great. so, provided that *model.theta* represents the log-probabilities and (hence the result of *brzPi + brzTheta * testData.toBreeze* is a big number too), how can I get back the *non-*log-probabilities which - apparently - are bounded between *0.0 and 1.0*?
*// Adamantios* On Tue, Sep 1, 2015 at 12:57 PM, Sean Owen <so...@cloudera.com> wrote: > (pedantic: it's the log-probabilities) > > On Tue, Sep 1, 2015 at 10:48 AM, Yanbo Liang <yblia...@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. > > > > 2015-08-28 20:38 GMT+08:00 Adamantios Corais < > adamantios.cor...@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 > >> > >> > > >