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* New tree configuration of the project:

Browse source 
.../
   ...           + -- EO
   |             |
   |             |
   +-- src ----- + -- EDO
   |             |
   |             |
   +-- test      + -- MO
   |             |
   |             |
   +-- tutorial  + -- MOEO
   |             |
   |             |
   +-- doc       + -- SMP
   |             |
   |             |
   ...           + -- EOMPI
                 |
                 |
                 + -- EOSERIAL

Question for current maintainers: ./README: new release?

Also:

* Moving out eompi & eoserial modules (issue #2).

* Correction of the errors when executing "make doc" command.

* Adding a solution for the conflicting headers problem (see the two CMake Cache
 Values: PROJECT_TAG & PROJECT_HRS_INSTALL_SUBPATH) (issue #1)

* Header inclusions:
        ** src: changing absolute paths into relative paths ('#include <...>' -> '#include "..."')
        ** test, tutorial: changing relative paths into absolute paths ('#include "..."' -> '#include <...>')

* Moving out some scripts from EDO -> to the root

* Add a new script for compilation and installation (see build_gcc_linux_install)

* Compilation with uBLAS library or EDO module: now ok

* Minor modifications on README & INSTALL files

* Comment eompi failed tests with no end

*** TODO: CPack (debian (DEB) & RedHat (RPM) packages) (issues #6 & #7) ***
This commit is contained in:
Adèle Harrissart 2014-08-04 13:40:28 +02:00
commit 490e837f7a
2359 changed files with 7688 additions and 16329 deletions
Download patch file Download diff file Expand all files Collapse all files

59
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###############################################################################
##
## CMakeLists file for unit test
##
###############################################################################
######################################################################################
### 1) Include the sources
######################################################################################
#include_directories(${CMAKE_CURRENT_SOURCE_DIR})
#include_directories(${CMAKE_SOURCE_DIR}/application/common)
include_directories(${EDO_TUTORIAL_DIR}/common)
#include_directories(${EO_SRC_DIR}/src)
#include_directories(${EDO_SRC_DIR}/src)
######################################################################################
### 2) Specify where CMake can find the libraries
######################################################################################
if(EDO_USE_LIB STREQUAL "Eigen3")
find_package(Eigen3)
include_directories( ${EIGEN3_INCLUDE_DIR} )
add_definitions( -DWITH_EIGEN )
elseif(EDO_USE_LIB STREQUAL "uBLAS")
find_package(Boost)
include_directories( ${Boost_INCLUDE_DIRS} )
add_definitions( -DWITH_BOOST )
else()
message(FATAL_ERROR "\n\nYou must set EDO_USE_LIB to either 'uBLAS' or 'Eigen3'.\n" )
endif()
######################################################################################
### 3) Define your targets and link the librairies
######################################################################################
set(SOURCES
#t-cholesky
t-variance
t-edoEstimatorNormalMulti
t-mean-distance
t-bounderno
t-uniform
t-continue
# t-dispatcher-round
t-repairer-modulo
t-binomial
t-binomialmulti
)
foreach(current ${SOURCES})
add_executable(${current} ${current}.cpp)
add_test(${current} ${current})
target_link_libraries(${current} eo eoutils edoutils)
install(TARGETS ${current} RUNTIME DESTINATION share/edo/test COMPONENT test)
endforeach()
######################################################################################

502
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#!/usr/bin/env python
from pylab import *
FILE_LOCATIONS = 'means_distances_results/files_description.txt'
data = []
locations = [ line.split()[0] for line in open( FILE_LOCATIONS ) ]
for cur_file in locations:
data.append( [ float(line.split()[7]) for line in open( cur_file ).readlines() ] )
print locations
#print data
boxplot( data )
show()

