On 27 October 2011 13:29, Alexandre Passos <[email protected]> wrote:
> On Wed, Oct 26, 2011 at 22:27, Alexandre Passos <[email protected]>
> wrote:
> > On Wed, Oct 26, 2011 at 22:15, Robert Layton <[email protected]>
> wrote:
> >> I am trying to implement the Adjusted Mutual Information in a stable
> way.
> >> Unfortunately, the third term for the Expected Mutual Information is not
> >> stable and can result in overflow issues with only a moderate number of
> >> samples (eg N=1000 fails). See
> >> here: http://en.wikipedia.org/wiki/Adjusted_mutual_information
> >> I think I've reduced the equation to a more stable
> >> format: https://github.com/robertlayton/scikit-learn/wiki/Reducing-EMI
> >> I would appreciate if someone could look through this an check:
> >> 1) That I did this correctly
> >> 2) That there isn't a better way (a better identity or efficient way to
> >> reduce factorials)
> >
> > Have you tried using scipy.special.gammaln, doing all the
> > multiplications and divisions with additions and subtractions in
> > logspace, and then exponentiating?
>
> And if this turns out to be too expensive you can probably get away
> with stirling's approximation for log n!
> http://en.wikipedia.org/wiki/Stirling%27s_approximation
>
>
> --
> - Alexandre
>
>
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That is an option. I wasn't sure how to use it though -> calculating the
factorial isn't the issue, its working with the really large numbers that
is. That is why I went with permutations, as the number should be lower.
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