From 8bbe907556041443a411b69c95d7a9cd3eb69dcc Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Fri, 5 Jul 2013 17:25:23 -0700 Subject: Replaced string constants in test --- mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) (limited to 'mllib') 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))) } -- cgit v1.2.3