104
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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) 2013 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#include <vector>
#include <iostream>
#include <string>
#include <cmath>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/ga.h> // for Bools
template<class T>
void print( T v, std::string sep=" " )
{
for( typename T::iterator it=v.begin(); it!=v.end(); ++it ) {
std::cout << *it << sep;
}
std::cout << std::endl;
}
int main(int ac, char** av)
{
typedef eoBit<double> Bools;
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int popsize = parser.createParam((unsigned int)100000, "popSize", "Population Size", 'P', section).value(); // P
unsigned int dim = parser.createParam((unsigned int)20, "dim", "Dimension size", 'd', section).value(); // d
double proba = parser.createParam((double)0.5, "proba", "Probability to estimate", 'b', section).value(); // b
if( parser.userNeedsHelp() ) {
parser.printHelp(std::cout);
exit(1);
}
make_help(parser);
// This generate a random boolean when called...
eoBooleanGenerator flip( proba );
// ... by this initializator...
eoInitFixedLength<Bools> init( dim, flip );
// ... when building this pop.
std::cout << "Init a pop of eoBit of size " << popsize << " at random with p(x=1)=" << proba << std::endl;
eoPop<Bools> pop( popsize, init );
// print( pop, "\n" );
// EDO triplet
edoBinomial<Bools> distrib(dim,0.0);
edoEstimatorBinomial<Bools> estimate;
edoSamplerBinomial<Bools> sample;
std::cout << "Estimate a distribution from the init pop" << std::endl;
distrib = estimate(pop);
print( distrib );
// std::cout << std::endl;
std::cout << "Sample a new pop from the init distrib" << std::endl;
pop.clear();
for( unsigned int i=0; i<popsize; ++i ) {
pop.push_back( sample( distrib ) );
}
// print( pop, "\n" );
std::cout << "Estimate a distribution from the sampled pop" << std::endl;
distrib = estimate(pop);
print( distrib );
// std::cout << std::endl;
double sum=0;
for( unsigned int i=0; i<distrib.size(); ++i ) {
sum += distrib[i];
}
double e = sum / dim;
std::cout << "Estimated initial proba = " << e << std::endl;
}

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test/edo/t-binomialmulti.cpp Executable file
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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) 2013 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#include <vector>
#include <iostream>
#include <string>
#include <cmath>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/ga.h> // for Bools
#ifdef WITH_EIGEN
#include <Eigen/Dense>
#endif // WITH_EIGEN
#ifdef WITH_EIGEN
// NOTE: a typedef on eoVector does not work, because of readFrom on a vector AtomType
// typedef eoVector<eoMinimizingFitness, std::vector<bool> > Bools;
class Bools : public std::vector<std::vector<bool> >, public EO<double>
{
public:
typedef std::vector<bool> AtomType;
};
#endif
int main(int ac, char** av)
{
#ifdef WITH_EIGEN
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int popsize = parser.createParam((unsigned int)100000, "popSize", "Population Size", 'P', section).value(); // P
unsigned int rows = parser.createParam((unsigned int)2, "lines", "Lines number", 'l', section).value(); // l
unsigned int cols = parser.createParam((unsigned int)3, "columns", "Columns number", 'c', section).value(); // c
double proba = parser.createParam((double)0.5, "proba", "Probability to estimate", 'b', section).value(); // b
if( parser.userNeedsHelp() ) {
parser.printHelp(std::cout);
exit(1);
}
make_help(parser);
std::cout << "Init distrib" << std::endl;
Eigen::MatrixXd initd = Eigen::MatrixXd::Constant(rows,cols,proba);
edoBinomialMulti<Bools> distrib( initd );
std::cout << distrib << std::endl;
edoEstimatorBinomialMulti<Bools> estimate;
edoSamplerBinomialMulti<Bools> sample;
std::cout << "Sample a pop from the init distrib" << std::endl;
eoPop<Bools> pop; pop.reserve(popsize);
for( unsigned int i=0; i < popsize; ++i ) {
pop.push_back( sample( distrib ) );
}
std::cout << "Estimate a distribution from the sampled pop" << std::endl;
distrib = estimate( pop );
std::cout << distrib << std::endl;
std::cout << "Estimated initial proba = " << distrib.mean() << std::endl;
#else
#ifdef WITH_BOOST
#pragma message "WARNING: there is no Boost::uBLAS implementation of t-binomialmulti, build WITH_EIGEN if you need it."
#endif
#endif // WITH_EIGEN
return 0;
}

40
test/edo/t-bounderno.cpp Executable file
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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>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include "Rosenbrock.h"
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
edoBounderNo< EOT > bounder;
return 0;
}

