diff options
Diffstat (limited to 'examples/src/main/python/kmeans.py')
| -rwxr-xr-x | examples/src/main/python/kmeans.py | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index 86ef6f32c8..1939150646 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -22,6 +22,7 @@ examples/src/main/python/mllib/kmeans.py. This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import sys @@ -47,12 +48,12 @@ def closestPoint(p, centers): if __name__ == "__main__": if len(sys.argv) != 4: - print >> sys.stderr, "Usage: kmeans <file> <k> <convergeDist>" + print("Usage: kmeans <file> <k> <convergeDist>", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of KMeans Clustering and is given + 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.""" + how to use MLlib's KMeans implementation.""", file=sys.stderr) sc = SparkContext(appName="PythonKMeans") lines = sc.textFile(sys.argv[1]) @@ -69,13 +70,13 @@ if __name__ == "__main__": pointStats = closest.reduceByKey( lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2)) newPoints = pointStats.map( - lambda (x, (y, z)): (x, y / z)).collect() + lambda xy: (xy[0], xy[1][0] / xy[1][1])).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) + print("Final centers: " + str(kPoints)) sc.stop() |