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import numpy as N
from gauss_mix import GM
from gmm_em import GMM, EM
from numpy.random import seed
def test_reg():
seed(0)
# Generate data with a few components
d = 2
k = 1
n = 500
w, mu, va = GM.gen_param(d, k)
gm = GM.fromvalues(w, mu, va)
data = gm.sample(n)
# Try to learn with an insane number of components
gmm = GMM(GM(d, 30), 'random')
em = EM()
like= em.train(data, gmm, 20, 1e-20)
# import pylab as P
# P.subplot(2, 1, 1)
# P.plot(data[:, 0], data[:, 1], '.')
# gmm.gm.plot()
# P.subplot(2, 1, 2)
# P.plot(like)
# print like
# P.show()
if __name__ == "__main__":
# import hotshot, hotshot.stats
# profile_file = 'manyk.prof'
# prof = hotshot.Profile(profile_file, lineevents=1)
# prof.runcall(test_reg)
# p = hotshot.stats.load(profile_file)
# print p.sort_stats('cumulative').print_stats(20)
# prof.close()
test_reg()
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