60#include <unordered_set>
64#define NANOFLANN_VERSION 0x163
67#if !defined(NOMINMAX) && \
68 (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
89 return static_cast<T
>(3.14159265358979323846);
96template <
typename T,
typename =
int>
107template <
typename T,
typename =
int>
121template <
typename Container>
122inline typename std::enable_if<has_resize<Container>::value,
void>::type
resize(
123 Container& c,
const size_t nElements)
132template <
typename Container>
133inline typename std::enable_if<!has_resize<Container>::value,
void>::type
134 resize(Container& c,
const size_t nElements)
136 if (nElements != c.size())
137 throw std::logic_error(
"Try to change the size of a std::array.");
143template <
typename Container,
typename T>
144inline typename std::enable_if<has_assign<Container>::value,
void>::type
assign(
145 Container& c,
const size_t nElements,
const T& value)
147 c.assign(nElements, value);
153template <
typename Container,
typename T>
154inline typename std::enable_if<!has_assign<Container>::value,
void>::type
155 assign(Container& c,
const size_t nElements,
const T& value)
157 for (
size_t i = 0; i < nElements; i++) c[i] = value;
164 template <
typename PairType>
165 bool operator()(
const PairType& p1,
const PairType& p2)
const
167 return p1.second < p2.second;
179template <
typename IndexType =
size_t,
typename DistanceType =
double>
183 ResultItem(
const IndexType index,
const DistanceType distance)
197 typename _DistanceType,
typename _IndexType = size_t,
198 typename _CountType =
size_t>
202 using DistanceType = _DistanceType;
203 using IndexType = _IndexType;
204 using CountType = _CountType;
214 : indices(
nullptr), dists(
nullptr), capacity(capacity_), count(0)
218 void init(IndexType* indices_, DistanceType* dists_)
224 dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
227 CountType size()
const {
return count; }
228 bool empty()
const {
return count == 0; }
229 bool full()
const {
return count == capacity; }
239 for (i = count; i > 0; --i)
243#ifdef NANOFLANN_FIRST_MATCH
244 if ((dists[i - 1] > dist) ||
245 ((dist == dists[i - 1]) && (indices[i - 1] > index)))
248 if (dists[i - 1] > dist)
253 dists[i] = dists[i - 1];
254 indices[i] = indices[i - 1];
265 if (count < capacity) count++;
271 DistanceType worstDist()
const {
return dists[capacity - 1]; }
281 typename _DistanceType,
typename _IndexType = size_t,
282 typename _CountType =
size_t>
286 using DistanceType = _DistanceType;
287 using IndexType = _IndexType;
288 using CountType = _CountType;
295 DistanceType maximumSearchDistanceSquared;
299 CountType capacity_, DistanceType maximumSearchDistanceSquared_)
304 maximumSearchDistanceSquared(maximumSearchDistanceSquared_)
308 void init(IndexType* indices_, DistanceType* dists_)
313 if (capacity) dists[capacity - 1] = maximumSearchDistanceSquared;
316 CountType size()
const {
return count; }
317 bool empty()
const {
return count == 0; }
318 bool full()
const {
return count == capacity; }
328 for (i = count; i > 0; --i)
332#ifdef NANOFLANN_FIRST_MATCH
333 if ((dists[i - 1] > dist) ||
334 ((dist == dists[i - 1]) && (indices[i - 1] > index)))
337 if (dists[i - 1] > dist)
342 dists[i] = dists[i - 1];
343 indices[i] = indices[i - 1];
354 if (count < capacity) count++;
360 DistanceType worstDist()
const {
return dists[capacity - 1]; }
371template <
typename _DistanceType,
typename _IndexType =
size_t>
375 using DistanceType = _DistanceType;
376 using IndexType = _IndexType;
379 const DistanceType radius;
381 std::vector<ResultItem<IndexType, DistanceType>>& m_indices_dists;
384 DistanceType radius_,
386 : radius(radius_), m_indices_dists(indices_dists)
391 void init() { clear(); }
392 void clear() { m_indices_dists.clear(); }
394 size_t size()
const {
return m_indices_dists.size(); }
395 size_t empty()
const {
return m_indices_dists.empty(); }
397 bool full()
const {
return true; }
406 if (dist < radius) m_indices_dists.emplace_back(index, dist);
410 DistanceType worstDist()
const {
return radius; }
418 if (m_indices_dists.empty())
419 throw std::runtime_error(
420 "Cannot invoke RadiusResultSet::worst_item() on "
421 "an empty list of results.");
422 auto it = std::max_element(
439void save_value(std::ostream& stream,
const T& value)
441 stream.write(
reinterpret_cast<const char*
>(&value),
sizeof(T));
445void save_value(std::ostream& stream,
const std::vector<T>& value)
447 size_t size = value.size();
448 stream.write(
reinterpret_cast<const char*
>(&size),
sizeof(
size_t));
449 stream.write(
reinterpret_cast<const char*
>(value.data()),
sizeof(T) * size);
453void load_value(std::istream& stream, T& value)
455 stream.read(
reinterpret_cast<char*
>(&value),
sizeof(T));
459void load_value(std::istream& stream, std::vector<T>& value)
462 stream.read(
reinterpret_cast<char*
>(&size),
sizeof(
size_t));
464 stream.read(
reinterpret_cast<char*
>(value.data()),
sizeof(T) * size);
486 class T,
class DataSource,
typename _DistanceType = T,
487 typename IndexType = uint32_t>
490 using ElementType = T;
491 using DistanceType = _DistanceType;
493 const DataSource& data_source;
495 L1_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
497 DistanceType evalMetric(
498 const T* a,
const IndexType b_idx,
size_t size,
499 DistanceType worst_dist = -1)
const
501 DistanceType result = DistanceType();
