def test2a(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
data = [generator.sample() for i in xrange(num_obs)]
# Gmm setup
num_mixtures = 2
gmm0 = make_gmm_diag(dimension, num_mixtures)
gmm1 = make_gmm_diag(dimension, num_mixtures)
mm = GmmMgr((gmm1,))
# Hmm setup
# A transition probability matrix with a p ~= 1 self-loop for the real state.
# The entry state feeds into the real state with p=1. We use p ~= 1 for the
# second self loop since we need *some* probability of finishing.
trans = array(((0.0, 1.0, 0.0),
(0.0, 0.999999999999, 0.000000000001),
(0.0, 0.0, 0.0)))
hmm0 = Hmm(1, log_domain=True)
hmm0.build_model(mm, (0,), 1, 1, trans)
print hmm0.to_string(True) + '\n'
print gmm0
print '\n\n'
# Try some adaptation
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
really_print = False
with DebugPrint("gaussian", "gaussian_pt", "gaussian_gmm_score") if really_print else DebugPrint():
gmm0.adapt(data, max_iters = num_passes)
print hmm0.to_string(True) + '\n'
print gmm0
def test1(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
GaussianMixtureModel.seed(0)
# Gmm setup
num_mixtures = 2
gmm0 = make_gmm(dimension, num_mixtures)
gmm1 = make_gmm(dimension, num_mixtures)
mm = GmmMgr((gmm1,))
# Hmm setup
hmm0 = Hmm(1, log_domain=True)
# A transition probability matrix with a p=1/2 exit for the real state.
# The entry state feeds into the real state with p=1.
trans = array(((0.0, 1.0, 0.0),
(0.0, 0.5, 0.5),
(0.0, 0.0, 0.0)))
hmm0.build_model(mm, (0,), 1, 1, trans)
print hmm0.to_string(True)
print gmm0
# Try some adaptation. Note that we are feeding the entire data set as one stream
# to the Hmm adaption call.
data = [generator.sample() for i in xrange(num_obs)]
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
gmm0.adapt(data, max_iters = num_passes)
print hmm0.to_string(True)
print gmm0
def test0(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
GaussianMixtureModel.seed(0)
mm = GmmMgr(dimension)
# Hmm setup
hmm0 = Hmm(0, log_domain=True)
# A transition probability matrix with no real state.
# The entry state feeds into the exit state with p=1.
trans = array(((0.0, 1.0),
(0.0, 0.0)))
hmm0.build_model(mm, (), 1, 1, trans)
print hmm0.to_string(True)
# Try some adaptation. Note that we are feeding the entire data set as one stream
# to the Hmm adaption call.
data = [generator.sample() for i in xrange(num_obs)]
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
with DebugPrint("hmm_gxfs", "hmm_aos") if False else DebugPrint():
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
