On Mon, Jan 25, 2010 at 5:16 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
On Mon, Jan 25, 2010 at 1:38 PM, Jan Strube <curiousjan@gmail.com> wrote:
Dear List,
I'm trying to speed up a piece of code that selects a subsample based on some criteria: Setup: I have two samples, raw and cut. Cut is a pure subset of raw, all elements in cut are also in raw, and cut is derived from raw by applying some cuts. Now I would like to select a random subsample of raw and find out how many are also in cut. In other words, some of those random events pass the cuts, others don't. So in principle I have
randomSample = np.random.random_integers(0, len(raw)-1, size=sampleSize) random_that_pass1 = [r for r in raw[randomSample] if r in cut]
This is fine (I hope), but slow.
You could construct raw2 and cut2 where each element placed in cut2 is removed from raw2:
idx = np.random.rand(n_in_cut2) > 0.5 # for example raw2 = raw[~idx] cut2 = raw[idx]
If you concatenate raw2 and cut2 you get raw (but reordered):
raw3 = np.concatenate((raw2, cut2), axis=0)
Any element in the subsample with an index of len(raw2) or greater is in cut. That makes counting fast.
There is a setup cost. So I guess it all depends on how many subsamples you need from one cut.
Not sure any of this works, just an idea. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
in1d or intersect in arraysetops should also work, pure python but well constructed and tested for performance. Josef