Github user GeorgeDittmar commented on a diff in the pull request:
https://github.com/apache/spark/pull/6112#discussion_r31382733
--- 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 {
+
+ //grab first active index and value by default
+ var maxIdx = indices(0)
+ var maxValue = 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){
--- End diff --
So are you thinking of the case where we have an inactive value thats set
to something like 1? I dont think the api allows you to do that. My
understanding of this case is that we will return idx=0 if 0 is the only max
value found. Its technically correct since that active zero happens at the very
beginning of the vector. I dont think we skip it due to the fact that someone
decided to create a sparse vector with an active zero value. I am pretty sure i
cover this case in my unit tests but I'll go back to the code real quick to
double check.
Also no worries. Better to find bugs than not right? lol.
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