foxes_opt.problems.layout.geom_layouts.objectives¶

Classes¶

Module Contents¶

class foxes_opt.problems.layout.geom_layouts.objectives.MaxDensity(problem: iwopy.Problem, dfactor: int = 1, min_dist: float | None = None, name: str = 'max_density')[source]¶

Bases: iwopy.Objective

Maximal turbine density objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

dfactor

Delta factor for grid spacing

min_dist

The minimal distance

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity: int) → None[source]¶

Initialize the object.

Parameters:¶

verbosity

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

dfactor : int = 1¶

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

min_dist¶

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names

class foxes_opt.problems.layout.geom_layouts.objectives.MaxGridSpacing(problem: iwopy.Problem, name: str = 'max_dxdy')[source]¶

Bases: iwopy.Objective

Maximal grid spacing objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names

class foxes_opt.problems.layout.geom_layouts.objectives.MeMiMaDist(problem: iwopy.Problem, scale: float = 500.0, c1: int = 1, c2: int = 1, c3: int = 1, name: str = 'MiMaMean')[source]¶

Bases: iwopy.Objective

Mean-min-max distance objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

scale

The distance scale

c1

Parameter for mean weighting

c2

Parameter for max diff weighting

c3

Parameter for min diff weighting

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

c1 : int = 1¶

c2 : int = 1¶

c3 : int = 1¶

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

scale : float = 500.0¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names

class foxes_opt.problems.layout.geom_layouts.objectives.OFixN(problem: iwopy.Problem, N: int, name: str = 'ofixN')[source]¶

Bases: iwopy.Objective

Fixed number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

N

The number of turbines

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

N¶

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names

class foxes_opt.problems.layout.geom_layouts.objectives.OMaxN(problem: iwopy.Problem, name: str = 'maxN')[source]¶

Bases: iwopy.Objective

Maximal number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray[source]¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names

class foxes_opt.problems.layout.geom_layouts.objectives.OMinN(problem: iwopy.Problem, name: str = 'ominN')[source]¶

Bases: OMaxN

Minimal number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

name

The constraint name

ana_deriv(vars_int, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array

The integer variable values, shape: (n_vars_int,)

vars_float: np.array

The float variable values, shape: (n_vars_float,)

var: int

The index of the differentiation float variable

components: list of int

The selected components, or None for all

Returns:¶

deriv: numpy.ndarray

The derivative values, shape: (n_sel_components,)

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray¶

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int

The integer variable values, shape: (n_vars_int,)

vars_float

The float variable values, shape: (n_vars_float,)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_sel_components,)

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: tuple[numpy.ndarray, numpy.ndarray], cmpnts: list[int] | None = None) → numpy.ndarray¶

Calculate values for all individuals of a population.

Parameters:¶

vars_int

The integer variable values, shape: (n_pop, n_vars_int)

vars_float

The float variable values, shape: (n_pop, n_vars_float)

problem_results

The results of the variable application to the problem

components

The selected components or None for all

Returns:¶

values

The component values, shape: (n_pop, n_sel_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

vars_int: np.array

The optimal integer variable values, shape: (n_vars_int,)

vars_float: np.array

The optimal float variable values, shape: (n_vars_float,)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

vars_int: np.array

The integer variable values of the final generation, shape: (n_pop, n_vars_int)

vars_float: np.array

The float variable values of the final generation, shape: (n_pop, n_vars_float)

problem_results: Any

The results of the variable application to the problem

verbosity: int

The verbosity level, 0 = silent

Returns:¶

values: np.array

The component values, shape: (n_pop, n_components)

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int

The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags

Bool array for component maximization, shape

n_components() → int¶

Returns the number of components of the function.

Returns:¶

value

The number of components.

classmethod new(objective_type, *args, **kwargs)¶

Run-time objective function factory.

Parameters:¶

objective_type: str

The selected derived class name

args: tuple, optional

Additional parameters for constructor

kwargs: dict, optional

Additional parameters for constructor

rename_vars_float(varmap)¶

Rename float variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict

The name mapping. Key: old name str, Value: new name str

vardeps_float()¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_float)

vardeps_int()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool

The dependencies of components on function variables, shape: (n_components, n_vars_int)

property component_names¶

The names of the components

Returns:¶

names: list of str

The component names

property initialized¶

Flag for finished initialization

Returns:¶

bool

True if initialization has been done

property n_vars_float¶

The number of float variables

Returns:¶

n: int

The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int

The number of int variables

name¶

problem¶

property var_names_float¶

The names of the float variables

Returns:¶

names: list of str

The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str

The integer variable names