fit
- fit(model, data, frequency=None, solver=ScipyMinimizer(options={}), *, features=None, **kwargs)[source]
Fit a model to data using either optimization or sampling.
This is a unified router. The execution path is determined by the type of solver provided.
- Parameters:
model (Model) – The parametric model to fit.
data (jnp.ndarray | skrf.Network | NetworkCollection) – The observed data (e.g., S-parameters).
frequency (Frequency | None, default=None) – The frequency sweep. Required if data is a raw array.
solver (Optimizer | Sampler, default=ScipyMinimizer()) – The solver to use. If an optimizer, routes to frequentist minimization via
pmrf.optimize.fit(). If a sampler, routes to Bayesian inference viapmrf.infer.condition().features (EvaluatorLike | None, default=None) – The specific circuit feature to evaluate. If None, it defers to the native default of the chosen solver backend (‘s’ for optimization, (‘s_re’, ‘s_im’) for inference).
**kwargs (dict) – Additional arguments passed directly to the underlying fit function.
- Returns:
A result object containing the newly fitted model. Depending on the solver, the model contains either optimized point-estimates or empirical posteriors.
- Return type: