Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5909#discussion_r35711056
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -498,6 +498,65 @@ class RowMatrix(
}
/**
+ * Compute QR decomposition for rowMatrix. The implementation is
designed to optimize the QR
+ * decomposition (factorizations) for the RowMatrix of a tall and skinny
shape, yet it applies
+ * to RowMatrix in general.
+ *
+ * Reference:
+ * Austin R. Benson, David F. Gleich, James Demmel. "Direct QR
factorizations for tall-and
+ * -skinny matrices in MapReduce architectures", 2013 IEEE
International Conference on Big Data
+ * @param computeQ: whether to computeQ, which is quite expensive.
+ * @return the decomposition result as (Option[Q], R), where Q is a
RowMatrix and R is Matrix.
+ */
+ def TSQR(computeQ: Boolean = false): (Option[RowMatrix], Matrix) = {
+ val col = numCols().toInt
+
+ // split rows horizontally into smaller matrices, and compute QR for
each of them
+ val blockQRs = rows.mapPartitions(rowsIterator =>{
+ val partRows = rowsIterator.toArray
+ val rowCount = partRows.size
+ var bdm = BDM.zeros[Double](partRows.size, col)
+ var i = 0
+ partRows.foreach(row =>{
+ bdm(i, ::) := row.toBreeze.t
+ i += 1
+ })
+
+ val blockQR = breeze.linalg.qr.reduced(bdm)
+ Iterator((blockQR.r, blockQR.q))
+ }).cache
+
+ // combine the R part from previous results horizontally into a tall
matrix
+ val blockRsRdd = blockQRs.map(_._1).collect()
+ val CombinedR = blockRsRdd.reduceLeft((r1, r2) => BDM.vertcat(r1, r2))
+
+ val CombinedRDecomposition = breeze.linalg.qr.reduced(CombinedR)
+ val finalR =
Matrices.fromBreeze(CombinedRDecomposition.r.toDenseMatrix)
+
+ val finalQ = if(computeQ){
--- End diff --
See my comments on the PR page about computing `Q`.
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