Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3288#discussion_r20557238
--- 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 --
`Tuple2[Int, Double]` is specialized in Scala:
https://github.com/scala/scala/blob/2.10.x/src/library/scala/Tuple2.scala, but
the iterator interface still won't be high-performance for numerical
computation. The iterator interface is not used in this PR, as we only need
`foreach`. Then the best for us is defining `foreach` directly:
~~~scala
def foreach(f: (Int, Double) => Unit)
~~~
( We could implement `ZippedTraversable2` but it doesn't seem to be
necessary.)
or
~~~scala
def foreach(skipZeros = True)(f: (Int, Double) => Unit)
~~~
This is a private function. We can change it when we see more use cases.
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