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 ?

Cheers,

David

P.S.

I modified sasha's version to account for the case where all elements are identical, which returned an empty array.

def unique_sasha(x):

s = sort(x)

r = empty(s.shape, float)

r[:-1] = s[1:]

r[-1] = NaN

return s[r != s]

2006/7/3, Robert Cimrman <cimrman3@ntc.zcu.cz>:

Sasha wrote:

> On 7/2/06, Norbert Nemec <Norbert.Nemec.list@gmx.de> wrote:

>> ...

>> Does anybody know about the internals of the python "set"? How is

>> .keys() implemented? I somehow have really doubts about the efficiency

>> of this method.

>>

> Set implementation (Objects/setobject.c) is a copy and paste job from

> dictobject with values removed. As a result it is heavily optimized

> for the case of string valued keys - a case that is almost irrelevant

> for numpy.

>

> I think something like the following (untested, 1d only) will probably

> be much faster and sorted:

>

> def unique(x):

> s = sort(x)

> r = empty_like(s)

> r[:-1] = s[1:]

> r[-1] = s[0]

> return s[r != s]

There are 1d array set operations like this already in numpy

(numpy/lib/arraysetops.py - unique1d, ...)

r.

Using Tomcat but need to do more? Need to support web services, security?

Get stuff done quickly with pre-integrated technology to make your job easier

Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo

http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642

_______________________________________________

Numpy-discussion mailing list

Numpy-discussion@lists.sourceforge.net

https://lists.sourceforge.net/lists/listinfo/numpy-discussion