def test9(num_obs):
"""
Test sequence scoring interface.
"""
num_states = 3
dimension = 2
# Data generator setup
target_means = ((1,1), (2,2), (3,3))
target_vars = ((0.1,0.1), (0.2,0.2), (0.3,0.3))
target_durations = (2, 3, 6)
num_steps = sum(target_durations)
generators = [SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE) for i in xrange(num_states)]
[m.set_model(tm, tv) for (m, tm, tv) in izip(generators, target_means, target_vars)]
SimpleGaussianModel.seed(0)
obs_gen = obs_generator(generators, target_durations)
# Gmm setup
num_states = 3
models = []
for i in xrange(num_states):
gmm = GaussianMixtureModel(dimension, GaussianMixtureModel.DIAGONAL_COVARIANCE, 1)
gmm.set_weights(array((1.0,)))
mu = array(((0.0,0.0),))
v = array(((1.0,1.0),))
gmm.set_model(mu, v)
models.append(gmm)
mm = GmmMgr(models)
models = range(num_states)
# Hmm setup
trans = array(((0.0, 1.0, 0.0, 0.0, 0.0),
(0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.5, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.5),
(0.0, 0.0, 0.0, 0.0, 0.0)))
hmm0 = Hmm(num_states)
hmm0.build_model(mm, models, 1, 1, trans)
print hmm0.to_string(full=True)
for i in xrange(num_obs):
obs = obs_gen.next()
scores = hmm0.forward_score_sequence(obs)
print scores
