David Huard wrote:
> Here is a quick benchmark between numpy's unique, unique1d and sasha's
> unique:
>
> x = rand(100000)*100
> x = x.astype('i')
>
> %timeit unique(x)
> 10 loops, best of 3: 525 ms per loop
>
> %timeit unique_sasha(x)
> 100 loops, best of 3: 10.7 ms per loop
>
> timeit unique1d(x)
> 100 loops, best of 3: 12.6 ms per loop
>
> So I wonder what is the added value of unique?
> Could unique1d simply become unique ?
It looks like unique1d and friends could use same facelifting with new
numpy features like boolean indexing :)
r.
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