502 const T* last = a + size;
503 const T* lastgroup = last - 3;
507 while (a < lastgroup)
509 const DistanceType diff0 =
510 std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
511 const DistanceType diff1 =
512 std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
513 const DistanceType diff2 =
514 std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
515 const DistanceType diff3 =
516 std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
517 result += diff0 + diff1 + diff2 + diff3;
519 if ((worst_dist > 0) && (result > worst_dist)) {
return result; }
525 result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
530 template <
typename U,
typename V>
531 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
533 return std::abs(a - b);
548 class T,
class DataSource,
typename _DistanceType = T,
549 typename IndexType = uint32_t>
552 using ElementType = T;
553 using DistanceType = _DistanceType;
555 const DataSource& data_source;
557 L2_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
559 DistanceType evalMetric(
560 const T* a,
const IndexType b_idx,
size_t size,
561 DistanceType worst_dist = -1)
const
563 DistanceType result = DistanceType();
564 const T* last = a + size;
565 const T* lastgroup = last - 3;
569 while (a < lastgroup)
571 const DistanceType diff0 =
572 a[0] - data_source.kdtree_get_pt(b_idx, d++);
573 const DistanceType diff1 =
574 a[1] - data_source.kdtree_get_pt(b_idx, d++);
575 const DistanceType diff2 =
576 a[2] - data_source.kdtree_get_pt(b_idx, d++);
577 const DistanceType diff3 =
578 a[3] - data_source.kdtree_get_pt(b_idx, d++);
580 diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
582 if ((worst_dist > 0) && (result > worst_dist)) {
return result; }
588 const DistanceType diff0 =
589 *a++ - data_source.kdtree_get_pt(b_idx, d++);
590 result += diff0 * diff0;
595 template <
typename U,
typename V>
596 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
598 return (a - b) * (a - b);
613 class T,
class DataSource,
typename _DistanceType = T,
614 typename IndexType = uint32_t>
617 using ElementType = T;
618 using DistanceType = _DistanceType;
620 const DataSource& data_source;
623 : data_source(_data_source)
627 DistanceType evalMetric(
628 const T* a,
const IndexType b_idx,
size_t size)
const
630 DistanceType result = DistanceType();
631 for (
size_t i = 0; i < size; ++i)
633 const DistanceType diff =
634 a[i] - data_source.kdtree_get_pt(b_idx, i);
635 result += diff * diff;
640 template <
typename U,
typename V>
641 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
643 return (a - b) * (a - b);
658 class T,
class DataSource,
typename _DistanceType = T,
659 typename IndexType = uint32_t>
662 using ElementType = T;
663 using DistanceType = _DistanceType;
665 const DataSource& data_source;
667 SO2_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
669 DistanceType evalMetric(
670 const T* a,
const IndexType b_idx,
size_t size)
const
673 a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1), size - 1);
678 template <
typename U,
typename V>
679 DistanceType
accum_dist(
const U a,
const V b,
const size_t)
const
681 DistanceType result = DistanceType();
682 DistanceType PI = pi_const<DistanceType>();
686 else if (result < -PI)
703 class T,
class DataSource,
typename _DistanceType = T,
704 typename IndexType = uint32_t>
707 using ElementType = T;
708 using DistanceType = _DistanceType;
714 : distance_L2_Simple(_data_source)
718 DistanceType evalMetric(
719 const T* a,
const IndexType b_idx,
size_t size)
const
721 return distance_L2_Simple.evalMetric(a, b_idx, size);
724 template <
typename U,
typename V>
725 DistanceType accum_dist(
const U a,
const V b,
const size_t idx)
const
727 return distance_L2_Simple.accum_dist(a, b, idx);
734 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
744 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
754 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
763 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
772 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
784enum class KDTreeSingleIndexAdaptorFlags
787 SkipInitialBuildIndex = 1
790inline std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type operator&(
791 KDTreeSingleIndexAdaptorFlags lhs, KDTreeSingleIndexAdaptorFlags rhs)
794 typename std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type;
795 return static_cast<underlying
>(lhs) &
static_cast<underlying
>(rhs);
802 size_t _leaf_max_size = 10,
803 KDTreeSingleIndexAdaptorFlags _flags =
804 KDTreeSingleIndexAdaptorFlags::None,
805 unsigned int _n_thread_build = 1)
806 : leaf_max_size(_leaf_max_size),
808 n_thread_build(_n_thread_build)
812 size_t leaf_max_size;
813 KDTreeSingleIndexAdaptorFlags flags;
814 unsigned int n_thread_build;
821 : eps(eps_), sorted(sorted_)
850 static constexpr size_t WORDSIZE = 16;
851 static constexpr size_t BLOCKSIZE = 8192;
862 void* base_ =
nullptr;
863 void* loc_ =
nullptr;
875 Size wastedMemory = 0;
890 while (base_ !=
nullptr)
893 void* prev = *(
static_cast<void**
>(base_));
910 const Size size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
915 if (size > remaining_)
917 wastedMemory += remaining_;
920 const Size blocksize =
921 size > BLOCKSIZE ? size + WORDSIZE : BLOCKSIZE + WORDSIZE;
924 void* m = ::malloc(blocksize);
927 fprintf(stderr,
"Failed to allocate memory.\n");
