jijzepttools.blackbox_optimization.problem#
Classes#
Functions#
|
Module Contents#
- CategoricalVar(name, categories)#
- class BlackboxProblem#
- name: str#
- description: str#
- sense: jijmodeling.ProblemSense#
- add_IntegerVar(name: str, lower_bound, upper_bound, shape: tuple | None = None, step: int = 1, description: str | None = None) jijmodeling.IntegerVar#
- add_CategoricalVar(name: str, categories: list[str | int], description: str | None = None) jijmodeling.BinaryVar#
- add_ContinuousVar(name: str, lower_bound: float, upper_bound: float, shape: tuple | None = None, description: str | None = None) jijmodeling.ContinuousVar#
- add_BinaryVar(name: str, shape: tuple | None = None, description: str | None = None) jijmodeling.BinaryVar#
- decision_variables_table() pandas.DataFrame#
- random_candidates(num_samples: int = 1, interpreter: jijmodeling.Interpreter | None = None) list[dict]#
- create_instance(instance_data: dict) ommx.v1.Instance#
- encode_to_array(dataset: pandas.DataFrame, deci_vars: list[ommx.v1.DecisionVariable], objectives: list[str]) tuple[numpy.ndarray, numpy.ndarray]#
- encode_dict_data(dict_deci_values: dict | pandas.Series, deci_vars: list[ommx.v1.DecisionVariable]) list#
- decode_from_solution(solution: ommx.v1.Solution) dict#