adaptive sampler for cmaes

edo/src/edoSamplerNormalAdaptive.h

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+/*
+The Evolving Distribution Objects framework (EDO) is a template-based,
+ANSI-C++ evolutionary computation library which helps you to write your
+own estimation of distribution algorithms.
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library 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
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+
+Copyright (C) 2010 Thales group
+*/
+/*
+Authors:
+    Johann Dréo <johann.dreo@thalesgroup.com>
+    Pierre Savéant <pierre.saveant@thalesgroup.com>
+*/
+
+#ifndef _edoSamplerNormalAdaptive_h
+#define _edoSamplerNormalAdaptive_h
+
+#include <cmath>
+#include <limits>
+
+#include <edoSampler.h>
+
+/** Sample points in a multi-normal law defined by a mean vector and a covariance matrix.
+ *
+ * Given M the mean vector and V the covariance matrix, of order n:
+ *   - draw a vector T in N(0,I) (i.e. each value is drawn in a normal law with mean=0 an stddev=1)
+ *   - compute the Cholesky decomposition L of V (i.e. such as V=LL*)
+ *   - return X = M + LT
+ */
+#ifdef WITH_EIGEN
+
+template< class EOT, typename EOD = edoNormalAdaptive< EOT > >
+class edoSamplerNormalAdaptive : public edoSampler< EOD >
+{
+public:
+    typedef typename EOT::AtomType AtomType;
+
+    typedef typename EOD::Vector Vector;
+    typedef typename EOD::Matrix Matrix;
+
+    edoSamplerNormalAdaptive( edoRepairer<EOT> & repairer ) 
+        : edoSampler< EOD >( repairer)
+    {}
+
+
+    EOT sample( EOD& distrib )
+    {
+        unsigned int size = distrib.size();
+        assert(size > 0);
+
+        // T = vector of size elements drawn in N(0,1)
+        Vector T( size );
+        for ( unsigned int i = 0; i < size; ++i ) {
+            T( i ) = rng.normal();
+        }
+        assert(T.innerSize() == size);
+        assert(T.outerSize() == 1);
+
+        //Vector t_sol = distrib.mean() + distrib.sigma() * distrib.coord_sys() * distrib.scaling() * T;
+        Vector sol = distrib.mean() + distrib.sigma()
+            * distrib.coord_sys().dot( distrib.scaling().dot( T ) );
+
+        // copy in the EOT structure (more probably a vector)
+        EOT solution( size );
+        for( unsigned int i = 0; i < size; i++ ) {
+            solution[i]= sol(i);
+        }
+
+        return solution;
+    }
+};
+#endif // WITH_EIGEN
+
+#endif // !_edoSamplerNormalAdaptive_h