250
test/edo/t-cholesky.cpp Executable file
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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>
*/
//#include <vector>
#include <cstdlib>
#include <iostream>
#include <sstream>
#include <limits>
#include <iomanip>
#include <ctime>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/es.h>
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;
}

44
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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>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include "Rosenbrock.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoUniform< EOT > Distrib;
int main(void)
{
eoState state;
edoContinue< Distrib >* continuator = new edoDummyContinue< Distrib >();
state.storeFunctor(continuator);
return 0;
}

81
test/edo/t-dispatcher-round.cpp Executable file
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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>
*/
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/es.h>
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
EOT sol;
sol.push_back(1.1);
sol.push_back(1.1);
sol.push_back(3.9);
sol.push_back(3.9);
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);
indexes1.push_back(2);
std::vector<unsigned int> indexes2;
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);
std::cout << sol << std::endl;
return 0;
}

198
test/edo/t-edoEstimatorNormalMulti.cpp Executable file
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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>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <sstream>
#include <iomanip>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
//#include <paradiseo/mo.h>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef EOT::AtomType AtomType;
#ifdef WITH_BOOST
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
typedef ublas::vector< AtomType > Vector;
typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
typedef edoNormalMulti<EOT>::Vector Vector;
typedef edoNormalMulti<EOT>::Matrix Matrix;
#endif
#endif
int main(int ac, char** av)
{
// (0) parser + eo routines
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int p_size = parser.createParam((unsigned int)100, "popSize", "Population Size", 'P', section).value(); // P
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value();
AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value();
AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value();
std::ostringstream ss;
ss << p_size << "_" << std::fixed << std::setprecision(1)
<< mean_value << "_" << covar1_value << "_" << covar2_value << "_"
<< covar3_value << "_gen";
std::string gen_filename = ss.str();
if( parser.userNeedsHelp() ) {
parser.printHelp(std::cout);
exit(1);
}
make_verbose(parser);
make_help(parser);
assert(p_size > 0);
assert(s_size > 0);
eoState state;
// (1) Population init and sampler
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
state.storeFunctor(init);
// create an empty pop and let the state handle the memory
// fill population thanks to eoInit instance
eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
// (2) distribution initial parameters
Vector mean( s_size );
for (unsigned int i = 0; i < s_size; ++i) {
mean( i ) = mean_value;
}
Matrix varcovar( s_size, s_size );
varcovar( 0, 0 ) = covar1_value;
varcovar( 0, 1 ) = covar2_value;
varcovar( 1, 1 ) = covar3_value;
Distrib distrib( mean, varcovar );
// (3a) distribution output preparation
edoDummyContinue< Distrib >* distrib_dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(distrib_dummy_continue);
edoCheckPoint< Distrib >* distrib_continue = new edoCheckPoint< Distrib >( *distrib_dummy_continue );
state.storeFunctor(distrib_continue);
edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
distrib_continue->add( *distrib_stat );
edoFileSnapshot* distrib_file_snapshot = new edoFileSnapshot( "TestResDistrib", 1, gen_filename );
state.storeFunctor(distrib_file_snapshot);
distrib_file_snapshot->add(*distrib_stat);
distrib_continue->add(*distrib_file_snapshot);
// (3b) distribution output
(*distrib_continue)( distrib );
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(
EOT(pop[0].size(), -5), EOT(pop[0].size(), 5), *gen
);
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
// (4) sampling phase
pop.clear();
for( unsigned int i = 0; i < p_size; ++i ) {
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
// (5) population output
eoContinue< EOT >* pop_cont = new eoGenContinue< EOT >( 2 ); // never reached fitness
state.storeFunctor(pop_cont);
eoCheckPoint< EOT >* pop_continue = new eoCheckPoint< EOT >( *pop_cont );
state.storeFunctor(pop_continue);
edoPopStat< EOT >* pop_stat = new edoPopStat<EOT>;
state.storeFunctor(pop_stat);
pop_continue->add(*pop_stat);
edoFileSnapshot* pop_file_snapshot = new edoFileSnapshot( "TestResPop", 1, gen_filename );
state.storeFunctor(pop_file_snapshot);
pop_file_snapshot->add(*pop_stat);
pop_continue->add(*pop_file_snapshot);
(*pop_continue)( pop );
// (6) estimation phase
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
distrib = (*estimator)( pop );
// (7) distribution output
(*distrib_continue)( distrib );
// (8) euclidianne distance estimation
Vector new_mean = distrib.mean();
Matrix new_varcovar = distrib.varcovar();
AtomType distance = 0;
for( unsigned int d = 0; d < s_size; ++d ) {
distance += pow( mean[ d ] - new_mean[ d ], 2 );
}
distance = sqrt( distance );
eo::log << eo::logging
<< "mean: " << mean << std::endl
<< "new mean: " << new_mean << std::endl
<< "distance: " << distance << std::endl
;
return 0;
}

