Hey Ian.
Sorry for being a bit absent from this discussion but I didn't think so 
much mail
would accumulate over one afternoon not in the office.

The ``coef_`` code was written quite recently by me. Before it was just 
buggy.
It was a bit tricky because of the weird format that LibSVM puts the 
alphas in
in the multi-class case. If I understood you correctly, though, there is no
real bug, right?

I am +1 on using the primal formulation for "predict" in the linear case
and I am +1 for computing "coef_" in "fit". It doesn't need to be a
property any more, then, right? Or was there some other magic attached?

I didn't do this in the first place because I thought this would be 
unnecessary
overhead. Though if "coef_" is used in predict any way, it is no 
overhead at all.

So a pull request doing this would be very welcome.
I am a bit busy atm with the approaching NIPS deadline but I can
review after that if no one else has time before.

Cheers,
Andy


On 05/24/2012 11:35 PM, Ian Goodfellow wrote:
> On Thu, May 24, 2012 at 5:09 PM, Gael Varoquaux
> <[email protected]>  wrote:
>> On Thu, May 24, 2012 at 05:07:59PM -0400, David Warde-Farley wrote:
>>> An alternative might be to just compute it in fit() if kernel == 'linear',
>>> and make the property function return the precomputed vector in that case.
>>> That probably minimizes the number of bugs introduced by either neglecting 
>>> to
>>> update current functionality to use the dirty bit or future, orthogonal
>>> functionality forgetting to respect the dirty bit.
>> I think that I would actually prefer this option.
> OK--you are aware that coef_ is only defined in the case where kernel
> == 'linear',
> right? So the property would basically become nothing more than check on that
> condition, followed by returning the value computed by fit.
>
>
>


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