Merge branch 'master' of ssh://eodev.git.sourceforge.net/gitroot/eodev/eodev
This commit is contained in:
Caner Candan
commit
d7cc58def3
13 changed files with 926 additions and 103 deletions
|
|
@ -59,6 +59,7 @@ Authors:
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#include "edoRepairer.h"
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#include "edoRepairerDispatcher.h"
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#include "edoRepairerRound.h"
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#include "edoRepairerModulo.h"
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#include "edoBounder.h"
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#include "edoBounderNo.h"
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#include "edoBounderBound.h"
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|
|
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@ -49,6 +49,10 @@ public:
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assert( this->max().size() > 0 );
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assert( sol.size() > 0);
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assert( sol.size() == this->min().size() );
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eo::log << eo::debug << "BounderUniform: from sol = " << sol;
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eo::log.flush();
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unsigned int size = sol.size();
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for (unsigned int d = 0; d < size; ++d) {
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@ -58,6 +62,8 @@ public:
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sol[d] = rng.uniform( this->min()[d], this->max()[d] );
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}
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} // for d in size
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eo::log << eo::debug << "\tto sol = " << sol << std::endl;
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}
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};
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100
edo/src/edoRepairerApply.h
Normal file
100
edo/src/edoRepairerApply.h
Normal file
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@ -0,0 +1,100 @@
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/*
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The Evolving Distribution Objects framework (EDO) is a template-based,
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ANSI-C++ evolutionary computation library which helps you to write your
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own estimation of distribution algorithms.
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This library is free software; you can redistribute it and/or
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||||
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.
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||||
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This library is distributed in the hope that it will be useful,
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||||
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
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Copyright (C) 2011 Thales group
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*/
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/*
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Authors:
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Johann Dréo <johann.dreo@thalesgroup.com>
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*/
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#ifndef _edoRepairerApply_h
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#define _edoRepairerApply_h
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#include <algorithm>
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#include "edoRepairer.h"
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template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType) >
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class edoRepairerApply : public edoRepairer<EOT>
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{
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public:
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edoRepairerApply( F function ) : _function(function) {}
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protected:
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F * _function;
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};
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/** Apply an arbitrary unary function as a repairer on each item of the solution
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*
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* By default, the signature of the expected function is "EOT::AtomType(EOT::AtomType)"
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*
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* @ingroup Repairers
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*/
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template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType)>
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class edoRepairerApplyUnary : public edoRepairerApply<EOT,F>
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{
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public:
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edoRepairerApplyUnary( F function ) : edoRepairerApply<EOT,F>(function) {}
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virtual void operator()( EOT& sol )
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{
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std::transform( sol.begin(), sol.end(), sol.begin(), *(this->_function) );
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sol.invalidate();
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}
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};
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/** Apply an arbitrary binary function as a repairer on each item of the solution,
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* the second argument of the function being fixed and given at instanciation.
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*
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* @see edoRepairerApplyUnary
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*
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* @ingroup Repairers
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*/
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template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType, typename EOT::AtomType)>
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class edoRepairerApplyBinary : public edoRepairerApply<EOT,F>
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{
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public:
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typedef typename EOT::AtomType ArgType;
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edoRepairerApplyBinary(
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F function,
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ArgType arg
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) : edoRepairerApply<EOT,F>(function), _arg(arg) {}
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virtual void operator()( EOT& sol )
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{
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// call the binary function on each item
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// TODO find a way to use std::transform here? Or would it be too bloated?
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for(typename EOT::iterator it = sol.begin(); it != sol.end(); ++it ) {
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*it = (*(this->_function))( *it, _arg );
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}
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sol.invalidate();
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}
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protected:
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ArgType _arg;
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};
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#endif // !_edoRepairerApply_h
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@ -38,23 +38,69 @@ Authors:
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* of indexes).
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*
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* Only work on EOT that implements the "push_back( EOT::AtomType )" and
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* "operator[](uint)" and "at(uint)" methods.
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* "operator[](uint)" and "at(uint)" methods (i.e. random access containers).
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*
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* Expects _addresses_ of the repairer operators.
