very nice. What's the purpose of the second `.argsort(0)` ? Doesn't
it also work without it, or am I missing something in how this works?>
Josef
On 2/10/09, Mark Janikas <mjani...@esri.com> wrote:
> Thanks to all for your replies. I want this to work on any vector so I was
> thinking this...?
>
> import numpy as np
> import timeit
> x = np.array([4.,5.,10.,3.,5.,6.,7.,2.,9.,1.])
> nx = 10
> ny = 100
>
> def weirdshuffle4(x, ny):
> nx = len(x)
> indices = np.random.random_sample((nx,ny)).argsort(0).argsort(0)
> return x[indices]
>
> t=timeit.Timer("weirdshuffle4(x,ny)", "from __main__ import *")
> print t.timeit(100)
>
> 0.0148663153873
>
>
> -----Original Message-----
> From: numpy-discussion-boun...@scipy.org
> [mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Keith Goodman
> Sent: Tuesday, February 10, 2009 12:59 PM
> To: Discussion of Numerical Python
> Subject: Re: [Numpy-discussion] Permutations in Simulations`
>
> On Tue, Feb 10, 2009 at 12:41 PM, Keith Goodman <kwgood...@gmail.com> wrote:
>> On Tue, Feb 10, 2009 at 12:28 PM, Keith Goodman <kwgood...@gmail.com>
>> wrote:
>>> On Tue, Feb 10, 2009 at 12:18 PM, Keith Goodman <kwgood...@gmail.com>
>>> wrote:
>>>> On Tue, Feb 10, 2009 at 11:29 AM, Mark Janikas <mjani...@esri.com>
>>>> wrote:
>>>>> I want to create an array that contains a column of permutations for
>>>>> each
>>>>> simulation:
>>>>>
>>>>> import numpy as NUM
>>>>>
>>>>> import numpy.random as RAND
>>>>>
>>>>> x = NUM.arange(4.)
>>>>>
>>>>> res = NUM.zeros((4,100))
>>>>>
>>>>>
>>>>> for sim in range(100):
>>>>>
>>>>> res[:,sim] = RAND.permutation(x)
>>>>>
>>>>>
>>>>> Is there a way to do this without a loop? Thanks so much ahead of
>>>>> time.
>>>>
>>>> Does this work? Might not be faster but it does avoid the loop.
>>>>
>>>> import numpy as np
>>>>
>>>> def weirdshuffle(nx, ny):
>>>> x = np.ones((nx,ny)).cumsum(0, dtype=np.int) - 1
>>>> yidx = np.ones((nx,ny)).cumsum(1, dtype=np.int) - 1
>>>> xidx = np.random.rand(nx,ny).argsort(0).argsort(0)
>>>> return x[xidx, yidx]
>>>
>>> Hey, it is faster for nx=4, ny=100
>>>
>>> def baseshuffle(nx, ny):
>>> x = np.arange(nx)
>>> res = np.zeros((nx,ny))
>>> for sim in range(ny):
>>> res[:,sim] = np.random.permutation(x)
>>> return res
>>>
>>>>> timeit baseshuffle(4,100)
>>> 1000 loops, best of 3: 1.11 ms per loop
>>>>> timeit weirdshuffle(4,100)
>>> 10000 loops, best of 3: 127 µs per loop
>>>
>>> OK, who can cut that time in half? My first try looks clunky.
>>
>> This is a little faster:
>>
>> def weirdshuffle2(nx, ny):
>> one = np.ones((nx,ny), dtype=np.int)
>> x = one.cumsum(0)
>> x -= 1
>> yidx = one.cumsum(1)
>> yidx -= 1
>> xidx = np.random.random_sample((nx,ny)).argsort(0).argsort(0)
>> return x[xidx, yidx]
>>
>>>> timeit weirdshuffle(4,100)
>> 10000 loops, best of 3: 129 µs per loop
>>>> timeit weirdshuffle2(4,100)
>> 10000 loops, best of 3: 106 µs per loop
>
> Sorry for all the mail.
>
> def weirdshuffle3(nx, ny):
> return np.random.random_sample((nx,ny)).argsort(0).argsort(0)
>
>>> timeit weirdshuffle(4,100)
> 10000 loops, best of 3: 128 µs per loop
>>> timeit weirdshuffle3(4,100)
> 10000 loops, best of 3: 37.5 µs per loop
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