smash.BayesResult#

class smash.BayesResult[source]#

Represents the Bayesian estimation or optimization result.

See also

Model.bayes_estimate

Estimate prior Model parameters/states using Bayesian approach.

Model.bayes_optimize

Optimize the Model using Bayesian approach.

Notes

This class is essentially a subclass of dict with attribute accessors.

Attributes:
datadict

Rrepresenting the generated spatially uniform Model parameters/sates and the corresponding cost values after running the simulations on this dataset. The keys are ‘cost’ and the names of Model parameters/states considered.

lcurvedict

The optimization results on the regularization parameter if the L-curve approach is used. The keys are

  • ‘alpha’ : a list of regularization parameters to be optimized.

  • ‘cost’ : a list of corresponding cost values.

  • ‘mahal_dist’ : a list of corresponding Mahalanobis distance values.

  • ‘var’ : a list of corresponding dictionaries. The keys are the names of the Model parameters/states, and each represents its variance.

  • ‘alpha_opt’ : the optimal value of the regularization parameter.

Methods

clear()

copy()

fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

pop(key[, default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()