928 throw std::bad_alloc();
932 static_cast<void**
>(m)[0] = base_;
935 remaining_ = blocksize - WORDSIZE;
936 loc_ =
static_cast<char*
>(m) + WORDSIZE;
939 loc_ =
static_cast<char*
>(loc_) + size;
954 template <
typename T>
957 T* mem =
static_cast<T*
>(this->malloc(
sizeof(T) * count));
969template <
int32_t DIM,
typename T>
972 using type = std::array<T, DIM>;
978 using type = std::vector<T>;
998 class Derived,
typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
999 typename index_t = uint32_t>
1007 obj.pool_.free_all();
1008 obj.root_node_ =
nullptr;
1009 obj.size_at_index_build_ = 0;
1012 using ElementType =
typename Distance::ElementType;
1013 using DistanceType =
typename Distance::DistanceType;
1014 using IndexType = index_t;
1021 using Offset =
typename decltype(vAcc_)::size_type;
1022 using Size =
typename decltype(vAcc_)::size_type;
1023 using Dimension = int32_t;
1047 Node *child1 =
nullptr, *child2 =
nullptr;
1055 ElementType low, high;
1060 Size leaf_max_size_ = 0;
1063 Size n_thread_build_ = 1;
1067 Size size_at_index_build_ = 0;
1091 Size
size(
const Derived& obj)
const {
return obj.size_; }
1094 Size
veclen(
const Derived& obj) {
return DIM > 0 ? DIM : obj.dim; }
1098 const Derived& obj, IndexType element, Dimension component)
const
1100 return obj.dataset_.kdtree_get_pt(element, component);
1109 return obj.pool_.usedMemory + obj.pool_.wastedMemory +
1110 obj.dataset_.kdtree_get_point_count() *
1115 const Derived& obj, Offset ind, Size count, Dimension element,
1116 ElementType& min_elem, ElementType& max_elem)
1118 min_elem = dataset_get(obj, vAcc_[ind], element);
1119 max_elem = min_elem;
1120 for (Offset i = 1; i < count; ++i)
1122 ElementType val = dataset_get(obj, vAcc_[ind + i], element);
1123 if (val < min_elem) min_elem = val;
1124 if (val > max_elem) max_elem = val;
1136 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox)
1138 assert(left < obj.dataset_.kdtree_get_point_count());
1140 NodePtr node = obj.pool_.template allocate<Node>();
1141 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1144 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1146 node->
child1 = node->child2 =
nullptr;
1151 for (Dimension i = 0; i < dims; ++i)
1153 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1154 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1156 for (Offset k = left + 1; k < right; ++k)
1158 for (Dimension i = 0; i < dims; ++i)
1160 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1161 if (bbox[i].low > val) bbox[i].low = val;
1162 if (bbox[i].high < val) bbox[i].high = val;
1170 DistanceType cutval;
1171 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1176 left_bbox[cutfeat].high = cutval;
1177 node->
child1 = this->divideTree(obj, left, left + idx, left_bbox);
1180 right_bbox[cutfeat].low = cutval;
1181 node->child2 = this->divideTree(obj, left + idx, right, right_bbox);
1183 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1184 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1186 for (Dimension i = 0; i < dims; ++i)
1188 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1189 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1207 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox,
1208 std::atomic<unsigned int>& thread_count, std::mutex& mutex)
1210 std::unique_lock<std::mutex> lock(mutex);
1211 NodePtr node = obj.pool_.template allocate<Node>();
1214 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1217 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1219 node->
child1 = node->child2 =
nullptr;
1224 for (Dimension i = 0; i < dims; ++i)
1226 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1227 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1229 for (Offset k = left + 1; k < right; ++k)
1231 for (Dimension i = 0; i < dims; ++i)
1233 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1234 if (bbox[i].low > val) bbox[i].low = val;
1235 if (bbox[i].high < val) bbox[i].high = val;
1243 DistanceType cutval;
1244 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1248 std::future<NodePtr> right_future;
1251 right_bbox[cutfeat].low = cutval;
1252 if (++thread_count < n_thread_build_)
1255 right_future = std::async(
1256 std::launch::async, &KDTreeBaseClass::divideTreeConcurrent,
1257 this, std::ref(obj), left + idx, right,
1258 std::ref(right_bbox), std::ref(thread_count),
1261 else { --thread_count; }
1264 left_bbox[cutfeat].high = cutval;
1265 node->
child1 = this->divideTreeConcurrent(
1266 obj, left, left + idx, left_bbox, thread_count, mutex);
1268 if (right_future.valid())
1271 node->child2 = right_future.get();
1276 node->child2 = this->divideTreeConcurrent(
1277 obj, left + idx, right, right_bbox, thread_count, mutex);
1280 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1281 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1283 for (Dimension i = 0; i < dims; ++i)
1285 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1286 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1294 const Derived& obj,
const Offset ind,
const Size count, Offset& index,
1295 Dimension& cutfeat, DistanceType& cutval,
const BoundingBox& bbox)
1297 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1298 const auto EPS =
static_cast<DistanceType
>(0.00001);
1299 ElementType max_span = bbox[0].high - bbox[0].low;
