1 files changed, 0 insertions, 57 deletions
diff --git a/pyspark/examples/logistic_regression.py b/pyspark/examples/logistic_regression.py
deleted file mode 100755
index f13698a86f..0000000000
--- a/pyspark/examples/logistic_regression.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""
-This example requires numpy (http://www.numpy.org/)
-"""
-from collections import namedtuple
-from math import exp
-from os.path import realpath
-import sys
-
-import numpy as np
-from pyspark import SparkContext
-
-
-N = 100000  # Number of data points
-D = 10  # Number of dimensions
-R = 0.7   # Scaling factor
-ITERATIONS = 5
-np.random.seed(42)
-
-
-DataPoint = namedtuple("DataPoint", ['x', 'y'])
-from lr import DataPoint  # So that DataPoint is properly serialized
-
-
-def generateData():
-    def generatePoint(i):
-        y = -1 if i % 2 == 0 else 1
-        x = np.random.normal(size=D) + (y * R)
-        return DataPoint(x, y)
-    return [generatePoint(i) for i in range(N)]
-
-
-if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, \
-            "Usage: PythonLR <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
-    points = sc.parallelize(generateData(), slices).cache()
-
-    # Initialize w to a random value
-    w = 2 * np.random.ranf(size=D) - 1
-    print "Initial w: " + str(w)
-
-    def add(x, y):
-        x += y
-        return x
-
-    for i in range(1, ITERATIONS + 1):
-        print "On iteration %i" % i
-
-        gradient = points.map(lambda p:
-            (1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x
-        ).reduce(add)
-        w -= gradient
-
-    print "Final w: " + str(w)