Module bayesvalidrox.surrogate_models.inputs
Expand source code
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
class Input:
"""
A class to define the uncertain input parameters.
Attributes
----------
Marginals : obj
Marginal objects. See `inputs.Marginal`.
Rosenblatt : bool
If Rossenblatt transformation is required for the dependent input
parameters.
Examples
-------
Marginals can be defined as following:
>>> 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 = input_data
"""
poly_coeffs_flag = True
def __init__(self):
self.Marginals = []
self.Rosenblatt = False
def add_marginals(self):
"""
Adds a new Marginal object to the input object.
Returns
-------
None.
"""
self.Marginals.append(Marginal())
# Nested class
class Marginal:
"""
An object containing the specifications of the marginals for each uncertain
parameter.
Attributes
----------
name : string
Name of the parameter. The default is `'$x_1$'`.
dist_type : string
Name of the distribution. The default is `None`.
parameters : list
List of the parameters corresponding to the distribution type. The
default is `None`.
input_data : array
Available input data. The default is `[]`.
moments : list
List of the moments.
"""
def __init__(self):
self.name = '$x_1$'
self.dist_type = None
self.parameters = None
self.input_data = []
self.moments = None
Classes
class Input
-
A class to define the uncertain input parameters.
Attributes
Marginals
:obj
- Marginal objects. See
inputs.Marginal
. Rosenblatt
:bool
- If Rossenblatt transformation is required for the dependent input parameters.
Examples
Marginals can be defined as following:
>>> 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 = input_data
Expand source code
class Input: """ A class to define the uncertain input parameters. Attributes ---------- Marginals : obj Marginal objects. See `inputs.Marginal`. Rosenblatt : bool If Rossenblatt transformation is required for the dependent input parameters. Examples ------- Marginals can be defined as following: >>> 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 = input_data """ poly_coeffs_flag = True def __init__(self): self.Marginals = [] self.Rosenblatt = False def add_marginals(self): """ Adds a new Marginal object to the input object. Returns ------- None. """ self.Marginals.append(Marginal())
Class variables
var poly_coeffs_flag
Methods
def add_marginals(self)
-
Adds a new Marginal object to the input object.
Returns
None.
Expand source code
def add_marginals(self): """ Adds a new Marginal object to the input object. Returns ------- None. """ self.Marginals.append(Marginal())
class Marginal
-
An object containing the specifications of the marginals for each uncertain parameter.
Attributes
name
:string
- Name of the parameter. The default is
'$x_1$'
. dist_type
:string
- Name of the distribution. The default is
None
. parameters
:list
- List of the parameters corresponding to the distribution type. The
default is
None
. input_data
:array
- Available input data. The default is
[]
. moments
:list
- List of the moments.
Expand source code
class Marginal: """ An object containing the specifications of the marginals for each uncertain parameter. Attributes ---------- name : string Name of the parameter. The default is `'$x_1$'`. dist_type : string Name of the distribution. The default is `None`. parameters : list List of the parameters corresponding to the distribution type. The default is `None`. input_data : array Available input data. The default is `[]`. moments : list List of the moments. """ def __init__(self): self.name = '$x_1$' self.dist_type = None self.parameters = None self.input_data = [] self.moments = None