ignore that
These operations are
* automatically available on any RDD of the right type (e.g. RDD[(Int, Int)]
through implicit
* conversions when you `import org.apache.spark.SparkContext._`.
--
Nan Zhu
On Wednesday, January 8, 2014 at 10:38 AM, Nan Zhu wrote:
> Hi, all
>
> I’m reading the source code of master branch
>
> there is a new predict() function in MatrixFactorizationModel
>
> /**
> * Predict the rating of many users for many products.
> * The output RDD has an element per each element in the input RDD
> (including all duplicates)
> * unless a user or product is missing in the training set.
> *
> * @param usersProducts RDD of (user, product) pairs.
> * @return RDD of Ratings.
> */
> def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
> val users = userFeatures.join(usersProducts).map{
> case (user, (uFeatures, product)) => (product, (user, uFeatures))
> }
> users.join(productFeatures).map {
> case (product, ((user, uFeatures), pFeatures)) =>
> val userVector = new DoubleMatrix(uFeatures)
> val productVector = new DoubleMatrix(pFeatures)
> Rating(user, product, userVector.dot(productVector))
> }
> }
>
>
> it seems that the author can directly call join with a RDD object?
>
> It’s a new feature in next version? I’m used to creating a PairRDDFunctions
> with the current RDD and then calls join, etc.
>
> Did I misunderstand something?
>
> Best,
>
> --
> Nan Zhu
>
