How to work with Matrix Multiplication in Apache Spark

Question

I am trying to perform matrix multiplication using Apache Spark and Java, where I need to create RDD that can represent matrix in Apache Spark. Can anyone help me with this?

Answer 1

Hey,

You can follow this below solution for your above query:

• IndexRowMatrix: Can be created directly from a RDD[IndexedRow] where IndexedRow consist of row index and org.apache.spark.mllib.linalg.Vector​

import org.apache.spark.mllib.linalg.{Vectors, Matrices}
import org.apache.spark.mllib.linalg.distributed.{IndexedRowMatrix,
  IndexedRow}

val rows =  sc.parallelize(Seq(
  (0L, Array(1.0, 0.0, 0.0)),
  (0L, Array(0.0, 1.0, 0.0)),
  (0L, Array(0.0, 0.0, 1.0)))
).map{case (i, xs) => IndexedRow(i, Vectors.dense(xs))}

val indexedRowMatrix = new IndexedRowMatrix(rows)

RowMatrix: Similar to IndexedRowMatrix but without meaningful row indices. Can be created directly from RDD[org.apache.spark.mllib.linalg.Vector]

import org.apache.spark.mllib.linalg.distributed.RowMatrix

val rowMatrix = new RowMatrix(rows.map(_.vector))     

BlockMatrix: Can be created from RDD[((Int, Int), Matrix)] where the first element of the tuple contains coordinates of the block and the second one is a local org.apache.spark.mllib.linalg.Matrix

val eye = Matrices.sparse(
  3, 3, Array(0, 1, 2, 3), Array(0, 1, 2), Array(1, 1, 1))

val blocks = sc.parallelize(Seq(
   ((0, 0), eye), ((1, 1), eye), ((2, 2), eye)))

val blockMatrix = new BlockMatrix(blocks, 3, 3, 9, 9)

Hope it helps.