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#include <boost/range/counting_range.hpp>
#include <boost/range/algorithm_ext/push_back.hpp>
#include <boost/range.hpp>
#include <queue>
#include <stack>
#include "../parallel/tbb.h"
template<class T>
dnn::KDTree<T>::
KDTree(const Traits& traits, HandleContainer&& handles, double _wassersteinPower):
traits_(traits), tree_(std::move(handles)), wassersteinPower(_wassersteinPower)
{ assert(wassersteinPower >= 1.0); init(); }
template<class T>
template<class Range>
dnn::KDTree<T>::
KDTree(const Traits& traits, const Range& range, double _wassersteinPower):
traits_(traits), wassersteinPower(_wassersteinPower)
{
assert( wassersteinPower >= 1.0);
init(range);
}
template<class T>
template<class Range>
void
dnn::KDTree<T>::
init(const Range& range)
{
size_t sz = std::distance(std::begin(range), std::end(range));
tree_.reserve(sz);
weights_.resize(sz, 0);
subtree_weights_.resize(sz, 0);
for (PointHandle h : range)
tree_.push_back(h);
init();
}
template<class T>
void
dnn::KDTree<T>::
init()
{
if (tree_.empty())
return;
#if defined(TBB)
task_group g;
g.run(OrderTree(tree_.begin(), tree_.end(), 0, traits()));
g.wait();
#else
OrderTree(tree_.begin(), tree_.end(), 0, traits()).serial();
#endif
for (size_t i = 0; i < tree_.size(); ++i)
indices_[tree_[i]] = i;
}
template<class T>
struct
dnn::KDTree<T>::OrderTree
{
OrderTree(HCIterator b_, HCIterator e_, size_t i_, const Traits& traits_):
b(b_), e(e_), i(i_), traits(traits_) {}
void operator()() const
{
if (e - b < 1000)
{
serial();
return;
}
HCIterator m = b + (e - b)/2;
CoordinateComparison cmp(i, traits);
std::nth_element(b,m,e, cmp);
size_t next_i = (i + 1) % traits.dimension();
task_group g;
if (b < m - 1) g.run(OrderTree(b, m, next_i, traits));
if (e > m + 2) g.run(OrderTree(m+1, e, next_i, traits));
g.wait();
}
void serial() const
{
std::queue<KDTreeNode> q;
q.push(KDTreeNode(b,e,i));
while (!q.empty())
{
HCIterator b, e; size_t i;
std::tie(b,e,i) = q.front();
q.pop();
HCIterator m = b + (e - b)/2;
CoordinateComparison cmp(i, traits);
std::nth_element(b,m,e, cmp);
size_t next_i = (i + 1) % traits.dimension();
// Replace with a size condition instead?
if (b < m - 1) q.push(KDTreeNode(b, m, next_i));
if (e - m > 2) q.push(KDTreeNode(m+1, e, next_i));
}
}
HCIterator b, e;
size_t i;
const Traits& traits;
};
template<class T>
template<class ResultsFunctor>
void
dnn::KDTree<T>::
search(PointHandle q, ResultsFunctor& rf) const
{
typedef typename HandleContainer::const_iterator HCIterator;
typedef std::tuple<HCIterator, HCIterator, size_t> KDTreeNode;
if (tree_.empty())
return;
DistanceType D = std::numeric_limits<DistanceType>::infinity();
// TODO: use tbb::scalable_allocator for the queue
std::queue<KDTreeNode> nodes;
nodes.push(KDTreeNode(tree_.begin(), tree_.end(), 0));
//std::cout << "started kdtree::search" << std::endl;
while (!nodes.empty())
{
HCIterator b, e; size_t i;
std::tie(b,e,i) = nodes.front();
nodes.pop();
CoordinateComparison cmp(i, traits());
i = (i + 1) % traits().dimension();
HCIterator m = b + (e - b)/2;
DistanceType dist = pow(traits().distance(q, *m), wassersteinPower) + weights_[m - tree_.begin()];
D = rf(*m, dist);
// we are really searching w.r.t L_\infty ball; could prune better with an L_2 ball
Coordinate diff = cmp.diff(q, *m); // diff returns signed distance
DistanceType diffToWasserPower = (diff > 0 ? 1.0 : -1.0) * pow(fabs(diff), wassersteinPower);
