Toy problems¶
The toy module provides toy models
,
distributions
and
error measures
that can be used for tests and in
examples.
Some toy classes provide extra functionality defined in the
pints.toy.ToyModel
and pints.toy.ToyLogPDF
classes.
- Toy base classes
- Annulus Distribution
- Beeler-Reuter Action Potential Model
ActionPotentialModel
ActionPotentialModel.initial_conditions()
ActionPotentialModel.n_outputs()
ActionPotentialModel.n_parameters()
ActionPotentialModel.set_initial_conditions()
ActionPotentialModel.set_solver_tolerances()
ActionPotentialModel.simulate()
ActionPotentialModel.simulate_all_states()
ActionPotentialModel.suggested_parameters()
ActionPotentialModel.suggested_times()
- Cone Distribution
- Constant Model
- Eight Schools distribution
- Fitzhugh-Nagumo Model
FitzhughNagumoModel
FitzhughNagumoModel.initial_conditions()
FitzhughNagumoModel.jacobian()
FitzhughNagumoModel.n_outputs()
FitzhughNagumoModel.n_parameters()
FitzhughNagumoModel.n_states()
FitzhughNagumoModel.set_initial_conditions()
FitzhughNagumoModel.simulate()
FitzhughNagumoModel.simulateS1()
FitzhughNagumoModel.suggested_parameters()
FitzhughNagumoModel.suggested_times()
- Gaussian distribution
- German Credit Hierarchical Logistic Distribution
- German Credit Logistic Distribution
- Goodwin oscillator model
GoodwinOscillatorModel
GoodwinOscillatorModel.initial_conditions()
GoodwinOscillatorModel.jacobian()
GoodwinOscillatorModel.n_outputs()
GoodwinOscillatorModel.n_parameters()
GoodwinOscillatorModel.n_states()
GoodwinOscillatorModel.set_initial_conditions()
GoodwinOscillatorModel.simulate()
GoodwinOscillatorModel.simulateS1()
GoodwinOscillatorModel.suggested_parameters()
GoodwinOscillatorModel.suggested_times()
- HES1 Michaelis-Menten Model
Hes1Model
Hes1Model.fixed_parameters()
Hes1Model.initial_conditions()
Hes1Model.jacobian()
Hes1Model.m0()
Hes1Model.n_outputs()
Hes1Model.n_parameters()
Hes1Model.n_states()
Hes1Model.set_fixed_parameters()
Hes1Model.set_initial_conditions()
Hes1Model.set_m0()
Hes1Model.simulate()
Hes1Model.simulateS1()
Hes1Model.simulate_all_states()
Hes1Model.suggested_parameters()
Hes1Model.suggested_times()
Hes1Model.suggested_values()
- High dimensional Gaussian distribution
- Hodgkin-Huxley IK Experiment Model
- Logistic model
- Lotka-Volterra model
LotkaVolterraModel
LotkaVolterraModel.initial_conditions()
LotkaVolterraModel.jacobian()
LotkaVolterraModel.n_outputs()
LotkaVolterraModel.n_parameters()
LotkaVolterraModel.n_states()
LotkaVolterraModel.set_initial_conditions()
LotkaVolterraModel.simulate()
LotkaVolterraModel.simulateS1()
LotkaVolterraModel.suggested_parameters()
LotkaVolterraModel.suggested_times()
LotkaVolterraModel.suggested_values()
- Multimodal Gaussian distribution
- Neal’s Funnel Distribution
- Parabolic error
- Repressilator model
- Rosenbrock function
- Simple Egg Box Distribution
- Simple Harmonic Oscillator model
- SIR Epidemiology model
- Twisted Gaussian distribution