def test_sample_nonrev_1(self):
P = sample_tmatrix(self.C, reversible=False)
assert np.all(P.shape == self.C.shape)
assert is_transition_matrix(P)
# same with boject
sampler = tmatrix_sampler(self.C, reversible=False)
P = sampler.sample()
assert np.all(P.shape == self.C.shape)
assert is_transition_matrix(P)
def _update_transition_matrix(model: _SampleStorage,
transition_matrix_prior,
initial_distribution_prior,
reversible: bool = True,
stationary: bool = False,
n_sampling_steps: int = 1000):
""" Updates the hidden-state transition matrix and the initial distribution """
C = _bd_util.count_matrix(model.hidden_trajs, 1,
model.transition_matrix.shape[0])
model.counts[...] = C
C = C + transition_matrix_prior
# check if we work with these options
if reversible and not is_connected(C, directed=True):
raise NotImplementedError(
'Encountered disconnected count matrix with sampling option reversible:\n '
f'{C}\nUse prior to ensure connectivity or use reversible=False.'
)
# ensure consistent sparsity pattern (P0 might have additional zeros because of underflows)
# TODO: these steps work around a bug in msmtools. Should be fixed there
P0 = transition_matrix(C,
reversible=reversible,
maxiter=10000,
warn_not_converged=False)
zeros = np.where(P0 + P0.T == 0)
C[zeros] = 0
# run sampler
Tij = sample_tmatrix(C,
nsample=1,
nsteps=n_sampling_steps,
reversible=reversible)
# INITIAL DISTRIBUTION
if stationary: # p0 is consistent with P
p0 = stationary_distribution(Tij, C=C)
else:
n0 = BayesianHMM._count_init(model.hidden_trajs,
model.transition_matrix.shape[0])
first_timestep_counts_with_prior = n0 + initial_distribution_prior
positive = first_timestep_counts_with_prior > 0
p0 = np.zeros(n0.shape, dtype=model.transition_matrix.dtype)
p0[positive] = np.random.dirichlet(
first_timestep_counts_with_prior[positive]
) # sample p0 from posterior
# update HMM with new sample
model.transition_matrix[...] = Tij
model.stationary_distribution[...] = p0