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
  • ToyLogPDF
    • ToyLogPDF.distance()
    • ToyLogPDF.evaluateS1()
    • ToyLogPDF.n_parameters()
    • ToyLogPDF.sample()
    • ToyLogPDF.suggested_bounds()
  • ToyModel
    • ToyModel.suggested_parameters()
    • ToyModel.suggested_times()
  • ToyODEModel
    • ToyODEModel._dfdp()
    • ToyODEModel._rhs()
    • ToyODEModel.initial_conditions()
    • ToyODEModel.jacobian()
    • ToyODEModel.n_states()
    • ToyODEModel.set_initial_conditions()
    • ToyODEModel.simulate()
    • ToyODEModel.simulateS1()
    • ToyODEModel.suggested_parameters()
    • ToyODEModel.suggested_times()
• Annulus Distribution
  • AnnulusLogPDF
    • AnnulusLogPDF.distance()
    • AnnulusLogPDF.evaluateS1()
    • AnnulusLogPDF.mean()
    • AnnulusLogPDF.mean_normed()
    • AnnulusLogPDF.moment_normed()
    • AnnulusLogPDF.n_parameters()
    • AnnulusLogPDF.r0()
    • AnnulusLogPDF.sample()
    • AnnulusLogPDF.sigma()
    • AnnulusLogPDF.suggested_bounds()
    • AnnulusLogPDF.var_normed()
• 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
  • ConeLogPDF
    • ConeLogPDF.CDF()
    • ConeLogPDF.beta()
    • ConeLogPDF.distance()
    • ConeLogPDF.evaluateS1()
    • ConeLogPDF.mean_normed()
    • ConeLogPDF.n_parameters()
    • ConeLogPDF.sample()
    • ConeLogPDF.suggested_bounds()
    • ConeLogPDF.var_normed()
• Constant Model
  • ConstantModel
    • ConstantModel.n_outputs()
    • ConstantModel.n_parameters()
    • ConstantModel.simulate()
    • ConstantModel.simulateS1()
• Eight Schools distribution
  • EightSchoolsLogPDF
    • EightSchoolsLogPDF.data()
    • EightSchoolsLogPDF.distance()
    • EightSchoolsLogPDF.evaluateS1()
    • EightSchoolsLogPDF.n_parameters()
    • EightSchoolsLogPDF.sample()
    • EightSchoolsLogPDF.suggested_bounds()
• 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
  • GaussianLogPDF
    • GaussianLogPDF.distance()
    • GaussianLogPDF.evaluateS1()
    • GaussianLogPDF.kl_divergence()
    • GaussianLogPDF.n_parameters()
    • GaussianLogPDF.sample()
    • GaussianLogPDF.suggested_bounds()
• German Credit Hierarchical Logistic Distribution
  • GermanCreditHierarchicalLogPDF
    • GermanCreditHierarchicalLogPDF.data()
    • GermanCreditHierarchicalLogPDF.distance()
    • GermanCreditHierarchicalLogPDF.evaluateS1()
    • GermanCreditHierarchicalLogPDF.n_parameters()
    • GermanCreditHierarchicalLogPDF.sample()
    • GermanCreditHierarchicalLogPDF.suggested_bounds()
• German Credit Logistic Distribution
  • GermanCreditLogPDF
    • GermanCreditLogPDF.data()
    • GermanCreditLogPDF.distance()
    • GermanCreditLogPDF.evaluateS1()
    • GermanCreditLogPDF.n_parameters()
    • GermanCreditLogPDF.sample()
    • GermanCreditLogPDF.suggested_bounds()
• 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
  • HighDimensionalGaussianLogPDF
    • HighDimensionalGaussianLogPDF.distance()
    • HighDimensionalGaussianLogPDF.evaluateS1()
    • HighDimensionalGaussianLogPDF.kl_divergence()
    • HighDimensionalGaussianLogPDF.n_parameters()
    • HighDimensionalGaussianLogPDF.rho()
    • HighDimensionalGaussianLogPDF.sample()
    • HighDimensionalGaussianLogPDF.suggested_bounds()
• Hodgkin-Huxley IK Experiment Model
  • HodgkinHuxleyIKModel
