60#include <unordered_set>
64#define NANOFLANN_VERSION 0x155
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;
165 typename _DistanceType,
typename _IndexType = size_t,
166 typename _CountType =
size_t>
170 using DistanceType = _DistanceType;
171 using IndexType = _IndexType;
172 using CountType = _CountType;
182 : indices(
nullptr), dists(
nullptr), capacity(capacity_), count(0)
186 void init(IndexType* indices_, DistanceType* dists_)
192 dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
195 CountType size()
const {
return count; }
196 bool empty()
const {
return count == 0; }
197 bool full()
const {
return count == capacity; }
207 for (i = count; i > 0; --i)
211#ifdef NANOFLANN_FIRST_MATCH
212 if ((dists[i - 1] > dist) ||
213 ((dist == dists[i - 1]) && (indices[i - 1] > index)))
216 if (dists[i - 1] > dist)
221 dists[i] = dists[i - 1];
222 indices[i] = indices[i - 1];
233 if (count < capacity) count++;
239 DistanceType worstDist()
const {
return dists[capacity - 1]; }
244 typename _DistanceType,
typename _IndexType = size_t,
245 typename _CountType =
size_t>
249 using DistanceType = _DistanceType;
250 using IndexType = _IndexType;
251 using CountType = _CountType;
258 DistanceType maximumSearchDistanceSquared;
262 CountType capacity_, DistanceType maximumSearchDistanceSquared_)
267 maximumSearchDistanceSquared(maximumSearchDistanceSquared_)
271 void init(IndexType* indices_, DistanceType* dists_)
276 if (capacity) dists[capacity - 1] = maximumSearchDistanceSquared;
279 CountType size()
const {
return count; }
280 bool empty()
const {
return count == 0; }
281 bool full()
const {
return count == capacity; }
291 for (i = count; i > 0; --i)
295#ifdef NANOFLANN_FIRST_MATCH
296 if ((dists[i - 1] > dist) ||
297 ((dist == dists[i - 1]) && (indices[i - 1] > index)))
300 if (dists[i - 1] > dist)
305 dists[i] = dists[i - 1];
306 indices[i] = indices[i - 1];
317 if (count < capacity) count++;
323 DistanceType worstDist()
const {
return dists[capacity - 1]; }
330 template <
typename PairType>
331 bool operator()(
const PairType& p1,
const PairType& p2)
const
333 return p1.second < p2.second;
345template <
typename IndexType =
size_t,
typename DistanceType =
double>
349 ResultItem(
const IndexType index,
const DistanceType distance)
350 : first(index), second(distance)
361template <
typename _DistanceType,
typename _IndexType =
size_t>
365 using DistanceType = _DistanceType;
366 using IndexType = _IndexType;
369 const DistanceType radius;
371 std::vector<ResultItem<IndexType, DistanceType>>& m_indices_dists;
374 DistanceType radius_,
376 : radius(radius_), m_indices_dists(indices_dists)
381 void init() { clear(); }
382 void clear() { m_indices_dists.clear(); }
384 size_t size()
const {
return m_indices_dists.size(); }
385 size_t empty()
const {
return m_indices_dists.empty(); }
387 bool full()
const {
return true; }
396 if (dist < radius) m_indices_dists.emplace_back(index, dist);
400 DistanceType worstDist()
const {
return radius; }
408 if (m_indices_dists.empty())
409 throw std::runtime_error(
410 "Cannot invoke RadiusResultSet::worst_item() on "
411 "an empty list of results.");
412 auto it = std::max_element(
423void save_value(std::ostream& stream,
const T& value)
425 stream.write(
reinterpret_cast<const char*
>(&value),
sizeof(T));
429void save_value(std::ostream& stream,
const std::vector<T>& value)
431 size_t size = value.size();
432 stream.write(
reinterpret_cast<const char*
>(&size),
sizeof(
size_t));
433 stream.write(
reinterpret_cast<const char*
>(value.data()),
sizeof(T) * size);
437void load_value(std::istream& stream, T& value)
439 stream.read(
reinterpret_cast<char*
>(&value),
sizeof(T));
443void load_value(std::istream& stream, std::vector<T>& value)
446 stream.read(
reinterpret_cast<char*
>(&size),
sizeof(
size_t));
448 stream.read(
reinterpret_cast<char*
>(value.data()),
sizeof(T) * size);
470 class T,
class DataSource,
typename _DistanceType = T,
471 typename IndexType = uint32_t>
474 using ElementType = T;
475 using DistanceType = _DistanceType;
477 const DataSource& data_source;
479 L1_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
481 DistanceType evalMetric(
482 const T* a,
const IndexType b_idx,
size_t size,
483 DistanceType worst_dist = -1)
const
485 DistanceType result = DistanceType();
486 const T* last = a + size;
487 const T* lastgroup = last - 3;
491 while (a < lastgroup)
493 const DistanceType diff0 =
494 std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
495 const DistanceType diff1 =
496 std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
497 const DistanceType diff2 =
498 std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
499 const DistanceType diff3 =
500 std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
501 result += diff0 + diff1 + diff2 + diff3;
503 if ((worst_dist > 0) && (result > worst_dist)) {
return result; }
509 result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
514 template <
typename U,
typename V>
515 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
517 return std::abs(a - b);
532 class T,
class DataSource,
typename _DistanceType = T,
