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import numpy as N
from numpy.random import randn
from scipy.sandbox.pyem import densities as D
from scipy.sandbox.pyem import _c_densities as DC
#import tables
def bench(func, mode = 'diag'):
#===========================================
# Diag Gaussian of dimension 20
#===========================================
d = 30
n = 1e5
niter = 10
print "Compute %d times densities, %d dimension, %d frames" % (niter, d, n)
# Generate a model with k components, d dimensions
mu = randn(1, d)
if mode == 'diag':
va = abs(randn(1, d))
elif mode == 'full':
va = randn(d, d)
va = N.dot(va, va.transpose())
X = randn(n, d)
for i in range(niter):
Y = func(X, mu, va)
def benchpy():
bench(D.gauss_den)
def benchc():
bench(DC.gauss_den)
def benchpyfull():
bench(D.gauss_den, 'full')
def benchcfull():
bench(DC.gauss_den, 'full')
if __name__ == "__main__":
import hotshot, hotshot.stats
profile_file = 'gdenpy.prof'
prof = hotshot.Profile(profile_file, lineevents=1)
prof.runcall(benchpy)
p = hotshot.stats.load(profile_file)
print p.sort_stats('cumulative').print_stats(20)
prof.close()
profile_file = 'gdenc.prof'
prof = hotshot.Profile(profile_file, lineevents=1)
prof.runcall(benchc)
p = hotshot.stats.load(profile_file)
print p.sort_stats('cumulative').print_stats(20)
prof.close()
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