    • HodgkinHuxleyIKModel.fold()
    • HodgkinHuxleyIKModel.n_outputs()
    • HodgkinHuxleyIKModel.n_parameters()
    • HodgkinHuxleyIKModel.simulate()
    • HodgkinHuxleyIKModel.suggested_duration()
    • HodgkinHuxleyIKModel.suggested_parameters()
    • HodgkinHuxleyIKModel.suggested_times()
• Logistic model
  • LogisticModel
    • LogisticModel.n_outputs()
    • LogisticModel.n_parameters()
    • LogisticModel.simulate()
    • LogisticModel.simulateS1()
    • LogisticModel.suggested_parameters()
    • LogisticModel.suggested_times()
• 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
  • MultimodalGaussianLogPDF
    • MultimodalGaussianLogPDF.distance()
    • MultimodalGaussianLogPDF.evaluateS1()
    • MultimodalGaussianLogPDF.kl_divergence()
    • MultimodalGaussianLogPDF.n_parameters()
    • MultimodalGaussianLogPDF.sample()
    • MultimodalGaussianLogPDF.suggested_bounds()
• Neal’s Funnel Distribution
  • NealsFunnelLogPDF
    • NealsFunnelLogPDF.distance()
    • NealsFunnelLogPDF.evaluateS1()
    • NealsFunnelLogPDF.kl_divergence()
    • NealsFunnelLogPDF.marginal_log_pdf()
    • NealsFunnelLogPDF.mean()
    • NealsFunnelLogPDF.n_parameters()
    • NealsFunnelLogPDF.sample()
    • NealsFunnelLogPDF.suggested_bounds()
    • NealsFunnelLogPDF.var()
• Parabolic error
  • ParabolicError
    • ParabolicError.evaluateS1()
    • ParabolicError.n_parameters()
    • ParabolicError.optimum()
• Repressilator model
  • RepressilatorModel
    • RepressilatorModel.n_outputs()
    • RepressilatorModel.n_parameters()
    • RepressilatorModel.simulate()
    • RepressilatorModel.suggested_parameters()
    • RepressilatorModel.suggested_times()
• Rosenbrock function
  • RosenbrockError
    • RosenbrockError.evaluateS1()
    • RosenbrockError.n_parameters()
    • RosenbrockError.optimum()
  • RosenbrockLogPDF
    • RosenbrockLogPDF.distance()
    • RosenbrockLogPDF.evaluateS1()
    • RosenbrockLogPDF.n_parameters()
    • RosenbrockLogPDF.optimum()
    • RosenbrockLogPDF.sample()
    • RosenbrockLogPDF.suggested_bounds()
• Simple Egg Box Distribution
  • SimpleEggBoxLogPDF
    • SimpleEggBoxLogPDF.distance()
    • SimpleEggBoxLogPDF.evaluateS1()
    • SimpleEggBoxLogPDF.kl_divergence()
    • SimpleEggBoxLogPDF.n_parameters()
    • SimpleEggBoxLogPDF.sample()
    • SimpleEggBoxLogPDF.suggested_bounds()
• Simple Harmonic Oscillator model
  • SimpleHarmonicOscillatorModel
    • SimpleHarmonicOscillatorModel.n_outputs()
    • SimpleHarmonicOscillatorModel.n_parameters()
    • SimpleHarmonicOscillatorModel.simulate()
    • SimpleHarmonicOscillatorModel.simulateS1()
    • SimpleHarmonicOscillatorModel.suggested_parameters()
    • SimpleHarmonicOscillatorModel.suggested_times()
• SIR Epidemiology model
  • SIRModel
    • SIRModel.n_outputs()
    • SIRModel.n_parameters()
    • SIRModel.simulate()
    • SIRModel.suggested_parameters()
    • SIRModel.suggested_times()
    • SIRModel.suggested_values()
• Twisted Gaussian distribution
  • TwistedGaussianLogPDF
    • TwistedGaussianLogPDF.distance()
    • TwistedGaussianLogPDF.evaluateS1()
    • TwistedGaussianLogPDF.kl_divergence()
    • TwistedGaussianLogPDF.n_parameters()
    • TwistedGaussianLogPDF.sample()
    • TwistedGaussianLogPDF.suggested_bounds()
    • TwistedGaussianLogPDF.untwist()