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diff --git a/python/examples/logistic_regression.py b/python/examples/logistic_regression.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.
-#
-
-"""
-A logistic regression implementation that uses NumPy (http://www.numpy.org)
-to act on batches of input data using efficient matrix operations.
-
-In practice, one may prefer to use the LogisticRegression algorithm in
-MLlib, as shown in python/examples/mllib/logistic_regression.py.
-"""
-
-from collections import namedtuple
-from math import exp
-from os.path import realpath
-import sys
-
-import numpy as np
-from pyspark import SparkContext
-
-
-D = 10  # Number of dimensions
-
-
-# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to
-# make further computations faster.
-# The data file contains lines of the form <label> <x1> <x2> ... <xD>. We load each block of these
-# into a NumPy array of size numLines * (D + 1) and pull out column 0 vs the others in gradient().
-def readPointBatch(iterator):
-    strs = list(iterator)
-    matrix = np.zeros((len(strs), D + 1))
-    for i in xrange(len(strs)):
-        matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ')
-    return [matrix]
-
-if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
-    iterations = int(sys.argv[3])
-
-    # Initialize w to a random value
-    w = 2 * np.random.ranf(size=D) - 1
-    print "Initial w: " + str(w)
-
-    # Compute logistic regression gradient for a matrix of data points
-    def gradient(matrix, w):
-        Y = matrix[:,0]    # point labels (first column of input file)
-        X = matrix[:,1:]   # point coordinates
-        # For each point (x, y), compute gradient function, then sum these up
-        return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)
-
-    def add(x, y):
-        x += y
-        return x
-
-    for i in range(iterations):
-        print "On iteration %i" % (i + 1)
-        w -= points.map(lambda m: gradient(m, w)).reduce(add)
-
-    print "Final w: " + str(w)