Christopher Barker wrote: > Wayne Watson wrote: > >> Yes, flat sounds useful here. However, numpy isn't bending over >> backwards to tie in conventional mathematical language into it. >> > > exactly -- it isn't bending over at all! (well a little -- see below). > numpy was designed for general purpose computational needs, not any one > branch of math. nd-arrays are very useful for lots of things. In > contrast, Matlab, for instance, was originally designed to be an easy > front-end to linear algebra package. Personally, when I used Matlab, I > found that very awkward -- I was usually writing 100s of lines of code > that had nothing to do with linear algebra, for every few lines that > actually did matrix math. So I much prefer numpy's way -- the linear > algebra lines of code are longer an more awkward, but the rest is much > better. > > The Matrix class is the exception to this: is was written to provide a > natural way to express linear algebra. However, things get a bit tricky > when you mix matrices and arrays, and even when sticking with matrices > there are confusions and limitations -- how do you express a row vs a > column vector? what do you get when you iterate over a matrix? etc. > > There has been a bunch of discussion about these issues, a lot of good > ideas, a little bit of consensus about how to improve it, but no one > with the skill to do it has enough motivation to do it. > I recently got motivated to get better linear algebra for Python; and startet submitting and writing on patches for Sage instead (which of course uses NumPy underneath). Sage has a strong concept of matrices and vectors, but not much numerical support, mainly exact or multi-precision arithmetic. So perhaps there will be more progress there; I'm not sure yet how far it will get or if anybody will join me in doing it...
To me that seems like the ideal way to split up code -- let NumPy/SciPy deal with the array-oriented world and Sage the closer-to-mathematics notation. I never liked the NumPy matrix class. I think this is mainly because my matrices are often, but not always, diagonal, which doesn't fit at all into NumPy's way of thinking about these things. (Also a 2D or 3D array could easily be a "vector", like if you want to linearily transform the values of the pixels in an image. So I think any Python linear algebra package has to attack things in a totally different way from numpy.matrix). Dag Sverre _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
