def build_an_hmm_example():
# i think the characters in each DiscreteDistribution definition, means the emission matrix for each state
# because it says the probability of seeing each character when the system is in that state
d1 = DiscreteDistribution({'A': 0.35, 'C': 0.20, 'G': 0.05, 'T': 0.40})
d2 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})
d3 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})
s1 = State(d1, name="s1")
s2 = State(d2, name="s2")
s3 = State(d3, name="s3")
model = HiddenMarkovModel('example')
model.add_states([s1, s2, s3])
model.add_transition(model.start, s1, 0.90)
model.add_transition(model.start, s2, 0.10)
model.add_transition(s1, s1, 0.80)
model.add_transition(s1, s2, 0.20)
model.add_transition(s2, s2, 0.90)
model.add_transition(s2, s3, 0.10)
model.add_transition(s3, s3, 0.70)
model.add_transition(s3, model.end, 0.30)
model.bake()
for i in range(len(model.states)):
print(model.states[i].name)
model.plot()
#print(model.log_probability(list('ACGACTATTCGAT')))
#print(", ".join(state.name for i, state in model.viterbi(list('ACGACTATTCGAT'))[1]))
print("forward:", model.forward(list('ACG')))
def build_the_same_model_in_test_sample_from_site_line_by_line():
# State olds emission distribution, but not
#transition distribution, because that's stored in the graph edges.
s1 = State(NormalDistribution(5, 1))
s2 = State(NormalDistribution(1, 7))
s3 = State(NormalDistribution(8, 2))
model = HiddenMarkovModel()
model.add_states(s1, s2, s3)
model.add_transition(model.start, s1, 1.0)
model.add_transition(s1, s1, 0.7)
model.add_transition(s1, s2, 0.3)
model.add_transition(s2, s2, 0.8)
model.add_transition(s2, s3, 0.2)
model.add_transition(s3, s3, 0.9)
model.add_transition(s3, model.end, 0.1)
model.bake()
model.plot()
