old_contents: #
  # 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 <file> <k> <max_iteration>"
          exit(-1)
      sc = SparkContext(appName="KMeans")
      lines = sc.textFile(sys.argv[1])
      data = lines.map(parseVector)
      k = int(sys.argv[2])
      max_iterations = int(sys.argv[3])
      model = KMeans.train(data, k, max_iterations)
      print "Final centers: " + str(model.clusterCenters)
  
new_contents: #
  # 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 <file> <k> <max_iteration>"
          exit(-1)
      sc = SparkContext(appName="PythonKMeans")
      lines = sc.textFile(sys.argv[1])
      data = lines.map(parseVector)
      k = int(sys.argv[2])
      max_iterations = int(sys.argv[3])
      model = KMeans.train(data, k, max_iterations)
      print "Final centers: " + str(model.clusterCenters)
  
subject: remove common folder