def dhmm_em(observation_i,prior,transition_matrix,emission_matrix,max_iter,thresh):
previous_loglik = -np.inf
loglik = 0
converged = False
num_iter = 1
LL = []
while (num_iter <= max_iter) and not converged :
#E step
[loglik,exp_num_visits1,exp_num_visitsT,exp_num_trans,exp_num_emit]=compute_ess_dhmm(observation_i,prior,transition_matrix,emission_matrix, 0)
#[loglik, prior,A,B] = compute_ess_dhmm(observation_i,hidden,prior,transition_matrix,emission_matrix, 0)
#print "-----------ITERATION "+str(num_iter)+"----------"
#M Step
#prior = prior
prior=normalize(exp_num_visits1)[0]
#print "AFTER M STEP:",prior
transition_matrix = mk_stochastic(exp_num_trans)
#emission_matrix = mk_stochastic(B)
emission_matrix=mk_stochastic(exp_num_emit)
num_iter = num_iter + 1
[converged,decrease] = em_converged(loglik, previous_loglik, thresh,False)
previous_loglik = loglik
LL.append(loglik)
#print "Log Likelihood:",LL
return [LL, prior, transition_matrix, emission_matrix, num_iter]
import numpy as np from underflow_normalize import normalize,mk_stochastic from sample import * from dhmm_em import dhmm_em O = 3 Q = 2 #"true" parameters prior0 = normalize(np.random.rand(Q,1))[0] prior0 =np.array([.5,.5]) transmat0 = mk_stochastic(np.random.rand(Q,Q)) transmat0=np.array([[.3 ,.7],[.7,.3]]) obsmat0 = mk_stochastic(np.random.rand(Q,O)) obsmat0=np.array([[.333,.333,.333],[.333,.333,.333]]) print "True Parameters" print "-"*80 print "Prior:",prior0 print "Observation",obsmat0 print "Transition:",transmat0 # training data T = 1 nex =5 [obs,hidden] = sample_dhmm(prior0, transmat0, obsmat0, T, nex) #print hidden,obs# initial guess of parameters prior1 = normalize(np.random.rand(Q,1))[0]
