Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/12402#discussion_r59834991 --- Diff: python/pyspark/ml/clustering.py --- @@ -22,7 +22,151 @@ from pyspark.mllib.common import inherit_doc __all__ = ['BisectingKMeans', 'BisectingKMeansModel', - 'KMeans', 'KMeansModel'] + 'KMeans', 'KMeansModel', 'GaussianMixture', 'GaussianMixtureModel'] + + +class GaussianMixtureModel(JavaModel, JavaMLWritable, JavaMLReadable): + """ + .. note:: Experimental + + Model fitted by GaussianMixtureModel. + + .. 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 self._call_java("gaussians") + + +@inherit_doc +class GaussianMixture(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed, + HasProbabilityCol, JavaMLWritable, JavaMLReadable): + """ + .. note:: Experimental + + Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm. + + >>> 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"]) + >>> gaussianmixture = GaussianMixture(k=3, tol=0.0001, + ... maxIter=50, seed=10) --- End diff -- alignment
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