Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15450#discussion_r84924203
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala ---
@@ -64,30 +66,55 @@ class KMeansSuite extends SparkFunSuite with
MLlibTestSparkContext {
assert(model.clusterCenters.head ~== center absTol 1E-5)
}
- test("no distinct points") {
+ test("fewer distinct points than clusters") {
val data = sc.parallelize(
Array(
Vectors.dense(1.0, 2.0, 3.0),
Vectors.dense(1.0, 2.0, 3.0),
Vectors.dense(1.0, 2.0, 3.0)),
2)
- val center = Vectors.dense(1.0, 2.0, 3.0)
- // Make sure code runs.
- var model = KMeans.train(data, k = 2, maxIterations = 1)
- assert(model.clusterCenters.size === 2)
- }
+ var model = KMeans.train(data, k = 2, maxIterations = 1,
initializationMode = "random")
+ assert(model.clusterCenters.length === 1)
- test("more clusters than points") {
- val data = sc.parallelize(
- Array(
- Vectors.dense(1.0, 2.0, 3.0),
- Vectors.dense(1.0, 3.0, 4.0)),
- 2)
+ model = KMeans.train(data, k = 2, maxIterations = 1,
initializationMode = "k-means||")
+ assert(model.clusterCenters.length === 1)
+ }
- // Make sure code runs.
- var model = KMeans.train(data, k = 3, maxIterations = 1)
- assert(model.clusterCenters.size === 3)
+ test("unique cluster centers") {
+ val rng = new Random(seed)
+ val numDistinctPoints = 10
+ val points = (0 until numDistinctPoints).map(i =>
Vectors.dense(Array.fill(3)(rng.nextDouble)))
+ val data = sc.parallelize(points.flatMap(Array.fill(1 +
rng.nextInt(3))(_)), 2)
+ val normedData = data.map(new VectorWithNorm(_))
+
+ // less centers than k
--- End diff --
Can we also test the "random" method here? I was going to suggest putting
the test cases inside `Seq("k-means||", "random").foreach { initMode =>`, but
we run the specific parallel case. Maybe just manually add it?
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