bayes
fun Hyperparameters.bayes(maxTrials: Int = 50, initialTrials: Int = -1, maximize: Boolean = true, acquisition: BayesianOptimization.Acquisition = BayesianOptimization.Acquisition.EXPECTED_IMPROVEMENT, exploration: Double = 0.01, kernel: MercerKernel<DoubleArray> = MaternKernel(1.0, 2.5), objective: (Properties) -> Double): BayesianOptimization.Result(source)
Executes Bayesian hyperparameter optimization.
Return
the optimization result.
Parameters
max
the total number of evaluations to perform.
initial
the number of initial random trials (-1 for heuristic default).
maximize
true to maximize the objective, false to minimize.
acquisition
the acquisition function strategy.
exploration
exploration parameter (ξ for EI/PI, κ for UCB).
kernel
the Mercer covariance kernel for GP surrogate modeling.
objective
the objective function evaluating a configuration.