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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 """A clustering model derived from the k-means method.
31
32 >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
33 >>> model = KMeans.train(
34 ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
35 >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
36 True
37 >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
38 True
39 >>> model = KMeans.train(sc.parallelize(data), 2)
40 >>> sparse_data = [
41 ... SparseVector(3, {1: 1.0}),
42 ... SparseVector(3, {1: 1.1}),
43 ... SparseVector(3, {2: 1.0}),
44 ... SparseVector(3, {2: 1.1})
45 ... ]
46 >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
47 >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
48 True
49 >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
50 True
51 >>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
52 True
53 >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
54 True
55 >>> type(model.clusterCenters)
56 <type 'list'>
57 """
59 self.centers = centers
60
61 @property
63 """Get the cluster centers, represented as a list of NumPy arrays."""
64 return self.centers
65
67 """Find the cluster to which x belongs in this model."""
68 best = 0
69 best_distance = float("inf")
70 for i in range(0, len(self.centers)):
71 distance = _squared_distance(x, self.centers[i])
72 if distance < best_distance:
73 best = i
74 best_distance = distance
75 return best
76
79 @classmethod
80 - def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
81 """Train a k-means clustering model."""
82 sc = data.context
83 dataBytes = _get_unmangled_double_vector_rdd(data)
84 ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
85 dataBytes._jrdd, k, maxIterations, runs, initializationMode)
86 if len(ans) != 1:
87 raise RuntimeError("JVM call result had unexpected length")
88 elif type(ans[0]) != bytearray:
89 raise RuntimeError("JVM call result had first element of type "
90 + type(ans[0]) + " which is not bytearray")
91 matrix = _deserialize_double_matrix(ans[0])
92 return KMeansModel([row for row in matrix])
93
96 import doctest
97 globs = globals().copy()
98 globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
99 (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
100 globs['sc'].stop()
101 if failure_count:
102 exit(-1)
103
104
105 if __name__ == "__main__":
106 _test()
107