mclovin wrote:
On Jul 4, 3:29 pm, MRAB <pyt...@mrabarnett.plus.com> wrote:
mclovin wrote:
[snip]
like I said I need to do this 480,000 times so to get this done
realistically I need to analyse about 5 a second. It appears that the
average matrix size contains about 15 million elements.
I threaded my program using your code and I did about 1,000 in an hour
so it is still much too slow.
When I selected 1 million random elements to count, 8 out of the top
10 of those were in the top 25 of the precise way and 18 of the 25
were in the top 25 of the precise way. so I suppose that could be an
option.
The values are integers, aren't they? What is the range of values?
There are appox 550k unique values with a range of 0-2million with
gaps.
I've done a little experimentation with lists (no numpy involved) and
found that I got a x2 speed increase if I did the counting using a list,
something like this:
counts = [0] * 2000000
for x in values:
counts[x] += 1
counts = dict(e for e in enumerate(values) if e[1] != 0)
--
http://mail.python.org/mailman/listinfo/python-list
