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Ruben Martinez-Cantin committed 55e8472

Correcting typos in documentation.

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doxygen/demos.dox

 
 \subsection reembodemo Demo in very high dimensions
 
-\b demo_reembo evaluates the REEMBO algorithm for optimization in very high dimensions. The idea is that Bayesian optimization can be used very high dimensions provided that the effective dimension is embedded in a lower space, by using random projections.
+\b demo_rembo evaluates the REMBO (Random EMbedding Bayesian
+Optimization) algorithm for optimization in very high dimensions. The
+idea is that Bayesian optimization can be used very high dimensions
+provided that the effective dimension is embedded in a lower space, by
+using random projections.
 
-In this case, we test it against an artificially augmented Branin function with 1000 dimensions where only 2 dimensions are actually relevant (but unknown). The function is defined in the file: \c braninghighdim
+In this case, we test it against an artificially augmented Branin
+function with 1000 dimensions where only 2 dimensions are actually
+relevant (but unknown). The function is defined in the file: 
+\c braninghighdim
 
-For details about REEMBO, see \cite ZiyuWang2013.
+For details about REMBO, see \cite ZiyuWang2013.
 
 */

doxygen/reference.dox

 
 \subsection pyusage Python callback/inheritance usage
 
-The file python/test.py provides examples of the two Python
+The file python/demo_quad.py provides examples of the two Python
 interfaces.
 
 \b Parameters: For both interfaces, the parameters are defined as a

matlab/demo_reembo.m

-% 
-% -------------------------------------------------------------------------
-%    This file is part of BayesOpt, an efficient C++ library for 
-%    Bayesian optimization.
-%
-%    Copyright (C) 2011-2013 Ruben Martinez-Cantin <rmcantin@unizar.es>
-%
-%    BayesOpt is free software: you can redistribute it and/or modify it 
-%    under the terms of the GNU General Public License as published by
-%    the Free Software Foundation, either version 3 of the License, or
-%    (at your option) any later version.
-%
-%    BayesOpt is distributed in the hope that it will be useful, but 
-%    WITHOUT ANY WARRANTY; without even the implied warranty of
-%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-%    GNU General Public License for more details.
-%
-%    You should have received a copy of the GNU General Public License
-%    along with BayesOpt.  If not, see <http://www.gnu.org/licenses/>.
-% ------------------------------------------------------------------------
-%
-clear all, close all
-addpath('testfunctions')
-
-params.n_iterations = 300;
-params.n_init_iterations = 50;
-params.crit_name = 'cEI';
-params.surr_name = 'sGaussianProcessNormal';
-params.noise = 0.005;
-params.kernel_name = 'kMaternISO3';
-params.kernel_hp_mean = [0.5];
-params.kernel_hp_std = [10];
-params.verbose_level = 0;
-params.log_filename = 'matbopt.log';
-
-n = 2;          % number of low dims (effective)
-nh = 1000;      % number of actual dims
-nreembo = 10;    % number of reembo iterations
-
-
-global MATRIX_A
-global truei
-
-truei = [150,237];
-
-lb = ones(n,1)*-sqrt(n);
-ub = ones(n,1)*sqrt(n);
-fun = 'braninhighdim';    % the function has an effective 2D
-values = zeros(nreembo,1);
-points = zeros(nreembo,n);
-
-for i=1:nreembo
-    disp('Continuous optimization');
-    MATRIX_A = randn(nh,n);
-    tic;
-    result = bayesopt(fun,n,params,lb,ub);
-    toc;
-
-    values(i) = braninhighdim(result);
-    hd_res = MATRIX_A*result';
-    points(i,:) = hd_res(truei)';
-    disp(hd_res(truei)); disp(values(i));
-end;
-
-[foo,id] = min(values);
-disp(points(id,:));

matlab/demo_rembo.m

+% 
+% -------------------------------------------------------------------------
+%    This file is part of BayesOpt, an efficient C++ library for 
+%    Bayesian optimization.
+%
+%    Copyright (C) 2011-2013 Ruben Martinez-Cantin <rmcantin@unizar.es>
+%
+%    BayesOpt is free software: you can redistribute it and/or modify it 
+%    under the terms of the GNU General Public License as published by
+%    the Free Software Foundation, either version 3 of the License, or
+%    (at your option) any later version.
+%
+%    BayesOpt is distributed in the hope that it will be useful, but 
+%    WITHOUT ANY WARRANTY; without even the implied warranty of
+%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%    GNU General Public License for more details.
+%
+%    You should have received a copy of the GNU General Public License
+%    along with BayesOpt.  If not, see <http://www.gnu.org/licenses/>.
+% ------------------------------------------------------------------------
+%
+clear all, close all
+addpath('testfunctions')
+
+params.n_iterations = 300;
+params.n_init_iterations = 50;
+params.crit_name = 'cEI';
+params.surr_name = 'sGaussianProcessNormal';
+params.noise = 0.005;
+params.kernel_name = 'kMaternISO3';
+params.kernel_hp_mean = [0.5];
+params.kernel_hp_std = [10];
+params.verbose_level = 0;
+params.log_filename = 'matbopt.log';
+
+n = 2;          % number of low dims (effective)
+nh = 1000;      % number of actual dims
+nreembo = 10;    % number of reembo iterations
+
+
+global MATRIX_A
+global truei
+
+truei = [150,237];
+
+lb = ones(n,1)*-sqrt(n);
+ub = ones(n,1)*sqrt(n);
+fun = 'braninhighdim';    % the function has an effective 2D
+values = zeros(nreembo,1);
+points = zeros(nreembo,n);
+
+for i=1:nreembo
+    disp('Continuous optimization');
+    MATRIX_A = randn(nh,n);
+    tic;
+    result = bayesopt(fun,n,params,lb,ub);
+    toc;
+
+    values(i) = braninhighdim(result);
+    hd_res = MATRIX_A*result';
+    points(i,:) = hd_res(truei)';
+    disp(hd_res(truei)); disp(values(i));
+end;
+
+[foo,id] = min(values);
+disp(points(id,:));