Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12402#discussion_r60271524
--- Diff: python/pyspark/ml/clustering.py ---
@@ -20,9 +20,150 @@
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import *
from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.stat.distribution import MultivariateGaussian
__all__ = ['BisectingKMeans', 'BisectingKMeansModel',
- 'KMeans', 'KMeansModel']
+ 'KMeans', 'KMeansModel',
+ 'GaussianMixture', 'GaussianMixtureModel']
+
+
+class GaussianMixtureModel(JavaModel, JavaMLWritable, JavaMLReadable):
+ """
+ .. note:: Experimental
+
+ Model fitted by GaussianMixture.
+
+ .. versionadded:: 2.0.0
+ """
+
+ @property
+ @since("2.0.0")
+ def weights(self):
+ """
+ Weights for each Gaussian distribution in the mixture, where
weights[i] is
+ the weight for Gaussian i, and weights.sum == 1.
+ """
+ return self._call_java("weights")
+
+ @property
+ @since("2.0.0")
+ def gaussians(self):
+ """
+ Array of MultivariateGaussian where gaussians[i] represents
+ the Multivariate Gaussian (Normal) Distribution for Gaussian i.
+ """
+ return [
+ MultivariateGaussian(gaussian[0], gaussian[1])
+ for gaussian in self._call_java("gaussiansPyDump")]
+
+
+@inherit_doc
+class GaussianMixture(JavaEstimator, HasFeaturesCol, HasPredictionCol,
HasMaxIter, HasTol, HasSeed,
+ HasProbabilityCol, JavaMLWritable, JavaMLReadable):
+ """
+ .. note:: Experimental
+
+ GaussianMixture clustering.
+
+ >>> from pyspark.mllib.linalg import Vectors
+
+ >>> data1 = [(Vectors.dense([-0.1, -0.05 ]),),
+ ... (Vectors.dense([-0.01, -0.1]),),
+ ... (Vectors.dense([0.9, 0.8]),),
+ ... (Vectors.dense([0.75, 0.935]),),
+ ... (Vectors.dense([-0.83, -0.68]),),
+ ... (Vectors.dense([-0.91, -0.76]),)]
+ >>> df1 = sqlContext.createDataFrame(data1, ["features"])
--- End diff --
rename to "df"
---
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