Sun Jun 9 10:27:40 CDT 2013
On 09.06.2013 12:10, firstname.lastname@example.org wrote:
> On Wed, May 29, 2013 at 3:19 PM, <email@example.com> wrote:
>> On Wed, May 29, 2013 at 12:25 PM, Julian Taylor
>> <firstname.lastname@example.org> wrote:
>>> On 05/29/2013 06:12 AM, email@example.com wrote:
>>>> On Tue, May 28, 2013 at 6:31 PM, Charles R Harris
>>>> <firstname.lastname@example.org> wrote:
>>>>> Hi All,
>>>>> There is a PR adding quickselect to numpy as a function `partition`.
>>>>> Comments on name and exposure in the numpy API are welcome.
here a a quick status report on the PR
I now implemented partitioning via the introselect algorithm which is a
quickselect median of 3 pivot with a cutoff on recursion depth to a
median of median of 5 pivot for O(N) worst case complexity.
Additionally it can stores its pivots for reuse in other partitions on
the same data to reduce the space required to be partitioned next time,
this is useful e.g. for multiple percentiles.
It is functionally ready, but there are still some API/ABI issues to be
Mainly deciding if we put the selection algorithms in the ABI for 1.8 or
not. Currently the ABI is unchanged so user types cannot make use of the
algorithms (they will fall back to quicksort).
The python api is now:
where kth is an integer or array of integers
it will move each index in kth into its final sorted position, so
np.partition(a, range(a.size)) results in a (inefficient) sort.
d = np.array([66, 81, 21, 75, 46, -6, 66, 86, 242, 47, 88, 79])
np.partition(d, (2, -2)) # (2, 8)
array([ -6, 21, 46, 47, 75, 66, 66, 79, 81, 86, 88, 242])
Multidimensional arrays will use the same array of kth, you cannot
partition each axis by different values, you would have to explicitly
loop to do that.
Median is implemented in terms of partitioning already, but percentile
I would suggest someone else than me gives a try at implementing
percentile in terms of partition to see if the documentation and api
make sense to others.
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