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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+A K-means clustering program using MLlib.
+
+This example requires NumPy (http://www.numpy.org/).
+"""
+
+import sys
+
+import numpy as np
+from pyspark import SparkContext
+from pyspark.mllib.clustering import KMeans
+
+
+def parseVector(line):
+ return np.array([float(x) for x in line.split(' ')])
+
+
+if __name__ == "__main__":
+ if len(sys.argv) < 4:
+ print >> sys.stderr, "Usage: kmeans <master> <file> <k>"
+ exit(-1)
+ sc = SparkContext(sys.argv[1], "KMeans")
+ lines = sc.textFile(sys.argv[2])
+ data = lines.map(parseVector)
+ k = int(sys.argv[3])
+ model = KMeans.train(data, k)
+ print "Final centers: " + str(model.clusterCenters)