/*
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 Dreo <johann.dreo@thalesgroup.com>
    Pierre Savéant <pierre.saveant@thalesgroup.com>
*/

#ifndef _edoNormalAdaptive_h
#define _edoNormalAdaptive_h

#include "edoDistrib.h"

#ifdef WITH_EIGEN

#include <Eigen/Dense>

/** @defgroup CMAES CMAES
 *
 * CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a stochastic,
 * derivative-free methods for numerical optimization of non-linear or
 * non-convex continuous optimization problems.
 *
 * @ingroup Algorithms
 */

/** @defgroup Adaptivenormal Adaptive normal
 *
 * A multi-variate normal distribution that can be updated via several components.
 * This is the data structure on which works the CMA-ES algorithm.
 *
 * @ingroup Distributions
 */

/** A normal distribution that can be updated via several components. This is the data structure on which works the CMA-ES
 * algorithm.
 *
 * This is *just* a data structure, the operators working on it are supposed to maintain its consistency (e.g. of the
 * covariance matrix against its eigen vectors).
 *
 * The distribution is defined by its mean, its covariance matrix (which can be decomposed in its eigen vectors and
 * values), a scaling factor (sigma) and the so-called evolution paths for the covariance and sigma.
 * evolution paths.
 *
 * NOTE: this is only available as an Eigen3 implementation (built WITH_EIGEN).
 *
 * @ingroup Distributions
 * @ingroup CMAES
 * @ingroup Adaptivenormal
 */
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; // column vectors ( n lines, 1 column)
    typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;

    edoNormalAdaptive( unsigned int dim = 1 ) :
        _dim(dim),
        _mean( Vector::Zero(dim) ),
        _C( Matrix::Identity(dim,dim) ),
        _B( Matrix::Identity(dim,dim) ),
        _D( Vector::Constant( dim, 1) ),
        _sigma(1.0),
        _p_c( Vector::Zero(dim) ),
        _p_s( Vector::Zero(dim) )
    {
        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 _C;}
    Matrix coord_sys()  const {return _B;}
    Vector scaling()    const {return _D;}
    double sigma()      const {return _sigma;}
    Vector path_covar() const {return _p_c;}
    Vector path_sigma() const {return _p_s;}

    //! Set the mean with an Eigen3 vector
    void mean(       Vector m ) { _mean = m;  assert( m.size() == _dim ); }

    /** Set the mean with an EOT instead of an Eigen3 mean
     *
     * Explicit copy of the EOT in a vector.
     */
    void mean(       EOT m )
    {
        Vector center( m.size() );
        for( unsigned int i=0, end=m.size(); i<end; ++i) {
            center[i] = m[i];
        }
        mean( center );
    }

    void covar(      Matrix c ) { _C = c;     assert( c.innerSize() == _dim && c.outerSize() == _dim ); }
    void coord_sys(  Matrix b ) { _B = b;     assert( b.innerSize() == _dim && b.outerSize() == _dim ); }
    void scaling(    Vector d ) { _D = d;     assert( d.size() == _dim ); }
    void sigma(      double s ) { _sigma = s; assert( s != 0.0 );}
    void path_covar( Vector p ) { _p_c = p;   assert( p.size() == _dim ); }
    void path_sigma( Vector p ) { _p_s = p;   assert( p.size() == _dim ); }

private:
    unsigned int _dim;
    Vector _mean; // mean vector
    Matrix _C; // covariance matrix
    Matrix _B; // eigen vectors / coordinates system
    Vector _D; // eigen values / scaling
    double _sigma; // absolute scaling of the distribution
    Vector _p_c; // evolution path for C
    Vector _p_s; // evolution path for sigma
};

#else
#pragma message "WARNING: there is no Boost::uBLAS implementation of edoNormalAdaptive, build WITH_EIGEN if you need it."
#endif // WITH_EIGEN

#endif // !_edoNormalAdaptive_h