Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/3288#discussion_r20553260
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
---
@@ -76,6 +76,22 @@ sealed trait Vector extends Serializable {
def copy: Vector = {
throw new NotImplementedError(s"copy is not implemented for
${this.getClass}.")
}
+
+ /**
+ * It will return the iterator for the active elements of dense and
sparse vector as
+ * (index, value) pair. Note that foreach method can be overridden for
better performance
+ * in different vector implementation.
+ *
+ * @param skippingZeros Skipping zero elements explicitly if true. It
will be useful when we
+ * iterator through dense vector having lots of
zero elements which
+ * we want to skip. Default is false.
+ * @return Iterator[(Int, Double)] where the first element in the tuple
is the index,
+ * and the second element is the corresponding value.
+ */
+ private[spark] def activeIterator(skippingZeros: Boolean):
Iterator[(Int, Double)]
--- End diff --
With the following code,
sample.activeIterator(false).foreach {
case (index, value) => if(value != 0.0) add(index, value)
}
It takes 61.809 for dense vector, and 54.626 for sparse vector.
The most expensive part is calling the anonymous function even when the
values are zero.
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