yunfengzhou-hub commented on a change in pull request #70:
URL: https://github.com/apache/flink-ml/pull/70#discussion_r835994436
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File path:
flink-ml-lib/src/test/java/org/apache/flink/ml/clustering/KMeansTest.java
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@@ -177,11 +177,20 @@ public void testFewerDistinctPointsThanCluster() {
KMeans kmeans = new KMeans().setK(2);
KMeansModel model = kmeans.fit(input);
Table output = model.transform(input)[0];
- List<Set<DenseVector>> expectedGroups =
-
Collections.singletonList(Collections.singleton(Vectors.dense(0.0, 0.1)));
- List<Set<DenseVector>> actualGroups =
- executeAndCollect(output, kmeans.getFeaturesCol(),
kmeans.getPredictionCol());
- assertTrue(CollectionUtils.isEqualCollection(expectedGroups,
actualGroups));
+
+ try {
Review comment:
I think the current definition of K and the expected behavior is
contradict. If we want to keep the same behavior, I think we should adopt one
of the followings:
- Change `K`'s description from `The number of clusters to create` to `The
max number of clusters to create`
- If there are fewer distinct points than clusters, the training process
would still create K clusters, but some of them are identical.
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