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diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
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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.
+#
+
+from numpy import array, dot
+from math import sqrt
+from pyspark import SparkContext
+from pyspark.mllib._common import \
+    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
+    _serialize_double_matrix, _deserialize_double_matrix, \
+    _serialize_double_vector, _deserialize_double_vector, \
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper
+
+class KMeansModel(object):
+    """A clustering model derived from the k-means method.
+
+    >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
+    >>> clusters = KMeans.train(sc, sc.parallelize(data), 2, maxIterations=10, runs=30, initialization_mode="random")
+    >>> clusters.predict(array([0.0, 0.0])) == clusters.predict(array([1.0, 1.0]))
+    True
+    >>> clusters.predict(array([8.0, 9.0])) == clusters.predict(array([9.0, 8.0]))
+    True
+    >>> clusters = KMeans.train(sc, sc.parallelize(data), 2)
+    """
+    def __init__(self, centers_):
+        self.centers = centers_
+
+    def predict(self, x):
+        """Find the cluster to which x belongs in this model."""
+        best = 0
+        best_distance = 1e75
+        for i in range(0, self.centers.shape[0]):
+            diff = x - self.centers[i]
+            distance = sqrt(dot(diff, diff))
+            if distance < best_distance:
+                best = i
+                best_distance = distance
+        return best
+
+class KMeans(object):
+    @classmethod
+    def train(cls, sc, data, k, maxIterations=100, runs=1,
+            initialization_mode="k-means||"):
+        """Train a k-means clustering model."""
+        dataBytes = _get_unmangled_double_vector_rdd(data)
+        ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd,
+                k, maxIterations, runs, initialization_mode)
+        if len(ans) != 1:
+            raise RuntimeError("JVM call result had unexpected length")
+        elif type(ans[0]) != bytearray:
+            raise RuntimeError("JVM call result had first element of type "
+                    + type(ans[0]) + " which is not bytearray")
+        return KMeansModel(_deserialize_double_matrix(ans[0]))
+
+def _test():
+    import doctest
+    globs = globals().copy()
+    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
+    (failure_count, test_count) = doctest.testmod(globs=globs,
+            optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()