![]() Threshold based on the difference in value of a fixed number of trials: if bigger than 0, it enables the tolerance criteria for stopping based in the history of rewards. Maximum number of calls to the function to be optimized limbo::defaults::opt_cmaes::BO_PARAM(double, fun_tolerance, - 1) Number of restarts of CMA-ES limbo::defaults::opt_cmaes::BO_PARAM(double, max_fun_evals, - 1) In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.įunctions limbo::defaults::opt_cmaes::BO_PARAM(int, restarts, 1) Optimization of Gaussian Process Hyperparameters using Rprop. reference : Blum, M., & Riedmiller, M.useful for control experiments (do not use this otherwise!).Meta-optimizer: run the same algorithm in parallel many times from different init points and return the maximum found among all the replicates (useful for local algorithms) See: īinding to gradient-free NLOpt algorithms. Only available if libcmaes is installed (see the compilation instructions)īinding to gradient-based NLOpt algorithms.Support bounded and unbounded optimization.our implementation is based on libcmaes ( ).int bins (number of bins for each dimensions)Īvailable optimizers ¶ group opt template struct Cmaes ¶Ĭovariance Matrix Adaptation Evolution Strategy by Hansen et al.The number of bins in the grid is ``bins“^number_of_dimensionsįor instance, if bins = 5 and there are 3 inputs dimensions, then the grid is 5*5*5=125, and random points will all be points of this grid. Grid-based random sampling: in effect, define a grid and takes random samples from this grid. Latin Hypercube sampling in ^n (LHS) Parameters:ĭo nothing (dummy initializer). opt::GridSearch otherwise (please do not use this: the algorithm will not work as expected!)Īvailable initializers ¶ group init template struct GridSampling ¶.opt::Cmaes if libcmaes was found but NLOpt was not found.opt::NLOptNoGrad if NLOpt was found in waf configure.This class takes the same template parameters as BoBase. The classic Bayesian optimization algorithm. ![]() template class limbo::bayes_opt:: BOptimizer ¶ Template void eval_and_add ( const StateFunction & seval, const Eigen::VectorXd & sample ) ¶Įvaluate a sample and add the result to the ‘database’ (sample / observations vectors) it does not update the model. Return the current iteration number void add_new_sample ( const Eigen::VectorXd & s, const Eigen::VectorXd & v ) ¶ Return the list of the points that have been evaluated so far (x) int current_iteration ( ) const ¶ Return the vector of points of observations (observations can be multi-dimensional, hence the VectorXd) f(x) const std::vector & samples ( ) const ¶ ![]() Return the name of the directory in which results (statistics) are written const std::vector & observations ( ) const ¶ for unit tests) const std::string & res_dir ( ) const ¶ Return true if the statitics are enabled (they can be disabled to avoid dumping data, e.g. Subclassed by limbo::bayes_opt::BOptimizer, limbo::experimental::bayes_opt::BoMulti, limbo::experimental::bayes_opt::CBOptimizerĭefault constructor BoBase ( const BoBase & other ) ¶Ĭopy is disabled (dangerous and useless) BoBase & operator= ( const BoBase & other ) ¶Ĭopy is disabled (dangerous and useless) bool stats_enabled ( ) const ¶ using Kernel_t = kernel::MaternFiveHalves.(meaning: kernel with automatic relevance determination and mean equals to the mean of the input data, that is, center the data automatically)įor Statistics, the default value is: boost::fusion::vector, stat::AggregatedObservations, stat::ConsoleSummary > typeįor GP, the default value is: model::GP >, This class is templated by several types with default values (thanks to boost::parameters). ![]() ![]()
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