[Numpy-discussion] New functions.

[Bruce Southey]

On Tue, May 31, 2011 at 9:26 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
>
>
> On Tue, May 31, 2011 at 8:00 PM, Skipper Seabold <jsseabold at gmail.com>
> wrote:
>>
>> On Tue, May 31, 2011 at 9:53 PM, Warren Weckesser
>> <warren.weckesser at enthought.com> wrote:
>> >
>> >
>> > On Tue, May 31, 2011 at 8:36 PM, Skipper Seabold <jsseabold at gmail.com>
>> > wrote:
>> >> I don't know if it's one pass off the top of my head, but I've used
>> >> percentile for interpercentile ranges.
>> >>
>> >> [docs]
>> >> [1]: X = np.random.random(1000)
>> >>
>> >> [docs]
>> >> [2]: np.percentile(X,[0,100])
>> >> [2]: [0.00016535235312509222, 0.99961513543316571]
>> >>
>> >> [docs]
>> >> [3]: X.min(),X.max()
>> >> [3]: (0.00016535235312509222, 0.99961513543316571)
>> >>
>> >
>> >
>> > percentile() isn't one pass; using percentile like that is much slower:
>> >
>> > In [25]: %timeit np.percentile(X,[0,100])
>> > 10000 loops, best of 3: 103 us per loop
>> >
>> > In [26]: %timeit X.min(),X.max()
>> > 100000 loops, best of 3: 11.8 us per loop
>> >
>>
>> Probably should've checked that before opening my mouth. Never
>> actually used it for a minmax, but it is faster than two calls to
>> scipy.stats.scoreatpercentile. Guess I'm +1 to fast order statistics.
>>
>
> So far the biggest interest seems to be in order statistics of various
> sorts, so to speak.
>
> Order Statistics
>
> minmax
> median
> k'th element
> largest/smallest k elements
>
> Other Statistics
>
> mean/std
>
> Nan functions
>
> nanadd
>
> Chuck
>
>
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>

How about including all or some of Keith's Bottleneck package?
He has tried to include some of the discussed functions and tried to
make them very fast.

Also, this Wikipedia "Algorithms for calculating variance"
(http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance) has
some basic info on calculating the variance as well as higher order
moments.However, there are probably more efficient algorithms
available.

Bruce