Toy base classes¶
- class pints.toy.ToyLogPDF[source]¶
Abstract base class for toy distributions.
Extends
pints.LogPDF
.- distance(samples)[source]¶
Calculates a measure of distance from
samples
to some characteristic of the underlying distribution.
- evaluateS1(x)¶
Evaluates this LogPDF, and returns the result plus the partial derivatives of the result with respect to the parameters.
The returned data is a tuple
(L, L')
whereL
is a scalar value andL'
is a sequence of lengthn_parameters
.Note that the derivative returned is of the log-pdf, so
L' = d/dp log(f(p))
, evaluated atp=x
.This is an optional method that is not always implemented.
- n_parameters()¶
Returns the dimension of the space this
LogPDF
is defined over.
- class pints.toy.ToyModel[source]¶
Defines an interface for toy problems.
Note that toy models should extend both
ToyModel
and one of the forward model classes, e.g.pints.ForwardModel
.
- class pints.toy.ToyODEModel[source]¶
Defines an interface for toy problems where the underlying model is an ordinary differential equation (ODE) that describes some time-series generating model.
Note that toy ODE models should extend both
pints.ToyODEModel
and one of the forward model classes, e.g.pints.ForwardModel
orpints.ForwardModelS1
.To use this class as the basis for a
pints.ForwardModel
, the method_rhs()
should be reimplemented.Models implementing
_rhs()
,jacobian()
and_dfdp()
can be used to create apints.ForwardModelS1
.- _dfdp(y, t, p)[source]¶
Returns the derivative of the ODE RHS at time
t
, with respect to model parametersp
.- Parameters:
y – The state vector at time
t
(with lengthn_outputs
).t – The time to evaluate at (as a scalar).
p – A vector of model parameters (of length
n_parameters
).
- Return type:
A matrix of dimensions
n_outputs
byn_parameters
.
- _rhs(y, t, p)[source]¶
Returns the evaluated RHS (
dy/dt
) for a given state vectory
, timet
, and parameter vectorp
.- Parameters:
y – The state vector at time
t
(with lengthn_outputs
).t – The time to evaluate at (as a scalar).
p – A vector of model parameters (of length
n_parameters
).
- Return type:
A vector of length
n_outputs
.
- jacobian(y, t, p)[source]¶
Returns the Jacobian (the derivative of the RHS ODE with respect to the outputs) at time
t
.- Parameters:
y – The state vector at time
t
(with lengthn_outputs
).t – The time to evaluate at (as a scalar).
p – A vector of model parameters (of length
n_parameters
).
- Return type:
A matrix of dimensions
n_outputs
byn_outputs
.
- n_states()[source]¶
Returns number of states in underlying ODE. Note: will not be same as
n_outputs()
for models where only a subset of states are observed.
- suggested_parameters()¶
Returns an NumPy array of the parameter values that are representative of the model.
For example, these parameters might reproduce a particular result that the model is famous for.
- suggested_times()¶
Returns an NumPy array of time points that is representative of the model