1300 for (Dimension i = 1; i < dims; ++i)
1302 ElementType span = bbox[i].high - bbox[i].low;
1303 if (span > max_span) { max_span = span; }
1305 ElementType max_spread = -1;
1307 ElementType min_elem = 0, max_elem = 0;
1308 for (Dimension i = 0; i < dims; ++i)
1310 ElementType span = bbox[i].high - bbox[i].low;
1311 if (span >= (1 - EPS) * max_span)
1313 ElementType min_elem_, max_elem_;
1314 computeMinMax(obj, ind, count, i, min_elem_, max_elem_);
1315 ElementType spread = max_elem_ - min_elem_;
1316 if (spread > max_spread)
1319 max_spread = spread;
1320 min_elem = min_elem_;
1321 max_elem = max_elem_;
1326 DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
1328 if (split_val < min_elem)
1330 else if (split_val > max_elem)
1336 planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
1338 if (lim1 > count / 2)
1340 else if (lim2 < count / 2)
1356 const Derived& obj,
const Offset ind,
const Size count,
1357 const Dimension cutfeat,
const DistanceType& cutval, Offset& lim1,
1362 Offset right = count - 1;
1365 while (left <= right &&
1366 dataset_get(obj, vAcc_[ind + left], cutfeat) < cutval)
1368 while (right && left <= right &&
1369 dataset_get(obj, vAcc_[ind + right], cutfeat) >= cutval)
1371 if (left > right || !right)
1373 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1384 while (left <= right &&
1385 dataset_get(obj, vAcc_[ind + left], cutfeat) <= cutval)
1387 while (right && left <= right &&
1388 dataset_get(obj, vAcc_[ind + right], cutfeat) > cutval)
1390 if (left > right || !right)
1392 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1399 DistanceType computeInitialDistances(
1400 const Derived& obj,
const ElementType* vec,
1401 distance_vector_t& dists)
const
1404 DistanceType dist = DistanceType();
1406 for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim_); ++i)
1408 if (vec[i] < obj.root_bbox_[i].low)
1411 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].low, i);
1414 if (vec[i] > obj.root_bbox_[i].high)
1417 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i);
1424 static void save_tree(
1425 const Derived& obj, std::ostream& stream,
const NodeConstPtr tree)
1427 save_value(stream, *tree);
1428 if (tree->child1 !=
nullptr) { save_tree(obj, stream, tree->child1); }
1429 if (tree->child2 !=
nullptr) { save_tree(obj, stream, tree->child2); }
1432 static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree)
1434 tree = obj.pool_.template allocate<Node>();
1435 load_value(stream, *tree);
1436 if (tree->child1 !=
nullptr) { load_tree(obj, stream, tree->child1); }
1437 if (tree->child2 !=
nullptr) { load_tree(obj, stream, tree->child2); }
1445 void saveIndex(
const Derived& obj, std::ostream& stream)
const
1447 save_value(stream, obj.size_);
1448 save_value(stream, obj.dim_);
1449 save_value(stream, obj.root_bbox_);
1450 save_value(stream, obj.leaf_max_size_);
1451 save_value(stream, obj.vAcc_);
1452 if (obj.root_node_) save_tree(obj, stream, obj.root_node_);
1462 load_value(stream, obj.size_);
1463 load_value(stream, obj.dim_);
1464 load_value(stream, obj.root_bbox_);
1465 load_value(stream, obj.leaf_max_size_);
1466 load_value(stream, obj.vAcc_);
1467 load_tree(obj, stream, obj.root_node_);
1513 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1514 typename index_t = uint32_t>
1517 KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, index_t>,
1518 Distance, DatasetAdaptor, DIM, index_t>
1524 Distance, DatasetAdaptor, DIM, index_t>&) =
delete;
1535 Distance, DatasetAdaptor, DIM, index_t>,
1536 Distance, DatasetAdaptor, DIM, index_t>;
1538 using Offset =
typename Base::Offset;
1539 using Size =
typename Base::Size;
1540 using Dimension =
typename Base::Dimension;
1542 using ElementType =
typename Base::ElementType;
1543 using DistanceType =
typename Base::DistanceType;
1544 using IndexType =
typename Base::IndexType;
1546 using Node =
typename Base::Node;
1547 using NodePtr = Node*;
1549 using Interval =
typename Base::Interval;
1579 template <
class... Args>
1581 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1583 : dataset_(inputData),
1584 indexParams(params),
1585 distance_(inputData, std::forward<Args>(args)...)
1587 init(dimensionality, params);
1591 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1593 : dataset_(inputData), indexParams(params), distance_(inputData)
1595 init(dimensionality, params);
1600 const Dimension dimensionality,
1601 const KDTreeSingleIndexAdaptorParams& params)
1603 Base::size_ = dataset_.kdtree_get_point_count();
1604 Base::size_at_index_build_ = Base::size_;
1605 Base::dim_ = dimensionality;
1606 if (DIM > 0) Base::dim_ = DIM;
1607 Base::leaf_max_size_ = params.leaf_max_size;
1608 if (params.n_thread_build > 0)
1610 Base::n_thread_build_ = params.n_thread_build;
1614 Base::n_thread_build_ =
1615 std::max(std::thread::hardware_concurrency(), 1u);
1618 if (!(params.flags &
1619 KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex))
1632 Base::size_ = dataset_.kdtree_get_point_count();
1633 Base::size_at_index_build_ = Base::size_;
1635 this->freeIndex(*
this);
1636 Base::size_at_index_build_ = Base::size_;
1637 if (Base::size_ == 0)
return;
1638 computeBoundingBox(Base::root_bbox_);
1640 if (Base::n_thread_build_ == 1)
1643 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
1647#ifndef NANOFLANN_NO_THREADS
1648 std::atomic<unsigned int> thread_count(0u);
