class BayesianHMMPosterior¶
- class deeptime.markov.hmm.BayesianHMMPosterior(prior: Optional[HiddenMarkovModel] = None, samples: Optional[List[HiddenMarkovModel]] = ())¶
Bayesian Hidden Markov model with samples of posterior and prior.
- Parameters:
prior (HiddenMarkovModel, optional, default=None) – The prior.
samples (list of HiddenMarkovModel, optional, default=()) – Sampled models.
See also
Attributes
Hidden state trajectories of sampled HMMs.
Lagtime of the models.
The prior model.
The sampled models.
Methods
ck_test
(models[, include_lag0, err_est, ...])Performs a Chapman Kolmogorov test.
copy
()Makes a deep copy of this model.
evaluate_samples
(quantity[, delimiter])Obtains a quantity (like an attribute or result of a method or a property) from each of the samples.
gather_stats
(quantity[, store_samples, ...])Obtain statistics about a sampled quantity.
get_params
([deep])Get the parameters.
set_params
(**params)Set the parameters of this estimator.
submodel
([states, obs])Creates a submodel from this model restricted to a selection of observable and hidden states.
submodel_largest
([directed, ...])Creates a submodel from the largest connected set.
submodel_populous
([directed, ...])Creates a submodel from the most populated connected set.
timescales
([k])Relaxation timescales corresponding to the eigenvalues.
- ck_test(models, include_lag0=True, err_est=False, progress=None, **kw)¶
Performs a Chapman Kolmogorov test. See
MarkovStateModel.ck_test
for more details
- copy() Model ¶
Makes a deep copy of this model.
- Returns:
A new copy of this model.
- Return type:
copy
- evaluate_samples(quantity, delimiter='/', *args, **kwargs)¶
Obtains a quantity (like an attribute or result of a method or a property) from each of the samples. Returns as list.
- Parameters:
quantity (str) – The quantity. Can be also deeper in the instance hierarchy, indicated by the delimiter.
delimiter (str, default='/') – The delimiter.
*args – Arguments passed to the evaluation point of the quantity.
**kwargs – Keyword arguments passed to the evaluation point of the quantity.
- Returns:
result – A list of the quantity evaluated on each of the samples. If can be converted to float ndarray then ndarray.
- Return type:
list of any or ndarray
- gather_stats(quantity, store_samples=False, delimiter='/', confidence=0.95, *args, **kwargs)¶
Obtain statistics about a sampled quantity. Can also be a chained call, separated by the delimiter.
- Parameters:
quantity (str) – name of attribute, which will be evaluated on samples
store_samples (bool, optional, default=False) – whether to store the samples (array).
delimiter (str, optional, default='/') – separator to call members of members
confidence (float, optional, default=0.95) – Size of the confidence intervals.
*args – pass-through
**kwargs – pass-through
- Returns:
statistics – The statistics
- Return type:
- get_params(deep=False)¶
Get the parameters.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- 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
- submodel(states=None, obs=None)¶
Creates a submodel from this model restricted to a selection of observable and hidden states.
- Parameters:
states (ndarray or None, optional, default=None) – The hidden states to restrict to. If None there is no restriction.
obs (ndarray or None, optional, default=None) – The observable states to restrict to. If None there is no restriction. Only makes sense with
DiscreteOutputModel
.
- Returns:
submodel – The submodel.
- Return type:
- submodel_largest(directed=True, connectivity_threshold='1/n', observe_nonempty=True, dtrajs=None)¶
Creates a submodel from the largest connected set.
- Parameters:
directed (bool, optional, default=True) – Whether the connectivity graph on the count matrix is interpreted as directed.
connectivity_threshold (float or '1/n', optional, default='1/n'.) – Connectivity threshold. counts that are below the specified value are disregarded when finding connected sets. In case of ‘1/n’, the threshold gets resolved to \(1 / n\_states\_full\).
observe_nonempty (bool, optional, default=True) – Whether to restrict to observable states which are observed in provided dtrajs. If True, dtrajs must not be None.
dtrajs (array_like or list of array_like, optional, default=None) – Time series on which is evaluated whether observable states in the model were actually observed.
- Returns:
submodel – The submodel.
- Return type:
- submodel_populous(directed=True, connectivity_threshold='1/n', observe_nonempty=True, dtrajs=None)¶
Creates a submodel from the most populated connected set.
- Parameters:
directed (bool, optional, default=True) – Whether the connectivity graph on the count matrix is interpreted as directed.
connectivity_threshold (float or '1/n', optional, default='1/n'.) – Connectivity threshold. counts that are below the specified value are disregarded when finding connected sets. In case of ‘1/n’, the threshold gets resolved to \(1 / n\_states\_full\).
observe_nonempty (bool, optional, default=True) – Whether to restrict to observable states which are observed in provided dtrajs. If True, dtrajs must not be None.
dtrajs (array_like or list of array_like, optional, default=None) – Time series on which is evaluated whether observable states in the model were actually observed and which states were the most populated.
- Returns:
submodel – The submodel.
- Return type:
- timescales(k=None)¶
Relaxation timescales corresponding to the eigenvalues.
- Parameters:
k (int, optional, default=None) – The number of timescales (excluding the stationary process).
- Returns:
timescales – Timescales of the prior and timescales of the samples.
- Return type:
tuple(iterable, iterable)
Hidden state trajectories of sampled HMMs. Available if the estimator was configured to save them, see
BayesianHMM.store_hidden
.
- property lagtime¶
Lagtime of the models.
- property prior: HiddenMarkovModel¶
The prior model.
- property samples: Optional[List[HiddenMarkovModel]]¶
The sampled models.