243
test/edo/t-mean-distance.cpp Executable file
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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>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <sys/stat.h>
#include <sys/types.h>
#include <sstream>
#include <iomanip>
#include <fstream>
#include <paradiseo/eo.h>
//#include <paradiseo/mo.h>
#include <paradiseo/edo.h>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef EOT::AtomType AtomType;
#ifdef WITH_BOOST
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
typedef ublas::vector< AtomType > Vector;
typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
typedef edoNormalMulti<EOT>::Vector Vector;
typedef edoNormalMulti<EOT>::Matrix Matrix;
#endif
#endif
int main(int ac, char** av)
{
// (0) parser + eo routines
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int r_max = parser.createParam((unsigned int)100, "run-number", "Number of run", 'r', section).value(); // r
unsigned int p_min = parser.createParam((unsigned int)10, "population-min", "Population min", 'p', section).value(); // p
unsigned int p_max = parser.createParam((unsigned int)1000, "population-max", "Population max", 'P', section).value(); // P
unsigned int p_step = parser.createParam((unsigned int)50, "population-step", "Population step", 't', section).value(); // t
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value(); // 1
AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value(); // 2
AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value(); // 3
std::string results_directory = parser.createParam((std::string)"means_distances_results", "results-directory", "Results directory", 'R', section).value(); // R
std::string files_description = parser.createParam((std::string)"files_description.txt", "files-description", "Files description", 'F', section).value(); // F
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
make_verbose(parser);
make_help(parser);
assert(r_max >= 1);
assert(s_size >= 2);
eo::log << eo::quiet;
::mkdir( results_directory.c_str(), 0755 );
for ( unsigned int p_size = p_min; p_size <= p_max; p_size += p_step )
{
assert(p_size >= p_min);
std::ostringstream desc_file;
desc_file << results_directory << "/" << files_description;
std::ostringstream cur_file;
cur_file << results_directory << "/pop_" << p_size << ".txt";
eo::log << eo::file( desc_file.str() ) << cur_file.str().c_str() << std::endl;
eo::log << eo::file( cur_file.str() );
eo::log << eo::logging << "run_number p_size s_size mean(0) mean(1) new-mean(0) new-mean(1) distance" << std::endl;
eo::log << eo::quiet;
for ( unsigned int r = 1; r <= r_max; ++r)
{
eoState state;
// (1) Population init and sampler
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
state.storeFunctor(init);
// create an empty pop and let the state handle the memory
// fill population thanks to eoInit instance
eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
// (2) distribution initial parameters
#ifdef WITH_BOOST
Vector mean( s_size, mean_value );
#else
#ifdef WITH_EIGEN
Vector mean( s_size );
mean = Vector::Constant( s_size, mean_value);
#endif
#endif
Matrix varcovar( s_size, s_size );
varcovar( 0, 0 ) = covar1_value;
varcovar( 0, 1 ) = covar2_value;
varcovar( 1, 1 ) = covar3_value;
Distrib distrib( mean, varcovar );
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
EOT(pop[0].size(), 5),
*gen);
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
// (4) sampling phase
pop.clear();
for (unsigned int i = 0; i < p_size; ++i)
{
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
// (6) estimation phase
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
distrib = (*estimator)( pop );
// (8) euclidianne distance estimation
Vector new_mean = distrib.mean();
Matrix new_varcovar = distrib.varcovar();
AtomType distance = 0;
for ( unsigned int d = 0; d < s_size; ++d )
{
distance += pow( mean[ d ] - new_mean[ d ], 2 );
}
distance = sqrt( distance );
eo::log << r << " " << p_size << " " << s_size << " "
<< mean(0) << " " << mean(1) << " "
<< new_mean(0) << " " << new_mean(1) << " "
<< distance << std::endl
;
}
}
return 0;
}

