Python dhmm_em Examples

Example #1

File: dhmm_demo.py Project: nipunbatra/PyHMM

prior_1=prior0
transmat1 = mk_stochastic(np.random.rand(Q,Q))
obsmat1 = mk_stochastic(np.random.rand(Q,O))

obs=np.array([[ 2,2,2,1,1,0,0,1,0,1]])
hidden=np.array([[1,0,0,1,0,1,0,1,1,0]])
transmat1=  np.array([[0.3143, 0.6857],[0.4807, 0.5193]])
obsmat1= np.array([[0.2535, 0.2571, 0.4893],[0.3934,0.4136,0.1930]])
prior_1=np.array([.6109,.3891])
print "Train initial guess parameters"
print "-"*80
print "Prior:",prior_1
print "Observation",obsmat1 
print "Transition:",transmat1
#transmat1=transmat0
#obsmat1=obsmat0
# prior1=prior0;
# transmat1=transmat0
# obsmat1=obsmat0    

# improve guess of parameters using EM
[LL, prior2, transmat2, obsmat2,nr_iter] = dhmm_em(obs[0],hidden[0], prior_1, transmat1, obsmat1, 10,.0001 );


print "Learnt Parameters"
print "Prior:",prior2
print "Transition:",transmat2
print "Observation:",obsmat2
import matplotlib.pyplot as plt
plt.plot(LL)
plt.show()

Example #2

File: dhmm_demo_multiple.py Project: nipunbatra/PyHMM

][1    2    1    2    2    1    1    2    1    1
][1    1    1    1    1    1    2    1    2    1
]])"""
transmat1 = np.array([[0.3143, 0.6857], [0.4807, 0.5193]])
obsmat1 = np.array([[0.2535, 0.2571, 0.4893], [0.3934, 0.4136, 0.1930]])
prior_1 = np.array([0.30, 0.70])
print "Train initial guess parameters"
print "-" * 80
print "Prior:", prior_1
print "Observation", obsmat1
print "Transition:", transmat1
# transmat1=transmat0
# obsmat1=obsmat0
# prior1=prior0;
# transmat1=transmat0
# obsmat1=obsmat0

# improve guess of parameters using EM

[LL, prior2, transmat2, obsmat2, nr_iter] = dhmm_em(obs, prior_1, transmat1, obsmat1, 3500, 0.0000001)


print "Learnt Parameters"
print "Prior:", prior2
print "Transition:", transmat2
print "Observation:", obsmat2
import matplotlib.pyplot as plt

# plt.plot(LL)
# plt.show()