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 > >
