namespace Diehard
{
default_random_engine ProblemTweet::random_engine = default_random_engine(random_device()());
ProblemTweet::ProblemTweet()
: ProblemDot(*GetRandomCapacities()) {}
shared_ptr<list<string> > ProblemTweet::GetTweets() const
{
list<const Node*> nodes_route;
auto node_goal = GetGoalNodeRandomly();
if (!node_goal) return nullptr;
auto node_ptr = node_goal;
while (node_ptr)
{
nodes_route.push_front(node_ptr);
if (node_ptr && node_ptr->GetSum() == 0) break;
node_ptr = GetFromNodeRandomly(node_ptr);
}
auto tweets = shared_ptr<list<string> >(new list<string>());
{
stringstream ss;
ss << "Capacities: " << GetName();
ss << " -> ";
ss << "Request: " << goal_sum;
tweets->push_back(ss.str());
}
for_each(nodes_route.begin(), nodes_route.end(), [&](const Node* node)
{
stringstream ss;
ss << "Bucket: " << node->GetName(false);
tweets->push_back(ss.str());
});
{
stringstream ss;
ss << "Final result: ";
for (Dimention d = 0; d < capacities.size(); d++)
{
if (d != 0) ss << "+";
ss << node_goal->volumes[d];
}
ss << "=" << node_goal->GetSum();
tweets->push_back(ss.str());
}
return tweets;
}
#pragma mark Random generator
const Node* ProblemTweet::GetGoalNodeRandomly() const
{
list<const Node*> nodes_goal;
for (auto& node : nodes)
{
if (
node.is_used &&
node.cost < Node::cost_max &&
node.GetSum() == goal_sum)
nodes_goal.push_back(&node);
}
return GetLowestCostRandomly(nodes_goal);
}
const Node* ProblemTweet::GetFromNodeRandomly(const Node* node)
{
list<const Node*> nodes_from(node->from.begin(), node->from.end());
return GetLowestCostRandomly(nodes_from);
}
const Node* ProblemTweet::GetLowestCostRandomly(const std::list<const Node*>& nodes)
{
Node::Cost cost_min = Node::cost_max;
for_each(nodes.begin(), nodes.end(), [&](const Node* node)
{
if (node->cost < cost_min) cost_min = node->cost;
});
vector<const Node*> nodes_lowcost;
for_each(nodes.begin(), nodes.end(), [&](const Node* node)
{
if (node->cost == cost_min) nodes_lowcost.push_back(node);
});
if (nodes_lowcost.empty())
{
return nullptr;
}
else
{
uniform_int_distribution<size_t> dist_idx(0, nodes_lowcost.size() - 1);
return nodes_lowcost[dist_idx(random_engine)];
}
}
shared_ptr<vector<Volume> > ProblemTweet::GetRandomCapacities()
{
uniform_int_distribution<Volume> dist_capacity(2, 200);
uniform_int_distribution<Dimention> dist_dimention(2, 4);
auto rand_capacity = bind(dist_capacity, random_engine);
auto dimention = dist_dimention(random_engine);
shared_ptr<vector<Volume> > capacities(new vector<Volume>(dimention));
for (Dimention i = 0; i < dimention; i++)
{
(*capacities)[i] = rand_capacity();
}
return capacities;
}
Volume ProblemTweet::GetRandomGoal()
{
Volume sum = 0;
for_each(capacities.begin(), capacities.end(), [&](Volume v) { sum += v; });
uniform_int_distribution<Volume> dist_goal(1, sum);
return dist_goal(random_engine);
}
}
std::vector<int> KMeansMethod::getInitialClusterOfInputs(const std::vector<std::vector<double>> &inputs, int dim, int countOfCluster) {
// メルセンヌツイスタの初期化
std::random_device random_device;
std::mt19937 mt(random_device());
std::vector<int> indexOfCluster;
// まず最初に乱数で一つだけ追加
std::uniform_int_distribution<int> score(0, inputs.size() - 1);
indexOfCluster.push_back(score(mt));
while (indexOfCluster.size() < countOfCluster) {
std::vector<double> distances;
for (int i = 0; i < inputs.size(); i++) {
std::vector<double> input = inputs[i];
// 一番近いクラスタとの距離
double minD = std::numeric_limits<double>::max();
for (int j = 0; j < indexOfCluster.size(); j++) {
int index = indexOfCluster[j];