1650 Base::root_node_ = this->divideTreeConcurrent(
1651 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
1653 throw std::runtime_error(
"Multithreading is disabled");
1677 template <
typename RESULTSET>
1679 RESULTSET& result,
const ElementType* vec,
1683 if (this->size(*
this) == 0)
return false;
1684 if (!Base::root_node_)
1685 throw std::runtime_error(
1686 "[nanoflann] findNeighbors() called before building the "
1688 float epsError = 1 + searchParams.eps;
1691 distance_vector_t dists;
1693 auto zero =
static_cast<decltype(result.worstDist())
>(0);
1694 assign(dists, (DIM > 0 ? DIM : Base::dim_), zero);
1695 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
1696 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
1698 if (searchParams.sorted) result.sort();
1700 return result.full();
1719 const ElementType* query_point,
const Size num_closest,
1720 IndexType* out_indices, DistanceType* out_distances)
const
1723 resultSet.init(out_indices, out_distances);
1724 findNeighbors(resultSet, query_point);
1725 return resultSet.size();
1748 const ElementType* query_point,
const DistanceType& radius,
1753 radius, IndicesDists);
1755 radiusSearchCustomCallback(query_point, resultSet, searchParams);
1764 template <
class SEARCH_CALLBACK>
1766 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
1769 findNeighbors(resultSet, query_point, searchParams);
1770 return resultSet.size();
1790 const ElementType* query_point,
const Size num_closest,
1791 IndexType* out_indices, DistanceType* out_distances,
1792 const DistanceType& radius)
const
1795 num_closest, radius);
1796 resultSet.init(out_indices, out_distances);
1797 findNeighbors(resultSet, query_point);
1798 return resultSet.size();
1809 Base::size_ = dataset_.kdtree_get_point_count();
1810 if (Base::vAcc_.size() != Base::size_) Base::vAcc_.resize(Base::size_);
1811 for (IndexType i = 0; i < static_cast<IndexType>(Base::size_); i++)
1815 void computeBoundingBox(BoundingBox& bbox)
1817 const auto dims = (DIM > 0 ? DIM : Base::dim_);
1819 if (dataset_.kdtree_get_bbox(bbox))
1825 const Size N = dataset_.kdtree_get_point_count();
1827 throw std::runtime_error(
1828 "[nanoflann] computeBoundingBox() called but "
1829 "no data points found.");
1830 for (Dimension i = 0; i < dims; ++i)
1832 bbox[i].low = bbox[i].high =
1833 this->dataset_get(*
this, Base::vAcc_[0], i);
1835 for (Offset k = 1; k < N; ++k)
1837 for (Dimension i = 0; i < dims; ++i)
1840 this->dataset_get(*
this, Base::vAcc_[k], i);
1841 if (val < bbox[i].low) bbox[i].low = val;
1842 if (val > bbox[i].high) bbox[i].high = val;
1854 template <
class RESULTSET>
1856 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
1858 const float epsError)
const
1861 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
1863 DistanceType worst_dist = result_set.worstDist();
1864 for (Offset i = node->node_type.lr.left;
1865 i < node->node_type.lr.right; ++i)
1867 const IndexType accessor = Base::vAcc_[i];
1868 DistanceType dist = distance_.evalMetric(
1869 vec, accessor, (DIM > 0 ? DIM : Base::dim_));
1870 if (dist < worst_dist)
1872 if (!result_set.addPoint(dist, Base::vAcc_[i]))
1884 Dimension idx = node->node_type.sub.divfeat;
1885 ElementType val = vec[idx];
1886 DistanceType diff1 = val - node->node_type.sub.divlow;
1887 DistanceType diff2 = val - node->node_type.sub.divhigh;
1891 DistanceType cut_dist;
1892 if ((diff1 + diff2) < 0)
1894 bestChild = node->child1;
1895 otherChild = node->child2;
1897 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
1901 bestChild = node->child2;
1902 otherChild = node->child1;
1904 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
1908 if (!searchLevel(result_set, vec, bestChild, mindist, dists, epsError))
1915 DistanceType dst = dists[idx];
1916 mindist = mindist + cut_dist - dst;
1917 dists[idx] = cut_dist;
1918 if (mindist * epsError <= result_set.worstDist())
1921 result_set, vec, otherChild, mindist, dists, epsError))
1940 Base::saveIndex(*
this, stream);
1948 void loadIndex(std::istream& stream) { Base::loadIndex(*
this, stream); }
1990 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1991 typename IndexType = uint32_t>
1994 KDTreeSingleIndexDynamicAdaptor_<
1995 Distance, DatasetAdaptor, DIM, IndexType>,
1996 Distance, DatasetAdaptor, DIM, IndexType>
2006 std::vector<int>& treeIndex_;
2012 Distance, DatasetAdaptor, DIM, IndexType>,
2013 Distance, DatasetAdaptor, DIM, IndexType>;
2015 using ElementType =
typename Base::ElementType;
2016 using DistanceType =
typename Base::DistanceType;
2018 using Offset =
typename Base::Offset;
2019 using Size =
typename Base::Size;
2020 using Dimension =
typename Base::Dimension;
2022 using Node =
typename Base::Node;
2023 using NodePtr = Node*;
2025 using Interval =
typename Base::Interval;
2050 const Dimension dimensionality,
const DatasetAdaptor& inputData,
2051 std::vector<int>& treeIndex,
2054 : dataset_(inputData),
2055 index_params_(params),
2056 treeIndex_(treeIndex),
2057 distance_(inputData)
2060 Base::size_at_index_build_ = 0;
2061 for (
auto& v : Base::root_bbox_) v = {};
2062 Base::dim_ = dimensionality;
2063 if (DIM > 0) Base::dim_ = DIM;
2064 Base::leaf_max_size_ = params.leaf_max_size;
2065 if (params.n_thread_build > 0)
2067 Base::n_thread_build_ = params.n_thread_build;
2071 Base::n_thread_build_ =
