Pkg.clone("https://github.com/ahwillia/ToyHMM.jl.git")

using ToyHMM

n_states = 2
n_outputs = 3
hmm = dHMM(n_states,n_outputs)

println(hmm.A) # state-transition matrix (randomly initialized, rows sum to 1)
println(hmm.B) # emmission matrix (randomly initialized, rows sum to 1)
println(hmm.p) # initial state probabilities (randomly initialized)

o = [1,1,2,1,1,2,1,2,1,3,3,3,3,2,2,3,3,3] # example observation sequence

ch = baum_welch!(hmm,o) # fit model using Expectation-Maximization

println(ch) # log-likelihood values, convergence history

println(hmm.A) # fitted values of the hmm model
println(hmm.B)
println(hmm.p)

println(viterbi(hmm,o)) # most likely state sequence given hmm params

using ToyHMM

n_states = 2
n_outputs = 3

# create a very long output sequence
true_model = dHMM(n_states,n_outputs)
(s,o) = generate(true_model,100_000)

# try to recover similar params by fitting new model
fit_model = dHMM(n_states,n_outputs)
@time ch = baum_welch!(fit_model,o)