55
test/edo/t-repairer-modulo.cpp Executable file
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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>
*/
#define _USE_MATH_DEFINES
#include <math.h>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/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;
}

43
test/edo/t-uniform.cpp Executable file
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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>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include "Rosenbrock.h"
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
eoState state;
edoUniform< EOT >* distrib = new edoUniform< EOT >( EOT(3, -1), EOT(3, 1) );
state.storeFunctor(distrib);
return 0;
}

83
test/edo/t-variance.cpp Executable file
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#include <iostream>
#include <vector>
#include <paradiseo/eo.h>
#include <paradiseo/edo.h>
#include <paradiseo/eo/es.h>
typedef eoReal<eoMinimizingFitness> Vec;
void check( Vec ex_mean, Vec ex_var, Vec es_mean, Vec es_var )
{
std::cout << "expected mean=" << ex_mean << " variance=" << ex_var << std::endl;
std::cout << "estimated mean=" << es_mean << " variance=" << es_var << std::endl;
for( unsigned int i=0; i<ex_mean.size(); ++i ) {
assert( es_mean[i] == ex_mean[i] );
assert( es_var[i] == ex_var[i] );
}
}
int main()
{
std::clog << "Variance computation on a simple vector" << std::endl;
eoPop<Vec> pop;
for( unsigned int i=1; i<7; ++i) {
Vec indiv(1,i);
pop.push_back( indiv );
std::clog << indiv << " ";
}
std::clog << std::endl;
edoEstimatorNormalMono<Vec> estimator;
edoNormalMono<Vec> distrib = estimator(pop);
Vec ex_mean(1,3.5);
Vec ex_var(1,17.5/6);
Vec es_mean = distrib.mean();
Vec es_var = distrib.variance();
check( ex_mean, ex_var, es_mean, es_var );
// Test with negative values
std::clog << "Variance computation on negative values" << std::endl;
pop.clear();
pop.push_back( Vec(1, 11.0) );
pop.push_back( Vec(1, -4.0) );
pop.push_back( Vec(1, 11.0) );
pop.push_back( Vec(1, 14.0) );
pop.push_back( Vec(1, -2.0) );
std::clog << pop << std::endl;
distrib = estimator(pop);
ex_mean = Vec(1,6.0);
ex_var = Vec(1,278.0/5);
es_mean = distrib.mean();
es_var = distrib.variance();
check( ex_mean, ex_var, es_mean, es_var );
// test single individual
std::clog << "Variance computation on a pop with a single individual" << std::endl;
pop.clear();
pop.push_back( Vec(1, 0.0) );
distrib = estimator(pop);
ex_mean = Vec(1,0.0);
ex_var = Vec(1,0.0);
es_mean = distrib.mean();
es_var = distrib.variance();
check( ex_mean, ex_var, es_mean, es_var );
}

18
test/edo/test_cov_parameters.py Executable file
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#!/usr/bin/env python
PSIZE = 10000
MEAN = 0
CMD = "./test/t-edoEstimatorNormalMulti -P=%s -m=%.1f -1=%.1f -2=%.1f -3=%.1f && ./gplot.py -r TestResPop -p -w 5 -u -g %s -G results_for_test_cov_parameters -f %s_gen1"
from os import system
from numpy import arange
if __name__ == '__main__':
for p1 in list(arange(0.1, 1.1, 0.1)):
for p2 in list(arange(-1., 0., 0.1)) + list(arange(0., 1.1, 0.1)):
for p3 in list(arange(0.1, 1.1, 0.1)):
gen = '%d_%.1f_%.1f_%.1f_%.1f' % (PSIZE, MEAN, p1, p2, p3)
cmd = CMD % ( PSIZE, MEAN, p1, p2, p3, gen, gen )
print cmd
system( cmd )