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*
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* Use the second template type if you want a different container to store
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* indexes. You can use any iterable. For example, you may want to use a set if
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* you need to be sure that indexes are use only once:
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* edoRepairerDispatcher<EOT, std::set<unsigned int> > rpd;
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* std::set<unsigned int> idx(1,1);
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* idx.insert(2);
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* rpd.add( idx, &repairer );
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*
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* A diagram trying to visually explain how it works:
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\ditaa
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|
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/-\ | /------------\
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| +---|---+ Dispatcher |
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| | v | |
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| |+-----+| --------------------------------+
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| || x_0 || +-+-+-+ | +------------\ | /-\
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| |+-----+| |2|3|5+*----*-* Repairer A +---|---+ |
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| || x_1 || +-+-+-+ | | | | v | |
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| |+-----+| | | | |+-----+| |
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| || x_2 || | | | || x_2 || |
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| |+-----+| | | | |+-----+| |
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| || x_3 || | | | || x_3 || |
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| |+-----+| | | | |+-----+| |
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| || x_4 || | | | || x_5 || |
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| |+-----+| | | | |+-----+| |
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| || x_5 || | | | | | | |
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| |+-----+| | | | +---|---+ |
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| || x_6 || | | \------------/ | \-/
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| |+-----+| <-------------------------------+
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| || x_7 || | |
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| |+-----+| +-+-+ | |
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| || x_8 || |2|3+*------+
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| |+-----+| +-+-+ |
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| || x_9 || |
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| |+-----+| +-+-+ | +------------\ /-\
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| | | | |1|5+*--------* Repairer B +-------+ |
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| | | | +-+-+ | | | | |
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| | | | | | | | |
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| | | | | | +-------+ |
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| +---|---+ | \------------/ \-/
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\-/ | \------------/
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v
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\endditaa
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* @example t-dispatcher-round.cpp
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*
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* @ingroup Repairers
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*/
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template < typename EOT >
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template < typename EOT, typename ICT = std::vector<unsigned int> >
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class edoRepairerDispatcher
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: public edoRepairer<EOT>,
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std::vector<
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std::pair< std::vector< unsigned int >, edoRepairer< EOT >* >
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std::pair< ICT, edoRepairer< EOT >* >
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>
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{
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public:
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//! Empty constructor
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edoRepairerDispatcher() :
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std::vector<
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@ -63,7 +109,7 @@ public:
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{}
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//! Constructor with a single index set and repairer operator
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edoRepairerDispatcher( std::vector<unsigned int> idx, edoRepairer<EOT>* op ) :
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edoRepairerDispatcher( ICT idx, edoRepairer<EOT>* op ) :
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std::vector<
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std::pair< std::vector< unsigned int >, edoRepairer< EOT >* >
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>()
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@ -72,7 +118,7 @@ public:
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}
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//! Add more indexes set and their corresponding repairer operator address to the list
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void add( std::vector<unsigned int> idx, edoRepairer<EOT>* op )
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void add( ICT idx, edoRepairer<EOT>* op )
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{
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assert( idx.size() > 0 );
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assert( op != NULL );
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@ -83,15 +129,27 @@ public:
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//! Repair a solution by calling several repair operator on subset of indexes
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virtual void operator()( EOT& sol )
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{
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// ipair is an iterator that points on a pair
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// std::cout << "in dispatcher, sol = " << sol << std::endl;
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// ipair is an iterator that points on a pair of <indexes,repairer>
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for( typename edoRepairerDispatcher<EOT>::iterator ipair = this->begin(); ipair != this->end(); ++ipair ) {
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assert( ipair->first.size() <= sol.size() ); // assert there is less indexes than items in the whole solution
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// a partial copy of the sol
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EOT partsol;
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// std::cout << "\tusing indexes = ";
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// j is an iterator that points on an uint
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for( std::vector< unsigned int >::iterator j = ipair->first.begin(); j != ipair->first.end(); ++j ) {
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// std::cout << *j << " ";
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// std::cout.flush();
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partsol.push_back( sol.at(*j) );
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} // for j
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// std::cout << std::endl;
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// std::cout << "\tpartial sol = " << partsol << std::endl;
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assert( partsol.size() > 0 );
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@ -108,6 +166,8 @@ public:
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} // for j
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} // context for k
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} // for ipair
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sol.invalidate();
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}
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};
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|
|
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47
edo/src/edoRepairerModulo.h
Normal file
47
edo/src/edoRepairerModulo.h
Normal file
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@ -0,0 +1,47 @@
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/*
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The Evolving Distribution Objects framework (EDO) is a template-based,
|
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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) 2011 Thales group
|
||||
*/
|
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/*
|
||||
Authors:
|
||||
Johann Dréo <johann.dreo@thalesgroup.com>
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*/
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#ifndef _edoRepairerModulo_h
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#define _edoRepairerModulo_h
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#include <cmath>
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#include "edoRepairerApply.h"
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/**
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*
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* @ingroup Repairers
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*/
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template < typename EOT >
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class edoRepairerModulo: public edoRepairerApplyBinary<EOT>
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{
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public:
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edoRepairerModulo<EOT>( double denominator ) : edoRepairerApplyBinary<EOT>( std::fmod, denominator ) {}
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};
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#endif // !_edoRepairerModulo_h
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|
@ -30,39 +30,81 @@ Authors:
|
|||
|
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#include <cmath>
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|
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#include "edoRepairer.h"
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#include "edoRepairerApply.h"
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|
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/**
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/** A repairer that calls "floor" on each items of a solution
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*
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* Just a proxy to "edoRepairerApplyUnary<EOT, EOT::AtomType(EOT::AtomType)> rep( std::floor);"
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*
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* @ingroup Repairers
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*/
|
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template < typename EOT >
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class edoRepairerFloor : public edoRepairer<EOT>
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class edoRepairerFloor : public edoRepairerApplyUnary<EOT>
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{
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public:
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virtual void operator()( EOT& sol )
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{
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for( unsigned int i=0; i < sol.size(); ++i ) {
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sol[i] = floor( sol[i] );
|
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}
|
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}
|
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edoRepairerFloor() : edoRepairerApplyUnary<EOT>( std::floor ) {}
|
||||
};
|
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|
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/**
|
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|
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/** A repairer that calls "ceil" on each items of a solution
|
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*
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* @see edoRepairerFloor
|
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*
|
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* @ingroup Repairers
|
||||
*/
|
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template < typename EOT >
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class edoRepairerCeil : public edoRepairer<EOT>
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class edoRepairerCeil : public edoRepairerApplyUnary<EOT>
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{
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public:
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virtual void operator()( EOT& sol )
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edoRepairerCeil() : edoRepairerApplyUnary<EOT>( std::ceil ) {}
|
||||
};
|
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// FIXME find a way to put this function as a member of edoRepairerRoundDecimals
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template< typename ArgType >
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ArgType edoRound( ArgType val, ArgType prec = 1.0 )
|
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{
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return (val > 0.0) ?
