Models¶
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class
stonesoup.models.base.
Model
[source]¶ Bases:
stonesoup.base.Base
Model type
Base/Abstract class for all models.
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abstract property
ndim
¶ Number of dimensions of model
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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
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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
)
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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
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abstract property
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class
stonesoup.models.base.
LinearModel
[source]¶ Bases:
stonesoup.models.base.Model
LinearModel class
Base/Abstract class for all linear models
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abstract
matrix
(**kwargs) → numpy.ndarray[source]¶ Model matrix
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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
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abstract
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class
stonesoup.models.base.
NonLinearModel
[source]¶ Bases:
stonesoup.models.base.Model
NonLinearModel class
Base/Abstract class for all non-linear models
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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
)
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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
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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
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abstract
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class
stonesoup.models.base.
TimeVariantModel
[source]¶ Bases:
stonesoup.models.base.Model
TimeVariantModel class
Base/Abstract class for all time-variant models
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class
stonesoup.models.base.
TimeInvariantModel
[source]¶ Bases:
stonesoup.models.base.Model
TimeInvariantModel class
Base/Abstract class for all time-invariant models
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class
stonesoup.models.base.
GaussianModel
[source]¶ Bases:
stonesoup.models.base.Model
GaussianModel class
Base/Abstract class for all Gaussian models
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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.
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
)
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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
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abstract
covar
(**kwargs) → stonesoup.types.array.CovarianceMatrix[source]¶ Model covariance
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