2072 std::max(std::thread::hardware_concurrency(), 1u);
2085 std::swap(Base::vAcc_, tmp.Base::vAcc_);
2086 std::swap(Base::leaf_max_size_, tmp.Base::leaf_max_size_);
2087 std::swap(index_params_, tmp.index_params_);
2088 std::swap(treeIndex_, tmp.treeIndex_);
2089 std::swap(Base::size_, tmp.Base::size_);
2090 std::swap(Base::size_at_index_build_, tmp.Base::size_at_index_build_);
2091 std::swap(Base::root_node_, tmp.Base::root_node_);
2092 std::swap(Base::root_bbox_, tmp.Base::root_bbox_);
2093 std::swap(Base::pool_, tmp.Base::pool_);
2102 Base::size_ = Base::vAcc_.size();
2103 this->freeIndex(*
this);
2104 Base::size_at_index_build_ = Base::size_;
2105 if (Base::size_ == 0)
return;
2106 computeBoundingBox(Base::root_bbox_);
2108 if (Base::n_thread_build_ == 1)
2111 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
2115#ifndef NANOFLANN_NO_THREADS
2116 std::atomic<unsigned int> thread_count(0u);
2118 Base::root_node_ = this->divideTreeConcurrent(
2119 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
2121 throw std::runtime_error(
"Multithreading is disabled");
2149 template <
typename RESULTSET>
2151 RESULTSET& result,
const ElementType* vec,
2155 if (this->size(*
this) == 0)
return false;
2156 if (!Base::root_node_)
return false;
2157 float epsError = 1 + searchParams.eps;
2160 distance_vector_t dists;
2163 dists, (DIM > 0 ? DIM : Base::dim_),
2164 static_cast<typename distance_vector_t::value_type>(0));
2165 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
2166 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
2167 return result.full();
2185 const ElementType* query_point,
const Size num_closest,
2186 IndexType* out_indices, DistanceType* out_distances,
2190 resultSet.init(out_indices, out_distances);
2191 findNeighbors(resultSet, query_point, searchParams);
2192 return resultSet.size();
2215 const ElementType* query_point,
const DistanceType& radius,
2220 radius, IndicesDists);
2221 const size_t nFound =
2222 radiusSearchCustomCallback(query_point, resultSet, searchParams);
2231 template <
class SEARCH_CALLBACK>
2233 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
2236 findNeighbors(resultSet, query_point, searchParams);
2237 return resultSet.size();
2243 void computeBoundingBox(BoundingBox& bbox)
2245 const auto dims = (DIM > 0 ? DIM : Base::dim_);
2248 if (dataset_.kdtree_get_bbox(bbox))
2254 const Size N = Base::size_;
2256 throw std::runtime_error(
2257 "[nanoflann] computeBoundingBox() called but "
2258 "no data points found.");
2259 for (Dimension i = 0; i < dims; ++i)
2261 bbox[i].low = bbox[i].high =
2262 this->dataset_get(*
this, Base::vAcc_[0], i);
2264 for (Offset k = 1; k < N; ++k)
2266 for (Dimension i = 0; i < dims; ++i)
2269 this->dataset_get(*
this, Base::vAcc_[k], i);
2270 if (val < bbox[i].low) bbox[i].low = val;
2271 if (val > bbox[i].high) bbox[i].high = val;
2281 template <
class RESULTSET>
2283 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
2285 const float epsError)
const
2288 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
2290 DistanceType worst_dist = result_set.worstDist();
2291 for (Offset i = node->node_type.lr.left;
2292 i < node->node_type.lr.right; ++i)
2294 const IndexType index = Base::vAcc_[i];
2295 if (treeIndex_[index] == -1)
continue;
2296 DistanceType dist = distance_.evalMetric(
2297 vec, index, (DIM > 0 ? DIM : Base::dim_));
2298 if (dist < worst_dist)
2300 if (!result_set.addPoint(
2301 static_cast<typename RESULTSET::DistanceType
>(dist),
2302 static_cast<typename RESULTSET::IndexType
>(
2315 Dimension idx = node->node_type.sub.divfeat;
2316 ElementType val = vec[idx];
2317 DistanceType diff1 = val - node->node_type.sub.divlow;
2318 DistanceType diff2 = val - node->node_type.sub.divhigh;
2322 DistanceType cut_dist;
2323 if ((diff1 + diff2) < 0)
2325 bestChild = node->child1;
2326 otherChild = node->child2;
2328 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
2332 bestChild = node->child2;
2333 otherChild = node->child1;
2335 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
2339 searchLevel(result_set, vec, bestChild, mindist, dists, epsError);
2341 DistanceType dst = dists[idx];
2342 mindist = mindist + cut_dist - dst;
2343 dists[idx] = cut_dist;
2344 if (mindist * epsError <= result_set.worstDist())
2346 searchLevel(result_set, vec, otherChild, mindist, dists, epsError);
2382 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
2383 typename IndexType = uint32_t>
2387 using ElementType =
typename Distance::ElementType;
2388 using DistanceType =
typename Distance::DistanceType;
2391 Distance, DatasetAdaptor, DIM>::Offset;
2393 Distance, DatasetAdaptor, DIM>::Size;
2395 Distance, DatasetAdaptor, DIM>::Dimension;
2398 Size leaf_max_size_;
2410 std::unordered_set<int> removedPoints_;
2417 Distance, DatasetAdaptor, DIM, IndexType>;
2418 std::vector<index_container_t> index_;
2430 int First0Bit(IndexType num)
2444 using my_kd_tree_t = KDTreeSingleIndexDynamicAdaptor_<
2445 Distance, DatasetAdaptor, DIM, IndexType>;
2446 std::vector<my_kd_tree_t> index(
2448 my_kd_tree_t(dim_ , dataset_, treeIndex_, index_params_));
2471 const int dimensionality,
const DatasetAdaptor& inputData,
2474 const size_t maximumPointCount = 1000000000U)
2475 : dataset_(inputData), index_params_(params), distance_(inputData)
2477 treeCount_ =
static_cast<size_t>(std::log2(maximumPointCount)) + 1;