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floor(val * prec + 0.5) / prec :
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ceil(val * prec - 0.5) / prec ;
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}
|
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|
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/** A repairer that round values at a given a precision.
|
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*
|
||||
* e.g. if prec=0.1, 8.06 will be rounded to 8.1
|
||||
*
|
||||
* @see edoRepairerFloor
|
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* @see edoRepairerCeil
|
||||
*
|
||||
* @ingroup Repairers
|
||||
*/
|
||||
template < typename EOT >
|
||||
class edoRepairerRoundDecimals : public edoRepairerApplyBinary<EOT>
|
||||
{
|
||||
public:
|
||||
typedef typename EOT::AtomType ArgType;
|
||||
|
||||
//! Generally speaking, we expect decimals being <= 1, but it can work for higher values
|
||||
edoRepairerRoundDecimals( ArgType decimals ) : edoRepairerApplyBinary<EOT>( edoRound<ArgType>, 1 / decimals )
|
||||
{
|
||||
for( unsigned int i=0; i < sol.size(); ++i ) {
|
||||
sol[i] = ceil( sol[i] );
|
||||
}
|
||||
assert( decimals <= 1.0 );
|
||||
assert( 1/decimals >= 1.0 );
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
/** A repairer that do a rounding around val+0.5
|
||||
*
|
||||
* @see edoRepairerRoundDecimals
|
||||
*
|
||||
* @ingroup Repairers
|
||||
*/
|
||||
template < typename EOT >
|
||||
class edoRepairerRound : public edoRepairerRoundDecimals<EOT>
|
||||
{
|
||||
public:
|
||||
edoRepairerRound() : edoRepairerRoundDecimals<EOT>( 1.0 ) {}
|
||||
};
|
||||
|
||||
#endif // !_edoRepairerRound_h
|
||||
|
|
|
|||
|
|
@ -28,6 +28,8 @@ Authors:
|
|||
#ifndef _edoSamplerNormalMono_h
|
||||
#define _edoSamplerNormalMono_h
|
||||
|
||||
#include <cmath>
|
||||
|
||||
#include <utils/eoRNG.h>
|
||||
|
||||
#include "edoSampler.h"
|
||||
|
|
@ -47,27 +49,25 @@ public:
|
|||
|
||||
edoSamplerNormalMono( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}
|
||||
|
||||
EOT sample( edoNormalMono< EOT >& distrib )
|
||||
EOT sample( edoNormalMono<EOT>& distrib )
|
||||
{
|
||||
unsigned int size = distrib.size();
|
||||
assert(size > 0);
|
||||
|
||||
// Point we want to sample to get higher a set of points
|
||||
// The point we want to draw
|
||||
// (coordinates in n dimension)
|
||||
// x = {x1, x2, ..., xn}
|
||||
EOT solution;
|
||||
|
||||
// Sampling all dimensions
|
||||
for (unsigned int i = 0; i < size; ++i)
|
||||
{
|
||||
for (unsigned int i = 0; i < size; ++i) {
|
||||
AtomType mean = distrib.mean()[i];
|
||||
AtomType variance = distrib.variance()[i];
|
||||
AtomType random = rng.normal(mean, variance);
|
||||
|
||||
assert(variance >= 0);
|
||||
// should use the standard deviation, which have the same scale than the mean
|
||||
AtomType random = rng.normal(mean, sqrt(variance) );
|
||||
|
||||
solution.push_back(random);
|
||||
}
|
||||
}
|
||||
|
||||
return solution;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -28,151 +28,391 @@ Authors:
|
|||
#ifndef _edoSamplerNormalMulti_h
|
||||
#define _edoSamplerNormalMulti_h
|
||||
|
||||
#include <cmath>
|
||||
#include <limits>
|
||||
|
||||
#include <edoSampler.h>
|
||||
#include <boost/numeric/ublas/lu.hpp>
|
||||
#include <boost/numeric/ublas/symmetric.hpp>
|
||||
|
||||
//! edoSamplerNormalMulti< EOT >
|
||||
|
||||
template< class EOT, typename D = edoNormalMulti< EOT > >
|
||||
class edoSamplerNormalMulti : public edoSampler< D >
|
||||
/** 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
|
||||
*/
|
||||
template< class EOT, typename EOD = edoNormalMulti< EOT > >
|
||||
class edoSamplerNormalMulti : public edoSampler< EOD >
|
||||
{
|
||||
public:
|
||||
typedef typename EOT::AtomType AtomType;
|
||||
|
||||
edoSamplerNormalMulti( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}
|
||||
|
||||
/** Cholesky decomposition, given a matrix V, return a matrix L
|
||||
* such as V = L L^T (L^T being the transposed of L).
|
||||
*
|
||||
* Need a symmetric and positive definite matrix as an input, which
|
||||
* should be the case of a non-ill-conditionned covariance matrix.
|
||||
* Thus, expect a (lower) triangular matrix.
|
||||
*/
|
||||
class Cholesky
|
||||
{
|
||||
public:
|
||||
Cholesky( const ublas::symmetric_matrix< AtomType, ublas::lower >& V)
|
||||
{
|
||||
unsigned int Vl = V.size1();
|
||||
//! The covariance-matrix is symetric
|
||||
typedef ublas::symmetric_matrix< AtomType, ublas::lower > CovarMat;
|
||||
|
||||
//! The factorization matrix is triangular
|
||||
// FIXME check if triangular types behaviour is like having 0
|
||||
typedef ublas::matrix< AtomType > FactorMat;
|
||||
|
||||
enum Method {
|
||||
//! use the standard algorithm, with square root @see factorize_LLT
|
||||
standard,
|
||||
//! use the algorithm using absolute value within the square root @see factorize_LLT_abs
|
||||
absolute,
|
||||
//! use the method that set negative square roots to zero @see factorize_LLT_zero
|
||||
zeroing,
|
||||
//! use the robust algorithm, without square root @see factorize_LDLT
|
||||
robust
|
||||
};
|
||||
Method _use;
|
||||
|
||||
|
||||
/** Instanciate without computing anything, you are responsible of
|
||||
* calling the algorithm and getting the result with operator()
|
||||
* */
|
||||
Cholesky( Cholesky::Method use = standard ) : _use(use) {}
|
||||
|
||||
|
||||
/** Computation is made at instanciation and then cached in a member variable,
|
||||
* use decomposition() to get the result.
