From 8bbe907556041443a411b69c95d7a9cd3eb69dcc Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@eecs.berkeley.edu>
Date: Fri, 5 Jul 2013 17:25:23 -0700
Subject: [PATCH] Replaced string constants in test
---
.../scala/spark/mllib/clustering/KMeansSuite.scala | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
index ae7cf57c42..cb096f39a9 100644
--- a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala
@@ -21,6 +21,8 @@ class KMeansSuite extends FunSuite with BeforeAndAfterAll {
val EPSILON = 1e-4
+ import KMeans.{RANDOM, K_MEANS_PARALLEL}
+
def prettyPrint(point: Array[Double]): String = point.mkString("(", ", ", ")")
def prettyPrint(points: Array[Array[Double]]): String = {
@@ -82,10 +84,11 @@ class KMeansSuite extends FunSuite with BeforeAndAfterAll {
model = KMeans.train(data, k=1, maxIterations=1, runs=5)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
- model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode="random")
+ model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
- model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode="k-means||")
+ model = KMeans.train(
+ data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
}
@@ -115,10 +118,10 @@ class KMeansSuite extends FunSuite with BeforeAndAfterAll {
model = KMeans.train(data, k=1, maxIterations=1, runs=5)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
- model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode="random")
+ model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
- model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode="k-means||")
+ model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0)))
}
--
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