optilab.functions package

Subpackages

• optilab.functions.benchmarks package
  • Submodules
  • optilab.functions.benchmarks.cec_objective_function module
  • Module contents
• optilab.functions.multimodal package
  • Submodules
  • optilab.functions.multimodal.ackley_function module
    • AckleyFunction
      • AckleyFunction.__init__()
      • AckleyFunction.__call__()
  • optilab.functions.multimodal.rastrigin_function module
    • RastriginFunction
      • RastriginFunction.__init__()
      • RastriginFunction.__call__()
  • optilab.functions.multimodal.rosenbrock_function module
    • RosenbrockFunction
      • RosenbrockFunction.__init__()
      • RosenbrockFunction.__call__()
  • Module contents
• optilab.functions.surrogate package
  • Submodules
  • optilab.functions.surrogate.knn_surrogate_objective_function module
    • KNNSurrogateObjectiveFunction
      • KNNSurrogateObjectiveFunction.__init__()
      • KNNSurrogateObjectiveFunction.train()
      • KNNSurrogateObjectiveFunction.__call__()
  • optilab.functions.surrogate.locally_weighted_polynomial_regression module
    • biquadratic_kernel_function()
    • LocallyWeightedPolynomialRegression
      • LocallyWeightedPolynomialRegression.__init__()
      • LocallyWeightedPolynomialRegression.set_covariance_matrix()
      • LocallyWeightedPolynomialRegression.train()
      • LocallyWeightedPolynomialRegression.__call__()
  • optilab.functions.surrogate.mlp_surrogate_objective_function module
    • MLPSurrogateObjectiveFunction
      • MLPSurrogateObjectiveFunction.__init__()
      • MLPSurrogateObjectiveFunction.train()
      • MLPSurrogateObjectiveFunction.__call__()
  • optilab.functions.surrogate.polynomial_regression module
    • PolynomialRegression
      • PolynomialRegression.__init__()
      • PolynomialRegression.train()
      • PolynomialRegression.__call__()
  • optilab.functions.surrogate.surrogate_objective_function module
    • SurrogateObjectiveFunction
      • SurrogateObjectiveFunction.__init__()
      • SurrogateObjectiveFunction.train()
      • SurrogateObjectiveFunction.__call__()
  • optilab.functions.surrogate.xgboost_surrogate_objective_function module
    • XGBoostSurrogateObjectiveFunction
      • XGBoostSurrogateObjectiveFunction.__init__()
      • XGBoostSurrogateObjectiveFunction.train()
      • XGBoostSurrogateObjectiveFunction.__call__()
  • Module contents
• optilab.functions.unimodal package
  • Submodules
  • optilab.functions.unimodal.bent_cigar_function module
    • BentCigarFunction
      • BentCigarFunction.__init__()
      • BentCigarFunction.__call__()
  • optilab.functions.unimodal.cumulative_squared_sums module
    • CumulativeSquaredSums
      • CumulativeSquaredSums.__init__()
      • CumulativeSquaredSums.__call__()
  • optilab.functions.unimodal.increasing_weight_cigar module
    • IncreasingWeightCigar
      • IncreasingWeightCigar.__init__()
      • IncreasingWeightCigar.__call__()
  • optilab.functions.unimodal.linear_function module
    • LinearFunction
      • LinearFunction.__init__()
      • LinearFunction.__call__()
  • optilab.functions.unimodal.sphere_function module
    • SphereFunction
      • SphereFunction.__init__()
      • SphereFunction.__call__()
  • Module contents

Submodules

optilab.functions.noisy_function module

Class that makes an objective function noisy using random normal distribution.

class NoisyFunction(function: ObjectiveFunction, noise: float)

Bases: ObjectiveFunction

Class that makes an objective function noisy using random normal distribution.

__init__(function: ObjectiveFunction, noise: float) → None

Class constructor.

Parameters:

• function – Objective function to noise.

• noise – Noise value of the function.

__call__(point: Point) → Point

Evaluate a single point with the objective function.

Parameters:

point – Point to be evaluated.

Raises:

ValueError – If dimensionality of x doesn’t match the dimensionality of the function.

Returns:

Evaluated point.

optilab.functions.objective_function module

Base class representing a callable objective function.

class ObjectiveFunction(name: str, dim: int, hyperparameters: Dict[str, Any] | None = None)

Bases: object

Base class representing a callable objective function.

__init__(name: str, dim: int, hyperparameters: Dict[str, Any] | None = None) → None

Class constructor.

Parameters:

• name – Name of the objective function.

• dim – Dimensionality of the function.

• hyperparameters – Dictionary with hyperparameters of the function.

__call__(point: Point) → Point

Evaluate a single point with the objective function.

Parameters:

point – Point to evaluate.

Raises:

ValueError – If dimensionality of x doesn’t match self.dim

Returns:

Evaluated point.

Module contents

Objective functions to be minimized by the optimizer.