|
||||
*
|
||||
* Use the standard unstable method by default.
|
||||
*/
|
||||
Cholesky(const CovarMat& V, Cholesky::Method use = standard ) :
|
||||
_use(use), _L(ublas::zero_matrix<AtomType>(V.size1(),V.size2()))
|
||||
{
|
||||
factorize( V );
|
||||
}
|
||||
|
||||
|
||||
/** Compute the factorization and return the result
|
||||
*/
|
||||
const FactorMat& operator()( const CovarMat& V )
|
||||
{
|
||||
factorize( V );
|
||||
return decomposition();
|
||||
}
|
||||
|
||||
//! The decomposition of the covariance matrix
|
||||
const FactorMat & decomposition() const
|
||||
{
|
||||
return _L;
|
||||
}
|
||||
|
||||
template<typename MT>
|
||||
std::string format(const MT& mat )
|
||||
{
|
||||
std::ostringstream out;
|
||||
|
||||
for( unsigned int i=0; i<mat.size1(); ++i) {
|
||||
out << std::endl;
|
||||
for( unsigned int j=0; j<mat.size2(); ++j) {
|
||||
out << mat(i,j) << "\t";
|
||||
} // columns
|
||||
} // rows
|
||||
|
||||
return out.str();
|
||||
}
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
//! The decomposition is a (lower) symetric matrix, just like the covariance matrix
|
||||
FactorMat _L;
|
||||
|
||||
/** Assert that the covariance matrix have the required properties and returns its dimension.
|
||||
*
|
||||
* Note: if compiled with NDEBUG, will not assert anything and just return the dimension.
|
||||
*/
|
||||
unsigned assert_properties( const CovarMat& V )
|
||||
{
|
||||
unsigned int Vl = V.size1(); // number of lines
|
||||
|
||||
// the result goes in _L
|
||||
_L = ublas::zero_matrix<AtomType>(Vl,Vl);
|
||||
|
||||
eo::log << eo::debug << std::endl << "Covariance matrix:" << format( V ) << std::endl;
|
||||
|
||||
#ifndef NDEBUG
|
||||
assert(Vl > 0);
|
||||
|
||||
unsigned int Vc = V.size2();
|
||||
|
||||
unsigned int Vc = V.size2(); // number of columns
|
||||
assert(Vc > 0);
|
||||
|
||||
assert( Vl == Vc );
|
||||
|
||||
_L.resize(Vl);
|
||||
// partial assert that V is semi-positive definite
|
||||
// assert that all diagonal elements are positives
|
||||
for( unsigned int i=0; i < Vl; ++i ) {
|
||||
assert( V(i,i) > 0 );
|
||||
}
|
||||
|
||||
unsigned int i,j,k;
|
||||
/* FIXME what is the more efficient way to check semi-positive definite? Candidates are:
|
||||
* perform the cholesky factorization
|
||||
* check if all eigenvalues are positives
|
||||
* check if all of the leading principal minors are positive
|
||||
*/
|
||||
#endif
|
||||
|
||||
// first column
|
||||
i=0;
|
||||
return Vl;
|
||||
}
|
||||
|
||||
// diagonal
|
||||
j=0;
|
||||
|
||||
/** Actually performs the factorization with the method given at
|
||||
* instanciation. Results are cached in _L.
|
||||
*/
|
||||
void factorize( const CovarMat& V )
|
||||
{
|
||||
if( _use == standard ) {
|
||||
factorize_LLT( V );
|
||||
} else if( _use == absolute ) {
|
||||
factorize_LLT_abs( V );
|
||||
} else if( _use == zeroing ) {
|
||||
factorize_LLT_zero( V );
|
||||
} else if( _use == robust ) {
|
||||
factorize_LDLT( V );
|
||||
}
|
||||
|
||||
eo::log << eo::debug << std::endl << "Decomposed matrix:" << format( _L ) << std::endl;
|
||||
}
|
||||
|
||||
|
||||
/** This standard algorithm makes use of square root and is thus subject
|
||||
* to round-off errors if the covariance matrix is very ill-conditioned.
|
||||
*
|
||||
* Compute L such that V = L L^T
|
||||
*
|
||||
* When compiled in debug mode and called on ill-conditionned matrix,
|
||||
* will raise an assert before calling the square root on a negative number.
|
||||
*/
|
||||
void factorize_LLT( const CovarMat& V)
|
||||
{
|
||||
unsigned int N = assert_properties( V );
|
||||
|
||||
unsigned int i=0, j=0, k;
|
||||
_L(0, 0) = sqrt( V(0, 0) );
|
||||
|
||||
// end of the column
|
||||
for ( j = 1; j < Vc; ++j )
|
||||
{
|
||||
for ( j = 1; j < N; ++j ) {
|
||||
_L(j, 0) = V(0, j) / _L(0, 0);
|
||||
}
|
||||
|
||||
// end of the matrix
|
||||
for ( i = 1; i < Vl; ++i ) // each column
|
||||
{
|
||||
|
||||
for ( i = 1; i < N; ++i ) { // each column
|
||||
// diagonal
|
||||
double sum = 0.0;
|
||||
for ( k = 0; k < i; ++k) {
|
||||
sum += _L(i, k) * _L(i, k);
|
||||
}
|
||||
|
||||
for ( k = 0; k < i; ++k)
|
||||
{
|
||||
// round-off errors may appear here
|
||||
assert( V(i,i) - sum >= 0 );
|
||||
_L(i,i) = sqrt( V(i,i) - sum );
|
||||
|
||||
for ( j = i + 1; j < N; ++j ) { // rows
|
||||
// one element
|
||||
sum = 0.0;
|
||||
for ( k = 0; k < i; ++k ) {
|
||||
sum += _L(j, k) * _L(i, k);
|
||||
}
|
||||
|
||||
_L(j, i) = (V(j, i) - sum) / _L(i, i);
|
||||
|
||||
} // for j in ]i,N[
|
||||
} // for i in [1,N[
|
||||
}
|
||||
|
||||
|
||||
/** This standard algorithm makes use of square root but do not fail
|
||||
* if the covariance matrix is very ill-conditioned.
