int main(int argc, char **argv)
{
population *pop; /* Population of solutions. */
entity *solution; /* Optimised solution. */
random_seed(23091975);
pop = ga_genesis_double(
50, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
NULL, /* GAgeneration_hook generation_hook */
test_iteration_callback, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
test_score, /* GAevaluate evaluate */
test_seed, /* GAseed seed */
NULL, /* GAadapt adapt */
NULL, /* GAselect_one select_one */
NULL, /* GAselect_two select_two */
ga_mutate_double_singlepoint_drift, /* GAmutate mutate */
NULL, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_simplex_parameters(
pop, /* population *pop */
4, /* const int num_dimensions */
0.5, /* const double Initial step size. */
test_to_double, /* const GAto_double to_double */
test_from_double /* const GAfrom_double from_double */
);
/* Evaluate and sort the initial population members (i.e. select best of 50 random solutions. */
ga_population_score_and_sort(pop);
/* Use the best population member. */
solution = ga_get_entity_from_rank(pop, 0);
ga_simplex(
pop, /* population *pop */
solution, /* entity *solution */
10000 /* const int max_iterations */
);
ga_extinction(pop);
exit(EXIT_SUCCESS);
}
int main(int argc, char **argv)
{
population *pop; /* Population of solutions. */
random_seed(23091975);
/* "Best Set" Multiobjective GA. */
printf("Using the Best Set Multiobjective GA varient.\n");
pop = ga_genesis_double(
100, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
test_generation_callback,/* GAgeneration_hook generation_hook */
NULL, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
test_score, /* GAevaluate evaluate */
test_seed, /* GAseed seed */
NULL, /* GAadapt adapt */
ga_select_one_bestof2, /* GAselect_one select_one */
ga_select_two_bestof2, /* GAselect_two select_two */
ga_mutate_double_singlepoint_drift, /* GAmutate mutate */
ga_crossover_double_doublepoints, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_parameters(
pop, /* population *pop */
GA_SCHEME_DARWIN, /* const ga_scheme_type scheme */
GA_ELITISM_BEST_SET_SURVIVE, /* const ga_elitism_type elitism */
0.8, /* double crossover */
0.2, /* double mutation */
0.0 /* double migration */
);
ga_population_set_fitness_dimensions(pop, 4);
ga_evolution(
pop, /* population *pop */
200 /* const int max_generations */
);
ga_extinction(pop);
/* "Pareto Set" Multiobjective GA. */
printf("Using the Pareto Set Multiobjective GA varient.\n");
pop = ga_genesis_double(
100, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
test_generation_callback,/* GAgeneration_hook generation_hook */
NULL, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
test_score, /* GAevaluate evaluate */
test_seed, /* GAseed seed */
NULL, /* GAadapt adapt */
ga_select_one_bestof2, /* GAselect_one select_one */
ga_select_two_bestof2, /* GAselect_two select_two */
ga_mutate_double_singlepoint_drift, /* GAmutate mutate */
ga_crossover_double_doublepoints, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_parameters(
pop, /* population *pop */
GA_SCHEME_DARWIN, /* const ga_scheme_type scheme */
GA_ELITISM_PARETO_SET_SURVIVE, /* const ga_elitism_type elitism */
0.8, /* double crossover */
0.2, /* double mutation */
0.0 /* double migration */
);
ga_population_set_fitness_dimensions(pop, 4);
ga_evolution(
pop, /* population *pop */
200 /* const int max_generations */
);
ga_extinction(pop);
exit(EXIT_SUCCESS);
}
void genetic_reconstruction(Image * _amp, Image * initial_support, Image * _exp_sigma,
Options * _opts, char * dir){
char prev_dir[1024];
real support_threshold = _opts->new_level;
population *pop; /* Population of solutions. */
random_seed(23091975);
stop_threshold = 10;
stop = 0;
support_size = -support_threshold;
opts = _opts;
amp = _amp;
exp_sigma = _exp_sigma;
init_log(&my_log);
my_log.threshold = support_threshold;
opts->cur_iteration = 0;
opts->flog = NULL;
if(opts->automatic){
opts->algorithm = HIO;
}
support = imgcpy(initial_support);
prev_support = imgcpy(initial_support);
/* Set the initial guess */
if(opts->image_guess){
real_in = imgcpy(opts->image_guess);
}else{
real_in = imgcpy(support);
}
/* make sure we make the input complex */
rephase(real_in);
/* Set random phases if needed */
if(opts->rand_phases){
/* set_rand_phases(real_in,img);*/
set_rand_ints(real_in,amp);
}
getcwd(prev_dir,1024);
mkdir(dir,0755);
chdir(dir);
write_png(support,"support.png",COLOR_JET);
write_png(real_in,"initial_guess.png",COLOR_JET);
write_png(initial_support,"initial_support.png",COLOR_JET);
if(get_algorithm(opts,&my_log) == HIO){
real_out = basic_hio_iteration(amp, real_in, support,opts,&my_log);
}else if(get_algorithm(opts,&my_log) == RAAR){
real_out = basic_raar_iteration(amp,exp_sigma, real_in, support,opts,&my_log);
}else if(get_algorithm(opts,&my_log) == HPR){
real_out = basic_hpr_iteration(amp, real_in, support,opts,&my_log);
}else{
fprintf(stderr,"Error: Undefined algorithm!\n");
exit(-1);
}
radius = opts->max_blur_radius;
pop = ga_genesis_double(
3, /* const int population_size */
1, /* const int num_chromo */
TSIZE(amp)*2, /* const int len_chromo */
test_generation_callback,/* GAgeneration_hook generation_hook */
NULL, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
test_score, /* GAevaluate evaluate */
test_seed, /* GAseed seed */
test_adaptation, /* GAadapt adapt */
ga_select_one_bestof2, /* GAselect_one select_one */
ga_select_two_bestof2, /* GAselect_two select_two */
ga_mutate_double_singlepoint_drift, /* GAmutate mutate */
ga_crossover_double_doublepoints, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_parameters(
pop, /* population *pop */
GA_SCHEME_LAMARCK_ALL, /* const ga_scheme_type scheme */
GA_ELITISM_PARENTS_SURVIVE, /* const ga_elitism_type elitism */
0.8, /* double crossover */
0.2, /* double mutation */
0.0 /* double migration */
);
ga_evolution(
pop, /* population *pop */
500 /* const int max_generations */
);
ga_extinction(pop);
exit(EXIT_SUCCESS);
}
int main(int argc, char **argv)
{
population *pop; /* Population of solutions. */
int i=0; /* Loop variable over strategies. */
entity *entity; /* Best ranked entity. */
random_seed(23091975);
log_init(LOG_NORMAL, NULL, NULL, FALSE);
while ( strategy[i].label != NULL )
{
if ( strategy[i].weighting_factor != strategy[i].weighting_factor2 )
{
printf( "Strategy %s ; C = %f ; F = rand( %f, %f )\n",
strategy[i].label,
strategy[i].crossover_factor,
strategy[i].weighting_factor, strategy[i].weighting_factor2 );
}
else
{
printf( "Strategy %s ; C = %f ; F = %f\n",
strategy[i].label,
strategy[i].crossover_factor,
strategy[i].weighting_factor );
}
pop = ga_genesis_double(
40, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
polynomial_generation_callback,/* GAgeneration_hook generation_hook */
NULL, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
polynomial_score, /* GAevaluate evaluate */
polynomial_seed, /* GAseed seed */
NULL, /* GAadapt adapt */
NULL, /* GAselect_one select_one */
NULL, /* GAselect_two select_two */
NULL, /* GAmutate mutate */
NULL, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_differentialevolution_parameters(
pop, strategy[i].strategy, strategy[i].crossover,
strategy[i].num_perturbed, strategy[i].weighting_factor, strategy[i].weighting_factor2,
strategy[i].crossover_factor
);
ga_differentialevolution(
pop, /* population *pop */
50 /* const int max_generations */
);
entity = ga_get_entity_from_rank(pop, 0);
printf( "Final: A = %f B = %f C = %f D = %f (fitness = %f)\n",
((double *)entity->chromosome[0])[0],
((double *)entity->chromosome[0])[1],
((double *)entity->chromosome[0])[2],
((double *)entity->chromosome[0])[3],
ga_entity_get_fitness(entity) );
ga_extinction(pop);
i++;
}
exit(EXIT_SUCCESS);
}
int
testVdwGA(){
//--------------------- protein -------------------------------------
string fileNameA ( "/mnt/2t/xushutan/project/gold_dock/1a9u_diverse500/1A9U_protein.mol2" ) ;
Molecular molA( fileNameA );
molA = readMolecularFile( fileNameA ).front();
mol = molA;
bindingAtomVec = getBindingSiteAtoms( mol, bindingCenter, bindingDiameter );
if(1){
cout<<"binding site atoms:"<<bindingAtomVec.size()<<endl;
for( size_t i=0; i<bindingAtomVec.size(); i++ ){
bindingAtomVec[i].print();
}
}
vector<Atom> protHbAcceptorAtoms = goldPar.getHydrogenBondAcceptorAtoms( bindingAtomVec );
vector<Atom> protHbDonorAtoms = goldPar.getHydrogenBondDonorAtoms( bindingAtomVec );
if(0){
cout<<"prot acceptor:"<<protHbAcceptorAtoms.size()<<endl;
for( size_t i=0; i<protHbAcceptorAtoms.size(); i++ ){
protHbAcceptorAtoms[i].print();
}
cout<<"prot donor:"<<protHbDonorAtoms.size()<<endl;
for( size_t i=0; i<protHbDonorAtoms.size(); i++ ){
protHbDonorAtoms[i].print();
}
}
//---------------------------- ligand -----------------------------------
string fileNameB = "/mnt/2t/xushutan/project/gold_dock/1a9u_diverse500/ligand_corina.mol2";
Molecular molB;
molB = readMolecularFile( fileNameB ).front();
vector<Atom> ligHbAcceptorAtoms = goldPar.getHydrogenBondAcceptorAtoms( molB.get_atomVec() );
vector<Atom> ligHbDonorAtoms = goldPar.getHydrogenBondDonorAtoms( molB.get_atomVec() );
if(0){
cout<<"lig acceptor:"<<ligHbAcceptorAtoms.size()<<endl;
for( size_t i=0; i<ligHbAcceptorAtoms.size(); i++ ){
ligHbAcceptorAtoms[i].print();
}
cout<<"lig donor:"<<ligHbDonorAtoms.size()<<endl;
for( size_t i=0; i<ligHbDonorAtoms.size(); i++ ){
ligHbDonorAtoms[i].print();
}
cout<<"-----------"<<endl;
}
Coord ligand_N25( 0.1835, 0.8981, 0.1207 );
Coord standardLigand_NC3( 3.576, 15.169, 28.381 );
Coord direction = standardLigand_NC3 - ligand_N25 ;
double distance = getCoordDis( standardLigand_NC3, ligand_N25 );
ligand = translateMol( molB, direction, distance );
ligand.print();
rotableBondVec = getRotableBonds( molB );
if(1){
cout<<"rotableBondNum:"<<rotableBondVec.size()<<endl;
for( size_t i=0; i<rotableBondVec.size(); i++ ){
rotableBondVec[i].print();
}
}
population *pop=NULL; /* The population of solutions. */
int chromoLength = 7 + rotableBondVec.size();
cout<<"chromolength:"<< rotableBondVec.size()<<endl;;
random_seed( 10000 );
pop = ga_genesis_double(
6, // population size
1, // num_chromo: ligand position, rotation and bond rotation
7 + rotableBondVec.size(), // chromo length, here 7 is position 3 + rotation 3 + translate distance 1
NULL,
NULL,
NULL,
NULL,
docking_score,
// ga_seed_printable_random,
initialChromo,
NULL,
ga_select_one_sus, /* GAselect_one select_one */
ga_select_two_sus, /* GAselect_two select_two */
ga_mutate_double_multipoint, /* GAmutate mutate */
ga_crossover_double_mean, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* void * userdata */
);
ga_population_set_parameters(
pop, /* population *pop */
GA_SCHEME_DARWIN, /* const ga_class_type class */
GA_ELITISM_PARENTS_DIE, /* const ga_elitism_type elitism */
0.9, /* double crossover */
0.2, /* double mutation */
0.0 /* double migration */
);
ga_evolution(
pop, /* population *pop */
int(2) /* const int max_generations */
);
printf( "Fitness score = %f\n", ga_get_entity_from_rank(pop,0)->fitness);
ga_extinction(pop); /* Deallocates all memory associated with the population and it's entities. */
exit(EXIT_SUCCESS);
}
