Source code for stonesoup.predictor.categorical

from ..base import Property
from ..models.transition.categorical import MarkovianTransitionModel
from ..predictor import Predictor
from ..predictor._utils import predict_lru_cache
from ..types.prediction import Prediction

[docs] class HMMPredictor(Predictor): r"""Hidden Markov model predictor Assumes transition model is time-invariant, and therefore care should be taken when predicting forward to the same time.""" transition_model: MarkovianTransitionModel = Property( doc="The transition model used to predict states forward in `time`." )
[docs] @predict_lru_cache() def predict(self, prior, timestamp=None, **kwargs): r"""Predicts a :class:`~.CategoricalState` forward using the :attr:`transition_model`. Parameters ---------- prior : :class:`~.CategoricalState` :math:`\alpha_{t-1}` timestamp : :class:`datetime.datetime`, optional :math:`t` **kwargs : These are passed to the :meth:`transition_model.function` method. Returns ------- : :class:`~.CategoricalStatePrediction` The predicted state. Notes ----- The Markovian transition model is time-invariant and the evaluated `time_interval` can be `None`. """ predict_over_interval = self._predict_over_interval(prior, timestamp) prediction_vector = self.transition_model.function(prior, time_interval=predict_over_interval, **kwargs) return Prediction.from_state(prior, prediction_vector, timestamp=timestamp, transition_model=self.transition_model)
def _predict_over_interval(self, prior, timestamp): """Private method to get the prediction interval (or None) Parameters ---------- prior : :class:`~.State` The prior state timestamp : :class:`datetime.datetime`, optional The (current) timestamp Returns ------- : :class:`datetime.timedelta` time interval to predict over """ # Deal with undefined timestamps if timestamp is None or prior.timestamp is None: predict_over_interval = None else: predict_over_interval = timestamp - prior.timestamp return predict_over_interval