Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/964#discussion_r13900006
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -201,6 +202,31 @@ class RowMatrix(
}
/**
+ * Multiply the Gramian matrix `A^T A` by a DenseVector on the right.
+ *
+ * @param v a local DenseVector whose length must match the number of
columns of this matrix.
+ * @return a local DenseVector representing the product.
+ */
+ private[mllib] def multiplyGramianMatrix(v: DenseVector): DenseVector = {
+ val n = numCols().toInt
+
+ val bv = rows.aggregate(BDV.zeros[Double](n))(
+ seqOp = (U, r) => {
+ val rBrz = r.toBreeze
+ val a = rBrz.dot(v.toBreeze)
+ rBrz match {
+ case _: BDV[_] => brzAxpy(a, rBrz.asInstanceOf[BDV[Double]], U)
--- End diff --
You might need to cast `DenseVector[Double]` to `Vector[Double]` to pass
compiler. I'm okay to check types here. But please add a default case that
throws exception for unrecognized types.
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