def test_exact_fhmm_train(self):
path = os.path.join(TESTDATA_PATH, 'X.mat')
self.assertTrue(os.path.exists(path))
X = loadmat(path)['X']
obs = [X]
fhmm_exact = ExactGaussianFHMM(n_chains=2, n_states=3, n_training_iterations=20, training_threshold=0.01, topology='full')
fhmm_exact.fit(obs)
print 'exact:', fhmm_exact.loglikelihood(obs[0])
fhmm_seq = SequentialGaussianFHMM(n_chains=2, n_states=3, n_training_iterations=20, training_threshold=0.01, topology='full')
fhmm_seq.fit(obs)
print 'seq:', fhmm_seq.loglikelihood(obs[0])
def test_forward(self):
chain0_means = np.array([[-5.99882413e+02, 9.99894546e+01],
[-3.00048799e+02, 1.99167070e+02],
[-4.28859220e-02, 2.99416348e+02]])
chain0_covars = np.array([[0.93523132, 0.75423995],
[0.58861593, 0.4016048],
[0.25641893, 1.60482337]])
chain0_logstart = np.array([-4.44089210e-16, -3.60436534e+01, -3.60436534e+01])
chain0_logtransmat = np.array([[-38.34619345, 0.0, -38.34619345],
[-38.34619345, -38.34619345, 0.0],
[-1.09861229, -1.09861229, -1.09861229]])
chain1_means = np.array([[-5.99882413e+02, 9.99894546e+01],
[-3.00048799e+02, 1.99167070e+02],
[-4.39995282e-02, 2.99415977e+02]])
chain1_covars = np.array([[3.73792529, 3.01395982],
[2.35146944, 1.60341962],
[1.3566332, 6.45346843]])
chain1_logstart = np.array([-4.44089210e-16, -3.60436534e+01, -3.60436534e+01])
chain1_logtransmat = np.array([[-3.83461934e+01, -3.71345941e-06, -1.25035485e+01],
[-3.83461897e+01, -2.35611266e+01, -5.85509419e-11],
[-2.58426449e+01, -2.58426449e+01, -1.19594334e-11]])
loglikelihood = -55.089351487070289
obs = [np.array([[-600, 100], [-300, 200], [0, 300]]) for _ in xrange(1)]
# Fit the FHMM on random noise b/c we need it to be fitted in order to manipulate the chains
fhmm = SequentialGaussianFHMM(n_chains=2, n_states=3)
fhmm.fit([np.random.random((3, 2))])
# Update chain0
fhmm.chains_[0]._means_ = chain0_means
fhmm.chains_[0]._covars_ = chain0_covars
fhmm.chains_[0]._log_startprob = chain0_logstart
fhmm.chains_[0]._log_transmat = chain0_logtransmat
# Update chain1
fhmm.chains_[1]._means_ = chain1_means
fhmm.chains_[1]._covars_ = chain1_covars
fhmm.chains_[1]._log_startprob = chain1_logstart
fhmm.chains_[1]._log_transmat = chain1_logtransmat
self.assertEqual(loglikelihood, fhmm.loglikelihood(obs[0]))
def setUp(self):
n_states = 3
self.hmms = [
HMMLearnGaussianHMM(n_states=n_states),
PomegranateGaussianHMM(n_states=n_states),
SequentialGaussianFHMM(n_states=n_states, n_chains=2)
]
r = np.random.randn
self.obs = [
np.array([[-600 + r(), 100 + r()], [-300 + r(), 200 + r()],
[0 + r(), 300 + r()]]) for _ in xrange(10)
]
def get_classifier(args):
if args.model == 'hmmlearn':
model = HMMLearnModel()
elif args.model == 'fhmm-exact':
model = ExactGaussianFHMM(n_chains=args.n_chains)
elif args.model == 'fhmm-seq':
model = SequentialGaussianFHMM(n_chains=args.n_chains)
elif args.model == 'pomegranate':
model = PomegranateModel()
else:
model = None
assert model is not None
model.n_training_iterations = args.n_iterations
model.n_states = args.n_states
model.topology = args.topology
return HMMClassifier(model, n_jobs=args.n_jobs)
def get_classifier(args):
if args.model == 'hmm':
model = GaussianHMM()
elif args.model == 'fhmm-exact':
model = ExactGaussianFHMM(n_chains=args.n_chains)
elif args.model == 'fhmm-seq':
model = SequentialGaussianFHMM(n_chains=args.n_chains)
else:
model = None
assert model is not None
model.n_training_iterations = args.n_training_iter
model.n_states = args.n_states
model.topology = args.topology
model.verbose = args.verbose
model.transition_init = args.transition_init
model.emission_init = args.emission_init
model.covar_type = args.covar_type
return HMMClassifier(model, n_jobs=args.n_jobs)