int main(int nargs, char *args[])
{
bayesopt::Parameters par;
if(nargs > 1) {
if(!bayesopt::utils::ParamLoader::load(args[1], par)) {
std::cout << "ERROR: provided file \"" << args[1] << "\" does not exist" << std::endl;
return -1;
}
}
else {
par = initialize_parameters_to_default();
par.n_iterations = 100;
par.n_init_samples = 2;
par.n_iter_relearn = 1;
par.random_seed = 10;
//set_surrogate(&par,"sStudentTProcessNIG");
par.l_type = L_MCMC;
par.sc_type = SC_MAP;
par.verbose_level = 1;
//bayesopt::utils::ParamLoader::save("bo_branin_display.txt", par);
}
boost::scoped_ptr<BraninNormalized> branin(new BraninNormalized(par));
GLOBAL_MATPLOT.init(branin.get(),2);
vectord sv(2);
sv(0) = 0.1239;
sv(1) = 0.8183;
GLOBAL_MATPLOT.setSolution(sv);
sv(0) = 0.5428;
sv(1) = 0.1517;
GLOBAL_MATPLOT.setSolution(sv);
sv(0) = 0.9617;
sv(1) = 0.1650;
GLOBAL_MATPLOT.setSolution(sv);
glutInit(&nargs, args);
glutCreateWindow(50,50,800,650);
glutDisplayFunc( display );
glutReshapeFunc( reshape );
glutIdleFunc( idle );
glutMotionFunc( motion );
glutMouseFunc( mouse );
glutPassiveMotionFunc(passive);
glutKeyboardFunc( keyboard );
glutMainLoop();
return 0;
}
int main(int nargs, char *args[])
{
bopt_params par = initialize_parameters_to_default();
par.n_iterations = 190;
par.random_seed = 0;
par.verbose_level = 1;
par.noise = 1e-10;
BraninNormalized branin(par);
vectord result(2);
branin.optimize(result);
std::cout << "Result: " << result << "->"
<< branin.evaluateSample(result) << std::endl;
branin.printOptimal();
return 0;
}
int main(int nargs, char *args[])
{
bayesopt::Parameters par;
if(nargs > 1){
if(!bayesopt::utils::ParamLoader::load(args[1], par)){
std::cout << "ERROR: provided file \"" << args[1] << "\" does not exist" << std::endl;
return -1;
}
}
else{
par = initialize_parameters_to_default();
par.n_iterations = 190;
par.random_seed = 0;
par.verbose_level = 1;
par.noise = 1e-10;
//bayesopt::utils::ParamLoader::save("system_opt.txt", par);
}
SystemCallsBranin branin(par);
vectord result(2);
branin.optimize(result);
std::cout << "Result: " << result << "->"
<< branin.evaluateSample(result) << std::endl;
branin.printOptimal();
// Remove results.txt file
std::string filename("results.txt");
if( remove( filename.c_str() ) == 0 ){
std::cout << "File \"" << filename << "\" successfully removed" << std::endl;
}
else{
std::cout << "Error: cannot remove \"" << filename << "\" file" << std::endl;
}
return 0;
}
int main(int nargs, char *args[])
{
bopt_params par = initialize_parameters_to_default();
par.verbose_level = 0;
par.noise = 1e-10;
par.force_jump = 30;
std::ofstream log;
std::clock_t start_t;
/* Branin */
log.open("branin.log");
par.n_init_samples = 5;
par.n_iterations = 195;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
BraninNormalized branin(par);
vectord result(2);
start_t = clock();
branin.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
branin.stepOptimization();
if (jj == 50)
{
result = branin.getFinalResult();
log << branin.evaluateSample(result) << ", ";
}
}
result = branin.getFinalResult();
log << branin.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
/* Camel */
log.open("camel.log");
par.n_init_samples = 5;
par.n_iterations = 95;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
ExampleCamelback camel(par);
vectord result(2);
vectord lb(2); lb(0) = -2; lb(1) = -1;
vectord ub(2); ub(0) = 2; ub(1) = 1;
camel.setBoundingBox(lb,ub);
start_t = clock();
camel.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
camel.stepOptimization();
if (jj == 50)
{
result = camel.getFinalResult();
log << camel.evaluateSample(result) << ", ";
}
}
result = camel.getFinalResult();
log << camel.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
/* Hart */
log.open("hart.log");
par.n_init_samples = 10;
par.n_iterations = 190;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
ExampleHartmann6 hart(par);
vectord result(6);
start_t = clock();
hart.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
hart.stepOptimization();
if (jj == 50)
{
result = hart.getFinalResult();
log << hart.evaluateSample(result) << ", ";
}
}
result = hart.getFinalResult();
log << hart.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
/***********************************************************************/
par.n_init_samples = 2;
par.n_iter_relearn = 1;
par.l_type = L_MCMC;
par.sc_type = SC_MAP;
/* Branin */
log.open("branin_mcmc.log");
par.n_iterations = 198;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
BraninNormalized branin(par);
vectord result(2);
start_t = clock();
branin.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
branin.stepOptimization();
if (jj == 50)
{
result = branin.getFinalResult();
log << branin.evaluateSample(result) << ", ";
}
}
result = branin.getFinalResult();
log << branin.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
/* Camel */
log.open("camel_mcmc.log");
par.n_iterations = 98;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
ExampleCamelback camel(par);
vectord result(2);
vectord lb(2); lb(0) = -2; lb(1) = -1;
vectord ub(2); ub(0) = 2; ub(1) = 1;
camel.setBoundingBox(lb,ub);
start_t = clock();
camel.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
camel.stepOptimization();
if (jj == 50)
{
result = camel.getFinalResult();
log << camel.evaluateSample(result) << ", ";
}
}
result = camel.getFinalResult();
log << camel.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
/* Hart */
log.open("hart_mcmc.log");
par.n_iterations = 198;
for (size_t ii = 0; ii < 10; ++ii)
{
par.random_seed = ii;
ExampleHartmann6 hart(par);
vectord result(6);
start_t = clock();
hart.initializeOptimization();
for (size_t jj = 0; jj < par.n_iterations; ++jj)
{
hart.stepOptimization();
if (jj == 50)
{
result = hart.getFinalResult();
log << hart.evaluateSample(result) << ", ";
}
}
result = hart.getFinalResult();
log << hart.evaluateSample(result) << ", ";
log << static_cast<double>(clock() - start_t) / static_cast<double>(CLOCKS_PER_SEC)
<< std::endl;
}
log.close();
return 0;
}
