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 >>> > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
