On Mon, 28 Apr 2008, Bill Spotz apparently wrote: > If matrix multiplication in my example is replaced with > np.dot() in yours, then when IS anything gained by using > matrices?
When matrix algebra is clearer than array algebra. But that is not the case for this algorithm. (Just the opposite, I would say.) Simple example: (X.T * X).I * (X.T * Y) (Not that this is computationally nice ...) > As for this example, my version should work with a properly > implemented sparse_matrix A, but the array approach precludes that. > That is to say, I could convert A to a matrix if it is provided as an > array, but you could not convert a sparse_matrix to an array. A.todense().A? (I don't think you can save on memory use without substantial changes...) In an earlier discussion, I suggested that if iteration over matrices were to yield 1d arrays, then iteration over sparse matrices should yield 1d "sparse arrays". Cheers, Alan _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion