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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.
#
"""
The K-means algorithm written from scratch against PySpark. In practice,
one may prefer to use the KMeans algorithm in MLlib, as shown in
examples/src/main/python/mllib/kmeans.py.
This example requires NumPy (http://www.numpy.org/).
"""
from __future__ import print_function
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) != 4:
print("Usage: kmeans <file> <k> <convergeDist>", file=sys.stderr)
exit(-1)
print("""WARN: This is a naive implementation of KMeans Clustering and is given
as an example! Please refer to examples/src/main/python/mllib/kmeans.py for an example on
how to use MLlib's KMeans implementation.""", file=sys.stderr)
sc = SparkContext(appName="PythonKMeans")
lines = sc.textFile(sys.argv[1])
data = lines.map(parseVector).cache()
K = int(sys.argv[2])
convergeDist = float(sys.argv[3])
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 (p1, c1), (p2, c2): (p1 + p2, c1 + c2))
newPoints = pointStats.map(
lambda st: (st[0], st[1][0] / st[1][1])).collect()
tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints)
for (iK, p) in newPoints:
kPoints[iK] = p
print("Final centers: " + str(kPoints))
sc.stop()
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