size_t lm = m + 1 + (e - (m+1))/2 - tree_.begin();
if (e > m + 1 && diffToWasserPower - subtree_weights_[lm] >= -D) {
nodes.push(KDTreeNode(m+1, e, i));
}
size_t rm = b + (m - b) / 2 - tree_.begin();
if (b < m && diffToWasserPower + subtree_weights_[rm] <= D) {
nodes.push(KDTreeNode(b, m, i));
}
}
//std::cout << "exited kdtree::search" << std::endl;
}
template<class T>
void
dnn::KDTree<T>::
increase_weight(PointHandle p, DistanceType w)
{
size_t idx = indices_[p];
// weight should only increase
assert( weights_[idx] <= w );
weights_[idx] = w;
typedef std::tuple<HCIterator, HCIterator> KDTreeNode;
// find the path down the tree to this node
// not an ideal strategy, but // it's not clear how to move up from the node in general
std::stack<KDTreeNode> s;
s.push(KDTreeNode(tree_.begin(),tree_.end()));
do
{
HCIterator b,e;
std::tie(b,e) = s.top();
size_t im = b + (e - b)/2 - tree_.begin();
if (idx == im)
break;
else if (idx < im)
s.push(KDTreeNode(b, tree_.begin() + im));
else // idx > im
s.push(KDTreeNode(tree_.begin() + im + 1, e));
} while(1);
// update subtree_weights_ on the path to the root
DistanceType min_w = w;
while (!s.empty())
{
HCIterator b,e;
std::tie(b,e) = s.top();
HCIterator m = b + (e - b)/2;
size_t im = m - tree_.begin();
s.pop();
// left and right children
if (b < m)
{
size_t lm = b + (m - b)/2 - tree_.begin();
if (subtree_weights_[lm] < min_w)
min_w = subtree_weights_[lm];
}
if (e > m + 1)
{
size_t rm = m + 1 + (e - (m+1))/2 - tree_.begin();
if (subtree_weights_[rm] < min_w)
min_w = subtree_weights_[rm];
}
if (weights_[im] < min_w) {
min_w = weights_[im];
}
if (subtree_weights_[im] < min_w ) // increase weight
subtree_weights_[im] = min_w;
else
break;
}
}
template<class T>
typename dnn::KDTree<T>::HandleDistance
dnn::KDTree<T>::
find(PointHandle q) const
{
dnn::NNRecord<HandleDistance> nn;
search(q, nn);
return nn.result;
}
template<class T>
typename dnn::KDTree<T>::Result
dnn::KDTree<T>::
findR(PointHandle q, DistanceType r) const
{
dnn::rNNRecord<HandleDistance> rnn(r);
search(q, rnn);
std::sort(rnn.result.begin(), rnn.result.end());
return rnn.result;
}
template<class T>
typename dnn::KDTree<T>::Result
dnn::KDTree<T>::
findK(PointHandle q, size_t k) const
{
dnn::kNNRecord<HandleDistance> knn(k);
search(q, knn);
std::sort(knn.result.begin(), knn.result.end());
return knn.result;
}
template<class T>
struct dnn::KDTree<T>::CoordinateComparison
{
CoordinateComparison(size_t i, const Traits& traits):
i_(i), traits_(traits) {}
bool operator()(PointHandle p1, PointHandle p2) const { return coordinate(p1) < coordinate(p2); }
Coordinate diff(PointHandle p1, PointHandle p2) const { return coordinate(p1) - coordinate(p2); }
Coordinate coordinate(PointHandle p) const { return traits_.coordinate(p, i_); }
size_t axis() const { return i_; }
private:
size_t i_;
const Traits& traits_;
};
template<class T>
void
dnn::KDTree<T>::
printWeights(void)
{
std::cout << "weights_:" << std::endl;
for(const auto ph : indices_) {
std::cout << "idx = " << ph.second << ": (" << (ph.first)->at(0) << ", " << (ph.first)->at(1) << ") weight = " << weights_[ph.second] << std::endl;
}
std::cout << "subtree_weights_:" << std::endl;
for(size_t idx = 0; idx < subtree_weights_.size(); ++idx) {
std::cout << idx << " : " << subtree_weights_[idx] << std::endl;
}
}
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