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# /usr/bin/python
# Last Change: Thu Sep 28 01:00 PM 2006 J
#TODO:
# - a demo for kmeans
import numpy as N
def _py_vq(data, code):
""" Please do not use directly. Use kmean instead"""
# No attempt to be efficient has been made...
(n, d) = data.shape
(k, d) = code.shape
label = N.zeros(n, int)
for i in range(n):
d = N.sum((data[i, :] - code) ** 2, 1)
label[i] = N.argmin(d)
return label
# Try to import pyrex function for vector quantization. If not available,
# falls back on pure python implementation.
#%KMEANIMPORT%
#try:
# from scipy.cluster.vq import kmeans as kmean
#except ImportError:
# try:
# from c_gmm import _vq
# except:
# print """c_gmm._vq not found, using pure python implementation instead.
# Kmean will be REALLY slow"""
# _vq = _py_vq
try:
from scipy.cluster.vq import vq
print "using scipy.cluster.vq"
def _vq(*args, **kw): return vq(*args, **kw)[0]
except ImportError:
try:
from c_gmm import _vq
print "using pyrex vq"
except ImportError:
print """c_gmm._vq not found, using pure python implementation instead.
Kmean will be REALLY slow"""
_vq = _py_vq
def kmean(data, init, iter = 10):
"""Simple kmean implementation for EM. Runs iter iterations.
returns a tuple (code, label), where code are the final
centroids, and label are the class label indec for each
frame (ie row) of data"""
data = N.atleast_2d(data)
init = N.atleast_2d(init)
(n, d) = data.shape
(k, d1) = init.shape
if not d == d1:
msg = "data and init centers do not have same dimensions..."
raise GmmParamError(msg)
code = N.asarray(init.copy())
for i in range(iter):
# Compute the nearest neighbour for each obs
# using the current code book
label = _vq(data, code)
# Update the code by computing centroids using the new code book
for j in range(k):
code[j,:] = N.mean(data[N.where(label==j)], axis=0)
return code, label
if __name__ == "__main__":
pass
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