adaptive operators that compiles (but still not work)

edo/src/edoEstimatorNormalAdaptive.h

@@ -0,0 +1,237 @@
+/*
+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 _edoEstimatorNormalAdaptive_h
+#define _edoEstimatorNormalAdaptive_h
+
+#ifdef WITH_EIGEN
+
+#include <algorithm>
+
+#include<Eigen/Dense>
+
+#include "edoNormalAdaptive.h"
+#include "edoEstimatorAdaptive.h"
+
+
+//! edoEstimatorNormalMulti< EOT >
+template< typename EOT, typename EOD = edoNormalAdaptive<EOT> >
+class edoEstimatorNormalAdaptive : public edoEstimatorAdaptive< EOD >
+{
+public:
+    typedef typename EOT::AtomType AtomType;
+    typedef typename EOD::Vector Vector;
+    typedef typename EOD::Matrix Matrix;
+
+    edoEstimatorNormalAdaptive( EOD& distrib, unsigned int mu ) :
+        edoEstimatorAdaptive<EOD>( distrib ),
+        _mu(mu),
+        _calls(0),
+        _eigeneval(0)
+    {}
+
+private:
+    Eigen::VectorXd edoCMAESweights( unsigned int pop_size )
+    {
+        // compute recombination weights
+        Eigen::VectorXd weights( pop_size );
+        double sum_w = 0;
+        for( unsigned int i = 0; i < _mu; ++i ) {
+            double w_i = log( _mu + 0.5 ) - log( i + 1 );
+            weights(i) = w_i;
+            sum_w += w_i;
+        }
+        // normalization of weights
+        weights /= sum_w;
+
+        return weights;
+    }
+
+public:
+    void resetCalls()
+    {
+        _calls = 0;
+    }
+
+    // update the distribution reference this->distribution()
+    edoNormalAdaptive<EOT> operator()( eoPop<EOT>& pop )
+    {
+
+        /**********************************************************************
+         * INITIALIZATION
+         *********************************************************************/
+
+        unsigned int N = pop[0].size(); // FIXME expliciter la dimension du pb ?
+        unsigned int lambda = pop.size();
+
+        // number of calls to the operator == number of generations
+        _calls++;
+        // number of "evaluations" until now
+        unsigned int counteval = _calls * lambda;
+
+        // Here, if we are in canonical CMA-ES,
+        // pop is supposed to be the mu ranked better solutions,
+        // as the rank mu selection is supposed to have occured.
+        Matrix arx( pop.size(), N );
+
+        // copy the pop (most probably a vector of vectors) in a Eigen3 matrix
+        for( unsigned int i = 0; i < lambda; ++i ) {
+            for( unsigned int d = 0; d < N; ++d ) {
+                arx(i,d) = pop[i][d];
+            } // dimensions
+        } // individuals
+
+        // muXone array for weighted recombination
+        Eigen::VectorXd weights = edoCMAESweights( N );
+
+        // FIXME exposer les constantes dans l'interface
+
+        // variance-effectiveness of sum w_i x_i
+        double mueff = pow(weights.sum(), 2) / (weights.array().square()).sum();
+
+        // time constant for cumulation for C
+        double cc = (4+mueff/N) / (N+4 + 2*mueff/N);
+
+        // t-const for cumulation for sigma control
+        double cs = (mueff+2) / (N+mueff+5);
+
+        // learning rate for rank-one update of C
+        double c1 = 2 / (pow(N+1.3,2)+mueff);
+
+        // and for rank-mu update
+        double cmu = 2 * (mueff-2+1/mueff) / ( pow(N+2,2)+mueff);
+
+        // damping for sigma
+        double damps = 1 + 2*std::max(0.0, sqrt((mueff-1)/(N+1))-1) + cs;
+
+
+        // shortcut to the referenced distribution
+        EOD& d = this->distribution();
+
+        // C^-1/2
+        Matrix invsqrtC =
+            d.coord_sys() * d.scaling().asDiagonal().inverse()
+            * d.coord_sys().transpose();
+
+        // expectation of ||N(0,I)|| == norm(randn(N,1))
+        double chiN = sqrt(N)*(1-1/(4*N)+1/(21*pow(N,2)));
+
+
+        /**********************************************************************
+         * WEIGHTED MEAN
+         *********************************************************************/
+
+        // compute weighted mean into xmean
+        Vector xold = d.mean();
+        d.mean( arx * weights );
+        Vector xmean = d.mean();
+
+
+        /**********************************************************************
+         * CUMULATION: UPDATE EVOLUTION PATHS
+         *********************************************************************/
+
+        // cumulation for sigma
+        d.path_sigma( 
+                (1.0-cs)*d.path_sigma() + sqrt(cs*(2.0-cs)*mueff)*invsqrtC*(xmean-xold)/d.sigma()
+                );
+
+        // sign of h
+        double hsig;
+        if( d.path_sigma().norm()/sqrt(1.0-pow((1.0-cs),(2.0*counteval/lambda)))/chiN
+                < 1.4 + 2.0/(N+1.0)
+          ) {
+            hsig = 1.0;
+        } else {
+            hsig = 0.0;
+        }
+
+        // cumulation for the covariance matrix
+        d.path_covar(
+                (1.0-cc)*d.path_covar() + hsig*sqrt(cc*(2.0-cc)*mueff)*(xmean-xold) / d.sigma()
+                );
+
+        Matrix artmp = (1.0/d.sigma()) * arx - xold.rowwise().replicate(_mu);
+
+
+        /**********************************************************************
+         * COVARIANCE MATRIX ADAPTATION
+         *********************************************************************/
+
+        d.covar(
+                (1-c1-cmu) * d.covar()                            // regard old matrix
+                + c1 * (d.path_covar()*d.path_covar().transpose() // plus rank one update
+                    + (1-hsig) * cc*(2-cc) * d.covar())   // minor correction if hsig==0
+                + cmu * artmp * weights.asDiagonal() * artmp.transpose() // plus rank mu update
+               );
+
+        // Adapt step size sigma
+        d.sigma( d.sigma() * exp((cs/damps)*(d.path_sigma().norm()/chiN - 1)) );
+
+
+
+        /**********************************************************************
+         * DECOMPOSITION OF THE COVARIANCE MATRIX
+         *********************************************************************/
+
+        // Decomposition of C into B*diag(D.^2)*B' (diagonalization)
+        if( counteval - _eigeneval > lambda/(c1+cmu)/N/10 ) {  // to achieve O(N^2)
+            _eigeneval = counteval;
+
+            // enforce symmetry of the covariance matrix
+            Matrix C = d.covar();
+            // FIXME edoEstimatorNormalAdaptive.h:213:44: erreur: expected primary-expression before ‘)’ token
+            // copy the upper part in the lower one
+            //C.triangularView<Eigen::Lower>() = C.adjoint();
+            // Matrix CS = C.triangularView<Eigen::Upper>() + C.triangularView<Eigen::StrictlyUpper>().transpose();
+            d.covar( C );
+
+            Eigen::SelfAdjointEigenSolver<Matrix> eigensolver( d.covar() ); // FIXME use JacobiSVD?
+            d.coord_sys( eigensolver.eigenvectors() );
+            Matrix D = eigensolver.eigenvalues().asDiagonal();
+
+            // from variance to standard deviations
+            D.cwiseSqrt();
+            d.scaling( D );
+        }
+
+        return d;
+    } // operator()
+
+protected:
+
+    unsigned int _mu;
+    unsigned int _calls;
+    unsigned int _eigeneval;
+
+
+    // EOD & distribution() inherited from edoEstimatorAdaptive
+};
+#endif // WITH_EIGEN
+
+#endif // !_edoEstimatorNormalAdaptive_h