533 typename IndexType = uint32_t>
536 using ElementType = T;
537 using DistanceType = _DistanceType;
539 const DataSource& data_source;
541 L2_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
543 DistanceType evalMetric(
544 const T* a,
const IndexType b_idx,
size_t size,
545 DistanceType worst_dist = -1)
const
547 DistanceType result = DistanceType();
548 const T* last = a + size;
549 const T* lastgroup = last - 3;
553 while (a < lastgroup)
555 const DistanceType diff0 =
556 a[0] - data_source.kdtree_get_pt(b_idx, d++);
557 const DistanceType diff1 =
558 a[1] - data_source.kdtree_get_pt(b_idx, d++);
559 const DistanceType diff2 =
560 a[2] - data_source.kdtree_get_pt(b_idx, d++);
561 const DistanceType diff3 =
562 a[3] - data_source.kdtree_get_pt(b_idx, d++);
564 diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
566 if ((worst_dist > 0) && (result > worst_dist)) {
return result; }
572 const DistanceType diff0 =
573 *a++ - data_source.kdtree_get_pt(b_idx, d++);
574 result += diff0 * diff0;
579 template <
typename U,
typename V>
580 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
582 return (a - b) * (a - b);
597 class T,
class DataSource,
typename _DistanceType = T,
598 typename IndexType = uint32_t>
601 using ElementType = T;
602 using DistanceType = _DistanceType;
604 const DataSource& data_source;
607 : data_source(_data_source)
611 DistanceType evalMetric(
612 const T* a,
const IndexType b_idx,
size_t size)
const
614 DistanceType result = DistanceType();
615 for (
size_t i = 0; i < size; ++i)
617 const DistanceType diff =
618 a[i] - data_source.kdtree_get_pt(b_idx, i);
619 result += diff * diff;
624 template <
typename U,
typename V>
625 DistanceType accum_dist(
const U a,
const V b,
const size_t)
const
627 return (a - b) * (a - b);
642 class T,
class DataSource,
typename _DistanceType = T,
643 typename IndexType = uint32_t>
646 using ElementType = T;
647 using DistanceType = _DistanceType;
649 const DataSource& data_source;
651 SO2_Adaptor(
const DataSource& _data_source) : data_source(_data_source) {}
653 DistanceType evalMetric(
654 const T* a,
const IndexType b_idx,
size_t size)
const
657 a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1), size - 1);
662 template <
typename U,
typename V>
663 DistanceType
accum_dist(
const U a,
const V b,
const size_t)
const
665 DistanceType result = DistanceType();
666 DistanceType PI = pi_const<DistanceType>();
670 else if (result < -PI)
687 class T,
class DataSource,
typename _DistanceType = T,
688 typename IndexType = uint32_t>
691 using ElementType = T;
692 using DistanceType = _DistanceType;
698 : distance_L2_Simple(_data_source)
702 DistanceType evalMetric(
703 const T* a,
const IndexType b_idx,
size_t size)
const
705 return distance_L2_Simple.evalMetric(a, b_idx, size);
708 template <
typename U,
typename V>
709 DistanceType accum_dist(
const U a,
const V b,
const size_t idx)
const
711 return distance_L2_Simple.accum_dist(a, b, idx);
718 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
728 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
738 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
747 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
756 template <
class T,
class DataSource,
typename IndexType = u
int32_t>
768enum class KDTreeSingleIndexAdaptorFlags
771 SkipInitialBuildIndex = 1
774inline std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type operator&(
775 KDTreeSingleIndexAdaptorFlags lhs, KDTreeSingleIndexAdaptorFlags rhs)
778 typename std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type;
779 return static_cast<underlying
>(lhs) &
static_cast<underlying
>(rhs);
786 size_t _leaf_max_size = 10,
787 KDTreeSingleIndexAdaptorFlags _flags =
788 KDTreeSingleIndexAdaptorFlags::None,
789 unsigned int _n_thread_build = 1)
790 : leaf_max_size(_leaf_max_size),
792 n_thread_build(_n_thread_build)
796 size_t leaf_max_size;
797 KDTreeSingleIndexAdaptorFlags flags;
798 unsigned int n_thread_build;
805 : eps(eps_), sorted(sorted_)
834 static constexpr size_t WORDSIZE = 16;
835 static constexpr size_t BLOCKSIZE = 8192;
846 void* base_ =
nullptr;
847 void* loc_ =
nullptr;
859 Size wastedMemory = 0;
874 while (base_ !=
nullptr)
877 void* prev = *(
static_cast<void**
>(base_));
894 const Size size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
899 if (size > remaining_)
901 wastedMemory += remaining_;
904 const Size blocksize =
905 size > BLOCKSIZE ? size + WORDSIZE : BLOCKSIZE + WORDSIZE;
908 void* m = ::malloc(blocksize);
911 fprintf(stderr,
"Failed to allocate memory.\n");
912 throw std::bad_alloc();
916 static_cast<void**
>(m)[0] = base_;
919 remaining_ = blocksize - WORDSIZE;
920 loc_ =
static_cast<char*
>(m) + WORDSIZE;
923 loc_ =
static_cast<char*
>(loc_) + size;
938 template <
typename T>
941 T* mem =
static_cast<T*
>(this->malloc(
sizeof(T) * count));
953template <
int32_t DIM,
typename T>
956 using type = std::array<T, DIM>;
962 using type = std::vector<T>;
982 class Derived,
typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
983 typename IndexType = uint32_t>
991 obj.pool_.free_all();
992 obj.root_node_ =
