本文整理汇总了C++中Gaussian::init_mu_sigma方法的典型用法代码示例。如果您正苦于以下问题：C++ Gaussian::init_mu_sigma方法的具体用法？C++ Gaussian::init_mu_sigma怎么用？C++ Gaussian::init_mu_sigma使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Gaussian的用法示例。
在下文中一共展示了Gaussian::init_mu_sigma方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: adjust_players
void TrueSkill::adjust_players(std::vector<Player*> players) {
Constants constants;
std::sort(players.begin(), players.end(), player_sorter());
std::vector<Variable*> ss, ps, ts, ds;
unsigned int i = 0, size = players.size();
double gammasqr = constants.GAMMA * constants.GAMMA,
betasqr = constants.BETA * constants.BETA;
for (i = 0; i < size; ++i) {
ss.push_back(new Variable());
ps.push_back(new Variable());
ts.push_back(new Variable());
}
for (i = 0; i < size - 1; ++i) {
ds.push_back(new Variable());
}
std::vector<PriorFactor*> skill;
for (i = 0; i < size; ++i) {
Player* pl = players[i];
Variable* s = ss[i];
Gaussian* gaussian = new Gaussian();
gaussian->init_mu_sigma(pl->mu, sqrt((pl->sigma * pl->sigma) + gammasqr));
skill.push_back(new PriorFactor(s, gaussian));
}
std::vector<LikelihoodFactor*> skill_to_perf;
for (i = 0; i < size; ++i) {
Variable* s = ss[i];
Variable* p = ps[i];
skill_to_perf.push_back(new LikelihoodFactor(s, p, betasqr));
}
std::vector<SumFactor*> perf_to_team;
for (i = 0; i < size; ++i) {
std::vector<Variable*>* p = new std::vector<Variable*>();
std::vector<double>* c = new std::vector<double>;
p->push_back(ps[i]);
c->push_back(1.0);
perf_to_team.push_back(new SumFactor(ts[i], p, c));
}
std::vector<SumFactor*> team_diff;
for (i = 0; i < size - 1; ++i) {
std::vector<Variable*>* p = new std::vector<Variable*>();
p->push_back(ts[i]);
p->push_back(ts[i + 1]);
std::vector<double>* c = new std::vector<double>;
c->push_back(1.0);
c->push_back(-1.0);
team_diff.push_back(new SumFactor(ds[i], p, c));
}
std::vector<TruncateFactor*> trunc;
for (i = 0; i < size - 1; ++i) {
TruncateFactor* tf;
if (players[i]->rank == players[i + 1]->rank) {
tf = new TruncateFactorDraw(ds[i], constants.EPSILON);
} else {
tf = new TruncateFactorWin(ds[i], constants.EPSILON);
}
trunc.push_back(tf);
}
for(std::vector<PriorFactor*>::iterator it = skill.begin(); it != skill.end(); ++it) {
(*it)->start();
}
for(std::vector<LikelihoodFactor*>::iterator it = skill_to_perf.begin(); it != skill_to_perf.end(); ++it) {
(*it)->update_value();
}
for(std::vector<SumFactor*>::iterator it = perf_to_team.begin(); it != perf_to_team.end(); ++it) {
(*it)->update_sum();
}
for (i = 0; i < 5; ++i) {
for(std::vector<SumFactor*>::iterator it = team_diff.begin(); it != team_diff.end(); ++it) {
(*it)->update_sum();
}
for(std::vector<TruncateFactor*>::iterator it = trunc.begin(); it != trunc.end(); ++it) {
(*it)->update();
}
for(std::vector<SumFactor*>::iterator it = team_diff.begin(); it != team_diff.end(); ++it) {
(*it)->update_term(0);
(*it)->update_term(1);
}
}
for(std::vector<SumFactor*>::iterator it = perf_to_team.begin(); it != perf_to_team.end(); ++it) {
(*it)->update_term(0);
}
for(std::vector<LikelihoodFactor*>::iterator it = skill_to_perf.begin(); it != skill_to_perf.end(); ++it) {
(*it)->update_mean();
//.........这里部分代码省略.........