While #9211 is a good start, it is pretty inefficient in terms of the fact
that it performs an O(nlogn) sort of the array. It is possible to reduce
the time to O(n) by using a similar partitioning algorithm to the one in
the C code of percentile. I will look into it as soon as I can.
-Joe
On Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan <chunwei.y...@gmail.com>
wrote:
> Just to provide some context, 9213 actually spawned off of this guy:
>
> https://github.com/numpy/numpy/pull/9211
>
> which might address the weighted inputs issue Joe brought up.
>
> C
>
> On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz <
> jfoxrabinov...@gmail.com> wrote:
>
>> I think that there would be a very good reason to have a separate
>> function if we were to introduce weights to the inputs, similarly to the
>> way that we have mean and average. This would have some (positive)
>> repercussions like making weighted histograms with the Freedman-Diaconis
>> binwidth estimator a possibility. I have had this change on the back-burner
>> for a long time, mainly because I was too lazy to figure out how to include
>> it in the C code. However, I will take a closer look.
>>
>> Regards,
>>
>> -Joe
>>
>>
>>
>> On Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan <chunwei.y...@gmail.com>
>> wrote:
>>
>>> There's an ongoing effort to introduce quantile() into numpy. You'd use
>>> it just like percentile(), but would input your q value in probability
>>> space (0.5 for 50%):
>>>
>>> https://github.com/numpy/numpy/pull/9213
>>>
>>> Since there's a great deal of overlap between these two functions, we'd
>>> like to solicit opinions on how to move forward on this.
>>>
>>> The current thinking is to tolerate the redundancy and keep both, using
>>> one as the engine for the other. I'm partial to having quantile because
>>> 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
>>>
>>> Best,
>>>
>>> C
>>>
>>> _______________________________________________
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>>>
>>>
>>
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