nullptr;
993 obj.size_at_index_build_ = 0;
996 using ElementType =
typename Distance::ElementType;
997 using DistanceType =
typename Distance::DistanceType;
1004 using Offset =
typename decltype(vAcc_)::size_type;
1005 using Size =
typename decltype(vAcc_)::size_type;
1006 using Dimension = int32_t;
1030 Node *child1 =
nullptr, *child2 =
nullptr;
1038 ElementType low, high;
1043 Size leaf_max_size_ = 0;
1046 Size n_thread_build_ = 1;
1050 Size size_at_index_build_ = 0;
1074 Size
size(
const Derived& obj)
const {
return obj.size_; }
1077 Size
veclen(
const Derived& obj) {
return DIM > 0 ? DIM : obj.dim; }
1081 const Derived& obj, IndexType element, Dimension component)
const
1083 return obj.dataset_.kdtree_get_pt(element, component);
1092 return obj.pool_.usedMemory + obj.pool_.wastedMemory +
1093 obj.dataset_.kdtree_get_point_count() *
1098 const Derived& obj, Offset ind, Size count, Dimension element,
1099 ElementType& min_elem, ElementType& max_elem)
1101 min_elem = dataset_get(obj, vAcc_[ind], element);
1102 max_elem = min_elem;
1103 for (Offset i = 1; i < count; ++i)
1105 ElementType val = dataset_get(obj, vAcc_[ind + i], element);
1106 if (val < min_elem) min_elem = val;
1107 if (val > max_elem) max_elem = val;
1119 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox)
1121 NodePtr node = obj.pool_.template allocate<Node>();
1122 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1125 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1127 node->
child1 = node->child2 =
nullptr;
1132 for (Dimension i = 0; i < dims; ++i)
1134 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1135 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1137 for (Offset k = left + 1; k < right; ++k)
1139 for (Dimension i = 0; i < dims; ++i)
1141 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1142 if (bbox[i].low > val) bbox[i].low = val;
1143 if (bbox[i].high < val) bbox[i].high = val;
1151 DistanceType cutval;
1152 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1157 left_bbox[cutfeat].high = cutval;
1158 node->
child1 = this->divideTree(obj, left, left + idx, left_bbox);
1161 right_bbox[cutfeat].low = cutval;
1162 node->child2 = this->divideTree(obj, left + idx, right, right_bbox);
1164 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1165 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1167 for (Dimension i = 0; i < dims; ++i)
1169 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1170 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1188 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox,
1189 std::atomic<unsigned int>& thread_count, std::mutex& mutex)
1191 std::unique_lock<std::mutex> lock(mutex);
1192 NodePtr node = obj.pool_.template allocate<Node>();
1195 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1198 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1200 node->
child1 = node->child2 =
nullptr;
1205 for (Dimension i = 0; i < dims; ++i)
1207 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1208 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1210 for (Offset k = left + 1; k < right; ++k)
1212 for (Dimension i = 0; i < dims; ++i)
1214 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1215 if (bbox[i].low > val) bbox[i].low = val;
1216 if (bbox[i].high < val) bbox[i].high = val;
1224 DistanceType cutval;
1225 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1229 std::future<NodePtr> right_future;
1232 right_bbox[cutfeat].low = cutval;
1233 if (++thread_count < n_thread_build_)
1236 right_future = std::async(
1237 std::launch::async, &KDTreeBaseClass::divideTreeConcurrent,
1238 this, std::ref(obj), left + idx, right,
1239 std::ref(right_bbox), std::ref(thread_count),
1248 left_bbox[cutfeat].high = cutval;
1249 node->
child1 = this->divideTreeConcurrent(
1250 obj, left, left + idx, left_bbox, thread_count, mutex);
1252 if (right_future.valid())
1255 node->child2 = right_future.get();
1260 node->child2 = this->divideTreeConcurrent(
1261 obj, left + idx, right, right_bbox, thread_count, mutex);
1264 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1265 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1267 for (Dimension i = 0; i < dims; ++i)
1269 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1270 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1278 const Derived& obj,
const Offset ind,
const Size count, Offset& index,
1279 Dimension& cutfeat, DistanceType& cutval,
const BoundingBox& bbox)
1281 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1282 const auto EPS =
static_cast<DistanceType
>(0.00001);
1283 ElementType max_span = bbox[0].high - bbox[0].low;
1284 for (Dimension i = 1; i < dims; ++i)
1286 ElementType span = bbox[i].high - bbox[i].low;
1287 if (span > max_span) { max_span = span; }
1289 ElementType max_spread = -1;
1291 ElementType min_elem = 0, max_elem = 0;
1292 for (Dimension i = 0; i < dims; ++i)
1294 ElementType span = bbox[i].high - bbox[i].low;
1295 if (span > (1 - EPS) * max_span)
1297 ElementType min_elem_, max_elem_;
1298 computeMinMax(obj, ind, count, i, min_elem_, max_elem_);
1299 ElementType spread = max_elem_ - min_elem_;
