My problem is not space, but time. I am creating a small array over and over, and this is turning out to be a bottleneck. My experiments suggest that problem is the allocation, not the random number generation. Allocating all the arrays as one n+1 dim and grabbing rows from it is faster than allocating the small arrays individually. I am iterating too many times to allocate everything at once though. I can just do a nested loop where create manageably large arrays in the outer and grab the rows in the inner, but I wanted something cleaner. Besides, I thought avoiding allocation altogether would be even faster.
cheers Daniel On 3/7/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote: > On 3/7/07, Robert Kern <[EMAIL PROTECTED]> wrote: > > > > Daniel Mahler wrote: > > > Is there an efficient way to fill an existing array with random > > > numbers without allocating a new array? > > > > No, sorry. > > > There is however an only moderately inefficient way if you are primarily > concerned with keeping your total memory usage down for some reason. In that > case, you can fill your array in chunks; for example getting 1000 random > numbers at a time from random.random and successively copying them into your > array. It's probably not worth the trouble unless you have a really big > array though. > > > -- > > //=][=\\ > > [EMAIL PROTECTED] > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
