On Fri, Jun 15, 2012 at 4:20 PM, Yaroslav Halchenko <[email protected]> wrote:
> Here is a comparison to output of my code (marked with >):
>
>  0.00458652660079 0.788017364828 0.00700027844478 0.00483928213727
>> 0.145564526722 0.480124905375 0.422482399359 0.217567496918
> 6.50616752373e-07 7.99461373461e-05 0.00700027844478 0.0094610687282
>> 0.120884106118 0.249205123601 0.422482399359 0.556949542822
> 0.00760091429285 0.819325410184 0.00909659010031 0.0094610687282
>> 0.108943679969 0.195731527474 0.556245690994 0.556949542822
> 9.30536435524e-08 9.96844926893e-06 0.00921024880111 0.0094610687282
>> 0.0362155612112 0.0648617517611 0.559754044038 0.556949542822

I was only looking at wikipedia, (I dowloaded the papers a while ago
but never read them)

https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py#L160
looks like a double sum, but wikipedia only has one sum, elementwise product.

When you have a nice implementation, I will (try to) borrow it :)

Josef

>
> ;)
>
> On Fri, 15 Jun 2012, [email protected] wrote:
>> >>>    here is my attempt:
>> >>>    
>> >>> [1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
>> >>>    i'll look at your code in detail later today to understand the uv=True
>
>> > trying to see how well this works, here's my gist
>> > https://gist.github.com/2938402
>> (univariate only)
>
>> > bretzel and cosine have essentially zero distance correlation ??
>
> --
> Yaroslav O. Halchenko
> Postdoctoral Fellow,   Department of Psychological and Brain Sciences
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
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