Convert Matrix to RowMatrix in Apache Spark using Scala -

i'd convert org.apache.spark.mllib.linalg.matrix org.apache.spark.mllib.linalg.distributed.rowmatrix

i can such:

val xx = x.computegramianmatrix()  //xx type org.apache.spark.mllib.linalg.matrix val xxs = xx.tostring() val xxr = xxs.split("\n").map(row => row.replace("   "," ").replace("  "," ").replace("  "," ").replace("  "," ").replace(" ",",").split(",")) val xxp = sc.parallelize(xxr) val xxd = xxp.map(ar => vectors.dense(ar.map(elm => elm.todouble))) val xxrm: rowmatrix = new rowmatrix(xxd) 

however, gross , total hack. can show me better way?

note using spark version 1.3.0

i suggest convert matrix rdd[vector] can automatically convert rowmatrix.

let's consider following example :

import org.apache.spark.rdd._ import org.apache.spark.mllib.linalg._   val densedata = seq(   vectors.dense(0.0, 1.0, 2.0),   vectors.dense(3.0, 4.0, 5.0),   vectors.dense(6.0, 7.0, 8.0),   vectors.dense(9.0, 0.0, 1.0) )  val dm: matrix = matrices.dense(3, 2, array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)) 

you'll need define method convert matrix rdd[vector]

def matrixtordd(m: matrix): rdd[vector] = {    val columns = m.toarray.grouped(m.numrows)    val rows = columns.toseq.transpose // skip if want column-major rdd.    val vectors = rows.map(row => new densevector(row.toarray))    sc.parallelize(vectors) } 

and can apply conversion on matrix :

 import org.apache.spark.mllib.linalg.distributed.rowmatrix  val rows = matrixtordd(dm)  val mat = new rowmatrix(rows) 

i hope can help!