Commit 5204aebe authored by Jean-Baptiste Mouret's avatar Jean-Baptiste Mouret
Browse files

improve BoBase documentation

parent 8cd0efe4
......@@ -92,14 +92,16 @@ namespace limbo {
class A4 = boost::parameter::void_,
class A5 = boost::parameter::void_>
// clang-format on
/** Base class for Bayesian optimizers
/**
\rst
Base class for Bayesian optimizers
**Parameters**
- ``bool Params::bayes_opt_bobase::stats_enabled``: activate / deactivate the statistics
Parameters:
- ``bool Params::bayes_opt_bobase::stats_enabled``: activate / deactivate the statistics
This class is templated by several types with default values (thanks to boost::parameters).
\rst
+----------------+---------+---------+---------------+
|type |typedef | argument| default |
+================+=========+=========+===============+
......@@ -113,27 +115,26 @@ namespace limbo {
+----------------+---------+---------+---------------+
|stopping crit. | stop_t | stopcrit| MaxIterations |
+----------------+---------+---------+---------------+
\endrst
\endrst
For GP, the default value is: ``model::GP<Params, kf_t, mean_t, opt_t>>``,
- with ``kf_t = kernel::SquaredExpARD<Params>``
- with ``mean_t = mean::Data<Params>``
- with ``opt_t = model::gp::KernelLFOpt<Params>``
- with ``kf_t = kernel::SquaredExpARD<Params>``
- with ``mean_t = mean::Data<Params>``
- with ``opt_t = model::gp::KernelLFOpt<Params>``
(meaning: kernel with automatic relevance determination and mean equals to the mean of the input data, that is, center the data automatically)
For Statistics, the default value is: ``boost::fusion::vector<stat::Samples<Params>, stat::AggregatedObservations<Params>, stat::ConsoleSummary<Params>>``
Example of customization:
- ``typedef kernel::MaternFiveHalfs<Params> Kernel_t;``
- ``typedef mean::Data<Params> Mean_t;``
- ``typedef model::GP<Params, Kernel_t, Mean_t> GP_t;``
- ``typedef acqui::UCB<Params, GP_t> Acqui_t;``
- ``bayes_opt::BOptimizer<Params, modelfun<GP_t>, acquifun<Acqui_t>> opt;``
Example of customization:
- typedef kernel::MaternFiveHalfs<Params> Kernel_t;
- typedef mean::Data<Params> Mean_t;
- typedef model::GP<Params, Kernel_t, Mean_t> GP_t;
- typedef acqui::UCB<Params, GP_t> Acqui_t;
- bayes_opt::BOptimizer<Params, modelfun<GP_t>, acquifun<Acqui_t>> opt;
@see limbo::bayes_opt::Boptimizer
*/
......
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