60#include <unordered_set>
64#define NANOFLANN_VERSION 0x161
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 NodePtr node = obj.pool_.template allocate<Node>();
1139 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1142 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1144 node->
child1 = node->child2 =
nullptr;
1149 for (Dimension i = 0; i < dims; ++i)
1151 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1152 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1154 for (Offset k = left + 1; k < right; ++k)
1156 for (Dimension i = 0; i < dims; ++i)
1158 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1159 if (bbox[i].low > val) bbox[i].low = val;
1160 if (bbox[i].high < val) bbox[i].high = val;
1168 DistanceType cutval;
1169 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1174 left_bbox[cutfeat].high = cutval;
1175 node->
child1 = this->divideTree(obj, left, left + idx, left_bbox);
1178 right_bbox[cutfeat].low = cutval;
1179 node->child2 = this->divideTree(obj, left + idx, right, right_bbox);
1181 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1182 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1184 for (Dimension i = 0; i < dims; ++i)
1186 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1187 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1205 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox,
1206 std::atomic<unsigned int>& thread_count, std::mutex& mutex)
1208 std::unique_lock<std::mutex> lock(mutex);
1209 NodePtr node = obj.pool_.template allocate<Node>();
1212 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1215 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1217 node->
child1 = node->child2 =
nullptr;
1222 for (Dimension i = 0; i < dims; ++i)
1224 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1225 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1227 for (Offset k = left + 1; k < right; ++k)
1229 for (Dimension i = 0; i < dims; ++i)
1231 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1232 if (bbox[i].low > val) bbox[i].low = val;
1233 if (bbox[i].high < val) bbox[i].high = val;
1241 DistanceType cutval;
1242 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1246 std::future<NodePtr> right_future;
1249 right_bbox[cutfeat].low = cutval;
1250 if (++thread_count < n_thread_build_)
1253 right_future = std::async(
1254 std::launch::async, &KDTreeBaseClass::divideTreeConcurrent,
1255 this, std::ref(obj), left + idx, right,
1256 std::ref(right_bbox), std::ref(thread_count),
1259 else { --thread_count; }
1262 left_bbox[cutfeat].high = cutval;
1263 node->
child1 = this->divideTreeConcurrent(
1264 obj, left, left + idx, left_bbox, thread_count, mutex);
1266 if (right_future.valid())
1269 node->child2 = right_future.get();
1274 node->child2 = this->divideTreeConcurrent(
1275 obj, left + idx, right, right_bbox, thread_count, mutex);
1278 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1279 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1281 for (Dimension i = 0; i < dims; ++i)
1283 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1284 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1292 const Derived& obj,
const Offset ind,
const Size count, Offset& index,
1293 Dimension& cutfeat, DistanceType& cutval,
const BoundingBox& bbox)
1295 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1296 const auto EPS =
static_cast<DistanceType
>(0.00001);
1297 ElementType max_span = bbox[0].high - bbox[0].low;
1298 for (Dimension i = 1; i < dims; ++i)
1300 ElementType span = bbox[i].high - bbox[i].low;
1301 if (span > max_span) { max_span = span; }
1303 ElementType max_spread = -1;
1305 ElementType min_elem = 0, max_elem = 0;
1306 for (Dimension i = 0; i < dims; ++i)
1308 ElementType span = bbox[i].high - bbox[i].low;
1309 if (span > (1 - EPS) * max_span)
1311 ElementType min_elem_, max_elem_;
1312 computeMinMax(obj, ind, count, i, min_elem_, max_elem_);
1313 ElementType spread = max_elem_ - min_elem_;
1314 if (spread > max_spread)
1317 max_spread = spread;
1318 min_elem = min_elem_;
1319 max_elem = max_elem_;
1324 DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
1326 if (split_val < min_elem)
1328 else if (split_val > max_elem)
1334 planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
1336 if (lim1 > count / 2)
1338 else if (lim2 < count / 2)
1354 const Derived& obj,
const Offset ind,
const Size count,
1355 const Dimension cutfeat,
const DistanceType& cutval, Offset& lim1,
1360 Offset right = count - 1;
1363 while (left <= right &&
1364 dataset_get(obj, vAcc_[ind + left], cutfeat) < cutval)