|
||||
* Here, we propagate the error by using the absolute value before
|
||||
* computing the square root.
|
||||
*
|
||||
* Be aware that this increase round-off errors, this is just a ugly
|
||||
* hack to avoid crash.
|
||||
*/
|
||||
void factorize_LLT_abs( const CovarMat & V)
|
||||
{
|
||||
unsigned int N = assert_properties( V );
|
||||
|
||||
unsigned int i=0, j=0, k;
|
||||
|
||||
_L(0, 0) = sqrt( V(0, 0) );
|
||||
|
||||
// end of the column
|
||||
for ( j = 1; j < N; ++j ) {
|
||||
_L(j, 0) = V(0, j) / _L(0, 0);
|
||||
}
|
||||
|
||||
// end of the matrix
|
||||
for ( i = 1; i < N; ++i ) { // each column
|
||||
// diagonal
|
||||
double sum = 0.0;
|
||||
for ( k = 0; k < i; ++k) {
|
||||
sum += _L(i, k) * _L(i, k);
|
||||
}
|
||||
|
||||
_L(i,i) = sqrt( fabs( V(i,i) - sum) );
|
||||
|
||||
for ( j = i + 1; j < Vl; ++j ) // rows
|
||||
{
|
||||
for ( j = i + 1; j < N; ++j ) { // rows
|
||||
// one element
|
||||
sum = 0.0;
|
||||
|
||||
for ( k = 0; k < i; ++k )
|
||||
{
|
||||
for ( k = 0; k < i; ++k ) {
|
||||
sum += _L(j, k) * _L(i, k);
|
||||
}
|
||||
|
||||
_L(j, i) = (V(j, i) - sum) / _L(i, i);
|
||||
}
|
||||
}
|
||||
|
||||
} // for j in ]i,N[
|
||||
} // for i in [1,N[
|
||||
}
|
||||
|
||||
const ublas::symmetric_matrix< AtomType, ublas::lower >& get_L() const {return _L;}
|
||||
|
||||
private:
|
||||
ublas::symmetric_matrix< AtomType, ublas::lower > _L;
|
||||
};
|
||||
/** This standard algorithm makes use of square root but do not fail
|
||||
* if the covariance matrix is very ill-conditioned.
|
||||
* Here, if the diagonal difference ir negative, we set it to zero.
|
||||
*
|
||||
* Be aware that this increase round-off errors, this is just a ugly
|
||||
* hack to avoid crash.
|
||||
*/
|
||||
void factorize_LLT_zero( const CovarMat & V)
|
||||
{
|
||||
unsigned int N = assert_properties( V );
|
||||
|
||||
edoSamplerNormalMulti( edoBounder< EOT > & bounder )
|
||||
: edoSampler< edoNormalMulti< EOT > >( bounder )
|
||||
{}
|
||||
unsigned int i=0, j=0, k;
|
||||
|
||||
EOT sample( edoNormalMulti< EOT >& distrib )
|
||||
{
|
||||
unsigned int size = distrib.size();
|
||||
_L(0, 0) = sqrt( V(0, 0) );
|
||||
|
||||
assert(size > 0);
|
||||
|
||||
|
||||
//-------------------------------------------------------------
|
||||
// Cholesky factorisation gererating matrix L from covariance
|
||||
// matrix V.
|
||||
// We must use cholesky.get_L() to get the resulting matrix.
|
||||
//
|
||||
// L = cholesky decomposition of varcovar
|
||||
//-------------------------------------------------------------
|
||||
|
||||
Cholesky cholesky( distrib.varcovar() );
|
||||
ublas::symmetric_matrix< AtomType, ublas::lower > L = cholesky.get_L();
|
||||
|
||||
//-------------------------------------------------------------
|
||||
|
||||
|
||||
//-------------------------------------------------------------
|
||||
// T = vector of size elements drawn in N(0,1) rng.normal(1.0)
|
||||
//-------------------------------------------------------------
|
||||
|
||||
ublas::vector< AtomType > T( size );
|
||||
|
||||
for ( unsigned int i = 0; i < size; ++i )
|
||||
{
|
||||
T( i ) = rng.normal( 1.0 );
|
||||
// end of the column
|
||||
for ( j = 1; j < N; ++j ) {
|
||||
_L(j, 0) = V(0, j) / _L(0, 0);
|
||||
}
|
||||
|
||||
//-------------------------------------------------------------
|
||||
// end of the matrix
|
||||
for ( i = 1; i < N; ++i ) { // each column
|
||||
// diagonal
|
||||
double sum = 0.0;
|
||||
for ( k = 0; k < i; ++k) {
|
||||
sum += _L(i, k) * _L(i, k);
|
||||
}
|
||||
|
||||
if( V(i,i) - sum >= 0 ) {
|
||||
_L(i,i) = sqrt( V(i,i) - sum);
|
||||
} else {
|
||||
_L(i,i) = std::numeric_limits<double>::epsilon();
|
||||
}
|
||||
|
||||
for ( j = i + 1; j < N; ++j ) { // rows
|
||||
// one element
|
||||
sum = 0.0;
|
||||
for ( k = 0; k < i; ++k ) {
|
||||
sum += _L(j, k) * _L(i, k);
|
||||
}
|
||||
|
||||
_L(j, i) = (V(j, i) - sum) / _L(i, i);
|
||||
|
||||
} // for j in ]i,N[
|
||||
} // for i in [1,N[
|
||||
}
|
||||
|
||||
|
||||
//-------------------------------------------------------------
|
||||
// LT = prod( L, T )
|
||||
//-------------------------------------------------------------
|
||||
/** This alternative algorithm do not use square root in an inner loop,
|
||||
* but only for some diagonal elements of the matrix D.
