Not that I know of. The algorithm is very simple, requiring a
relatively small addition to the current introselect algorithm used
for `np.partition`. My biggest hurdle is figuring out how the calling
machinery really works so that I can figure out which input type
permutations I need to generate, and how to get the right backend
running for a given function call.
-Joe
On Thu, Aug 3, 2017 at 1:00 PM, Chun-Wei Yuan <chunwei.y...@gmail.com> wrote:
> Any way I can help expedite this?
>
> On Fri, Jul 21, 2017 at 4:42 PM, Chun-Wei Yuan <chunwei.y...@gmail.com>
> wrote:
>>
>> That would be great. I just used np.argsort because it was familiar to
>> me. Didn't know about the C code.
>>
>> On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz
>> <jfoxrabinov...@gmail.com> wrote:
>>>
>>> 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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>>>>>
>>>>
>>>>
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