def _updateTransitionMatrix(self):
"""
Updates the hidden-state transition matrix and the initial distribution
"""
# TRANSITION MATRIX
C = self.model.count_matrix() + self.prior_C # posterior count matrix
# check if we work with these options
if self.reversible and not _tmatrix_disconnected.is_connected(C, strong=True):
raise NotImplementedError(
"Encountered disconnected count matrix with sampling option reversible:\n "
+ str(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 = msmest.transition_matrix(C, reversible=self.reversible, maxiter=10000, warn_not_converged=False)
zeros = np.where(P0 + P0.T == 0)
C[zeros] = 0
# run sampler
Tij = msmest.sample_tmatrix(
C, nsample=1, nsteps=self.transition_matrix_sampling_steps, reversible=self.reversible
)
# INITIAL DISTRIBUTION
if self.stationary: # p0 is consistent with P
p0 = _tmatrix_disconnected.stationary_distribution(Tij, C=C)
else:
n0 = self.model.count_init().astype(float)
p0 = np.random.dirichlet(n0 + self.prior_n0) # sample p0 from posterior
# update HMM with new sample
self.model.update(p0, Tij)
def _update_transition_matrix(self, model):
""" Updates the hidden-state transition matrix and the initial distribution """
C = model.count_matrix() + self.prior_C # posterior count matrix
# check if we work with these options
if self.reversible and not msmest.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 = msmest.transition_matrix(C, reversible=self.reversible, maxiter=10000, warn_not_converged=False)
zeros = np.where(P0 + P0.T == 0)
C[zeros] = 0
# run sampler
Tij = msmest.sample_tmatrix(C, nsample=1, nsteps=self.transition_matrix_sampling_steps,
reversible=self.reversible)
# INITIAL DISTRIBUTION
if self.stationary: # p0 is consistent with P
p0 = _tmatrix_disconnected.stationary_distribution(Tij, C=C)
else:
n0 = model.count_init().astype(float)
first_timestep_counts_with_prior = n0 + self.prior_n0
positive = first_timestep_counts_with_prior > 0
p0 = np.zeros_like(n0)
p0[positive] = np.random.dirichlet(first_timestep_counts_with_prior[positive]) # sample p0 from posterior
# update HMM with new sample
model.update(p0, Tij)
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 _updateTransitionMatrix(self):
"""
Updates the hidden-state transition matrix
"""
C = self.model.count_matrix()
# apply prior
C += self.prior
# sample T-matrix
Tij = msmest.sample_tmatrix(
C, nsample=1, nsteps=self.transition_matrix_sampling_steps, reversible=self.reversible
)
self.model.update(Tij)
def _updateTransitionMatrix(self):
"""
Updates the hidden-state transition matrix
"""
C = self.model.count_matrix()
# apply prior
C += self.prior
# sample T-matrix
Tij = msmest.sample_tmatrix(
C,
nsample=1,
nsteps=self.transition_matrix_sampling_steps,
reversible=self.reversible)
self.model.update(Tij)