def test_invalid_ctor_args(self):
with self.assertRaises(ValueError):
# not row stochastic
DiscreteOutputModel(
np.array([[0.5, 0.3, 0.2, 0.1], [0.5, 0.3, 0.2, 0.0]]))
# no-op: this does not raise
DiscreteOutputModel(np.array([[0., 1., 0., 0.], [1., 0., 0., 0.]]),
prior=np.random.normal(size=(2, 4)).astype(
np.float64))
with self.assertRaises(ValueError):
# prior has wrong shape, raise
DiscreteOutputModel(np.array([[0., 1., 0., 0.], [1., 0., 0., 0.]]),
prior=np.random.normal(size=(2, 5)).astype(
np.float64))
def __init__(
self,
transition_model,
output_model: Union[np.ndarray, OutputModel],
initial_distribution: Optional[np.ndarray] = None,
likelihoods: Optional[np.ndarray] = None,
state_probabilities: Optional[List[np.ndarray]] = None,
initial_count: Optional[np.ndarray] = None,
hidden_state_trajectories: Optional[Iterable[np.ndarray]] = None,
stride: Union[int, str] = 1,
observation_symbols: Optional[np.ndarray] = None,
observation_symbols_full: Optional[np.ndarray] = None):
super().__init__()
if isinstance(transition_model, np.ndarray):
from deeptime.markov.msm import MarkovStateModel
transition_model = MarkovStateModel(transition_model)
if isinstance(output_model, np.ndarray):
output_model = DiscreteOutputModel(output_model)
if transition_model.n_states != output_model.n_hidden_states:
raise ValueError("Transition model must describe hidden states")
if initial_distribution is None:
# uniform
initial_distribution = np.ones(
transition_model.n_states) / transition_model.n_states
if initial_distribution.shape[0] != transition_model.n_states:
raise ValueError(
"Initial distribution over hidden states must be of length {}".
format(transition_model.n_states))
self._transition_model = transition_model
self._output_model = output_model
self._initial_distribution = initial_distribution
self._likelihoods = likelihoods
self._state_probabilities = state_probabilities
self._initial_count = initial_count
self._hidden_state_trajectories = hidden_state_trajectories
if observation_symbols is None and output_model.n_observable_states >= 0:
observation_symbols = np.arange(output_model.n_observable_states)
observation_symbols_full = observation_symbols
self._observation_symbols = observation_symbols
self._observation_symbols_full = observation_symbols_full
if not (isinstance(stride, Integral) or
(isinstance(stride, str) and stride == 'effective')):
raise ValueError(
"Stride argument must either be an integer value or 'effective', "
"but was: {}".format(stride))
self._stride = stride