Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/6112#discussion_r31380991
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
---
@@ -717,6 +719,49 @@ class SparseVector(
new SparseVector(size, ii, vv)
}
}
+
+ override def argmax: Int = {
+ if (size == 0) {
+ -1
+ } else {
+
+ var maxIdx = 0
+ var maxValue = if(indices(0) != 0) 0.0 else values(0)
+
+ foreachActive { (i, v) =>
+ if (v > maxValue) {
+ maxIdx = i
+ maxValue = v
+ }
+ }
+
+ // look for inactive values incase all active node values are
negative
+ if(size != values.size && maxValue < 0){
+ maxIdx = calcInactiveIdx(indices(0))
+ maxValue = 0
+ }
+ maxIdx
+ }
+ }
+
+ /**
+ * Calculates the first instance of an inactive node in a sparse vector
and returns the Idx
+ * of the element.
+ * @param idx starting index of computation
+ * @return index of first inactive node or -1 if it cannot find one
+ */
+ private[SparseVector] def calcInactiveIdx(idx: Int): Int ={
--- End diff --
yes. sorry about that. How about something like this?
val maxIdx = {
var indexPresent = true
var j = 0
while (indexPresent) {
if (!indices.contains(j)) indexPresent = false
j += 1
}
j
}
I have no opposition to the present function, except for `calcInactiveIdx`
should be `calcFirstInactiveIdx` and I find it slightly odd that a function
that calculates the first inactive index should have a parameter ;)
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