VaryingParameter#
- pydantic model optimas.core.VaryingParameter(name, lower_bound, upper_bound, is_fidelity=False, fidelity_target_value=None, default_value=None, dtype=<class 'float'>)#
Defines an input parameter to be varied during optimization.
- Parameters:
- namestr
The name of the parameter.
- lower_bound, upper_boundfloat
Lower and upper bounds of the range in which the parameter can vary.
- is_fidelitybool, optional
Indicates whether the parameter is a fidelity. Only needed for multifidelity optimization.
- fidelity_target_valuefloat, optional
The target value of the fidelity. Only needed for multifidelity optimization.
- default_valuefloat, optional
Default value of the parameter when it is not being varied. Only needed for some generators.
- dtypedata-type
The data type of the parameter. Any object that can be converted to a numpy dtype.
- fix_value(value)#
Fix the value of the parameter.
The value must be within the range of the parameter.
- Parameters:
- valuefloat
The value to which the parameter will be fixed.
- free_value()#
Free the value of the parameter.
- model_post_init(context, /)#
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that’s what pydantic-core passes when calling it.
- Args:
self: The BaseModel instance. context: The context.
- update_range(lower_bound, upper_bound)#
Update range of the parameter.
- Parameters:
- lower_bound, upper_boundfloat
Lower and upper bounds of the range in which the parameter can vary.
- property is_fixed#
Get whether the parameter is fixed to a certain value.