2479 dim_ = dimensionality;
2481 if (DIM > 0) dim_ = DIM;
2482 leaf_max_size_ = params.leaf_max_size;
2484 const size_t num_initial_points = dataset_.kdtree_get_point_count();
2485 if (num_initial_points > 0) { addPoints(0, num_initial_points - 1); }
2491 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
2496 const Size count = end - start + 1;
2498 treeIndex_.resize(treeIndex_.size() + count);
2499 for (IndexType idx = start; idx <= end; idx++)
2501 const int pos = First0Bit(pointCount_);
2502 maxIndex = std::max(pos, maxIndex);
2503 treeIndex_[pointCount_] = pos;
2505 const auto it = removedPoints_.find(idx);
2506 if (it != removedPoints_.end())
2508 removedPoints_.erase(it);
2509 treeIndex_[idx] = pos;
2512 for (
int i = 0; i < pos; i++)
2514 for (
int j = 0; j < static_cast<int>(index_[i].vAcc_.size());
2517 index_[pos].vAcc_.push_back(index_[i].vAcc_[j]);
2518 if (treeIndex_[index_[i].vAcc_[j]] != -1)
2519 treeIndex_[index_[i].vAcc_[j]] = pos;
2521 index_[i].vAcc_.clear();
2523 index_[pos].vAcc_.push_back(idx);
2527 for (
int i = 0; i <= maxIndex; ++i)
2529 index_[i].freeIndex(index_[i]);
2530 if (!index_[i].vAcc_.empty()) index_[i].buildIndex();
2537 if (idx >= pointCount_)
return;
2538 removedPoints_.insert(idx);
2539 treeIndex_[idx] = -1;
2558 template <
typename RESULTSET>
2560 RESULTSET& result,
const ElementType* vec,
2563 for (
size_t i = 0; i < treeCount_; i++)
2565 index_[i].findNeighbors(result, &vec[0], searchParams);
2567 return result.full();
2598 bool row_major =
true>
2603 using num_t =
typename MatrixType::Scalar;
2604 using IndexType =
typename MatrixType::Index;
2605 using metric_t =
typename Distance::template traits<
2606 num_t,
self_t, IndexType>::distance_t;
2610 row_major ? MatrixType::ColsAtCompileTime
2611 : MatrixType::RowsAtCompileTime,
2618 using Size =
typename index_t::Size;
2619 using Dimension =
typename index_t::Dimension;
2623 const Dimension dimensionality,
2624 const std::reference_wrapper<const MatrixType>& mat,
2625 const int leaf_max_size = 10,
const unsigned int n_thread_build = 1)
2626 : m_data_matrix(mat)
2628 const auto dims = row_major ? mat.get().cols() : mat.get().rows();
2629 if (
static_cast<Dimension
>(dims) != dimensionality)
2630 throw std::runtime_error(
2631 "Error: 'dimensionality' must match column count in data "
2633 if (DIM > 0 &&
static_cast<int32_t
>(dims) != DIM)
2634 throw std::runtime_error(
2635 "Data set dimensionality does not match the 'DIM' template "
2640 leaf_max_size, nanoflann::KDTreeSingleIndexAdaptorFlags::None,
2650 const std::reference_wrapper<const MatrixType> m_data_matrix;
2661 const num_t* query_point,
const Size num_closest,
2662 IndexType* out_indices, num_t* out_distances)
const
2665 resultSet.init(out_indices, out_distances);
2672 const self_t& derived()
const {
return *
this; }
2673 self_t& derived() {
return *
this; }
2676 Size kdtree_get_point_count()
const
2679 return m_data_matrix.get().rows();
2681 return m_data_matrix.get().cols();
2685 num_t kdtree_get_pt(
const IndexType idx,
size_t dim)
const
2688 return m_data_matrix.get().coeff(idx, IndexType(dim));
2690 return m_data_matrix.get().coeff(IndexType(dim), idx);
2698 template <
class BBOX>
2699 bool kdtree_get_bbox(BBOX& )
const
// end of grouping
Definition nanoflann.hpp:1001
void freeIndex(Derived &obj)
Definition nanoflann.hpp:1005
BoundingBox root_bbox_
Definition nanoflann.hpp:1079
Size veclen(const Derived &obj)
Definition nanoflann.hpp:1094
void saveIndex(const Derived &obj, std::ostream &stream) const
Definition nanoflann.hpp:1445
Size usedMemory(Derived &obj)
Definition nanoflann.hpp:1107
typename array_or_vector< DIM, DistanceType >::type distance_vector_t
Definition nanoflann.hpp:1076
void planeSplit(const Derived &obj, const Offset ind, const Size count, const Dimension cutfeat, const DistanceType &cutval, Offset &lim1, Offset &lim2)
Definition nanoflann.hpp:1355
NodePtr divideTree(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox)
Definition nanoflann.hpp:1135
std::vector< IndexType > vAcc_
Definition nanoflann.hpp:1019
Size size(const Derived &obj) const
Definition nanoflann.hpp:1091
NodePtr divideTreeConcurrent(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox, std::atomic< unsigned int > &thread_count, std::mutex &mutex)
Definition nanoflann.hpp:1206
void loadIndex(Derived &obj, std::istream &stream)
Definition nanoflann.hpp:1460
PooledAllocator pool_
Definition nanoflann.hpp:1088
ElementType dataset_get(const Derived &obj, IndexType element, Dimension component) const
Helper accessor to the dataset points:
Definition nanoflann.hpp:1097
typename array_or_vector< DIM, Interval >::type BoundingBox
Definition nanoflann.hpp:1072
Definition nanoflann.hpp:1519
bool searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:1855
void saveIndex(std::ostream &stream) const
Definition nanoflann.hpp:1938
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1747
void init_vind()
Definition nanoflann.hpp:1806
void buildIndex()
Definition nanoflann.hpp:1630
const DatasetAdaptor & dataset_
Definition nanoflann.hpp:1527
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t > &)=delete
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1678
Size rknnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const DistanceType &radius) const
