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Diffstat (limited to 'docs/mllib-clustering.md')
-rw-r--r-- | docs/mllib-clustering.md | 8 |
1 files changed, 8 insertions, 0 deletions
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 0b6db4fcb7..f5aa15b7d9 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -173,6 +173,7 @@ to the algorithm. We then output the parameters of the mixture model. {% highlight scala %} import org.apache.spark.mllib.clustering.GaussianMixture +import org.apache.spark.mllib.clustering.GaussianMixtureModel import org.apache.spark.mllib.linalg.Vectors // Load and parse the data @@ -182,6 +183,10 @@ val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))) // Cluster the data into two classes using GaussianMixture val gmm = new GaussianMixture().setK(2).run(parsedData) +// Save and load model +gmm.save(sc, "myGMMModel") +val sameModel = GaussianMixtureModel.load(sc, "myGMMModel") + // output parameters of max-likelihood model for (i <- 0 until gmm.k) { println("weight=%f\nmu=%s\nsigma=\n%s\n" format @@ -231,6 +236,9 @@ public class GaussianMixtureExample { // Cluster the data into two classes using GaussianMixture GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); + // Save and load GaussianMixtureModel + gmm.save(sc, "myGMMModel") + GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc, "myGMMModel") // Output the parameters of the mixture model for(int j=0; j<gmm.k(); j++) { System.out.println("weight=%f\nmu=%s\nsigma=\n%s\n", |