[Numpy-discussion] indexing, searchsorting, ...
kwgoodman at gmail.com
Mon Jan 25 17:16:19 EST 2010
On Mon, Jan 25, 2010 at 1:38 PM, Jan Strube <curiousjan at gmail.com> wrote:
> Dear List,
> I'm trying to speed up a piece of code that selects a subsample based on some criteria:
> 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.
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