Across models, weights should be implemented such that duplicating samples
would give identical results to corresponding integer weights. That is true
here, to my understanding, if we remove the stochasticity such that all
identical samples have their update occur at once.
On 25 June 2015 at 19:28, Daniel Sullivan <dbsulliva...@gmail.com> wrote:
> Hi Anton,
>
> The update for each sample is just multiplied by the sample_weight and
> the class_weight before it's applied to the weight vector (coef_). So
> if your sample is [1, 2, 3], your gradient is .1, your weight for this
> sample is .2, and your eta is 5.0 your weight_vector (coef_) would be
> updated by [+(1 * .1 * .2 * 5.0), +(2 * .1 * .2 * 5.0), +(3 *.1
> * .2 * 5.0)]. Does that help?
>
> Danny
>
> On Thu, Jun 25, 2015 at 10:19 AM, Anton Suchaneck <a.suchan...@gmail.com>
> wrote:
> > Hello!
> >
> > How can I find out what the precise mathematical treatment of the
> > sample_weights for SGDClassifier in the partial_fit setting is?
> >
> > Cheers,
> > Anton
> >
> >
> >
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