|
||||
*
|
||||
* Computes L and D such as V = L D L^T.
|
||||
* The factorized matrix is (L D^1/2), because V = (L D^1/2) (L D^1/2)^T
|
||||
*/
|
||||
void factorize_LDLT( const CovarMat& V)
|
||||
{
|
||||
// use "int" everywhere, because of the "j-1" operation
|
||||
int N = assert_properties( V );
|
||||
// example of an invertible matrix whose decomposition is undefined
|
||||
assert( V(0,0) != 0 );
|
||||
|
||||
FactorMat L = ublas::zero_matrix<AtomType>(N,N);
|
||||
FactorMat D = ublas::zero_matrix<AtomType>(N,N);
|
||||
D(0,0) = V(0,0);
|
||||
|
||||
for( int j=0; j<N; ++j ) { // each columns
|
||||
L(j, j) = 1;
|
||||
|
||||
D(j,j) = V(j,j);
|
||||
for( int k=0; k<=j-1; ++k) { // sum
|
||||
D(j,j) -= L(j,k) * L(j,k) * D(k,k);
|
||||
}
|
||||
|
||||
for( int i=j+1; i<N; ++i ) { // remaining rows
|
||||
|
||||
L(i,j) = V(i,j);
|
||||
for( int k=0; k<=j-1; ++k) { // sum
|
||||
L(i,j) -= L(i,k)*L(j,k) * D(k,k);
|
||||
}
|
||||
L(i,j) /= D(j,j);
|
||||
|
||||
} // for i in rows
|
||||
} // for j in columns
|
||||
|
||||
// now compute the final symetric matrix: _L = L D^1/2
|
||||
// remember that V = ( L D^1/2) ( L D^1/2)^T
|
||||
|
||||
// fortunately, the square root of a diagonal matrix is the square
|
||||
// root of all its elements
|
||||
FactorMat D12 = D;
|
||||
for(int i=0; i<N; ++i) {
|
||||
D12(i,i) = sqrt(D(i,i));
|
||||
}
|
||||
|
||||
// the factorization is thus _L*D^1/2
|
||||
_L = ublas::prod( L, D12);
|
||||
}
|
||||
|
||||
}; // class Cholesky
|
||||
|
||||
|
||||
edoSamplerNormalMulti( edoRepairer<EOT> & repairer, typename Cholesky::Method use = Cholesky::absolute )
|
||||
: edoSampler< EOD >( repairer), _cholesky(use)
|
||||
{}
|
||||
|
||||
|
||||
EOT sample( EOD& distrib )
|
||||
{
|
||||
unsigned int size = distrib.size();
|
||||
assert(size > 0);
|
||||
|
||||
// L = cholesky decomposition of varcovar
|
||||
const typename Cholesky::FactorMat& L = _cholesky( distrib.varcovar() );
|
||||
|
||||
// T = vector of size elements drawn in N(0,1)
|
||||
ublas::vector< AtomType > T( size );
|
||||
for ( unsigned int i = 0; i < size; ++i ) {
|
||||
T( i ) = rng.normal();
|
||||
}
|
||||
|
||||
// LT = L * T
|
||||
ublas::vector< AtomType > LT = ublas::prod( L, T );
|
||||
|
||||
//-------------------------------------------------------------
|
||||
|
||||
|
||||
//-------------------------------------------------------------
|
||||
// solution = means + LT
|
||||
//-------------------------------------------------------------
|
||||
|
||||
ublas::vector< AtomType > mean = distrib.mean();
|
||||
|
||||
ublas::vector< AtomType > ublas_solution = mean + LT;
|
||||
|
||||
EOT solution( size );
|
||||
|
||||
std::copy( ublas_solution.begin(), ublas_solution.end(), solution.begin() );
|
||||
|
||||
//-------------------------------------------------------------
|
||||
|
||||
return solution;
|
||||
}
|
||||
|
||||
protected:
|
||||
Cholesky _cholesky;
|
||||
};
|
||||
|
||||
#endif // !_edoSamplerNormalMulti_h
|
||||
|
|
|
|||
|
|
@ -33,12 +33,14 @@ LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
|
|||
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/application/common)
|
||||
|
||||
SET(SOURCES
|
||||
t-cholesky
|
||||
t-edoEstimatorNormalMulti
|
||||
t-mean-distance
|
||||
t-bounderno
|
||||
t-uniform
|
||||
t-continue
|
||||
t-dispatcher-round
|
||||
t-repairer-modulo
|
||||
)
|
||||
|
||||
FOREACH(current ${SOURCES})
|
||||
|
|
|
|||
250
edo/test/t-cholesky.cpp
Normal file
250
edo/test/t-cholesky.cpp
Normal file
|
|
@ -0,0 +1,250 @@
|
|||
|
||||
/*
|
||||
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>
|
||||
*/
|
||||
|
||||
//#include <vector>
|
||||
#include <cstdlib>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <limits>
|
||||
#include <iomanip>
|
||||
#include <ctime>
|
||||
|
||||
#include <eo>
|
||||
#include <es.h>
|
||||
#include <edo>
|
||||
|
||||
typedef eoReal< eoMinimizingFitness > EOT;
|
||||
typedef edoNormalMulti<EOT> EOD;
|
||||
|
||||
|
||||
void setformat( std::ostream& out )
|
||||
{
|
||||
out << std::right;
|
||||
out << std::setfill(' ');
|
||||
out << std::setw( 5 + std::numeric_limits<double>::digits10);
|
||||
out << std::setprecision(std::numeric_limits<double>::digits10);
|
||||
out << std::setiosflags(std::ios_base::showpoint);
|
||||
}
|
||||
|
||||
|
||||
template<typename MT>
|
||||
std::string format(const MT& mat )
|
||||
{
|
||||
std::ostringstream out;
|
||||
setformat(out);
|
||||
|
||||
for( unsigned int i=0; i<mat.size1(); ++i) {
|
||||
for( unsigned int j=0; j<mat.size2(); ++j) {
|
||||
out << mat(i,j) << "\t";
|
||||
} // columns
|
||||
out << std::endl;
|
||||
} // rows
|
||||
|
||||
return out.str();
|
||||
}
|
||||
|
||||
|
||||
template< typename T >
|
||||
T round( T val, T prec = 1.0 )
|
||||
{
|
||||
return (val > 0.0) ?
