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authorReza Zadeh <rizlar@gmail.com>2013-12-27 00:41:46 -0500
committerReza Zadeh <rizlar@gmail.com>2013-12-27 00:41:46 -0500
commit3369c2d48795d831acd841b8ecb67b5a84083883 (patch)
tree02a7d0f893c723344d76ab799133f7c17fac4ba5 /mllib
parentbdb5037987d472c31f4d7891e60a8997e0e9bdcc (diff)
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cleanup documentation
Diffstat (limited to 'mllib')
-rw-r--r--mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala4
1 files changed, 2 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala
index a799aa3280..1c9f67e265 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala
@@ -29,7 +29,7 @@ import org.jblas.{DoubleMatrix, Singular, MatrixFunctions}
* Given an m x n matrix A, this will compute matrices U, S, V such that
* A = U * S * V'
*
- * There is no restriction on m, but we require n doubles to be held in memory.
+ * There is no restriction on m, but we require n^2 doubles to fit in memory.
* Further, n should be less than m.
*
* This is computed by first computing A'A = V S^2 V',
@@ -81,7 +81,7 @@ object SVD {
}.reduceByKey(_+_)
- // Constructi jblas A^T A locally
+ // Construct jblas A^T A locally
val ata = DoubleMatrix.zeros(n, n)
for(entry <- emits.toArray) {
ata.put(entry._1._1-1, entry._1._2-1, entry._2)