Sorry about that, yes, it should be uniformVectorRDD. Thanks Sean!
Burak
On Mon, Feb 9, 2015 at 2:05 AM, Sean Owen 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 wrote:
> > Thanks a lot!
> > Can I ask
Yes the example given here should have used uniformVectorRDD. Then it's correct.
On Mon, Feb 9, 2015 at 9:56 AM, Luca Puggini 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
>
> 20
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 :
> Hi,
>
> You can do the following:
> ```
> import org.apache.spark.mllib.linalg.distributed.RowMatrix
> import o
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, numParti
