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author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-07-05 17:25:23 -0700 |
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committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-07-05 17:25:23 -0700 |
commit | 8bbe907556041443a411b69c95d7a9cd3eb69dcc (patch) | |
tree | c20c4e87f8ffcee2e82281e409daaaf399e512b0 /mllib | |
parent | 653043beb6681c57a02a3e8dde837d7dbc1e44bf (diff) | |
download | spark-8bbe907556041443a411b69c95d7a9cd3eb69dcc.tar.gz spark-8bbe907556041443a411b69c95d7a9cd3eb69dcc.tar.bz2 spark-8bbe907556041443a411b69c95d7a9cd3eb69dcc.zip |
Replaced string constants in test
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala | 11 |
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))) } |