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18 from numpy import array, dot
19 from math import sqrt
20 from pyspark import SparkContext
21 from pyspark.mllib._common import \
22 _get_unmangled_rdd, _get_unmangled_double_vector_rdd, _squared_distance, \
23 _serialize_double_matrix, _deserialize_double_matrix, \
24 _serialize_double_vector, _deserialize_double_vector, \
25 _get_initial_weights, _serialize_rating, _regression_train_wrapper
26 from pyspark.mllib.linalg import SparseVector
30
31 """A clustering model derived from the k-means method.
32
33 >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
34 >>> model = KMeans.train(
35 ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
36 >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
37 True
38 >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
39 True
40 >>> model = KMeans.train(sc.parallelize(data), 2)
41 >>> sparse_data = [
42 ... SparseVector(3, {1: 1.0}),
43 ... SparseVector(3, {1: 1.1}),
44 ... SparseVector(3, {2: 1.0}),
45 ... SparseVector(3, {2: 1.1})
46 ... ]
47 >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
48 >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
49 True
50 >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
51 True
52 >>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
53 True
54 >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
55 True
56 >>> type(model.clusterCenters)
57 <type 'list'>
58 """
59
61 self.centers = centers
62
63 @property
65 """Get the cluster centers, represented as a list of NumPy arrays."""
66 return self.centers
67
69 """Find the cluster to which x belongs in this model."""
70 best = 0
71 best_distance = float("inf")
72 for i in range(0, len(self.centers)):
73 distance = _squared_distance(x, self.centers[i])
74 if distance < best_distance:
75 best = i
76 best_distance = distance
77 return best
78
81
82 @classmethod
83 - def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
84 """Train a k-means clustering model."""
85 sc = data.context
86 dataBytes = _get_unmangled_double_vector_rdd(data)
87 ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
88 dataBytes._jrdd, k, maxIterations, runs, initializationMode)
89 if len(ans) != 1:
90 raise RuntimeError("JVM call result had unexpected length")
91 elif type(ans[0]) != bytearray:
92 raise RuntimeError("JVM call result had first element of type "
93 + type(ans[0]) + " which is not bytearray")
94 matrix = _deserialize_double_matrix(ans[0])
95 return KMeansModel([row for row in matrix])
96
99 import doctest
100 globs = globals().copy()
101 globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
102 (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
103 globs['sc'].stop()
104 if failure_count:
105 exit(-1)
106
107
108 if __name__ == "__main__":
109 _test()
110