The log probabilities are unlikely to be very large, though the
probabilities may be very small. The direct answer is to exponentiate
brzPi + brzTheta * testData.toBreeze -- apply exp(x).
I have forgotten whether the probabilities are normalized already
though. If not you'll have to normalize to get them to sum to 1 and be
real class probabilities. This is better done in log space though.
On Thu, Sep 10, 2015 at 5:12 PM, Adamantios Corais
<adamantios.cor...@gmail.com> wrote:
> 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
>> >>
>> >>
>> >
>
>
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