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authorYuhao Yang <hhbyyh@gmail.com>2015-01-25 22:18:09 -0800
committerXiangrui Meng <meng@databricks.com>2015-01-25 22:18:09 -0800
commit81251682edfb6a0de47e9dd72b2f63ee298fca33 (patch)
tree87c9d5e7643fc73cc78fa1661cac691bcaf51a22 /mllib
parent8df9435512e4b4df12b55143a910d9ce3d4bd62c (diff)
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[SPARK-5384][mllib] Vectors.sqdist returns inconsistent results for sparse/dense vectors when the vectors have different lengths
JIRA issue: https://issues.apache.org/jira/browse/SPARK-5384 Currently `Vectors.sqdist` return inconsistent result for sparse/dense vectors when the vectors have different lengths, please refer to JIRA for sample PR scope: Unify the sqdist logic for dense/sparse vectors and fix the inconsistency, also remove the possible sparse to dense conversion in the original code. For reviewers: Maybe we should first discuss what's the correct behavior. 1. Vectors for sqdist must have the same length, like in breeze? 2. If they can have different lengths, what's the correct result for sqdist? (should the extra part get into calculation?) I'll update PR with more optimization and additional ut afterwards. Thanks. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #4183 from hhbyyh/fixDouble and squashes the following commits: 1f17328 [Yuhao Yang] limit PR scope to size constraints only 54cbf97 [Yuhao Yang] fix Vectors.sqdist inconsistence
Diffstat (limited to 'mllib')
-rw-r--r--mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala11
1 files changed, 6 insertions, 5 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 7ee0224ad4..b3022add38 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -333,7 +333,7 @@ object Vectors {
math.pow(sum, 1.0 / p)
}
}
-
+
/**
* Returns the squared distance between two Vectors.
* @param v1 first Vector.
@@ -341,8 +341,9 @@ object Vectors {
* @return squared distance between two Vectors.
*/
def sqdist(v1: Vector, v2: Vector): Double = {
+ require(v1.size == v2.size, "vector dimension mismatch")
var squaredDistance = 0.0
- (v1, v2) match {
+ (v1, v2) match {
case (v1: SparseVector, v2: SparseVector) =>
val v1Values = v1.values
val v1Indices = v1.indices
@@ -350,12 +351,12 @@ object Vectors {
val v2Indices = v2.indices
val nnzv1 = v1Indices.size
val nnzv2 = v2Indices.size
-
+
var kv1 = 0
var kv2 = 0
while (kv1 < nnzv1 || kv2 < nnzv2) {
var score = 0.0
-
+
if (kv2 >= nnzv2 || (kv1 < nnzv1 && v1Indices(kv1) < v2Indices(kv2))) {
score = v1Values(kv1)
kv1 += 1
@@ -397,7 +398,7 @@ object Vectors {
val nnzv1 = indices.size
val nnzv2 = v2.size
var iv1 = if (nnzv1 > 0) indices(kv1) else -1
-
+
while (kv2 < nnzv2) {
var score = 0.0
if (kv2 != iv1) {