text: """
      Logistic regression using MLlib.
      
      This example requires NumPy (http://www.numpy.org/).
      """
  from __future__ import print_function
  
  import sys
  
  from pyspark import SparkContext
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.mllib.classification import LogisticRegressionWithSGD
  
  
  def parsePoint(line):
      """
          Parse a line of text into an MLlib LabeledPoint object.
          """
      values = [float(s) for s in line.split(' ')]
      if values[0] == -1:   # Convert -1 labels to 0 for MLlib
          values[0] = 0
      return LabeledPoint(values[0], values[1:])
  
  
  if __name__ == "__main__":
      if len(sys.argv) != 3:
          print("Usage: logistic_regression <file> <iterations>", file=sys.stderr)
          sys.exit(-1)
      sc = SparkContext(appName="PythonLR")
      points = sc.textFile(sys.argv[1]).map(parsePoint)
      iterations = int(sys.argv[2])
      model = LogisticRegressionWithSGD.train(points, iterations)
      print("Final weights: " + str(model.weights))
      print("Final intercept: " + str(model.intercept))
      sc.stop()
  