|
||||
floor(val * prec + 0.5) / prec :
|
||||
ceil(val * prec - 0.5) / prec ;
|
||||
}
|
||||
|
||||
|
||||
template<typename MT>
|
||||
bool equal( const MT& M1, const MT& M2, double prec /* = 1/std::numeric_limits<double>::digits10 ???*/ )
|
||||
{
|
||||
if( M1.size1() != M2.size1() || M1.size2() != M2.size2() ) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for( unsigned int i=0; i<M1.size1(); ++i ) {
|
||||
for( unsigned int j=0; j<M1.size2(); ++j ) {
|
||||
if( round(M1(i,j),prec) != round(M2(i,j),prec) ) {
|
||||
std::cout << "round(M(" << i << "," << j << "," << prec << ") == "
|
||||
<< round(M1(i,j),prec) << " != " << round(M2(i,j),prec) << std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
template<typename MT >
|
||||
MT error( const MT& M1, const MT& M2 )
|
||||
{
|
||||
assert( M1.size1() == M2.size1() && M1.size1() == M2.size2() );
|
||||
|
||||
MT Err = ublas::zero_matrix<double>(M1.size1(),M1.size2());
|
||||
|
||||
for( unsigned int i=0; i<M1.size1(); ++i ) {
|
||||
for( unsigned int j=0; j<M1.size2(); ++j ) {
|
||||
Err(i,j) = M1(i,j) - M2(i,j);
|
||||
}
|
||||
}
|
||||
|
||||
return Err;
|
||||
}
|
||||
|
||||
|
||||
template<typename MT >
|
||||
double trigsum( const MT& M )
|
||||
{
|
||||
double sum;
|
||||
for( unsigned int i=0; i<M.size1(); ++i ) {
|
||||
for( unsigned int j=i; j<M.size2(); ++j ) { // triangular browsing
|
||||
sum += fabs( M(i,j) ); // absolute deviation
|
||||
}
|
||||
}
|
||||
return sum;
|
||||
}
|
||||
|
||||
|
||||
template<typename T>
|
||||
double sum( const T& c )
|
||||
{
|
||||
return std::accumulate(c.begin(), c.end(), 0);
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
srand(time(0));
|
||||
|
||||
unsigned int N = 4; // size of matrix
|
||||
unsigned int R = 1000; // nb of repetitions
|
||||
|
||||
if( argc >= 2 ) {
|
||||
N = std::atoi(argv[1]);
|
||||
}
|
||||
if( argc >= 3 ) {
|
||||
R = std::atoi(argv[2]);
|
||||
}
|
||||
|
||||
std::cout << "Usage: t-cholesky [matrix size] [repetitions]" << std::endl;
|
||||
std::cout << "matrix size = " << N << std::endl;
|
||||
std::cout << "repetitions = " << R << std::endl;
|
||||
|
||||
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::CovarMat CovarMat;
|
||||
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::FactorMat FactorMat;
|
||||
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTz( edoSamplerNormalMulti<EOT,EOD>::Cholesky::zeroing );
|
||||
edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
|
||||
|
||||
std::vector<double> s0,s1,s2,s3;
|
||||
for( unsigned int n=0; n<R; ++n ) {
|
||||
|
||||
// a variance-covariance matrix of size N*N
|
||||
CovarMat V(N,N);
|
||||
|
||||
// random covariance matrix
|
||||
for( unsigned int i=0; i<N; ++i) {
|
||||
V(i,i) = std::pow(rand(),2); // variance should be >= 0
|
||||
for( unsigned int j=i+1; j<N; ++j) {
|
||||
V(i,j) = rand();
|
||||
}
|
||||
}
|
||||
|
||||
FactorMat L0 = LLT(V);
|
||||
CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
|
||||
s0.push_back( trigsum(error(V,V0)) );
|
||||
|
||||
FactorMat L1 = LLTa(V);
|
||||
CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
|
||||
s1.push_back( trigsum(error(V,V1)) );
|
||||
|
||||
FactorMat L2 = LLTz(V);
|
||||
CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
|
||||
s2.push_back( trigsum(error(V,V2)) );
|
||||
|
||||
FactorMat L3 = LDLT(V);
|
||||
CovarMat V3 = ublas::prod( L3, ublas::trans(L3) );
|
||||
s3.push_back( trigsum(error(V,V3)) );
|
||||
}
|
||||
|
||||
std::cout << "Average error:" << std::endl;
|
||||
std::cout << "\tLLT: " << sum(s0)/R << std::endl;
|
||||
std::cout << "\tLLTa: " << sum(s1)/R << std::endl;
|
||||
std::cout << "\tLLTz: " << sum(s2)/R << std::endl;
|
||||
std::cout << "\tLDLT: " << sum(s3)/R << std::endl;
|
||||
|
||||
// double precision = 1e-15;
|
||||
// if( argc >= 4 ) {
|
||||
// precision = std::atof(argv[3]);
|
||||
// }
|
||||
// std::cout << "precision = " << precision << std::endl;
|
||||
// std::cout << "usage: t-cholesky [N] [precision]" << std::endl;
|
||||
// std::cout << "N = " << N << std::endl;
|
||||
// std::cout << "precision = " << precision << std::endl;
|
||||
// std::string linesep = "--------------------------------------------------------------------------------------------";
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// setformat(std::cout);
