1 files changed, 0 insertions, 73 deletions
diff --git a/python/examples/kmeans.py b/python/examples/kmeans.py
deleted file mode 100755
index d8387b0b18..0000000000
--- a/python/examples/kmeans.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# 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.
-#
-
-"""
-The K-means algorithm written from scratch against PySpark. In practice,
-one may prefer to use the KMeans algorithm in MLlib, as shown in
-python/examples/mllib/kmeans.py.
-
-This example requires NumPy (http://www.numpy.org/).
-"""
-
-import sys
-
-import numpy as np
-from pyspark import SparkContext
-
-
-def parseVector(line):
-    return np.array([float(x) for x in line.split(' ')])
-
-
-def closestPoint(p, centers):
-    bestIndex = 0
-    closest = float("+inf")
-    for i in range(len(centers)):
-        tempDist = np.sum((p - centers[i]) ** 2)
-        if tempDist < closest:
-            closest = tempDist
-            bestIndex = i
-    return bestIndex
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 5:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonKMeans")
-    lines = sc.textFile(sys.argv[2])
-    data = lines.map(parseVector).cache()
-    K = int(sys.argv[3])
-    convergeDist = float(sys.argv[4])
-
-    kPoints = data.takeSample(False, K, 1)
-    tempDist = 1.0
-
-    while tempDist > convergeDist:
-        closest = data.map(
-            lambda p : (closestPoint(p, kPoints), (p, 1)))
-        pointStats = closest.reduceByKey(
-            lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2))
-        newPoints = pointStats.map(
-            lambda (x, (y, z)): (x, y / z)).collect()
-
-        tempDist = sum(np.sum((kPoints[x] - y) ** 2) for (x, y) in newPoints)
-
-        for (x, y) in newPoints:
-            kPoints[x] = y
-
-    print "Final centers: " + str(kPoints)