Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3643#discussion_r21863319
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala ---
@@ -264,6 +263,92 @@ object MLUtils {
}
Vectors.fromBreeze(vector1)
}
+
+ /**
+ * Returns the squared distance between two Vectors.
+ */
+ private[util] def vectorSquaredDistance(v1: Vector, v2: Vector): Double
= {
+ var squaredDistance = 0.0
+ (v1, v2) match {
+ case (v1: SparseVector, v2: SparseVector) =>
+ val v1Values = v1.values
+ val v1Indices = v1.indices
+ val v2Values = v2.values
+ val v2Indices = v2.indices
+ val nnzv1 = v1Indices.size
+ val nnzv2 = v2Indices.size
+
+ var kv1 = 0
+ var kv2 = 0
+ var score = 0.0
+ while (kv1 < nnzv1) {
+ val iv1 = v1Indices(kv1)
+
+ if (kv2 >= nnzv2 || iv1 < v2Indices(kv2)) {
+ score = v1Values(kv1)
+ squaredDistance += score * score
+ }
+ while (kv2 < nnzv2 && v2Indices(kv2) < iv1) {
+ score = v2Values(kv2)
+ squaredDistance += score * score
+ kv2 += 1
+ }
+ if (kv2 < nnzv2 && v2Indices(kv2) == iv1) {
+ score = v1Values(kv1) - v2Values(kv2)
+ squaredDistance += score * score
+ kv2 += 1
+ }
+ kv1 += 1
+ }
+
+ case (v1: SparseVector, v2: DenseVector) if v1.indices.length /
v1.size < 0.5 =>
+ squaredDistance = vectorSquaredDistance(v1, v2)
+
+ case (v1: DenseVector, v2: SparseVector) if v2.indices.length /
v2.size < 0.5 =>
+ squaredDistance = vectorSquaredDistance(v2, v1)
+
+ case (v1, v2) =>
--- End diff --
I'd recommend putting a comment here about this case handling dense and
approximately dense vectors.
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