本文整理汇总了C++中boost::container::flat_map::empty方法的典型用法代码示例。如果您正苦于以下问题：C++ flat_map::empty方法的具体用法？C++ flat_map::empty怎么用？C++ flat_map::empty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boost::container::flat_map的用法示例。
在下文中一共展示了flat_map::empty方法的2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: if
boost::optional<reaction_task_t> find_automatic_reaction_task(const settler_ai_t &ai) {
if (automatic_reactions.empty()) return boost::optional<reaction_task_t>{};
boost::optional<reaction_task_t> result;
// Iterate through available reactions
for (auto outerit=automatic_reactions.begin(); outerit != automatic_reactions.end(); ++outerit) {
// Is the workshop busy?
auto busy_finder = workshop_claimed.find(outerit->first);
if (busy_finder == workshop_claimed.end()) {
// Iterate available automatic reactions
for (const std::string &reaction_name : outerit->second) {
auto reaction = reaction_defs.find(reaction_name);
if (reaction != reaction_defs.end()) {
// Is the settler allowed to do this?
int target_category = -1;
if (reaction->second.skill == "Carpentry") {
target_category = JOB_CARPENTRY;
} else if (reaction->second.skill == "Masonry") {
target_category = JOB_MASONRY;
}
if (target_category == -1 || ai.permitted_work[target_category]) {
// Are the inputs available?
bool available = true;
std::vector<std::pair<std::size_t,bool>> components;
for (auto &input : reaction->second.inputs) {
const int n_available = available_items_by_reaction_input(input);
if (n_available < input.quantity) {
available = false;
} else {
// Claim an item and push its # to the list
std::size_t item_id = claim_item_by_reaction_input(input);
components.push_back(std::make_pair(item_id,false));
}
}
if (available) {
// Components are available, build job and return it
result = reaction_task_t{outerit->first, reaction->second.name, reaction->second.tag, components};
workshop_claimed.insert(outerit->first);
return result;
} else {
for (auto comp : components) {
unclaim_by_id(comp.first);
}
}
}
}
}
}
}
return result;
}示例2: FillInPredictives
void Restaurant::FillInPredictives(const std::vector<double>& parent_predictives,
int type,
const LambdaManagerInterface& lmanager,
int depth,
const HPYParameter& hpy_parameter,
std::vector<double>& predictives) const {
auto floor_it = floor2c_t_.begin();
auto type_it = type2internal_.find(type);
predictives[0] = parent_predictives[0];
if (floor2c_t_.empty()) {
for (size_t i = 1; i < predictives.size(); ++i) {
double lambda = lmanager.lambda(i, depth);
predictives[i] = lambda * parent_predictives[i] + (1 - lambda) * predictives[0];
}
return;
}
if ((*floor_it).first == 0) {
auto& c_t = (*floor_it).second;
predictives[0] *= (hpy_parameter.concentration(depth, 0) + hpy_parameter.discount(depth, 0) * c_t.second) / (c_t.first + hpy_parameter.concentration(depth, 0));
if (type_it != type2internal_.end()) {
auto& sections = (*type_it).second.sections_;
if (!sections.empty()) {
auto section_begin = sections.begin();
if ((*section_begin).first == 0) {
int cw = (*section_begin).second.customers;
int tw = (*section_begin).second.tables;
predictives[0] += (cw - hpy_parameter.discount(depth, 0) * tw) / (c_t.first + hpy_parameter.concentration(depth, 0));
}
}
}
}
for (size_t i = 1; i < predictives.size(); ++i) {
double lambda = lmanager.lambda(i, depth);
predictives[i] = lambda * parent_predictives[i] + (1 - lambda) * predictives[0];
}
if ((*floor_it).first == 0) {
++floor_it;
}
for (; floor_it != floor2c_t_.end(); ++floor_it) {
auto floor_id = (*floor_it).first;
auto& c_t = (*floor_it).second;
tmp_c_[floor_id] = c_t.first;
predictives[floor_id] *=
(hpy_parameter.concentration(depth, floor_id)
+ hpy_parameter.discount(depth, floor_id) * c_t.second)
/ (c_t.first + hpy_parameter.concentration(depth, floor_id));
}
if (type_it == type2internal_.end()) return;
auto& sections = (*type_it).second.sections_;
auto section_it = sections.begin();
if (section_it != sections.end() && (*section_it).first == 0) {
++section_it;
}
for (; section_it != sections.end(); ++section_it) {
auto floor_id = (*section_it).first;
auto& section = (*section_it).second;
int cw = section.customers;
int tw = section.tables;
predictives[floor_id] +=
(cw - hpy_parameter.discount(depth, floor_id) * tw)
/ (tmp_c_[floor_id] + hpy_parameter.concentration(depth, floor_id));
}
}