Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7177#discussion_r33747859
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -282,18 +282,30 @@ class PowerIterationClusteringModel(JavaModelWrapper,
JavaSaveable, JavaLoader):
Model produced by [[PowerIterationClustering]].
- >>> data = [(0, 1, 1.0), (0, 2, 1.0), (1, 3, 1.0), (2, 3, 1.0),
- ... (0, 3, 1.0), (1, 2, 1.0), (0, 4, 0.1)]
+ >>> data = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (1, 3,
1.0),
+ ... (2, 3, 1.0), (3, 4, 0.1), (4, 5, 1.0), (4, 15, 1.0), (5, 6, 1.0),
+ ... (6, 7, 1.0), (7, 8, 1.0), (8, 9, 1.0), (9, 10, 1.0), (10, 11, 1.0),
+ ... (11, 12, 1.0), (12, 13, 1.0), (13, 14, 1.0), (14, 15, 1.0)]
>>> rdd = sc.parallelize(data, 2)
>>> model = PowerIterationClustering.train(rdd, 2, 100)
>>> model.k
2
+ >>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
+ >>> sum([x.cluster != result[3].cluster for x in result if x.id < 3])
--- End diff --
Yes, I agree to keep it simple. But the output of the following code is not
determinate.
```>>> sorted(model.assignments().collect(), key=lambda x: x.id)```
It can only ensure ids [0-3] and [4-15] are clustered into two group, but
the group id is not determinate. So output the assignments directly is not a
good idea.
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