inline bool check_dist_and_prob(const char* function, const RealType& r, RealType p, RealType prob, RealType* result, const Policy& pol)
{
if(check_dist(function, r, p, result, pol) && detail::check_probability(function, prob, result, pol) == false)
{
return false;
}
return true;
} // check_dist_and_prob
inline bool check_dist_and_prob(const char* function, RealType p, RealType prob, RealType* result, const Policy& /* pol */)
{
if(check_dist(function, p, result, Policy()) && detail::check_probability(function, prob, result, Policy()) == false)
{
return false;
}
return true;
}
inline bool check_dist_and_prob(const char* function, RealType p, RealType prob, RealType* result, const Policy& /* pol */)
{
if((check_dist(function, p, result, Policy(), typename policies::method_error_check<Policy>::type()) && detail::check_probability(function, prob, result, Policy())) == false)
{
return false;
}
return true;
}
inline bool check_dist_and_k(const char* function, const RealType& r, const RealType& p, RealType k, RealType* result, const Policy& pol)
{
if(check_dist(function, r, p, result, pol) == false)
{
return false;
}
if( !(lslboost::math::isfinite)(k) || (k < 0) )
{ // Check k failures.
*result = policies::raise_domain_error<RealType>(
function,
"Number of failures argument is %1%, but must be >= 0 !", k, pol);
return false;
}
return true;
} // Check_dist_and_k
inline bool check_dist_and_k(const char* function, const RealType& p, RealType k, RealType* result, const Policy& pol)
{
if(check_dist(function, p, result, Policy()) == false)
{
return false;
}
if(!(boost::math::isfinite)(k) || !((k == 0) || (k == 1)))
{
*result = policies::raise_domain_error<RealType>(
function,
"Number of successes argument is %1%, but must be 0 or 1 !", k, pol);
return false;
}
return true;
}
float count_dist(float xdep, float ydep, float xarr, float yarr)
{
float dist_long;
float dist_larg;
float hypothenuse;
if (xdep == xarr || ydep == yarr)
return (check_dist(xdep, ydep, xarr, yarr));
else
{
dist_long = yarr - ydep;
dist_larg = xarr - ydep;
hypothenuse = sqrt((dist_long * dist_long) + (dist_larg * dist_larg));
return (hypothenuse);
}
}
inline bool check_dist_and_k(const char* function, const RealType& N, const RealType& p, RealType k, RealType* result, const Policy& pol)
{
if(check_dist(function, N, p, result, pol) == false)
return false;
if((k < 0) || !(boost::math::isfinite)(k))
{
*result = policies::raise_domain_error<RealType>(
function,
"Number of Successes argument is %1%, but must be >= 0 !", k, pol);
return false;
}
if(k > N)
{
*result = policies::raise_domain_error<RealType>(
function,
"Number of Successes argument is %1%, but must be <= Number of Trials !", k, pol);
return false;
}
return true;
}
inline bool check_dist_and_prob(const char* function, const RealType& alpha, const RealType& beta, RealType p, RealType* result, const Policy& pol)
{
return check_dist(function, alpha, beta, result, pol)
&& check_prob(function, p, result, pol);
} // bool check_dist_and_prob
inline bool check_dist(const char* function, const RealType& p, RealType* result, const Policy& /* pol */)
{
return check_dist(function, p, result, Policy(), typename policies::constructor_error_check<Policy>::type());
}
inline bool check_dist_and_x(const char* function, const RealType& alpha, const RealType& beta, RealType x, RealType* result, const Policy& pol)
{
return check_dist(function, alpha, beta, result, pol)
&& beta_detail::check_x(function, x, result, pol);
} // bool check_dist_and_x
/**
* Principal function of the Genetic Algorithm
* \fn shooter_repartition genetic algorithm heuristic for selection of towers
**/
void shooter_repartition(const char * input_file_name, const float mutation_prob, const int population_size,
const int nb_children, const int nb_iteration, const int dist, float taux_var, bool sim)
{
vector<tower>* towers = new vector<tower>();
int k, n;
tower_parse(input_file_name, *towers, k, n);
srand((unsigned int) time(NULL));
if (!is_easy_solution(k, n)) {
vector<vector<tower>*>* population = generate_initial_pop(towers, k, dist, taux_var);
sort_population(population);
vector<vector<tower>*>* children;
// population growth until reaching population_size
while ((int) population->size() < population_size) {
children = reproduction_iteration(nb_children, mutation_prob, population, towers, dist, taux_var);
sort_population(children);
for (int i = 0; i < (int) children->size(); i++) {
if ((int) population->size() < population_size) { //do not oversize the population_size && !find_indiv(population, (*children)[i])
population->push_back((*children)[i]);
}
else {
break;
}
}
}
// reordering population
sort_population(population);
// iteration of genetic algorithm
for (int j = 0; j < nb_iteration; j++) {
children = reproduction_iteration(nb_children, mutation_prob, population, towers, dist, taux_var);
for (int i = 0; i < (int) children->size(); i++) {
child_insertion((*children)[i], population);
}
}
// printing the results
for (int i = 0; i < 10 ; i++) {
cout << i+1 << " : ";
print_indiv((*population)[i]);
}
if(sim)
simulate_day(population->front(), towers, dist, taux_var);
for (vector<vector<tower>*>::iterator it = population->begin(); it != population->end(); it++){
delete *it;
}
delete population;
}
else { // generate all the permutations and verify which is the best;
int tmp_eval = 0, best_eval = 0;
int nb_perm = binomial_coef(n,k);
int t_size = towers->size();
int *perm_indexes = (int*) malloc( k * sizeof(int)); //build index tracker to avoid repeating indivs
vector<tower> *tmp_indiv, *best_indiv;
tmp_indiv = new vector<tower>();
best_indiv = new vector<tower>();
for(int l = 0; l<k; l++){
perm_indexes[l] = l;
tmp_indiv->push_back((*towers)[l]);
}
// spread var for variance
spread_indiv_var(tmp_indiv, towers, taux_var);
*best_indiv = *tmp_indiv;
best_eval = check_dist(best_indiv, dist) ? eval_indiv(best_indiv) : 0;
for (int j = 0; j < nb_perm; j++){
//build and eval permutation
for (int i = 0; i < k ; i++){
tmp_indiv->at(i) = towers->at(perm_indexes[i]);
}
spread_indiv_var(tmp_indiv, towers, taux_var);
if(check_dist(tmp_indiv,dist)) {
tmp_eval = eval_indiv(tmp_indiv);
if (tmp_eval > best_eval) {
best_eval = tmp_eval;
*best_indiv = *tmp_indiv;
}
}
//update of indexes tracker
perm_indexes[k-1]++;
for( int m = k-2; m >= 0; m--){ //go to next permutation
if (perm_indexes[m+1] > t_size-(k-m-1)){
perm_indexes[m]++;
for(int p = m+1; p < k; p++){//correcting overflow
perm_indexes[p] = perm_indexes[p-1]+1;
}
}
}
}
print_indiv(best_indiv);
if (sim)
simulate_day(best_indiv, towers, dist, taux_var);
free(perm_indexes);
delete best_indiv;
delete tmp_indiv;
}
delete towers;
}
/**
* Reproduction between two individu
* Mix : [1 2 3 4 5] + [15 16 17 18 19] = [1 2 17 18 19] and [15 16 3 4 5]
**/
vector<tower>* mix(vector<tower>* parent1, vector<tower>* parent2, const int dist, vector<tower>* towers, float taux_var){
vector<tower>* child1 = new vector<tower>();
vector<tower>* child2 = new vector<tower>();
for(int i = 0; i < ((int) parent1->size()/2); i++){
child1->push_back((*parent1)[i]);
child2->push_back((*parent2)[i]);
}
for(int i = ((int) parent1->size()/2); i < (int) parent1->size(); i++){
if (find(child1->begin(), child1->end(), (*parent2)[i]) != child1->end() || find(child2->begin(), child2->end(), (*parent1)[i]) != child2->end()) { // ensure the unicity of tower
if (find(child1->begin(), child1->end(), (*parent1)[i]) != child1->end() || find(child2->begin(), child2->end(), (*parent2)[i]) != child2->end()) { // ensure the unicity of tower
delete child1;
delete child2;
return new vector<tower>();
}
else {
child1->push_back((*parent1)[i]);
child2->push_back((*parent2)[i]);
}
}
else {
child1->push_back((*parent2)[i]);
child2->push_back((*parent1)[i]);
}
}
if (*child1 == *parent1 || *child2 == *parent2 || *child1 == *parent2 || *child2 == *parent1)
{
delete child1;
delete child2;
return new vector<tower>();
}
// reset child and spread var
spread_indiv_var(child1, towers, taux_var);
spread_indiv_var(child2, towers, taux_var);
if(*child1 < *child2){
if (check_dist(child2, dist)) {
delete child1;
return child2;
}
else {
if (check_dist(child1, dist)) {// if the first child don't respect the distance try it for the orher child
delete child2;
return child1;
}
else {
delete child1;
delete child2;
return new vector<tower>();
}
}
}
else {
if (check_dist(child1, dist)) {
delete child2;
return child1;
}
else {
if (check_dist(child2, dist)) {// if the first child don't respect the distance try it for the orher child
delete child1;
return child2;
}
else {
delete child1;
delete child2;
return new vector<tower>();
}
}
}
}