std::vector<double> cluster = inputs[index];
double d = 0;
for (int k = 0; k < dim; k++) {
d += pow(cluster[k] - input[k], 2.0);
}
if (d < minD) {
minD = d;
}
}
distances.push_back(minD);
}
// 重み付き確率計算のために距離を範囲に変換
double index = 0;
for (int i = 0; i < distances.size(); i++) {
index += distances[i];
distances[i] = index;
}
// 0 ~ 最大値までの乱数生成
std::uniform_real_distribution<double> score(0.0, index);
double random = score(mt);
// 乱数がどこに含まれるかを探索
int result = -1;
for (int i = 0; i < distances.size(); i++) {
if (distances[i] >= random) {
result = i;
break;
}
}
// 選択されたクラスタを追加
if (result >= 0) {
indexOfCluster.push_back(result);
}
}
std::vector<int> clusterOfInputs;
// 入力をそれぞれ一番近いクラスタに付与
for (int i = 0; i < inputs.size(); i++) {
std::vector<double> input = inputs[i];
double minD = std::numeric_limits<double>::max();
int minIndex = 0;
// 一番近いクラスタを検索
for (int j = 0; j < countOfCluster; j++) {
int index = indexOfCluster[j];
std::vector<double> cluster = inputs[index];
double d = 0;
for (int k = 0; k < dim; k++) {
d += pow(input[k] - cluster[k], 2.0);
}
if (d < minD) {
minD = d;
minIndex = j;
}
}
clusterOfInputs.push_back(minIndex);
}
return clusterOfInputs;
}
int main()
{
std::vector<int> vec(10);
std::cout << "iota:" << '\n';
boost::iota(vec, 1);
for (auto e : vec) std::cout << e << " ";
std::cout << '\n';
std::cout << "reversed:" << '\n';
for (auto e : vec | boost::adaptors::reversed) std::cout << e << " ";
std::cout << '\n';
std::cout << "sums:" << '\n';
std::vector<int> sums(10);
boost::partial_sum(vec, begin(sums));
for (auto e : sums) std::cout << e << " ";
std::cout << '\n';
// perhaps as exercise?
std::cout << "evens:" << '\n';
std::vector<int> evens(10);
boost::fill(evens, 2); // std::vector<int> evens(10, 2);
boost::partial_sum(evens, begin(evens));
for (auto e : evens) std::cout << e << " ";
std::cout << '\n';
boost::partial_sum(evens | boost::adaptors::reversed, begin(evens));
for (auto e : evens) std::cout << e << " ";
std::cout << '\n';
// perhaps as exercise?
std::cout << "factorials:" << '\n';
std::vector<int> factorials(10);
boost::partial_sum(vec, begin(factorials), std::multiplies<int>());
for (auto e : factorials) std::cout << e << " ";
std::cout << '\n';
std::cout << "die:" << '\n';
std::random_device random_device;
std::default_random_engine random_engine(random_device());
int a = 1, b = 6;
std::uniform_int_distribution<int> uniform_distribution(a, b);
auto die = std::bind(uniform_distribution, random_engine);
std::cout << die() << '\n';
std::cout << "die throws:" << '\n';
std::vector<int> die_throws(10);
boost::generate(die_throws, die); // readability: "generate die_throws using die [duh]"
for (auto e : die_throws) std::cout << e << " ";
std::cout << '\n';
std::cout << "die throws, from #2 to #5:" << '\n';
for (auto e : die_throws | boost::adaptors::sliced(2, 5)) std::cout << e << " ";
std::cout << '\n';
auto count_unique_low = boost::count_if( die_throws | boost::adaptors::uniqued, [](int result) { return result <= 3; } );
auto count_unique_high = boost::count_if( die_throws | boost::adaptors::uniqued, [](int result) { return result > 3; } );
std::cout << "count_unique_low = " << count_unique_low << '\n';
std::cout << "count_unique_high = " << count_unique_high << '\n';
/*
std::vector<int> v;
boost::push_front(v, boost::istream_range<int>(std::cin));
for (auto e : v) std::cout << e << " ";
std::cout << '\n';
*/
std::vector<int> v;
boost::push_back(v, boost::irange(100, 200, 3)); // first, last, step-size
for (auto e : v) std::cout << e << " ";
std::cout << '\n';
}
