lindong28 commented on a change in pull request #70:
URL: https://github.com/apache/flink-ml/pull/70#discussion_r836004126
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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:
Following Spark's implementation, I believe expected interpretation is
`The max number of clusters to create...`. It also seems to make user's lifer
easier to allowing the algorithm to automatically handle the case where the
number of unique input data is less than `K`.
@zhipeng93 what do you think?
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