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| author | nate.crosswhite <nate.crosswhite@stresearch.com> | 2015-01-21 10:32:10 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-01-21 10:32:10 -0800 |
| commit | 7450a992b3b543a373c34fc4444a528954ac4b4a (patch) | |
| tree | 1e3c63168367b3a25335f34dc0d8a58ffa39477f /python/pyspark/mllib/clustering.py | |
| parent | aa1e22b17b4ce885febe6970a2451c7d17d0acfb (diff) | |
| download | spark-7450a992b3b543a373c34fc4444a528954ac4b4a.tar.gz spark-7450a992b3b543a373c34fc4444a528954ac4b4a.tar.bz2 spark-7450a992b3b543a373c34fc4444a528954ac4b4a.zip | |
[SPARK-4749] [mllib]: Allow initializing KMeans clusters using a seed
This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark
Author: nate.crosswhite <nate.crosswhite@stresearch.com>
Author: nxwhite-str <nxwhite-str@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes #3610 from nxwhite-str/master and squashes the following commits:
a2ebbd3 [nxwhite-str] Merge pull request #1 from mengxr/SPARK-4749-kmeans-seed
7668124 [Xiangrui Meng] minor updates
f8d5928 [nate.crosswhite] Addressing PR issues
277d367 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
9156a57 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
5d087b4 [nate.crosswhite] Adding KMeans train with seed and Scala unit test
616d111 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
35c1884 [nate.crosswhite] Add kmeans initial seed to pyspark API
Diffstat (limited to 'python/pyspark/mllib/clustering.py')
| -rw-r--r-- | python/pyspark/mllib/clustering.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index e2492eef5b..6b713aa393 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -78,10 +78,10 @@ class KMeansModel(object): class KMeans(object): @classmethod - def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"): + def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", seed=None): """Train a k-means clustering model.""" model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations, - runs, initializationMode) + runs, initializationMode, seed) centers = callJavaFunc(rdd.context, model.clusterCenters) return KMeansModel([c.toArray() for c in centers]) |