# Source code for stonesoup.types.prediction

```
import copy
import datetime
from typing import Sequence
from .array import CovarianceMatrix
from .base import Type
from .state import (State, GaussianState, EnsembleState,
ParticleState, MultiModelParticleState, RaoBlackwellisedParticleState,
SqrtGaussianState, InformationState, TaggedWeightedGaussianState,
WeightedGaussianState, CategoricalState, ASDGaussianState,
BernoulliParticleState)
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')
prior: State = Property(default=None)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.prior and hasattr(self.prior, 'hypothesis'):
self.prior = copy.copy(self.prior)
# Stop repeated linking back which will eat memory
if self.prior.hypothesis and hasattr(self.prior.hypothesis, 'prediction'):
self.prior.hypothesis.prediction = copy.copy(self.prior.hypothesis.prediction)
if hasattr(self.prior.hypothesis.prediction, 'prior'):
self.prior.hypothesis.prediction.prior = None
[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 ASDGaussianStatePrediction(Prediction, ASDGaussianState):
""" ASDGaussianStatePrediction type
This is a simple ASDGaussian state prediction object, which, as the name
suggests, is described by a Gaussian distribution.
"""
act_timestamp: datetime.datetime = Property(
doc="The timestamp for which the state is predicted")
[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 ASDGaussianMeasurementPrediction(MeasurementPrediction, ASDGaussianState):
"""ASD Gaussian Measurement Prediction"""
cross_covar: CovarianceMatrix = Property(
doc="The state-measurement cross covariance matrix", default=None)
[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 MultiModelParticleStatePrediction(Prediction, MultiModelParticleState):
"""MultiModelParticleStatePrediction type
This is a simple multi-model Particle state prediction object.
"""
[docs]
class RaoBlackwellisedParticleStatePrediction(Prediction, RaoBlackwellisedParticleState):
"""RaoBlackwellisedParticleStatePrediction type
This is a simple Rao Blackwellised Particle state prediction object.
"""
[docs]
class BernoulliParticleStatePrediction(Prediction, BernoulliParticleState):
"""BernoulliParticleStatePrediction type
This is a simple Bernoulli Particle state 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
```