How to compute the inverse of a RowMatrix in Apache Spark?

Question

I have a X, distributed matrix, in RowMatrix form. I am using Spark 1.3.0. I need to be able to calculate X inverse.

Answer 1

I had problems using this function with option

conf.set("spark.sql.shuffle.partitions", "12")

The rows in RowMatrix got shuffled.

Here is an update that worked for me

import org.apache.spark.mllib.linalg.{DenseMatrix,DenseVector}
import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix

def computeInverse(X: IndexedRowMatrix)
: DenseMatrix = 
{
  val nCoef = X.numCols.toInt
  val svd = X.computeSVD(nCoef, computeU = true)
  if (svd.s.size < nCoef) {
    sys.error(s"IndexedRowMatrix.computeInverse called on singular matrix.")
  }

  // Create the inv diagonal matrix from S 
  val invS = DenseMatrix.diag(new DenseVector(svd.s.toArray.map(x => math.pow(x, -1))))

  // U cannot be a RowMatrix
  val U = svd.U.toBlockMatrix().toLocalMatrix().multiply(DenseMatrix.eye(svd.U.numRows().toInt)).transpose

  val V = svd.V
  (V.multiply(invS)).multiply(U)
}