bayesvalidrox.surrogate_models.input_space.InputSpace¶

class bayesvalidrox.surrogate_models.input_space.InputSpace(input_object, meta_Model_type='pce')¶

Bases: object

This class generates the input space for the metamodel from the distributions provided using the Input object.

Attributes¶

Inputobj: Input object containing the parameter marginals, i.e. name, distribution type and distribution parameters or available raw data.
meta_Model_typestr: Type of the meta_Model_type.

Methods

`__init__`(input_object[, meta_Model_type])
`build_polytypes`(rosenblatt)	Creates the polynomial types to be passed to univ_basis_vals method of the MetaModel object.
`check_valid_inputs`()	Check if the given InputObj is valid to use for further calculations: 1) Has some Marginals 2) The Marginals have valid priors 3) All Marginals given as the same type (samples vs dist)
`init_param_space`([max_deg])	Initializes parameter space.
`transform`(X[, params, method])	Transforms the samples via either a Rosenblatt or an isoprobabilistic transformation.

build_polytypes(rosenblatt)¶

Creates the polynomial types to be passed to univ_basis_vals method of the MetaModel object.

check_valid_inputs() → None¶: Check if the given InputObj is valid to use for further calculations: 1) Has some Marginals 2) The Marginals have valid priors 3) All Marginals given as the same type (samples vs dist)

Returns¶

None

init_param_space(max_deg=1)¶

Initializes parameter space.

raw_dataarray of shape (n_params, n_samples): Raw data.
bound_tupleslist of tuples: A list containing lower and upper bounds of parameters.

transform(X, params=None, method=None)¶

Transforms the samples via either a Rosenblatt or an isoprobabilistic transformation.

Xarray of shape (n_samples,n_params): Samples to be transformed.
paramslist: Parameters for laguerre/gamma-type distribution.
methodstring: If transformation method is ‘user’ transform X, else just pass X.