1300 if (spread > max_spread)
1303 max_spread = spread;
1304 min_elem = min_elem_;
1305 max_elem = max_elem_;
1310 DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
1312 if (split_val < min_elem)
1314 else if (split_val > max_elem)
1320 planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
1322 if (lim1 > count / 2)
1324 else if (lim2 < count / 2)
1340 const Derived& obj,
const Offset ind,
const Size count,
1341 const Dimension cutfeat,
const DistanceType& cutval, Offset& lim1,
1346 Offset right = count - 1;
1349 while (left <= right &&
1350 dataset_get(obj, vAcc_[ind + left], cutfeat) < cutval)
1352 while (right && left <= right &&
1353 dataset_get(obj, vAcc_[ind + right], cutfeat) >= cutval)
1355 if (left > right || !right)
1357 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1368 while (left <= right &&
1369 dataset_get(obj, vAcc_[ind + left], cutfeat) <= cutval)
1371 while (right && left <= right &&
1372 dataset_get(obj, vAcc_[ind + right], cutfeat) > cutval)
1374 if (left > right || !right)
1376 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1383 DistanceType computeInitialDistances(
1384 const Derived& obj,
const ElementType* vec,
1385 distance_vector_t& dists)
const
1388 DistanceType dist = DistanceType();
1390 for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim_); ++i)
1392 if (vec[i] < obj.root_bbox_[i].low)
1395 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].low, i);
1398 if (vec[i] > obj.root_bbox_[i].high)
1401 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i);
1408 static void save_tree(
1409 const Derived& obj, std::ostream& stream,
const NodeConstPtr tree)
1411 save_value(stream, *tree);
1412 if (tree->child1 !=
nullptr) { save_tree(obj, stream, tree->child1); }
1413 if (tree->child2 !=
nullptr) { save_tree(obj, stream, tree->child2); }
1416 static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree)
1418 tree = obj.pool_.template allocate<Node>();
1419 load_value(stream, *tree);
1420 if (tree->child1 !=
nullptr) { load_tree(obj, stream, tree->child1); }
1421 if (tree->child2 !=
nullptr) { load_tree(obj, stream, tree->child2); }
1429 void saveIndex(
const Derived& obj, std::ostream& stream)
const
1431 save_value(stream, obj.size_);
1432 save_value(stream, obj.dim_);
1433 save_value(stream, obj.root_bbox_);
1434 save_value(stream, obj.leaf_max_size_);
1435 save_value(stream, obj.vAcc_);
1436 if (obj.root_node_) save_tree(obj, stream, obj.root_node_);
1446 load_value(stream, obj.size_);
1447 load_value(stream, obj.dim_);
1448 load_value(stream, obj.root_bbox_);
1449 load_value(stream, obj.leaf_max_size_);
1450 load_value(stream, obj.vAcc_);
1451 load_tree(obj, stream, obj.root_node_);
1497 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1498 typename IndexType = uint32_t>
1501 KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>,
1502 Distance, DatasetAdaptor, DIM, IndexType>
1508 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
1519 Distance, DatasetAdaptor, DIM, IndexType>,
1520 Distance, DatasetAdaptor, DIM, IndexType>;
1522 using Offset =
typename Base::Offset;
1523 using Size =
typename Base::Size;
1524 using Dimension =
typename Base::Dimension;
1526 using ElementType =
typename Base::ElementType;
1527 using DistanceType =
typename Base::DistanceType;
1529 using Node =
typename Base::Node;
1530 using NodePtr = Node*;
1532 using Interval =
typename Base::Interval;
1562 template <
class... Args>
1564 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1566 : dataset_(inputData),
1567 indexParams(params),
1568 distance_(inputData, std::forward<Args>(args)...)
1570 init(dimensionality, params);
1574 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1576 : dataset_(inputData), indexParams(params), distance_(inputData)
1578 init(dimensionality, params);
1583 const Dimension dimensionality,
1584 const KDTreeSingleIndexAdaptorParams& params)
1586 Base::size_ = dataset_.kdtree_get_point_count();
1587 Base::size_at_index_build_ = Base::size_;
1588 Base::dim_ = dimensionality;
1589 if (DIM > 0) Base::dim_ = DIM;
1590 Base::leaf_max_size_ = params.leaf_max_size;
1591 if (params.n_thread_build > 0)
1593 Base::n_thread_build_ = params.n_thread_build;
1597 Base::n_thread_build_ =
1598 std::max(std::thread::hardware_concurrency(), 1u);
1601 if (!(params.flags &
1602 KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex))
1615 Base::size_ = dataset_.kdtree_get_point_count();
1616 Base::size_at_index_build_ = Base::size_;
1618 this->freeIndex(*
this);
1619 Base::size_at_index_build_ = Base::size_;
1620 if (Base::size_ == 0)
return;
1621 computeBoundingBox(Base::root_bbox_);
1623 if (Base::n_thread_build_ == 1)
1626 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
1630 std::atomic<unsigned int> thread_count(0u);
1632 Base::root_node_ = this->divideTreeConcurrent(
1633 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
1656 template <
typename RESULTSET>
1658 RESULTSET& result,
const ElementType* vec,
1662 if (this->size(*
this) == 0)
return false;
1663 if (!Base::root_node_)
1664 throw std::runtime_error(
1665 "[nanoflann] findNeighbors() called before building the "
