Models
- class stonesoup.models.base.Model[source]
Bases:
stonesoup.base.Base
Model type
Base/Abstract class for all models.
- abstract function(state: State, noise: Union[bool, numpy.ndarray] = False, **kwargs) Union[stonesoup.types.array.StateVector, stonesoup.types.array.StateVectors] [source]
Model function \(f_k(x(k),w(k))\)
- Parameters
state (State) – An input state
noise (
numpy.ndarray
or bool) – An externally generated random process noise sample (the default is False, in which case no noise will be added if ‘True’, the output ofrvs()
is used)
- Returns
The StateVector(s) with the model function evaluated.
- Return type
StateVector
orStateVectors
- abstract rvs(num_samples: int = 1, **kwargs) Union[stonesoup.types.array.StateVector, stonesoup.types.array.StateVectors] [source]
Model noise/sample generation function
Generates noise samples from the model.
- Parameters
num_samples (scalar, optional) – The number of samples to be generated (the default is 1)
- Returns
noise – A set of Np samples, generated from the model’s noise distribution.
- Return type
2-D array of shape (
ndim
,num_samples
)
- abstract pdf(state1: State, state2: State, **kwargs) Union[stonesoup.types.numeric.Probability, numpy.ndarray] [source]
Model pdf/likelihood evaluation function
Evaluates the pdf/likelihood of
state1
, given the statestate2
which is passed tofunction()
.- Parameters
- Returns
The likelihood of
state1
, givenstate2
- Return type
Probability
orndarray
ofProbability
- class stonesoup.models.base.LinearModel[source]
Bases:
stonesoup.models.base.Model
LinearModel class
Base/Abstract class for all linear models
- abstract matrix(**kwargs) numpy.ndarray [source]
Model matrix
- function(state: State, noise: Union[bool, numpy.ndarray] = False, **kwargs) Union[stonesoup.types.array.StateVector, stonesoup.types.array.StateVectors] [source]
Model linear function \(f_k(x(k),w(k)) = F_k(x_k) + w_k\)
- Parameters
state (State) – An input state
noise (
numpy.ndarray
or bool) – An externally generated random process noise sample (the default is False, in which case no noise will be added if ‘True’, the output ofrvs()
is added)
- Returns
The StateVector(s) with the model function evaluated.
- Return type
StateVector
orStateVectors
- jacobian(state: State, **kwargs) numpy.ndarray [source]
Model jacobian matrix \(H_{jac}\)
- Parameters
state (
State
) – An input state- Returns
The model jacobian matrix evaluated around the given state vector.
- Return type
numpy.ndarray
of shape (ndim_meas
,ndim_state
)
- class stonesoup.models.base.NonLinearModel[source]
Bases:
stonesoup.models.base.Model
NonLinearModel class
Base/Abstract class for all non-linear models
- jacobian(state: State, **kwargs) numpy.ndarray [source]
Model Jacobian matrix \(H_{jac}\)
- Parameters
state (
State
) – An input state- Returns
:class:`numpy.ndarray` of shape (attr – The model Jacobian matrix evaluated around the given state vector.
- Return type
~ndim_meas,
ndim_state
)
- class stonesoup.models.base.ReversibleModel[source]
Bases:
stonesoup.models.base.NonLinearModel
Non-linear model containing sufficient co-ordinate information such that the linear co-ordinate conversions can be calculated from the non-linear counterparts.
Contains an inverse function which computes the reverse of the relevant linear-to-non-linear function
- abstract inverse_function(detection: Detection, **kwargs) stonesoup.types.array.StateVector [source]
Takes in the result of the function and computes the inverse function, returning the initial input of the function.
- Parameters
detection (
Detection
) – Input state (non-linear format)- Returns
The linear co-ordinates
- Return type
- class stonesoup.models.base.TimeVariantModel[source]
Bases:
stonesoup.models.base.Model
TimeVariantModel class
Base/Abstract class for all time-variant models
- class stonesoup.models.base.TimeInvariantModel[source]
Bases:
stonesoup.models.base.Model
TimeInvariantModel class
Base/Abstract class for all time-invariant models
- class stonesoup.models.base.GaussianModel(seed: Optional[int] = None)[source]
Bases:
stonesoup.models.base.Model
GaussianModel class
Base/Abstract class for all Gaussian models
- Parameters
seed (
Union[int, NoneType]
, optional) – Seed for random number generation
- rvs(num_samples: int = 1, random_state=None, **kwargs) Union[stonesoup.types.array.StateVector, stonesoup.types.array.StateVectors] [source]
Model noise/sample generation function
Generates noise samples from the model.
In mathematical terms, this can be written as:
\[v_t \sim \mathcal{N}(0,Q)\]where \(v_t =\)
noise
and \(Q\) =covar
.- Parameters
num_samples (scalar, optional) – The number of samples to be generated (the default is 1)
- Returns
noise – A set of Np samples, generated from the model’s noise distribution.
- Return type
2-D array of shape (
ndim
,num_samples
)
- pdf(state1: State, state2: State, **kwargs) Union[stonesoup.types.numeric.Probability, numpy.ndarray] [source]
Model pdf/likelihood evaluation function
Evaluates the pdf/likelihood of
state1
, given the statestate2
which is passed tofunction()
.In mathematical terms, this can be written as:
\[p = p(y_t | x_t) = \mathcal{N}(y_t; x_t, Q)\]where \(y_t\) =
state_vector1
, \(x_t\) =state_vector2
and \(Q\) =covar
.- Parameters
- Returns
The likelihood of
state1
, givenstate2
- Return type
Probability
orndarray
ofProbability
- abstract covar(**kwargs) stonesoup.types.array.CovarianceMatrix [source]
Model covariance