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example.py
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example.py
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#!/usr/bin/env python2.7
# example.py: Yet Another Hidden Markov Model library
# Contact: Jacob Schreiber ( jmschreiber91@gmail.com )
# Adam Novak ( anovak1@ucsc.edu )
"""
A simple example highlighting how to build a model using states, add
transitions, and then run the algorithms, including showing how training
on a sequence improves the probability of the sequence.
"""
import random
from yahmm import *
random.seed(0)
model = Model(name="ExampleModel")
distribution = UniformDistribution(0.0, 1.0)
state = State(distribution, name="uniform")
state2 = State(NormalDistribution(0, 2), name="normal")
silent = State(None, name="silent")
model.add_state(state)
model.add_state(state2)
model.add_transition(state, state, 0.4)
model.add_transition(state, state2, 0.4)
model.add_transition(state2, state2, 0.4)
model.add_transition(state2, state, 0.4)
model.add_transition(model.start, state, 0.5)
model.add_transition(model.start, state2, 0.5)
model.add_transition(state, model.end, 0.2)
model.add_transition(state2, model.end, 0.2)
model.bake()
sequence = model.sample()
print sequence
print
print model.forward(sequence)[ len(sequence), model.end_index ]
print
print model.backward(sequence)[0,model.start_index]
print
print model.forward_backward(sequence)
print
model.train( [ sequence ] )
print model.forward(sequence)[ len(model.states), model.end_index ]
print
print model.backward(sequence)[0,model.start_index]
print
print model.forward_backward(sequence)
print