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authorMatei Zaharia <matei@eecs.berkeley.edu>2013-07-05 17:25:23 -0700
committerMatei Zaharia <matei@eecs.berkeley.edu>2013-07-05 17:25:23 -0700
commit8bbe907556041443a411b69c95d7a9cd3eb69dcc (patch)
treec20c4e87f8ffcee2e82281e409daaaf399e512b0 /mllib
parent653043beb6681c57a02a3e8dde837d7dbc1e44bf (diff)
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Replaced string constants in test
Diffstat (limited to 'mllib')
-rw-r--r--mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala11
1 files 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)))
}