1667 float epsError = 1 + searchParams.eps;
1670 distance_vector_t dists;
1672 auto zero =
static_cast<decltype(result.worstDist())
>(0);
1673 assign(dists, (DIM > 0 ? DIM : Base::dim_), zero);
1674 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
1675 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
1676 return result.full();
1695 const ElementType* query_point,
const Size num_closest,
1696 IndexType* out_indices, DistanceType* out_distances)
const
1699 resultSet.init(out_indices, out_distances);
1700 findNeighbors(resultSet, query_point);
1701 return resultSet.size();
1724 const ElementType* query_point,
const DistanceType& radius,
1729 radius, IndicesDists);
1731 radiusSearchCustomCallback(query_point, resultSet, searchParams);
1732 if (searchParams.sorted)
1743 template <
class SEARCH_CALLBACK>
1745 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
1748 findNeighbors(resultSet, query_point, searchParams);
1749 return resultSet.size();
1769 const ElementType* query_point,
const Size num_closest,
1770 IndexType* out_indices, DistanceType* out_distances,
1771 const DistanceType& radius)
const
1774 num_closest, radius);
1775 resultSet.init(out_indices, out_distances);
1776 findNeighbors(resultSet, query_point);
1777 return resultSet.size();
1788 Base::size_ = dataset_.kdtree_get_point_count();
1789 if (Base::vAcc_.size() != Base::size_) Base::vAcc_.resize(Base::size_);
1790 for (Size i = 0; i < Base::size_; i++) Base::vAcc_[i] = i;
1793 void computeBoundingBox(BoundingBox& bbox)
1795 const auto dims = (DIM > 0 ? DIM : Base::dim_);
1797 if (dataset_.kdtree_get_bbox(bbox))
1803 const Size N = dataset_.kdtree_get_point_count();
1805 throw std::runtime_error(
1806 "[nanoflann] computeBoundingBox() called but "
1807 "no data points found.");
1808 for (Dimension i = 0; i < dims; ++i)
1810 bbox[i].low = bbox[i].high =
1811 this->dataset_get(*
this, Base::vAcc_[0], i);
1813 for (Offset k = 1; k < N; ++k)
1815 for (Dimension i = 0; i < dims; ++i)
1818 this->dataset_get(*
this, Base::vAcc_[k], i);
1819 if (val < bbox[i].low) bbox[i].low = val;
1820 if (val > bbox[i].high) bbox[i].high = val;
1832 template <
class RESULTSET>
1834 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
1836 const float epsError)
const
1839 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
1841 DistanceType worst_dist = result_set.worstDist();
1842 for (Offset i = node->node_type.lr.left;
1843 i < node->node_type.lr.right; ++i)
1845 const IndexType accessor = Base::vAcc_[i];
1846 DistanceType dist = distance_.evalMetric(
1847 vec, accessor, (DIM > 0 ? DIM : Base::dim_));
1848 if (dist < worst_dist)
1850 if (!result_set.addPoint(dist, Base::vAcc_[i]))
1862 Dimension idx = node->node_type.sub.divfeat;
1863 ElementType val = vec[idx];
1864 DistanceType diff1 = val - node->node_type.sub.divlow;
1865 DistanceType diff2 = val - node->node_type.sub.divhigh;
1869 DistanceType cut_dist;
1870 if ((diff1 + diff2) < 0)
1872 bestChild = node->child1;
1873 otherChild = node->child2;
1875 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
1879 bestChild = node->child2;
1880 otherChild = node->child1;
1882 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
1886 if (!searchLevel(result_set, vec, bestChild, mindist, dists, epsError))
1893 DistanceType dst = dists[idx];
1894 mindist = mindist + cut_dist - dst;
1895 dists[idx] = cut_dist;
1896 if (mindist * epsError <= result_set.worstDist())
1899 result_set, vec, otherChild, mindist, dists, epsError))
1918 Base::saveIndex(*
this, stream);
1926 void loadIndex(std::istream& stream) { Base::loadIndex(*
this, stream); }
1968 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1969 typename IndexType = uint32_t>
1972 KDTreeSingleIndexDynamicAdaptor_<
1973 Distance, DatasetAdaptor, DIM, IndexType>,
1974 Distance, DatasetAdaptor, DIM, IndexType>
1984 std::vector<int>& treeIndex_;
1990 Distance, DatasetAdaptor, DIM, IndexType>,
1991 Distance, DatasetAdaptor, DIM, IndexType>;
1993 using ElementType =
typename Base::ElementType;
1994 using DistanceType =
typename Base::DistanceType;
1996 using Offset =
typename Base::Offset;
1997 using Size =
typename Base::Size;
1998 using Dimension =
typename Base::Dimension;
2000 using Node =
typename Base::Node;
2001 using NodePtr = Node*;
2003 using Interval =
typename Base::Interval;
2028 const Dimension dimensionality,
const DatasetAdaptor& inputData,
2029 std::vector<int>& treeIndex,
2032 : dataset_(inputData),
2033 index_params_(params),
2034 treeIndex_(treeIndex),
2035 distance_(inputData)
2038 Base::size_at_index_build_ = 0;
2039 for (
auto& v : Base::root_bbox_) v = {};
2040 Base::dim_ = dimensionality;
2041 if (DIM > 0) Base::dim_ = DIM;
2042 Base::leaf_max_size_ = params.leaf_max_size;
2043 if (params.n_thread_build > 0)
2045 Base::n_thread_build_ = params.n_thread_build;
2049 Base::n_thread_build_ =
2050 std::max(std::thread::hardware_concurrency(), 1u);
2063 std::swap(Base::vAcc_, tmp.Base::vAcc_);
2064 std::swap(Base::leaf_max_size_, tmp.Base::leaf_max_size_);
2065 std::swap(index_params_, tmp.index_params_);
2066 std::swap(treeIndex_, tmp.treeIndex_);