1366 while (right && left <= right &&
1367 dataset_get(obj, vAcc_[ind + right], cutfeat) >= cutval)
1369 if (left > right || !right)
1371 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1382 while (left <= right &&
1383 dataset_get(obj, vAcc_[ind + left], cutfeat) <= cutval)
1385 while (right && left <= right &&
1386 dataset_get(obj, vAcc_[ind + right], cutfeat) > cutval)
1388 if (left > right || !right)
1390 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1397 DistanceType computeInitialDistances(
1398 const Derived& obj,
const ElementType* vec,
1399 distance_vector_t& dists)
const
1402 DistanceType dist = DistanceType();
1404 for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim_); ++i)
1406 if (vec[i] < obj.root_bbox_[i].low)
1409 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].low, i);
1412 if (vec[i] > obj.root_bbox_[i].high)
1415 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i);
1422 static void save_tree(
1423 const Derived& obj, std::ostream& stream,
const NodeConstPtr tree)
1425 save_value(stream, *tree);
1426 if (tree->child1 !=
nullptr) { save_tree(obj, stream, tree->child1); }
1427 if (tree->child2 !=
nullptr) { save_tree(obj, stream, tree->child2); }
1430 static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree)
1432 tree = obj.pool_.template allocate<Node>();
1433 load_value(stream, *tree);
1434 if (tree->child1 !=
nullptr) { load_tree(obj, stream, tree->child1); }
1435 if (tree->child2 !=
nullptr) { load_tree(obj, stream, tree->child2); }
1443 void saveIndex(
const Derived& obj, std::ostream& stream)
const
1445 save_value(stream, obj.size_);
1446 save_value(stream, obj.dim_);
1447 save_value(stream, obj.root_bbox_);
1448 save_value(stream, obj.leaf_max_size_);
1449 save_value(stream, obj.vAcc_);
1450 if (obj.root_node_) save_tree(obj, stream, obj.root_node_);
1460 load_value(stream, obj.size_);
1461 load_value(stream, obj.dim_);
1462 load_value(stream, obj.root_bbox_);
1463 load_value(stream, obj.leaf_max_size_);
1464 load_value(stream, obj.vAcc_);
1465 load_tree(obj, stream, obj.root_node_);
1511 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1512 typename index_t = uint32_t>
1515 KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, index_t>,
1516 Distance, DatasetAdaptor, DIM, index_t>
1522 Distance, DatasetAdaptor, DIM, index_t>&) =
delete;
1533 Distance, DatasetAdaptor, DIM, index_t>,
1534 Distance, DatasetAdaptor, DIM, index_t>;
1536 using Offset =
typename Base::Offset;
1537 using Size =
typename Base::Size;
1538 using Dimension =
typename Base::Dimension;
1540 using ElementType =
typename Base::ElementType;
1541 using DistanceType =
typename Base::DistanceType;
1542 using IndexType =
typename Base::IndexType;
1544 using Node =
typename Base::Node;
1545 using NodePtr = Node*;
1547 using Interval =
typename Base::Interval;
1577 template <
class... Args>
1579 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1581 : dataset_(inputData),
1582 indexParams(params),
1583 distance_(inputData, std::forward<Args>(args)...)
1585 init(dimensionality, params);
1589 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1591 : dataset_(inputData), indexParams(params), distance_(inputData)
1593 init(dimensionality, params);
1598 const Dimension dimensionality,
1599 const KDTreeSingleIndexAdaptorParams& params)
1601 Base::size_ = dataset_.kdtree_get_point_count();
1602 Base::size_at_index_build_ = Base::size_;
1603 Base::dim_ = dimensionality;
1604 if (DIM > 0) Base::dim_ = DIM;
1605 Base::leaf_max_size_ = params.leaf_max_size;
1606 if (params.n_thread_build > 0)
1608 Base::n_thread_build_ = params.n_thread_build;
1612 Base::n_thread_build_ =
1613 std::max(std::thread::hardware_concurrency(), 1u);
1616 if (!(params.flags &
1617 KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex))
1630 Base::size_ = dataset_.kdtree_get_point_count();
1631 Base::size_at_index_build_ = Base::size_;
1633 this->freeIndex(*
this);
1634 Base::size_at_index_build_ = Base::size_;
1635 if (Base::size_ == 0)
return;
1636 computeBoundingBox(Base::root_bbox_);
1638 if (Base::n_thread_build_ == 1)
1641 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
1645#ifndef NANOFLANN_NO_THREADS
1646 std::atomic<unsigned int> thread_count(0u);
1648 Base::root_node_ = this->divideTreeConcurrent(
1649 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
1651 throw std::runtime_error(
"Multithreading is disabled");
1675 template <
typename RESULTSET>
1677 RESULTSET& result,
const ElementType* vec,
1681 if (this->size(*
this) == 0)
return false;
1682 if (!Base::root_node_)
1683 throw std::runtime_error(