Definition nanoflann.hpp:1789
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1765
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:1557
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:1948
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:1553
KDTreeSingleIndexAdaptor(const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms, Args &&... args)
Definition nanoflann.hpp:1580
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances) const
Definition nanoflann.hpp:1718
Definition nanoflann.hpp:1997
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2214
KDTreeSingleIndexDynamicAdaptor_(const Dimension dimensionality, const DatasetAdaptor &inputData, std::vector< int > &treeIndex, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams())
Definition nanoflann.hpp:2049
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:2028
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:2002
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2232
KDTreeSingleIndexDynamicAdaptor_(const KDTreeSingleIndexDynamicAdaptor_ &rhs)=default
void buildIndex()
Definition nanoflann.hpp:2100
void saveIndex(std::ostream &stream)
Definition nanoflann.hpp:2357
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:2032
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:2364
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2184
void searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:2282
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2150
KDTreeSingleIndexDynamicAdaptor_ operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs)
Definition nanoflann.hpp:2081
Definition nanoflann.hpp:2385
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2559
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:2405
void removePoint(size_t idx)
Definition nanoflann.hpp:2535
void addPoints(IndexType start, IndexType end)
Definition nanoflann.hpp:2494
KDTreeSingleIndexDynamicAdaptor(const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams(), const size_t maximumPointCount=1000000000U)
Definition nanoflann.hpp:2470
std::vector< int > treeIndex_
Definition nanoflann.hpp:2409
const std::vector< index_container_t > & getAllIndices() const
Definition nanoflann.hpp:2423
Dimension dim_
Dimensionality of each data point.
Definition nanoflann.hpp:2414
KDTreeSingleIndexDynamicAdaptor(const KDTreeSingleIndexDynamicAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
Definition nanoflann.hpp:200
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:236
Definition nanoflann.hpp:849
~PooledAllocator()
Definition nanoflann.hpp:885
void free_all()
Definition nanoflann.hpp:888
void * malloc(const size_t req_size)
Definition nanoflann.hpp:904
T * allocate(const size_t count=1)
Definition nanoflann.hpp:955
PooledAllocator()
Definition nanoflann.hpp:880
Definition nanoflann.hpp:284
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:325
Definition nanoflann.hpp:373
ResultItem< IndexType, DistanceType > worst_item() const
Definition nanoflann.hpp:416
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:404
std::enable_if< has_assign< Container >::value, void >::type assign(Container &c, const size_t nElements, const T &value)
Definition nanoflann.hpp:144
T pi_const()
Definition nanoflann.hpp:87
std::enable_if< has_resize< Container >::value, void >::type resize(Container &c, const size_t nElements)
Definition nanoflann.hpp:122
Definition nanoflann.hpp:162
bool operator()(const PairType &p1, const PairType &p2) const
Definition nanoflann.hpp:165
Definition nanoflann.hpp:1054
Definition nanoflann.hpp:1029
DistanceType divlow
The values used for subdivision.
Definition nanoflann.hpp:1042
Offset right
Indices of points in leaf node.
Definition nanoflann.hpp:1036
union nanoflann::KDTreeBaseClass::Node::@0 node_type
Dimension divfeat
Definition nanoflann.hpp:1040
Node * child1
Definition nanoflann.hpp:1047
Definition nanoflann.hpp:2600
void query(const num_t *query_point, const Size num_closest, IndexType *out_indices, num_t *out_distances) const
Definition nanoflann.hpp:2660
KDTreeEigenMatrixAdaptor(const self_t &)=delete
typename index_t::Offset Offset
Definition nanoflann.hpp:2617
KDTreeEigenMatrixAdaptor(const Dimension dimensionality, const std::reference_wrapper< const MatrixType > &mat, const int leaf_max_size=10, const unsigned int n_thread_build=1)
Constructor: takes a const ref to the matrix object with the data points.
Definition nanoflann.hpp:2622
Definition nanoflann.hpp:800
Definition nanoflann.hpp:489
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Definition nanoflann.hpp:472
Definition nanoflann.hpp:181
DistanceType second
Distance from sample to query point.
Definition nanoflann.hpp:189
IndexType first
Index of the sample in the dataset.
Definition nanoflann.hpp:188
Definition nanoflann.hpp:661
DistanceType accum_dist(const U a, const V b, const size_t) const
Definition nanoflann.hpp:679
Definition nanoflann.hpp:706
Definition nanoflann.hpp:819
bool sorted
distance (default: true)
Definition nanoflann.hpp:826
float eps
search for eps-approximate neighbours (default: 0)
Definition nanoflann.hpp:825
Definition nanoflann.hpp:971
Definition nanoflann.hpp:109
Definition nanoflann.hpp:98
Definition nanoflann.hpp:736
Definition nanoflann.hpp:733
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Definition nanoflann.hpp:771