Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/4059#discussion_r23399900 --- Diff: python/pyspark/mllib/clustering.py --- @@ -86,6 +86,68 @@ def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||" return KMeansModel([c.toArray() for c in centers]) +class GaussianMixtureModel(object): + + """A clustering model derived from the Gaussian Mixture Model method. + + >>> from numpy import array + >>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1, + ... 0.9,0.8,0.75,0.935, + ... -0.83,-0.68,-0.91,-0.76 ]).reshape(6,2)) + >>> model = GaussianMixtureEM.train(clusterdata_1, 3, 0.0001, 3205, 10) + >>> labels = model.predictLabels(clusterdata_1).collect() + >>> labels[0]==labels[2] + True + >>> labels[3]==labels[4] + False + >>> labels[4]==labels[5] + True + >>> clusterdata_2 = sc.parallelize(array([-5.1971, -2.5359, -3.8220, + ... -5.2211, -5.0602, 4.7118, + ... 6.8989, 3.4592, 4.6322, + ... 5.7048, 4.6567, 5.5026, + ... 4.5605, 5.2043, 6.2734]).reshape(5,3)) + >>> model = GaussianMixtureEM.train(clusterdata_2, 2, 0.0001, 150, 10) + >>> labels = model.predictLabels(clusterdata_2).collect() + >>> labels[0]==labels[1]==labels[2] + True + >>> labels[3]==labels[4] + True + """ + + def __init__(self, weight, mu, sigma): + self.weight = weight + self.mu = mu + self.sigma = sigma + + def predictLabels(self, X): + """ + Find the cluster to which the points in X has maximum membership + in this model. + """ + cluster_labels = self.predictSoft(X).map(lambda x: x.index(max(x))) + return cluster_labels + + def predictSoft(self, X): + """ + Find the membership of each point in X to all clusters in this model. --- End diff -- What is the type of the return value? Is it a matrix or an array? This is important for Python users.
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