Hi Luca, You can tackle this using RowMatrix (spark-shell example): ``` 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 data: RDD[Vector] = RandomRDDs.normalVectorRDD(sc, n, k, numPartitions, seed) val matrix = new RowMatrix(data, n, k) ``` You can find more tutorials here: https://spark-summit.org/2013/exercises/index.html Best, Burak On Fri, Feb 6, 2015 at 10:03 AM, Luca Puggini <lucapug...@gmail.com> wrote: > Hi all, > this is my first email with this mailing list and I hope that I am not > doing anything wrong. > > I am currently trying to define a distributed matrix with n rows and k > columns where each element is randomly sampled by a uniform distribution. > How can I do that? > > It would be also nice if you can suggest me any good guide that I can use > to start working with Spark. (The quick start tutorial is not enough for me > ) > > Thanks a lot ! >