2067 std::swap(Base::size_, tmp.Base::size_);
2068 std::swap(Base::size_at_index_build_, tmp.Base::size_at_index_build_);
2069 std::swap(Base::root_node_, tmp.Base::root_node_);
2070 std::swap(Base::root_bbox_, tmp.Base::root_bbox_);
2071 std::swap(Base::pool_, tmp.Base::pool_);
2080 Base::size_ = Base::vAcc_.size();
2081 this->freeIndex(*
this);
2082 Base::size_at_index_build_ = Base::size_;
2083 if (Base::size_ == 0)
return;
2084 computeBoundingBox(Base::root_bbox_);
2086 if (Base::n_thread_build_ == 1)
2089 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
2093 std::atomic<unsigned int> thread_count(0u);
2095 Base::root_node_ = this->divideTreeConcurrent(
2096 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
2123 template <
typename RESULTSET>
2125 RESULTSET& result,
const ElementType* vec,
2129 if (this->size(*
this) == 0)
return false;
2130 if (!Base::root_node_)
return false;
2131 float epsError = 1 + searchParams.eps;
2134 distance_vector_t dists;
2137 dists, (DIM > 0 ? DIM : Base::dim_),
2138 static_cast<typename distance_vector_t::value_type>(0));
2139 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
2140 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
2141 return result.full();
2159 const ElementType* query_point,
const Size num_closest,
2160 IndexType* out_indices, DistanceType* out_distances,
2164 resultSet.init(out_indices, out_distances);
2165 findNeighbors(resultSet, query_point, searchParams);
2166 return resultSet.size();
2189 const ElementType* query_point,
const DistanceType& radius,
2194 radius, IndicesDists);
2195 const size_t nFound =
2196 radiusSearchCustomCallback(query_point, resultSet, searchParams);
2197 if (searchParams.sorted)
2208 template <
class SEARCH_CALLBACK>
2210 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
2213 findNeighbors(resultSet, query_point, searchParams);
2214 return resultSet.size();
2220 void computeBoundingBox(BoundingBox& bbox)
2222 const auto dims = (DIM > 0 ? DIM : Base::dim_);
2225 if (dataset_.kdtree_get_bbox(bbox))
2231 const Size N = Base::size_;
2233 throw std::runtime_error(
2234 "[nanoflann] computeBoundingBox() called but "
2235 "no data points found.");
2236 for (Dimension i = 0; i < dims; ++i)
2238 bbox[i].low = bbox[i].high =
2239 this->dataset_get(*
this, Base::vAcc_[0], i);
2241 for (Offset k = 1; k < N; ++k)
2243 for (Dimension i = 0; i < dims; ++i)
2246 this->dataset_get(*
this, Base::vAcc_[k], i);
2247 if (val < bbox[i].low) bbox[i].low = val;
2248 if (val > bbox[i].high) bbox[i].high = val;
2258 template <
class RESULTSET>
2260 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
2262 const float epsError)
const
2265 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
2267 DistanceType worst_dist = result_set.worstDist();
2268 for (Offset i = node->node_type.lr.left;
2269 i < node->node_type.lr.right; ++i)
2271 const IndexType index = Base::vAcc_[i];
2272 if (treeIndex_[index] == -1)
continue;
2273 DistanceType dist = distance_.evalMetric(
2274 vec, index, (DIM > 0 ? DIM : Base::dim_));
2275 if (dist < worst_dist)
2277 if (!result_set.addPoint(
2278 static_cast<typename RESULTSET::DistanceType
>(dist),
2279 static_cast<typename RESULTSET::IndexType
>(
2292 Dimension idx = node->node_type.sub.divfeat;
2293 ElementType val = vec[idx];
2294 DistanceType diff1 = val - node->node_type.sub.divlow;
2295 DistanceType diff2 = val - node->node_type.sub.divhigh;
2299 DistanceType cut_dist;
2300 if ((diff1 + diff2) < 0)
2302 bestChild = node->child1;
2303 otherChild = node->child2;
2305 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
2309 bestChild = node->child2;
2310 otherChild = node->child1;
2312 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
2316 searchLevel(result_set, vec, bestChild, mindist, dists, epsError);
2318 DistanceType dst = dists[idx];
2319 mindist = mindist + cut_dist - dst;
2320 dists[idx] = cut_dist;
2321 if (mindist * epsError <= result_set.worstDist())
2323 searchLevel(result_set, vec, otherChild, mindist, dists, epsError);
2359 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
2360 typename IndexType = uint32_t>
2364 using ElementType =
typename Distance::ElementType;
2365 using DistanceType =
typename Distance::DistanceType;
2368 Distance, DatasetAdaptor, DIM>::Offset;
2370 Distance, DatasetAdaptor, DIM>::Size;
2372 Distance, DatasetAdaptor, DIM>::Dimension;
2375 Size leaf_max_size_;
2387 std::unordered_set<int> removedPoints_;
2394 Distance, DatasetAdaptor, DIM, IndexType>;
2395 std::vector<index_container_t> index_;
2407 int First0Bit(IndexType num)
2421 using my_kd_tree_t = KDTreeSingleIndexDynamicAdaptor_<
2422 Distance, DatasetAdaptor, DIM, IndexType>;
2423 std::vector<my_kd_tree_t> index(
2425 my_kd_tree_t(dim_ , dataset_, treeIndex_, index_params_));
2448 const int dimensionality,
const DatasetAdaptor& inputData,
2451 const size_t maximumPointCount = 1000000000U)
2452 : dataset_(inputData), index_params_(params), distance_(inputData)
2454 treeCount_ =
static_cast<size_t>(std::log2(maximumPointCount)) + 1;
2456 dim_ = dimensionality;
2458 if (DIM > 0) dim_ = DIM;
