Source code for stonesoup.types.prediction

# -*- coding: utf-8 -*-
from typing import Sequence

from .array import CovarianceMatrix
from .base import Type
from .state import (State, GaussianState, ParticleState, EnsembleState,
                    SqrtGaussianState, InformationState, TaggedWeightedGaussianState,
                    WeightedGaussianState, CategoricalState)
from ..base import Property
from ..models.transition.base import TransitionModel
from ..types.state import CreatableFromState, CompositeState


[docs]class Prediction(Type, CreatableFromState): """ Prediction type This is the base prediction class. """ transition_model: TransitionModel = Property( default=None, doc='The transition model used to make the prediction')
[docs]class MeasurementPrediction(Type, CreatableFromState): """ Prediction type This is the base measurement prediction class. """
[docs]class StatePrediction(Prediction, State): """ StatePrediction type Most simple state prediction type, which only has time and a state vector. """
[docs]class InformationStatePrediction(Prediction, InformationState): """ InformationStatePrediction type Information state prediction type: contains state vector, precision matrix and timestamp """
[docs]class StateMeasurementPrediction(MeasurementPrediction, State): """ MeasurementPrediction type Most simple measurement prediction type, which only has time and a state vector. """
[docs]class GaussianStatePrediction(Prediction, GaussianState): """ GaussianStatePrediction type This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution. """
[docs]class SqrtGaussianStatePrediction(Prediction, SqrtGaussianState): """ SqrtGaussianStatePrediction type This is a Gaussian state prediction object, with the covariance held as the square root of the covariance matrix """
[docs]class WeightedGaussianStatePrediction(Prediction, WeightedGaussianState): """ WeightedGaussianStatePrediction type This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution with an associated weight. """
[docs]class TaggedWeightedGaussianStatePrediction(Prediction, TaggedWeightedGaussianState): """ TaggedWeightedGaussianStatePrediction type This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution, with an associated weight and unique tag. """
[docs]class GaussianMeasurementPrediction(MeasurementPrediction, GaussianState): """ GaussianMeasurementPrediction type This is a simple Gaussian measurement prediction object, which, as the name suggests, is described by a Gaussian distribution. """ cross_covar: CovarianceMatrix = Property( default=None, doc="The state-measurement cross covariance matrix") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.cross_covar is not None \ and self.cross_covar.shape[1] != self.state_vector.shape[0]: raise ValueError("cross_covar should have the same number of " "columns as the number of rows in state_vector")
# Don't need to support Sqrt Covar for MeasurementPrediction CreatableFromState.class_mapping[MeasurementPrediction][SqrtGaussianState] = \ GaussianMeasurementPrediction
[docs]class ParticleStatePrediction(Prediction, ParticleState): """ParticleStatePrediction type This is a simple Particle state prediction object. """
[docs]class ParticleMeasurementPrediction(MeasurementPrediction, ParticleState): """MeasurementStatePrediction type This is a simple Particle measurement prediction object. """
[docs]class EnsembleStatePrediction(Prediction, EnsembleState): """EnsembleStatePrediction type This is a simple Ensemble measurement prediction object. """
[docs]class EnsembleMeasurementPrediction(MeasurementPrediction, EnsembleState): """EnsembleMeasurementPrediction type This is a simple Ensemble measurement prediction object. """
[docs]class CategoricalStatePrediction(Prediction, CategoricalState): """Categorical state prediction type"""
[docs]class CategoricalMeasurementPrediction(MeasurementPrediction, CategoricalState): """Categorical measurement prediction type"""
[docs]class CompositePrediction(Prediction, CompositeState): """Composite prediction type Composition of :class:`~.Prediction`. """ sub_states: Sequence[Prediction] = Property( doc="Sequence of sub-predictions comprising the composite prediction. All sub-predictions " "must have matching timestamp. Must not be empty.")
Prediction.register(CompositeState) # noqa: E305
[docs]class CompositeMeasurementPrediction(MeasurementPrediction, CompositeState): """Composite measurement prediction type Composition of :class:`~.MeasurementPrediction`. """ sub_states: Sequence[MeasurementPrediction] = Property( default=None, doc="Sequence of sub-measurement-predictions comprising the composite measurement " "prediction. All sub-measurement-predictions must have matching timestamp.")
MeasurementPrediction.register(CompositeState) # noqa: E305