Ruben Martinez-Cantin avatar Ruben Martinez-Cantin committed 323dd8f

Minor tweaks

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Files changed (4)

 The online HTML version of these docs:
 <http://rmcantin.bitbucket.org/html/>
 
+- Download: <https://bitbucket.org/rmcantin/bayesopt>
+- Mirror: <https://github.com/rmcantin/bayesopt>
+
 Bayesian optimization uses a distribution over functions to build a
 metamodel of the unknown function for we are looking the extrema,
 and then apply some active learning strategy to select the query

app/bo_display.cpp

   size_t dim = 1;
   bopt_params parameters = initialize_parameters_to_default();
   parameters.n_init_samples = 10;
-  parameters.n_iter_relearn = 0;
+  parameters.n_iter_relearn = 10;
   parameters.n_iterations = 300;
+  parameters.verbose_level = 2;
+
+  // Surrogate models
   //parameters.surr_name = "sStudentTProcessNIG";
   parameters.surr_name = "sGaussianProcessNormal";
 
+  // Criterion models
+  // parameters.crit_name = "cEI";
+  // parameters.crit_params[0] = 1;
   parameters.crit_name = "cLCB";
   parameters.crit_params[0] = 5;
   parameters.n_crit_params = 1;
 
+  // Kernel models
   parameters.kernel.name = "kSum(kPoly3,kRQISO)";
-  parameters.kernel.hp_mean[0] = 1;
-  parameters.kernel.hp_mean[1] = 1;
-  parameters.kernel.hp_mean[2] = 1;
-  parameters.kernel.hp_mean[3] = 1;
-  parameters.kernel.hp_std[0] = 5;
-  parameters.kernel.hp_std[1] = 5;
-  parameters.kernel.hp_std[2] = 5;
-  parameters.kernel.hp_std[3] = 5;
-  parameters.kernel.n_hp = 4;
+  double mean[128] = {1, 1, 1, 1};
+  double std[128] = {5, 5, 5, 5};
+  size_t nhp = 4;
+  memcpy(parameters.kernel.hp_mean, mean, nhp * sizeof(double));
+  memcpy(parameters.kernel.hp_std,std, nhp * sizeof(double));
+  parameters.kernel.n_hp = nhp;
 
-  parameters.verbose_level = 2;
+  // parameters.kernel.name = "kSEISO";
+  // parameters.kernel.hp_mean[0] = 1;
+  // parameters.kernel.hp_std[0] = 5;
+  // parameters.kernel.n_hp = 1;
+
+
 
   state_ii = 0;
 

doxygen/introduction.dox

 \f]
 where
 \f[
-  \epsilon(\mathbf{x}) \sim \mathcal{NP} \left( 0, \sigma^2_s (\mathbf{K}(\theta) + \sigma^2_n \I) \right)
+  \epsilon(\mathbf{x}) \sim \mathcal{NP} \left( 0, \sigma^2_s (\mathbf{K}(\theta) + \sigma^2_n \mathbf{I}) \right)
 \f]
 The term \f$\mathcal{NP}\f$ means a non-parametric process, which can
 make reference to a Gaussian process \f$\mathcal{GP}\f$ or a Student's
   title = {Bayesian Approach to Global Optimization},
   publisher = {Kluwer Academic Publishers},
   year = {1989},
-  editor = {Michiel HazewinkeI},
   author = {Mockus, Jonas},
   volume = {37},
   series = {Mathematics and Its Applications},
   timestamp = {2010.06.18}
 }
 
-@ARTICLE{O'Hagan1992,
+@ARTICLE{OHagan1992,
   author = {Anthony O'Hagan},
   title = {Some {B}ayesian Numerical Analysis},
   journal = {Bayesian Statistics},
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