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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