bayesvalidrox.surrogate_models.inputs.Input¶

class bayesvalidrox.surrogate_models.inputs.Input¶

Bases: object

A class to define the uncertain input parameters.

Attributes¶

Marginalsobj: Marginal objects. See inputs.Marginal.
Rosenblattbool: If Rossenblatt transformation is required for the dependent input parameters.

Examples¶

Marginals can be defined as following:

>>> inputs = Inputs()
>>> inputs.add_marginals()
>>> inputs.marginals[0].name = 'X_1'
>>> inputs.marginals[0].dist_type = 'uniform'
>>> inputs.marginals[0].parameters = [-5, 5]

If there is no common data is avaliable, the input data can be given as following:

>>> inputs.add_marginals()
>>> inputs.marginals[0].name = 'X_1'
>>> inputs.marginals[0].input_data = [0,0,1,0]

__init__()¶

Methods

`__init__`()
`add_marginals`([name, dist_type, parameters, ...])	Adds a new Marginal object to the input object.

Attributes

poly_coeffs_flag

add_marginals(name='$x_1$', dist_type=None, parameters=None, input_data=None)¶

Adds a new Marginal object to the input object.

Parameters¶

namestring, optional: Name of the parameter. The default is ‘$x_1$’
dist_typestring, optional: Type of distribution of the marginal. This parameter has to be given in combination with parameters. The default is None.
parameterslist, optional: List of parameters of distribution of type dist_type. The default is None.

input_data : np.array, optional

Returns¶

None.