2459 leaf_max_size_ = params.leaf_max_size;
2461 const size_t num_initial_points = dataset_.kdtree_get_point_count();
2462 if (num_initial_points > 0) { addPoints(0, num_initial_points - 1); }
2468 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
2473 const Size count = end - start + 1;
2475 treeIndex_.resize(treeIndex_.size() + count);
2476 for (IndexType idx = start; idx <= end; idx++)
2478 const int pos = First0Bit(pointCount_);
2479 maxIndex = std::max(pos, maxIndex);
2480 treeIndex_[pointCount_] = pos;
2482 const auto it = removedPoints_.find(idx);
2483 if (it != removedPoints_.end())
2485 removedPoints_.erase(it);
2486 treeIndex_[idx] = pos;
2489 for (
int i = 0; i < pos; i++)
2491 for (
int j = 0; j < static_cast<int>(index_[i].vAcc_.size());
2494 index_[pos].vAcc_.push_back(index_[i].vAcc_[j]);
2495 if (treeIndex_[index_[i].vAcc_[j]] != -1)
2496 treeIndex_[index_[i].vAcc_[j]] = pos;
2498 index_[i].vAcc_.clear();
2500 index_[pos].vAcc_.push_back(idx);
2504 for (
int i = 0; i <= maxIndex; ++i)
2506 index_[i].freeIndex(index_[i]);
2507 if (!index_[i].vAcc_.empty()) index_[i].buildIndex();
2514 if (idx >= pointCount_)
return;
2515 removedPoints_.insert(idx);
2516 treeIndex_[idx] = -1;
2535 template <
typename RESULTSET>
2537 RESULTSET& result,
const ElementType* vec,
2540 for (
size_t i = 0; i < treeCount_; i++)
2542 index_[i].findNeighbors(result, &vec[0], searchParams);
2544 return result.full();
2575 bool row_major =
true>
2580 using num_t =
typename MatrixType::Scalar;
2581 using IndexType =
typename MatrixType::Index;
2582 using metric_t =
typename Distance::template traits<
2583 num_t,
self_t, IndexType>::distance_t;
2587 row_major ? MatrixType::ColsAtCompileTime
2588 : MatrixType::RowsAtCompileTime,
2595 using Size =
typename index_t::Size;
2596 using Dimension =
typename index_t::Dimension;
2600 const Dimension dimensionality,
2601 const std::reference_wrapper<const MatrixType>& mat,
2602 const int leaf_max_size = 10)
2603 : m_data_matrix(mat)
2605 const auto dims = row_major ? mat.get().cols() : mat.get().rows();
2606 if (
static_cast<Dimension
>(dims) != dimensionality)
2607 throw std::runtime_error(
2608 "Error: 'dimensionality' must match column count in data "
2610 if (DIM > 0 &&
static_cast<int32_t
>(dims) != DIM)
2611 throw std::runtime_error(
2612 "Data set dimensionality does not match the 'DIM' template "
2625 const std::reference_wrapper<const MatrixType> m_data_matrix;
2636 const num_t* query_point,
const Size num_closest,
2637 IndexType* out_indices, num_t* out_distances)
const
2640 resultSet.init(out_indices, out_distances);
2647 const self_t& derived()
const {
return *
this; }
2648 self_t& derived() {
return *
this; }
2651 Size kdtree_get_point_count()
const
2654 return m_data_matrix.get().rows();
2656 return m_data_matrix.get().cols();
2660 num_t kdtree_get_pt(
const IndexType idx,
size_t dim)
const
2663 return m_data_matrix.get().coeff(idx, IndexType(dim));
2665 return m_data_matrix.get().coeff(IndexType(dim), idx);
2673 template <
class BBOX>
2674 bool kdtree_get_bbox(BBOX& )
const
// end of grouping
Definition nanoflann.hpp:985
Size usedMemory(Derived &obj)
Definition nanoflann.hpp:1090
NodePtr divideTreeConcurrent(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox, std::atomic< unsigned int > &thread_count, std::mutex &mutex)
Definition nanoflann.hpp:1187
void planeSplit(const Derived &obj, const Offset ind, const Size count, const Dimension cutfeat, const DistanceType &cutval, Offset &lim1, Offset &lim2)
Definition nanoflann.hpp:1339
typename array_or_vector< DIM, DistanceType >::type distance_vector_t
Definition nanoflann.hpp:1059
std::vector< IndexType > vAcc_
Definition nanoflann.hpp:1002
Size size(const Derived &obj) const
Definition nanoflann.hpp:1074
void freeIndex(Derived &obj)
Definition nanoflann.hpp:989
void loadIndex(Derived &obj, std::istream &stream)
Definition nanoflann.hpp:1444
typename array_or_vector< DIM, Interval >::type BoundingBox
Definition nanoflann.hpp:1055
BoundingBox root_bbox_
Definition nanoflann.hpp:1062
void saveIndex(const Derived &obj, std::ostream &stream) const
Definition nanoflann.hpp:1429
PooledAllocator pool_
Definition nanoflann.hpp:1071
NodePtr divideTree(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox)
Definition nanoflann.hpp:1118
ElementType dataset_get(const Derived &obj, IndexType element, Dimension component) const
Helper accessor to the dataset points:
Definition nanoflann.hpp:1080
Size veclen(const Derived &obj)
Definition nanoflann.hpp:1077
Definition nanoflann.hpp:1503
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1744
Size rknnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const DistanceType &radius) const
Definition nanoflann.hpp:1768
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
KDTreeSingleIndexAdaptor(const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms, Args &&... args)
Definition nanoflann.hpp:1563
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1723
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:1540
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:1926
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:1536
