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import numpy as np
from scipy.cluster import vq
def python_vq(all_data,code_book):
import time
t1 = time.time()
codes1,dist1 = vq.vq(all_data,code_book)
t2 = time.time()
#print 'fast (double):', t2 - t1
#print ' first codes:', codes1[:5]
#print ' first dist:', dist1[:5]
#print ' last codes:', codes1[-5:]
#print ' last dist:', dist1[-5:]
float_obs = all_data.astype(np.float32)
float_code = code_book.astype(np.float32)
t1 = time.time()
codes1,dist1 = vq.vq(float_obs,float_code)
t2 = time.time()
#print 'fast (float):', t2 - t1
#print ' first codes:', codes1[:5]
#print ' first dist:', dist1[:5]
#print ' last codes:', codes1[-5:]
#print ' last dist:', dist1[-5:]
return codes1,dist1
def read_data(name):
f = open(name,'r')
data = []
for line in f.readlines():
data.append(map(float,string.split(line)))
f.close()
return array(data)
def main():
np.random.seed((1000,1000))
Ncodes = 40
Nfeatures = 16
Nobs = 4000
code_book = np.random.normal(0,1,(Ncodes,Nfeatures))
features = np.random.normal(0,1,(Nobs,Nfeatures))
codes,dist = python_vq(features,code_book)
if __name__ == '__main__':
main()
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