Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5909#discussion_r35774677
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -498,6 +498,50 @@ class RowMatrix(
}
/**
+ * Compute QR decomposition for [[RowMatrix]]. The implementation is
designed to optimize the QR
+ * decomposition (factorization) for the [[RowMatrix]] of a tall and
skinny shape.
+ * Reference:
+ * Paul G. Constantine, David F. Gleich. "Tall and skinny QR
factorizations in MapReduce
+ * architectures" ([[http://dx.doi.org/10.1145/1996092.1996103]])
+ *
+ * @param computeQ: whether to computeQ
+ * @return QRDecomposition(Q, R), Q = null if computeQ = false.
+ */
+ def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix,
Matrix] = {
+ val col = numCols().toInt
+ // split rows horizontally into smaller matrices, and compute QR for
each of them
+ val blockQRs = rows.glom().map{ partRows =>
+ val bdm = BDM.zeros[Double](partRows.length, col)
+ var i = 0
+ partRows.foreach{ row =>
+ bdm(i, ::) := row.toBreeze.t
+ i += 1
+ }
+ breeze.linalg.qr.reduced(bdm).r
+ }.cache()
+
+ // combine the R part from previous results vertically into a tall
matrix
+ val combinedR = blockQRs.treeReduce((r1, r2) => BDM.vertcat(r1, r2))
+ val breezeR = breeze.linalg.qr.reduced(combinedR).r.toDenseMatrix
+ val finalR = Matrices.fromBreeze(breezeR)
+ val finalQ = if (computeQ) {
+ try {
+ val invR = inv(breezeR)
+ this.multiply(Matrices.fromBreeze(invR))
+ }
+ catch {
+ case err: MatrixSingularException =>
+ logWarning("R is not invertible and return Q as null")
+ null
+ }
+ }
+ else {
--- End diff --
same here: `} else {`
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