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- import org.apache.spark.mllib.linalg.{Vectors,Vector,Matrix,SingularValueDecomposition,DenseMatrix,DenseVector}
- import org.apache.spark.mllib.linalg.distributed.RowMatrix
- def computeInverse(X: RowMatrix): DenseMatrix = {
- val nCoef = X.numCols.toInt
- val svd = X.computeSVD(nCoef, computeU = true)
- if (svd.s.size < nCoef) {
- sys.error(s"RowMatrix.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 = new DenseMatrix(svd.U.numRows().toInt,svd.U.numCols().toInt,svd.U.rows.collect.flatMap(x => x.toArray))
- // If you could make V distributed, then this may be better. However its alreadly local...so maybe this is fine.
- val V = svd.V
- // inv(X) = V*inv(S)*transpose(U) --- the U is already transposed.
- (V.multiply(invS)).multiply(U)
- }
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