Sorry about that, yes, it should be uniformVectorRDD. Thanks Sean! Burak
On Mon, Feb 9, 2015 at 2:05 AM, Sean Owen <so...@cloudera.com> wrote: > Yes the example given here should have used uniformVectorRDD. Then it's > correct. > > On Mon, Feb 9, 2015 at 9:56 AM, Luca Puggini <lucapug...@gmail.com> wrote: > > Thanks a lot! > > Can I ask why this code generates a uniform distribution? > > > > If dist is N(0,1) data should be N(-1, 2). > > > > Let me know. > > Thanks, > > Luca > > > > 2015-02-07 3:00 GMT+00:00 Burak Yavuz <brk...@gmail.com>: > >> > >> Hi, > >> > >> You can do the following: > >> ``` > >> import org.apache.spark.mllib.linalg.distributed.RowMatrix > >> import org.apache.spark.mllib.random._ > >> > >> // sc is the spark context, numPartitions is the number of partitions > you > >> want the RDD to be in > >> val dist: RDD[Vector] = RandomRDDs.normalVectorRDD(sc, n, k, > >> numPartitions, seed) > >> // make the distribution uniform between (-1, 1) > >> val data = dist.map(_ * 2 - 1) > >> val matrix = new RowMatrix(data, n, k) > >> > >> On Feb 6, 2015 11:18 AM, "Donbeo" <lucapug...@gmail.com> wrote: > >>> > >>> Hi > >>> I would like to know how can I generate a random matrix where each > >>> element > >>> come from a uniform distribution in -1, 1 . > >>> > >>> In particular I would like the matrix be a distributed row matrix with > >>> dimension n x p > >>> > >>> Is this possible with mllib? Should I use another library? > >>> > >>> > >>> > >>> -- > >>> View this message in context: > >>> > http://apache-spark-user-list.1001560.n3.nabble.com/generate-a-random-matrix-with-uniform-distribution-tp21538.html > >>> Sent from the Apache Spark User List mailing list archive at > Nabble.com. > >>> > >>> --------------------------------------------------------------------- > >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > >>> For additional commands, e-mail: user-h...@spark.apache.org > >>> > > >
