inline void Restaurant::FillInPredictivesNonGraphical(
const std::vector<double>& parent_predictives,
int type,
int depth,
const HPYParameter& hpy_parameter,
std::vector<double>& predictives) const {
for (size_t i = 0; i < parent_predictives.size(); ++i) {
predictives[i] = parent_predictives[i];
}
for (auto floor_it = floor2c_t_.begin(); 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));
}
auto type_it = type2internal_.find(type);
if (type_it == type2internal_.end()) return;
auto& sections = (*type_it).second.sections_;
auto section_it = sections.begin();
for (; section_it != sections.end(); ++section_it) {
auto floor_id = (*section_it).first;
auto& section = (*section_it).second;
auto cw = section.customers;
auto tw = section.tables;
predictives[floor_id] +=
(cw - hpy_parameter.discount(depth, floor_id) * tw)
/ (tmp_c_[floor_id] + hpy_parameter.concentration(depth, floor_id));
}
}
void FloorSampler::TakeInSectionLL(
const boost::container::flat_map<topic_t, std::pair<int, int> >& floor2c_t,
int add_customers,
int add_tables,
int current_k,
int depth) {
int s = pdf_.size();
auto it = floor2c_t.begin();
int j = 0;
if (!include_zero_) {
j = 1;
if (it != floor2c_t.end() && (*it).first == 0) ++it;
}
for (; j < s; ++j) {
if (it == floor2c_t.end() || (*it).first > j) {
assert(j != current_k); // current restaurant should never be empty
pdf_[j] += arrangement_part_[depth]->log_p(add_customers, add_tables, 0, 0);
} else {
assert((*it).first == j);
int c = (*it).second.first;
int t = (*it).second.second;
if (j == current_k) {
c -= add_customers;
t -= add_tables;
}
pdf_[j] += arrangement_part_[depth]->log_p(add_customers, add_tables, c, t);
++it;
}
}
}
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;
}
std::vector<HeroProfile*> heroProfiles()
{
std::vector<HeroProfile*> profiles;
profiles.reserve(globalHeroTable.size());
transform(globalHeroTable.begin(), globalHeroTable.end(), back_inserter(profiles),
[](boost::container::flat_map<int, HeroProfile*>::const_reference value){ return value.second; });
std::sort(profiles.begin(), profiles.end(), [](HeroProfile* first, HeroProfile* second){
return (first->tableId-9999.5)*(second->tableId-9999.5) < 0 ? first->tableId >= 10000 : first->tableId < second->tableId; });
return profiles;
}
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));
}
}