edo/src/edoNormalAdaptive.h

@@ -39,6 +39,7 @@ template < typename EOT >
 class edoNormalAdaptive : public edoDistrib< EOT >
 {
 public:
+    //typedef EOT EOType;
     typedef typename EOT::AtomType AtomType;
     typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
     typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
@@ -55,13 +56,40 @@ public:
         assert( dim > 0);
     }
 
+    edoNormalAdaptive( unsigned int dim, 
+            Vector mean,
+            Matrix C,
+            Matrix B,
+            Vector D,
+            double sigma,
+            Vector p_c,
+            Vector p_s
+        ) :
+        _mean( mean ),
+        _C( C ),
+        _B( B ),
+        _D( D ),
+        _sigma(sigma),
+        _p_c( p_c ),
+        _p_s( p_s )
+    {
+        assert( dim > 0);
+        assert( _mean.innerSize() == dim );
+        assert( _C.innerSize() == dim && _C.outerSize() == dim );
+        assert( _B.innerSize() == dim && _B.outerSize() == dim );
+        assert( _D.innerSize() == dim );
+        assert( _sigma != 0.0 );
+        assert( _p_c.innerSize() == dim );
+        assert( _p_s.innerSize() == dim );
+    }
+
     unsigned int size()
     {
         return _mean.innerSize();
     }
 
     Vector mean() const {return _mean;}
-    Matrix covar() const {return _covar;}
+    Matrix covar() const {return _C;}
     Matrix coord_sys() const {return _B;}
     Vector scaling() const {return _D;}
     double sigma() const {return _sigma;}
@@ -73,8 +101,8 @@ public:
     void coord_sys(  Matrix b ) { _B = b; }
     void scaling(    Vector d ) { _D = d; }
     void sigma(      double s ) { _sigma = s; }
-    void path_covar( Vector p ) { _path_covar = p; }
+    void path_covar( Vector p ) { _p_c = p; }
-    void path_sigma( Vector p ) { _path_sigma = p; }
+    void path_sigma( Vector p ) { _p_s = p; }
 
 private:
     Vector _mean; // 

edo/src/edoSamplerNormalAdaptive.h

@@ -69,9 +69,9 @@ public:
         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() * (distrib.scaling().dot(T) );
-        Vector sol = distrib.mean() + distrib.sigma()
+        /*Vector sol = distrib.mean() + distrib.sigma()
-            * distrib.coord_sys().dot( distrib.scaling().dot( T ) );
+            * distrib.coord_sys().dot( distrib.scaling().dot( T ) );*/
 
         // copy in the EOT structure (more probably a vector)
         EOT solution( size );