[Numpy-discussion] Permutations in Simulations`

[Mark Janikas]

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-bounces at scipy.org [mailto:numpy-discussion-bounces at 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 <kwgoodman at gmail.com> wrote:
> On Tue, Feb 10, 2009 at 12:28 PM, Keith Goodman <kwgoodman at gmail.com> wrote:
>> On Tue, Feb 10, 2009 at 12:18 PM, Keith Goodman <kwgoodman at gmail.com> wrote:
>>> On Tue, Feb 10, 2009 at 11:29 AM, Mark Janikas <mjanikas at 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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