|
||||
//
|
||||
// std::cout << "Covariance matrix" << std::endl << format(V) << std::endl;
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
|
||||
// FactorMat L0 = LLT(V);
|
||||
// CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
|
||||
// CovarMat E0 = error(V,V0);
|
||||
// std::cout << "LLT" << std::endl << format(E0) << std::endl;
|
||||
// std::cout << trigsum(E0) << std::endl;
|
||||
// std::cout << "LLT" << std::endl << format(L0) << std::endl;
|
||||
// std::cout << "LLT covar" << std::endl << format(V0) << std::endl;
|
||||
// assert( equal(V0,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
|
||||
// FactorMat L1 = LLTa(V);
|
||||
// CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
|
||||
// CovarMat E1 = error(V,V1);
|
||||
// std::cout << "LLT abs" << std::endl << format(E1) << std::endl;
|
||||
// std::cout << trigsum(E1) << std::endl;
|
||||
// std::cout << "LLT abs" << std::endl << format(L1) << std::endl;
|
||||
// std::cout << "LLT covar" << std::endl << format(V1) << std::endl;
|
||||
// assert( equal(V1,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
//
|
||||
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
|
||||
// FactorMat L2 = LDLT(V);
|
||||
// CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
|
||||
// CovarMat E2 = error(V,V2);
|
||||
// std::cout << "LDLT" << std::endl << format(E2) << std::endl;
|
||||
// std::cout << trigsum(E2) << std::endl;
|
||||
// std::cout << "LDLT" << std::endl << format(L2) << std::endl;
|
||||
// std::cout << "LDLT covar" << std::endl << format(V2) << std::endl;
|
||||
// assert( equal(V2,V,precision) );
|
||||
// std::cout << linesep << std::endl;
|
||||
|
||||
}
|
||||
|
|
@ -38,10 +38,18 @@ int main(void)
|
|||
sol.push_back(1.1);
|
||||
sol.push_back(3.9);
|
||||
sol.push_back(3.9);
|
||||
// we expect {1,2,3,4}
|
||||
sol.push_back(5.4);
|
||||
sol.push_back(5.6);
|
||||
sol.push_back(7.011);
|
||||
sol.push_back(8.09);
|
||||
sol.push_back(8.21);
|
||||
|
||||
std::cout << "expect: INVALID 9 1 2 3 4 5 6 7 8.1 8.2" << std::endl;
|
||||
|
||||
edoRepairer<EOT>* rep1 = new edoRepairerFloor<EOT>();
|
||||
edoRepairer<EOT>* rep2 = new edoRepairerCeil<EOT>();
|
||||
edoRepairer<EOT>* rep3 = new edoRepairerRound<EOT>();
|
||||
edoRepairer<EOT>* rep4 = new edoRepairerRoundDecimals<EOT>( 10 );
|
||||
|
||||
std::vector<unsigned int> indexes1;
|
||||
indexes1.push_back(0);
|
||||
|
|
@ -51,8 +59,19 @@ int main(void)
|
|||
indexes2.push_back(1);
|
||||
indexes2.push_back(3);
|
||||
|
||||
std::vector<unsigned int> indexes3;
|
||||
indexes3.push_back(4);
|
||||
indexes3.push_back(5);
|
||||
|
||||
std::vector<unsigned int> indexes4;
|
||||
indexes4.push_back(6);
|
||||
indexes4.push_back(7);
|
||||
indexes4.push_back(8);
|
||||
|
||||
edoRepairerDispatcher<EOT> repare( indexes1, rep1 );
|
||||
repare.add( indexes2, rep2 );
|
||||
repare.add( indexes3, rep3 );
|
||||
repare.add( indexes4, rep4 );
|
||||
|
||||
repare(sol);
|
||||
|
||||
|
|
|
|||
55
edo/test/t-repairer-modulo.cpp
Normal file
55
edo/test/t-repairer-modulo.cpp
Normal file
|
|
@ -0,0 +1,55 @@
|
|||
/*
|
||||
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>
|
||||
*/
|
||||
|
||||
#define _USE_MATH_DEFINES
|
||||
#include <math.h>
|
||||
|
||||
#include <eo>
|
||||
#include <edo>
|
||||
#include <es.h>
|
||||
|
||||
typedef eoReal< eoMinimizingFitness > EOT;
|
||||
|
||||
int main(void)
|
||||
{
|
||||
EOT sol;
|
||||
sol.push_back( M_PI * 1 );
|
||||
sol.push_back( M_PI * 2 );
|
||||
sol.push_back( M_PI * 3 );
|
||||
sol.push_back( M_PI * 4 );
|
||||
sol.push_back( M_PI * 4 + M_PI / 2 );
|
||||
sol.push_back( M_PI * 5 + M_PI / 2 );
|
||||
// we expect {pi,0,pi,0,pi/2,pi+pi/2}
|
||||
std::cout << "expect: INVALID 4 3.14159 0 3.14159 0 1.5708 4.71239" << std::endl;
|
||||
|
||||
edoRepairer<EOT>* repare = new edoRepairerModulo<EOT>( 2 * M_PI ); // modulo 2pi
|
||||
|
||||
(*repare)(sol);
|
||||
|
||||
std::cout << sol << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -216,6 +216,7 @@ public :
|
|||
/** Gaussian deviate
|
||||
|
||||
Zero mean Gaussian deviate with standard deviation 1.
|
||||
Note: Use the Marsaglia polar method.
|
||||
|
||||
@return Random Gaussian deviate
|
||||
*/
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue