void DrawDetectedPoints(const vector<Point2f>* initPts, const vector<Point2f>* updPts,
const vector<FatoStatus>* ptsStatus, const vector<int>* ptsIds, Mat&out)
{
int imgOffset = out.cols / 2;
random_device rd;
default_random_engine dre(rd());
uniform_int_distribution<unsigned int> uniform_dist(0, 255);
for (size_t i = 0; i < updPts->size(); i++)
{
const int id = ptsIds->at(i);
if (ptsStatus->at(i) == FatoStatus::MATCH)
{
Scalar color(uniform_dist(dre), uniform_dist(dre), uniform_dist(dre));
const Point2f& src = initPts->at(id);
Point2f dst = updPts->at(i);
dst.x += imgOffset;
circle(out, src, 3, color, 1);
circle(out, dst, 3, color, 1);
line(out, src, dst, color, 1);
}
}
}
int Enemy::randomNumber(int min, int max)
{
random_device dev;
default_random_engine dre(dev());
uniform_int_distribution<int> dist(min, max);
return dist(dre);
}
void
RilConsumer::ReceiveSocketData(nsAutoPtr<UnixSocketRawData>& aMessage)
{
MOZ_ASSERT(NS_IsMainThread());
nsRefPtr<DispatchRILEvent> dre(new DispatchRILEvent(aMessage.forget()));
mDispatcher->PostTask(dre);
}
void Graph::teleportRabbit() {
Vertex* destination = nullptr;
while(destination == nullptr || destination == getCowVertex() || destination == getRabbitVertex()) {
std::random_device dev;
std::default_random_engine dre(dev());
std::uniform_int_distribution<int> dist1(0, this->getVertices()->size() - 1);
int index = dist1(dre);
destination = this->getVertices()->at(index);
}
this->rabbit->setDestination(destination);
while(this->rabbit->getRoute()->size() > 0) {
this->rabbit->getRoute()->pop_back();
}
this->setRabbitVertex(destination);
}
void
Tester::run(const unsigned int highpower) {
unsigned int seed = 12345;
std::default_random_engine dre(seed);
std::uniform_int_distribution<> dis(1, 10000);
for (int n = 0; n < (REPS * 2) + pow(2, highpower); ++n) {
RAN_NUMS.push_back(dis(dre));
}
TestFHeap(highpower);
TestBHeap(highpower);
//TestDijkstra(highpower);
}
void MainMenuState::loadAdvertisement()
{
if (_advertisementIndex >= 0)
_game->getEngine()->getDrawEngine()->unload("advertisement");
std::random_device dev;
std::default_random_engine dre(dev());
std::uniform_int_distribution<int> randomAdvertisement(0, _advertismentList->size() - 1);
int advertisementIndex = 0;
do
advertisementIndex = randomAdvertisement(dre);
while (advertisementIndex == _advertisementIndex);
_advertisementIndex = advertisementIndex;
_game->getEngine()->getDrawEngine()->load("advertisement", "assets\\advertisements\\" + _advertismentList->at(advertisementIndex));
const std::array<int, 2> size = _game->getEngine()->getDrawEngine()->getImageSize("advertisement");
_advertisementX = _advertisementStartX + (size[0] / 2);
_advertisementY = _advertisementStartY + (size[1] / 2);
}
individual combine(individual i1, individual i2){
std::cerr<<"Warning: for sex aware objective function set the sexAware \n "
<<"boolean flag of the combination strategy to true! Otherwise\n"
<<" the sex of the individual has no effect."<<std::endl;
std::vector<std::pair<float, float> > newGenotype(i2.getNdim());
/* the get chromosome method gives a random selection of allele genes
* which is not neccesarrily the gene sequence that has been represented in the
* phenotype of the individual!
*
* This simulates the process of meiosis - an integral part of
* sexual reproduction.
*
* Possible improvements could inlcude gene methylation simulation and other
* acquired genetic traits other than simple mutation.
*
* When the strategy is sex aware the returned individual is returned with the objective function
* already calculated in order to reduce the number of objective function calculations.
*
* The objective function value of the male individual is estimated as
* the best objective function of his mother. That allows for possible hidden beneficial traits to
* pass to next generations and not wiped out by evolutionary pressure.
*
*/
std::vector<float > v1 = i1.getChromosome();
std::vector<float > v2 = i2.getChromosome();
std::uniform_real_distribution<float> dre(0.0,1.0);
assert(REset == true);
for(unsigned int i=0;i<i1.getNdim();i++){
float rn = dre(re);
std::pair<float, float> newGene;
newGene.first = rn>0.5?v1.at(i):v2.at(i);
newGene.second = rn>0.5?v2.at(i):v1.at(i); // saving the non expressed genes in the non expressed chromosome
//Mutation in reality is most probable to happen during meiosis - here it goes:
if(MRSet){
//std::cout<<"mutating gene gene after recombination"<<std::endl;
float rn2 = dre(re);
newGene.first += (0.5-rn2)*mutRates.at(i);
newGene.second += (0.5-rn2)*mutRates.at(i);
}
newGenotype.at(i) = newGene;
}
individual indd(newGenotype);
indd.setSex(dre(re)>0.5?MALE:FEMALE);
if(sexAware){
if(indd.getSex()==MALE){
/*
* If the created individual is a Male (objective function should not be calculated) then
* he inherits the objective function of his mother in order to reduce the objective function calcu
* lations and increase population variability. Hopefully beneficial genes are surviving generations
* when they are part of less fit individuals that way.
*/
indd.setEstimatedObjectiveFunction(i1.getSex()==FEMALE?i1.getObjectiveFunction():i2.getObjectiveFunction());
}
}
return indd;
}
void background_Thread() {
std::default_random_engine dre(static_cast<int>(time(0)));
std::this_thread::sleep_for(std::chrono::seconds(dre()%2));
std::lock_guard<std::mutex> lock(m);
std::cout << "Woke up" << "\n";
}
