GaussianIVPFilter¶
-
class
probnum.diffeq.
GaussianIVPFilter
(ivp, gaussfilt, with_smoothing)[source]¶ Bases:
probnum.diffeq.ODESolver
ODE solver that behaves like a Gaussian filter.
This is based on continuous-discrete Gaussian filtering.
Note: this is specific for IVPs and does not apply without further considerations to, e.g., BVPs.
- Parameters
gaussfilt (gaussianfilter.GaussianFilter) – e.g. the return value of ivp_to_ukf(), ivp_to_ekf1().
Notes
gaussfilt.dynamicmodel contains the prior,
gaussfilt.measurementmodel contains the information about the ODE right hand side function,
gaussfilt.initialdistribution contains the information about the initial values.
Attributes Summary
Methods Summary
Returns t0 and y0 (for the solver, which might be different to ivp.y0)
method_callback
(time, current_guess, …)Optional callback.
postprocess
(times, rvs)Rescale covariances with sigma square estimate, (if specified) smooth the estimate, return ODESolution.
solve
(steprule)Solve an IVP.
step
(t, t_new, current_rv)Gaussian IVP filter step as nonlinear Kalman filtering with zero data.
Attributes Documentation
-
prior
¶
Methods Documentation
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method_callback
(time, current_guess, current_error)¶ Optional callback.
Can be overwritten. Do this as soon as it is clear that the current guess is accepted, but before storing it. No return. For example: tune hyperparameters (sigma).
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postprocess
(times, rvs)[source]¶ Rescale covariances with sigma square estimate, (if specified) smooth the estimate, return ODESolution.