great. so, provided that *model.theta* represents the logprobabilities and
(hence the result of *brzPi + brzTheta * testData.toBreeze* is a big number
too), how can I get back the *non*logprobabilities which  apparently 
are bounded between *0.0 and 1.0*?
*// Adamantios*
On Tue, Sep 1, 2015 at 12:57 PM, Sean Owen <sowen@cloudera.com> wrote:
> (pedantic: it's the logprobabilities)
>
> On Tue, Sep 1, 2015 at 10:48 AM, Yanbo Liang <ybliang8@gmail.com> wrote:
> > Actually
> > brzPi + brzTheta * testData.toBreeze
> > is the probabilities of the input Vector on each class, however it's a
> > Breeze Vector.
> > Pay attention the index of this Vector need to map to the corresponding
> > label index.
> >
> > 20150828 20:38 GMT+08:00 Adamantios Corais <
> adamantios.corais@gmail.com>:
> >>
> >> Hi,
> >>
> >> I am trying to change the following code so as to get the probabilities
> of
> >> the input Vector on each class (instead of the class itself with the
> highest
> >> probability). I know that this is already available as part of the most
> >> recent release of Spark but I have to use Spark 1.1.0.
> >>
> >> Any help is appreciated.
> >>
> >>> override def predict(testData: Vector): Double = {
> >>> labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
> >>> }
> >>
> >>
> >>>
> >>>
> https://github.com/apache/spark/blob/v1.1.0/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
> >>
> >>
> >> // Adamantios
> >>
> >>
> >
>
