Forgot to add the more recent training material: https://databricks-training.s3.amazonaws.com/index.html
On Fri, Feb 6, 2015 at 12:12 PM, Burak Yavuz <brk...@gmail.com> wrote: > 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 ! >> > >
