be two distinct data types. In most cases, it's best that conversions between the two should be explicit. > >> A list of matrices seems to be a logical structure. > > Yes, and it's the only option if you want to make a list of matrices > of different shapes, but I frequently have a need for things like a > list of per-point transformation matrices. Each column from each of > those matrices can be thought of as a vector. Sometimes its > convenient to consider all the X basis vectors together, for instance, > which is a simple and efficient M[:,:,0] slice if I have all the data > in a 3-D array, but it's a slow list comprehension plus matrix > constructor if I have the matrices in a list -- something like > matrix([m[:,0] for m in M]) > but that line is probably incorrect. Logically, this makes sense, where M is a list of matrices. My guess is that it would be a little faster to build one larger matrix and then slice it as needed. > >> PyMatrix deals with >> lists in building a larger matrix from sub-matrices. >> >> Suppose that we have matrices A (3, 4), B (3, 6), C (4, 2) and D (4, 8). >> >> Then E= M([[A, B], [C, D]]) gives E (7, 10). > > Numpy generally tries to treat all lists and tuples as array literals. > That's not likely to change. That need no be a problem is there is clarity of thinking about the essential difference between the matrix data type (even if is is built as a sub-type of the array) and the multi-dimensional array. > > --bb Colin W. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
