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# Python linear algebra module -- requesting comments on interface

 Hi, I'm in the process of writing a Python linear algebra module. The current targeted interface is: http://oregonstate.edu/~barnesc/temp/linalg/ The interface was originally based on Raymond Hettinger's Matfunc [1]. However, it has evolved so that now it is nearly identical to JAMA [2], the Java matrix library. I am soliticing comments on this interface. Please post up any criticism that you have. Even small things -- if something isn't right, it's better to fix it now than later. I have not made source code available yet, since the current code is missing the decompositions and doesn't match the new interface. I'm in the process of rewritting the code to match the new interface. You can e-mail me and ask for the old code if you're curious or skeptical. [1]. http://users.rcn.com/python/download/python.htm [2]. http://math.nist.gov/javanumerics/jama/ --------------------------------------------- Brief comparison with Numeric --------------------------------------------- Numeric and linalg serve different purposes. Numeric is intended to be a general purpose array extension. It takes a "kitchen sink" approach, and includes every function which could potentially be useful for array manipulations. Linalg is intended to handle real/complex vectors and matrices, for scientific and 3D applications. It has a more restricted scope. Because it is intended for 3D applications, it is optimized for dimension 2, 3, 4 operations. For the typical matrix operations, the linalg interface is much intuitive than Numeric's. Real and imaginary components are always cast to doubles, so no headaches are created if a matrix is instantiated from a list of integers. Unlike Numeric, the * operator performs matrix multiplication, A**-1 computes the matrix inverse, A == B returns True or False, and the 2-norm and cross product functions exist. As previously stated, linalg is optimized for matrix arithmetic with small matrices (size 2, 3, 4). A (somewhat out of date) set of microbenchmarks [3] [4] show that linalg is roughly an order of magnitude faster than Numeric for dimension 3 vectors and matrices. [3]. Microbenchmarks without psyco: http://oregonstate.edu/~barnesc/temp/ numeric_vs_linalg_prelim-2005-09-07.pdf [4]. Microbenchmarks with psyco: http://oregonstate.edu/~barnesc/temp/ numeric_vs_linalg_prelim_psyco-2005-09-07.pdf __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com Sep 9 '05 #1
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 C. Barnes wrote: Hi, I'm in the process of writing a Python linear algebra module. The current targeted interface is: http://oregonstate.edu/~barnesc/temp/linalg/ Is this going to become free software. If yes, what license will you use? So my suggestions: In cases like these ones: random_matrix(m, n=-1) zero_matrix(m, n=-1) ... I think it's better to set the default value to "None" instead of a number: random_matrix(m, n=None) zero_matrix(m, n=None) IMHO, this is more intuitive and more "pythonic". I also suggest to make the "random function" choosable: random_matrix(m, n=None, randfunc=random.random) random_vector(n, randfunc=random.random) This way it's more easy for those who want another range of numbers, or want another kind of distribution of the random numbers. At the top of your documentation, there is a link "overview", which is broken: See _overview_ for a quick start. Greets, Volker -- Volker Grabsch ---<<(())>>--- \frac{\left|\vartheta_0\times\{\ell,\kappa\in\Re\} \right|}{\sqrt [G]{-\Gamma(\alpha)\cdot\mathcal{B}^{\left[\oint\!c_\hbar\right]}}} Sep 9 '05 #2
 Since one of the module's targeted applications is for 3D applications, I think there should be some specific support for applying the Matrix-vector product operation to a sequence of vectors instead of only one at a time -- and it should be possible to optimize the module's code for this common case. I'd also like to see some special specific errors defined and raised from the Matrix det(), inverse(), and transpose() methods when the operation is attempted on an ill-formed matrices (e.g. for non-square, non-invertible, singular cases). This would allow client code to handle errors better. Very nice work overall, IMHO. Best, -Martin Sep 9 '05 #3