# [Numpy-discussion] weighted random integers

josef.pktd@gmai... josef.pktd@gmai...
Fri Sep 10 19:32:13 CDT 2010

```On Fri, Sep 10, 2010 at 8:28 PM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Fri, Sep 10, 2010 at 6:15 PM, Charles R Harris
> <charlesr.harris@gmail.com> wrote:
>>
>>
>> On Fri, Sep 10, 2010 at 5:46 PM, <josef.pktd@gmail.com> wrote:
>>>
>>> I saw some questions on the web how to create non-uniform random
>>> integers in python.
>>>
>>> I don't know what the best way is but here is another way that looks
>>> reasonably fast
>>>
>>> >>> rvs = np.dot(np.random.multinomial(1, [0.1, 0.2, 0.5, 0.2],
>>> >>> size=1000000),np.arange(4))
>>>
>>> >>> np.bincount(rvs)/1000000.
>>> array([ 0.099741,  0.199943,  0.499317,  0.200999])
>>>
>>
>> This looks like a good case for the inverse cdf approach, at least for
>> smallish ranges of integers. Searchsorted on an array of appropriate values
>> should do the trick.
>>
>
> For instance, weight 0..3 by 1..4, then
>
> In [14]: w = arange(1,5)
>
> In [15]: p = cumsum(w)/float(w.sum())
>
> In [16]: bincount(p.searchsorted(random(1000000)))/1e6
> Out[16]: array([ 0.100336,  0.200382,  0.299132,  0.40015 ])

Looks good, it feels faster and takes less memory, I guess.

Josef

>
> Chuck
>
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>
```