Man pages for SBOAtools
Secretary Bird Optimization for Continuous Optimization and Neural Network Training
| apply_minmax | Apply min-max normalization using existing bounds |
| clip_bounds | Clip values to lower and upper bounds |
| denormalize_minmax | Reverse min-max normalization |
| forward_mlp | Forward pass for a single-hidden-layer MLP |
| get_benchmark | Get a built-in benchmark definition |
| levy_flight | Lévy flight step generator |
| list_benchmarks | List built-in benchmark functions |
| mae_vec | Mean absolute error |
| mape_vec | Mean absolute percentage error |
| mlp_mse_fitness | MSE fitness function for MLP training |
| normalize_minmax | Min-max normalization |
| plot.sboa | Plot method for SBOA objects |
| plot.sboa_mlp | Plot method for SBOA-MLP objects |
| predict.sboa_mlp | Predict method for SBOA-MLP objects |
| print.sboa | Print method for SBOA objects |
| print.sboa_mlp | Print method for SBOA-MLP objects |
| rmse_vec | Root mean squared error |
| rsq_vec | R-squared |
| sboa | Secretary Bird Optimization Algorithm |
| sboa_mlp | Train a single-hidden-layer MLP using SBOA |
| sigmoid | Sigmoid activation function |
| unpack_mlp_params | Unpack MLP parameter vector |
