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#pragma once
#include "StarRandom.hpp"
namespace Star {
template <typename Item>
struct WeightedPool {
public:
typedef pair<double, Item> ItemsType;
typedef List<ItemsType> ItemsList;
WeightedPool();
template <typename Container>
explicit WeightedPool(Container container);
void add(double weight, Item item);
void clear();
ItemsList const& items() const;
size_t size() const;
pair<double, Item> const& at(size_t index) const;
double weight(size_t index) const;
Item const& item(size_t index) const;
bool empty() const;
// Return item using the given randomness source
Item select(RandomSource& rand) const;
// Return item using the global randomness source
Item select() const;
// Return item using fast static randomness from the given seed
Item select(uint64_t seed) const;
// Return a list of n items which are selected uniquely (by index), where
// n is the lesser of the desiredCount and the size of the pool.
// This INFLUENCES PROBABILITIES so it should not be used where a
// correct statistical distribution is required.
List<Item> selectUniques(size_t desiredCount) const;
List<Item> selectUniques(size_t desiredCount, uint64_t seed) const;
size_t selectIndex(RandomSource& rand) const;
size_t selectIndex() const;
size_t selectIndex(uint64_t seed) const;
private:
size_t selectIndex(double target) const;
ItemsList m_items;
double m_totalWeight;
};
template <typename Item>
WeightedPool<Item>::WeightedPool()
: m_totalWeight(0.0) {}
template <typename Item>
template <typename Container>
WeightedPool<Item>::WeightedPool(Container container)
: WeightedPool() {
for (auto const& pair : container)
add(get<0>(pair), get<1>(pair));
}
template <typename Item>
void WeightedPool<Item>::add(double weight, Item item) {
if (weight <= 0.0)
return;
m_items.append({weight, std::move(item)});
m_totalWeight += weight;
}
template <typename Item>
void WeightedPool<Item>::clear() {
m_items.clear();
m_totalWeight = 0.0;
}
template <typename Item>
auto WeightedPool<Item>::items() const -> ItemsList const & {
return m_items;
}
template <typename Item>
size_t WeightedPool<Item>::size() const {
return m_items.count();
}
template <typename Item>
pair<double, Item> const& WeightedPool<Item>::at(size_t index) const {
return m_items.at(index);
}
template <typename Item>
double WeightedPool<Item>::weight(size_t index) const {
return at(index).first;
}
template <typename Item>
Item const& WeightedPool<Item>::item(size_t index) const {
return at(index).second;
}
template <typename Item>
bool WeightedPool<Item>::empty() const {
return m_items.empty();
}
template <typename Item>
Item WeightedPool<Item>::select(RandomSource& rand) const {
if (m_items.empty())
return Item();
return m_items[selectIndex(rand)].second;
}
template <typename Item>
Item WeightedPool<Item>::select() const {
if (m_items.empty())
return Item();
return m_items[selectIndex()].second;
}
template <typename Item>
Item WeightedPool<Item>::select(uint64_t seed) const {
if (m_items.empty())
return Item();
return m_items[selectIndex(seed)].second;
}
template <typename Item>
List<Item> WeightedPool<Item>::selectUniques(size_t desiredCount) const {
return selectUniques(desiredCount, Random::randu64());
}
template <typename Item>
List<Item> WeightedPool<Item>::selectUniques(size_t desiredCount, uint64_t seed) const {
size_t targetCount = std::min(desiredCount, size());
Set<size_t> indices;
while (indices.size() < targetCount)
indices.add(selectIndex(++seed));
List<Item> result;
for (size_t i : indices)
result.append(m_items[i].second);
return result;
}
template <typename Item>
size_t WeightedPool<Item>::selectIndex(RandomSource& rand) const {
return selectIndex(rand.randd());
}
template <typename Item>
size_t WeightedPool<Item>::selectIndex() const {
return selectIndex(Random::randd());
}
template <typename Item>
size_t WeightedPool<Item>::selectIndex(uint64_t seed) const {
return selectIndex(staticRandomDouble(seed));
}
template <typename Item>
size_t WeightedPool<Item>::selectIndex(double target) const {
if (m_items.empty())
return NPos;
// Test a randomly generated target against each weighted item in turn, and
// see if that weighted item's weight value crosses the target. This way, a
// random item is picked from the list, but (roughly) weighted to be
// proportional to its weight over the weight of all entries.
//
// TODO: This is currently O(n), but can easily be made O(log(n)) by using a
// tree. If this shows up in performance measurements, this is an obvious
// improvement.
double accumulatedWeight = 0.0f;
for (size_t i = 0; i < m_items.size(); ++i) {
accumulatedWeight += m_items[i].first / m_totalWeight;
if (target <= accumulatedWeight)
return i;
}
// If we haven't crossed the target, just assume floating point error has
// caused us to not quite make it to the last item.
return m_items.size() - 1;
}
}
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