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances) const
Definition nanoflann.hpp:1694
void init_vind()
Definition nanoflann.hpp:1785
const DatasetAdaptor & dataset_
Definition nanoflann.hpp:1511
void saveIndex(std::ostream &stream) const
Definition nanoflann.hpp:1916
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1657
void buildIndex()
Definition nanoflann.hpp:1613
bool searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:1833
Definition nanoflann.hpp:1975
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2188
KDTreeSingleIndexDynamicAdaptor_(const Dimension dimensionality, const DatasetAdaptor &inputData, std::vector< int > &treeIndex, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams())
Definition nanoflann.hpp:2027
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:2006
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:1980
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2209
KDTreeSingleIndexDynamicAdaptor_(const KDTreeSingleIndexDynamicAdaptor_ &rhs)=default
void buildIndex()
Definition nanoflann.hpp:2078
void saveIndex(std::ostream &stream)
Definition nanoflann.hpp:2334
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:2010
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:2341
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2158
void searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:2259
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2124
KDTreeSingleIndexDynamicAdaptor_ operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs)
Definition nanoflann.hpp:2059
Definition nanoflann.hpp:2362
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2536
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:2382
void removePoint(size_t idx)
Definition nanoflann.hpp:2512
void addPoints(IndexType start, IndexType end)
Definition nanoflann.hpp:2471
KDTreeSingleIndexDynamicAdaptor(const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams(), const size_t maximumPointCount=1000000000U)
Definition nanoflann.hpp:2447
std::vector< int > treeIndex_
Definition nanoflann.hpp:2386
const std::vector< index_container_t > & getAllIndices() const
Definition nanoflann.hpp:2400
Dimension dim_
Dimensionality of each data point.
Definition nanoflann.hpp:2391
KDTreeSingleIndexDynamicAdaptor(const KDTreeSingleIndexDynamicAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
Definition nanoflann.hpp:168
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:204
Definition nanoflann.hpp:833
~PooledAllocator()
Definition nanoflann.hpp:869
void free_all()
Definition nanoflann.hpp:872
void * malloc(const size_t req_size)
Definition nanoflann.hpp:888
T * allocate(const size_t count=1)
Definition nanoflann.hpp:939
PooledAllocator()
Definition nanoflann.hpp:864
Definition nanoflann.hpp:247
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:288
Definition nanoflann.hpp:363
ResultItem< IndexType, DistanceType > worst_item() const
Definition nanoflann.hpp:406
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:394
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:328
bool operator()(const PairType &p1, const PairType &p2) const
Definition nanoflann.hpp:331
Definition nanoflann.hpp:1037
Definition nanoflann.hpp:1012
DistanceType divlow
The values used for subdivision.
Definition nanoflann.hpp:1025
Node * child1
Definition nanoflann.hpp:1030
Dimension divfeat
Definition nanoflann.hpp:1023
Offset right
Indices of points in leaf node.
Definition nanoflann.hpp:1019
union nanoflann::KDTreeBaseClass::Node::@0 node_type
Definition nanoflann.hpp:2577
void query(const num_t *query_point, const Size num_closest, IndexType *out_indices, num_t *out_distances) const
Definition nanoflann.hpp:2635
KDTreeEigenMatrixAdaptor(const Dimension dimensionality, const std::reference_wrapper< const MatrixType > &mat, const int leaf_max_size=10)
Constructor: takes a const ref to the matrix object with the data points.
Definition nanoflann.hpp:2599
KDTreeEigenMatrixAdaptor(const self_t &)=delete
typename index_t::Offset Offset
Definition nanoflann.hpp:2594
Definition nanoflann.hpp:784
Definition nanoflann.hpp:473
Definition nanoflann.hpp:535
Definition nanoflann.hpp:600
Definition nanoflann.hpp:456
Definition nanoflann.hpp:347
DistanceType second
Distance from sample to query point.
Definition nanoflann.hpp:355
IndexType first
Index of the sample in the dataset.
Definition nanoflann.hpp:354
Definition nanoflann.hpp:645
DistanceType accum_dist(const U a, const V b, const size_t) const
Definition nanoflann.hpp:663
Definition nanoflann.hpp:690
Definition nanoflann.hpp:803
bool sorted
distance (default: true)
Definition nanoflann.hpp:810
float eps
search for eps-approximate neighbours (default: 0)
Definition nanoflann.hpp:809
Definition nanoflann.hpp:955
Definition nanoflann.hpp:109
Definition nanoflann.hpp:98
Definition nanoflann.hpp:720
Definition nanoflann.hpp:717
Definition nanoflann.hpp:730
Definition nanoflann.hpp:740
Definition nanoflann.hpp:737
Definition nanoflann.hpp:727
Definition nanoflann.hpp:749
Definition nanoflann.hpp:746
Definition nanoflann.hpp:758
Definition nanoflann.hpp:755