Loading...
Searching...
No Matches
libcmaes::RSVMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > Class Template Reference
Inheritance diagram for libcmaes::RSVMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >:
Public Member Functions | |
| RSVMSurrogateStrategy (FitFunc &func, CMAParameters< TGenoPheno > ¶meters) | |
 Public Member Functions inherited from libcmaes::ACMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | |
| ACMSurrogateStrategy (FitFunc &func, CMAParameters< TGenoPheno > ¶meters) | |
| constructor | |
| dMat | ask () |
| Generates a set of candidate points. Uses the pre-sampling of a larger than usual number of offprings, controled by 'lambdaprime', as needed. | |
| void | eval (const dMat &candidates, const dMat &phenocandidates=dMat(0, 0)) |
| Evaluates a set of candiates against the objective function or the surrogate model, as needed. | |
| void | tell () |
| Updates the state of the stochastic search, and prepares for the next iteration by training the surrogate model, as needed. | |
| int | optimize () |
| bool | do_train () const |
| whether to train the model | |
| void | set_lambdaprime (const int &lp) |
| sets the number of true objective function calls per iteration | |
| int | get_lambdaprime () const |
| returns the number of calls to the true objective function per iteration | |
| void | set_prelambda (const int &pl) |
| sets the number of pre-screened offsprings (sampled) | |
| int | get_prelambda () const |
| returns the current number of pre-screened offpsrings (sampled) | |
| void | set_theta_sel0 (const double &s) |
| sets the standard deviation of selection sampling step 0 | |
| double | get_theta_sel0 () const |
| returns the standard deviation of selection sampling step 0 | |
| void | set_theta_sel1 (const double &s) |
| sets the standard deviation of selection sampling step 1 | |
| double | get_theta_sel1 () const |
| returns the standard deviation of selection sampling step 0 | |
| Public Member Functions inherited from libcmaes::SurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | |
| SurrogateStrategy (FitFunc &func, CMAParameters< TGenoPheno > ¶meters) | |
| constructor | |
| int | train (const std::vector< Candidate > &candidates, const dMat &cov) |
| train a surrogate model | |
| int | predict (std::vector< Candidate > &candidates, const dMat &cov) |
| predict from a surrogate model | |
| double | compute_error (const std::vector< Candidate > &test_set, const dMat &cov=dMat(0, 0)) |
| compute surrogate model error (copies and sorts the test_set) | |
| bool | do_train () const |
| conditionals on training, to be specialized in inherited surrogate strategies | |
| void | set_ftrain (const CSurrFunc &train) |
| sets the training function | |
| void | set_fpredict (const SurrFunc &predict) |
| sets the prediction function | |
| void | set_l (const int &l) |
| sets the size of the training set (number of points) | |
| int | get_l () const |
| gets the size of the training set (number of points) | |
| void | set_exploit (const bool &exploit) |
| sets whether to exploit the surrogate model | |
| bool | get_exploit () const |
| gets the state of surrogate model exploitation | |
| double | get_train_error () const |
| returns the surrogate model training error | |
| double | get_test_error () const |
| returns the surrogate model test error | |
| void | set_train_error (const double &err) |
| sets training error | |
| void | set_test_error (const double &err) |
| sets the test error and updates the smoothed test err. | |
| void | add_to_training_set (const Candidate &c) |
| adds a point to the training set (candidate = points + objective function value) | |
| void | set_nsteps (const int &nsteps) |
| sets the lifelength of the surrogate, i.e. the number of steps in between to training steps | |
| void | reset_training_set () |
| resets training set and related information, useful when using algorithms with restarts | |
| int | get_nsteps () const |
| returns the current surrogate lifelength | |
Public Attributes | |
| RankingSVM< RBFKernel > | _rsvm |
| int | _rsvm_iter = 1e6 |
Additional Inherited Members | |
| Protected Member Functions inherited from libcmaes::ACMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | |
| void | pre_selection_eval (const dMat &candidates) |
| pre-selection + candidate evaluation scheme. Called by eval, evaluates lambdaprime candidates with surrogate model, then subsample the population in order to evaluate them with the original objective function, test the surrogate model and grow the training set with new points | |
| Protected Attributes inherited from libcmaes::ACMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | |
| double | _prelambda = 500 |
| double | _theta_sel0 = 0.4 |
| double | _theta_sel1 = 0.8 |
| int | _lambdaprime |
| Protected Attributes inherited from libcmaes::SurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno > | |
| bool | _exploit = true |
| int | _l = 200 |
| std::vector< Candidate > | _tset |
| CSurrFunc | _train |
| SurrFunc | _predict |
| double | _train_err = 0.0 |
| double | _test_err = 0.0 |
| double | _smooth_test_err = 0.5 |
| double | _beta_err = 0.2 |
| int | _nsteps = 1 |
| int | _auto_nsteps = false |
Member Data Documentation
◆ _rsvm_iter
template<template< class U, class V > class TStrategy, class TCovarianceUpdate = CovarianceUpdate, class TGenoPheno = GenoPheno<NoBoundStrategy>>
| int libcmaes::RSVMSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::_rsvm_iter = 1e6 |
number of iterations for optimizing the ranking SVM
The documentation for this class was generated from the following file:
- include/libcmaes/surrogates/rsvm_surr_strategy.hpp
