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
|
from __future__ import division, print_function, absolute_import
from scipy import *
from scipy.cluster import vq
def cluster_data(data,cluster_cnt,iter=20,thresh=1e-5):
""" Group data into a number of common clusters
data -- 2D array of data points. Each point is a row in the array.
cluster_cnt -- The number of clusters to use
iter -- number of iterations to use for kmeans algorithm
thresh -- distortion threshold for kmeans algorithm
return -- list of 2D arrays. Each array contains the data points
that belong to a specific cluster.
Uses kmeans algorithm to find the clusters.
"""
wh_data = vq.whiten(data)
code_book,dist = vq.kmeans(wh_data,cluster_cnt,iter,thresh)
code_ids, distortion = vq.vq(wh_data,code_book)
clusters = []
for i in range(len(code_book)):
cluster = compress(code_ids == i,data,0)
clusters.append(cluster)
return clusters
if __name__ == "__main__":
data = array(((400, 79, 5.4),
(180, 76, 4.5),
(28, 25, 30.),
(270, 81, 5.0),
(185, 78, 4.6)))
clusters = cluster_data(data,2)
for i in range(len(clusters)):
print('cluster %d:' % i)
print(clusters[i])
|
