class OutputModel¶

class deeptime.markov.hmm.OutputModel(n_hidden_states: int, n_observable_states: int, ignore_outliers: bool = True)¶

Output model superclass. Contains basic functionality and interfaces which output models are supposed to implement.

Parameters:

n_hidden_states (int) – Number of hidden states.
n_observable_states (int) – Number of observable states if discrete state space, otherwise -1
ignore_outliers (bool, optional, default=True) – By outliers observations that have zero probability given the current model are meant. ignore_outliers=True means that outliers will be treated as if no observation was made, which is equivalent to making this observation with equal probability from any hidden state. ignore_outliers=False means that an Exception or in the worst case an unhandled crash will occur if an outlier is observed.

Attributes

`ignore_outliers`	By outliers observations that have zero probability given the current model are meant.
`n_hidden_states`	Number of hidden states.
`n_observable_states`	Number of observable states, can be -1 if not applicable (e.g., in a continous observable space).

Methods

`copy`()	Makes a deep copy of this model.
`fit`(observations, weights)	Fits the output model given the observations and weights.
`generate_observation_trajectory`(...)	Generates a synthetic trajectory in observation space given a trajectory in hidden state space.
`get_params`([deep])	Get the parameters.
`sample`(observations_per_state)	Samples a new set of parameters for this output model given a list of observations for each hidden state.
`set_params`(**params)	Set the parameters of this estimator.
`submodel`([states, obs])	Restricts this model to a set of hidden states and observable states (if applicable).
`to_state_probability_trajectory`(observations)	Converts a list of observations to hidden state probabilities, i.e., for each observation \(o_t\), one obtains a vector \(p_t\in\mathbb{R}^{n_\mathrm{hidden}}\) denoting how probable that particular observation is in one of the hidden states.

copy() → Model¶

Makes a deep copy of this model.

Returns:: A new copy of this model.
Return type:: copy

abstract fit(observations: List[ndarray], weights: List[ndarray])¶

Fits the output model given the observations and weights.

Parameters:

observations (list of ndarray) – A list of K observation trajectories
weights (list of ndarray) – A list of K weight matrices, each having length T_k of their corresponding observation trajectory. Evaluating weights[k][t,n] should yield the weight assignment from observations[k][t] to state index n.

Returns:

self – Reference to self.

Return type:

OutputModel

abstract generate_observation_trajectory(hidden_state_trajectory: ndarray) → ndarray¶

Generates a synthetic trajectory in observation space given a trajectory in hidden state space.

Parameters:: hidden_state_trajectory ((T, 1) ndarray) – Hidden state trajectory.
Returns:: observations – Observation timeseries \(\{o_t\}_t\), where \(o_t\) is the observation associated to hidden state \(s_t\).
Return type:: (T, d) ndarray

get_params(deep=False)¶

Get the parameters.

Returns:: params – Parameter names mapped to their values.
Return type:: mapping of string to any

abstract sample(observations_per_state: List[ndarray]) → None¶

Samples a new set of parameters for this output model given a list of observations for each hidden state.

Parameters:: observations_per_state (list of ndarray) – The observations per state, i.e., len(observations_per_state) must evaluate to the value of n_hidden_states.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: object

abstract submodel(states: Optional[ndarray] = None, obs: Optional[ndarray] = None)¶

Restricts this model to a set of hidden states and observable states (if applicable).

Parameters:

states (ndarray, optional, default=None) – The hidden states to restrict to, per default no restriction.
obs (ndarray, optional, default=None) – The observable states to restrict to (if applicable), per default no restriction.

Returns:

submodel – The restricted output model.

Return type:

OutputModel

abstract to_state_probability_trajectory(observations: ndarray) → ndarray¶

Converts a list of observations to hidden state probabilities, i.e., for each observation \(o_t\), one obtains a vector \(p_t\in\mathbb{R}^{n_\mathrm{hidden}}\) denoting how probable that particular observation is in one of the hidden states.

Parameters:: observations ((T, d) ndarray) – Array of observations.
Returns:: state_probabilities – State probability trajectory.
Return type:: (T, n_hidden) ndarray

property ignore_outliers: bool¶: By outliers observations that have zero probability given the current model are meant. ignore_outliers=True means that outliers will be treated as if no observation was made, which is equivalent to making this observation with equal probability from any hidden state. ignore_outliers=False means that an Exception or in the worst case an unhandled crash will occur if an outlier is observed.

property n_hidden_states: int¶: Number of hidden states.

property n_observable_states: int¶: Number of observable states, can be -1 if not applicable (e.g., in a continous observable space).