-
Notifications
You must be signed in to change notification settings - Fork 0
/
kmean.py
46 lines (33 loc) · 1.17 KB
/
kmean.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
from __future__ import print_function
import sys
import re
import numpy as np
from pyspark import SparkContext
from pyspark.ml.clustering import KMeans, KMeansModel
from pyspark.mllib.linalg import VectorUDT, _convert_to_vector
from pyspark.sql import SQLContext
from pyspark.sql.types import Row, StructField, StructType
def parseVector(line):
array = np.array([float(x) for x in line.split(' ')])
return _convert_to_vector(array)
if __name__ == "__main__":
FEATURES_COL = "features"
if len(sys.argv) != 3:
print("Usage: kmeans_example.py <file> <k>", file=sys.stderr)
exit(-1)
path = sys.argv[1]
k = sys.argv[2]
sc = SparkContext(appName="Jay")
sqlContext = SQLContext(sc)
lines = sc.textFile(path)
data = lines.map(parseVector)
row_rdd = data.map(lambda x: Row(x))
schema = StructType([StructField(FEATURES_COL, VectorUDT(), False)])
df = sqlContext.createDataFrame(row_rdd, schema)
kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol(FEATURES_COL)
model = kmeans.fit(df)
centers = model.clusterCenters()
print("Cluster Centers: ")
for center in centers:
print(center)
sc.stop()