1684 "[nanoflann] findNeighbors() called before building the "
1686 float epsError = 1 + searchParams.eps;
1689 distance_vector_t dists;
1691 auto zero =
static_cast<decltype(result.worstDist())
>(0);
1692 assign(dists, (DIM > 0 ? DIM : Base::dim_), zero);
1693 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
1694 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
1696 if (searchParams.sorted) result.sort();
1698 return result.full();
1717 const ElementType* query_point,
const Size num_closest,
1718 IndexType* out_indices, DistanceType* out_distances)
const
1721 resultSet.init(out_indices, out_distances);
1722 findNeighbors(resultSet, query_point);
1723 return resultSet.size();
1746 const ElementType* query_point,
const DistanceType& radius,
1751 radius, IndicesDists);
1753 radiusSearchCustomCallback(query_point, resultSet, searchParams);
1762 template <
class SEARCH_CALLBACK>
1764 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
1767 findNeighbors(resultSet, query_point, searchParams);
1768 return resultSet.size();
1788 const ElementType* query_point,
const Size num_closest,
1789 IndexType* out_indices, DistanceType* out_distances,
1790 const DistanceType& radius)
const
1793 num_closest, radius);
1794 resultSet.init(out_indices, out_distances);
1795 findNeighbors(resultSet, query_point);
1796 return resultSet.size();
1807 Base::size_ = dataset_.kdtree_get_point_count();
1808 if (Base::vAcc_.size() != Base::size_) Base::vAcc_.resize(Base::size_);
1809 for (Size i = 0; i < Base::size_; i++) Base::vAcc_[i] = i;
1812 void computeBoundingBox(BoundingBox& bbox)
1814 const auto dims = (DIM > 0 ? DIM : Base::dim_);
1816 if (dataset_.kdtree_get_bbox(bbox))
1822 const Size N = dataset_.kdtree_get_point_count();
1824 throw std::runtime_error(
1825 "[nanoflann] computeBoundingBox() called but "
1826 "no data points found.");
1827 for (Dimension i = 0; i < dims; ++i)
1829 bbox[i].low = bbox[i].high =
1830 this->dataset_get(*
this, Base::vAcc_[0], i);
1832 for (Offset k = 1; k < N; ++k)
1834 for (Dimension i = 0; i < dims; ++i)
1837 this->dataset_get(*
this, Base::vAcc_[k], i);
1838 if (val < bbox[i].low) bbox[i].low = val;
1839 if (val > bbox[i].high) bbox[i].high = val;
1851 template <
class RESULTSET>
1853 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
1855 const float epsError)
const
1858 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
1860 DistanceType worst_dist = result_set.worstDist();
1861 for (Offset i = node->node_type.lr.left;
1862 i < node->node_type.lr.right; ++i)
1864 const IndexType accessor = Base::vAcc_[i];
1865 DistanceType dist = distance_.evalMetric(
1866 vec, accessor, (DIM > 0 ? DIM : Base::dim_));
1867 if (dist < worst_dist)
1869 if (!result_set.addPoint(dist, Base::vAcc_[i]))
1881 Dimension idx = node->node_type.sub.divfeat;
1882 ElementType val = vec[idx];
1883 DistanceType diff1 = val - node->node_type.sub.divlow;
1884 DistanceType diff2 = val - node->node_type.sub.divhigh;
1888 DistanceType cut_dist;
1889 if ((diff1 + diff2) < 0)
1891 bestChild = node->child1;
1892 otherChild = node->child2;
1894 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
1898 bestChild = node->child2;
1899 otherChild = node->child1;
1901 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
1905 if (!searchLevel(result_set, vec, bestChild, mindist, dists, epsError))
1912 DistanceType dst = dists[idx];
1913 mindist = mindist + cut_dist - dst;
1914 dists[idx] = cut_dist;
1915 if (mindist * epsError <= result_set.worstDist())
1918 result_set, vec, otherChild, mindist, dists, epsError))
1937 Base::saveIndex(*
this, stream);
1945 void loadIndex(std::istream& stream) { Base::loadIndex(*
this, stream); }
1987 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1988 typename IndexType = uint32_t>
1991 KDTreeSingleIndexDynamicAdaptor_<
1992 Distance, DatasetAdaptor, DIM, IndexType>,
1993 Distance, DatasetAdaptor, DIM, IndexType>
2003 std::vector<int>& treeIndex_;
2009 Distance, DatasetAdaptor, DIM, IndexType>,
2010 Distance, DatasetAdaptor, DIM, IndexType>;
2012 using ElementType =
typename Base::ElementType;
2013 using DistanceType =
typename Base::DistanceType;
2015 using Offset =
typename Base::Offset;
2016 using Size =
typename Base::Size;
2017 using Dimension =
typename Base::Dimension;
2019 using Node =
typename Base::Node;
2020 using NodePtr = Node*;
2022 using Interval =
typename Base::Interval;
2047 const Dimension dimensionality,
const DatasetAdaptor& inputData,
2048 std::vector<int>& treeIndex,
2051 : dataset_(inputData),
2052 index_params_(params),
2053 treeIndex_(treeIndex),
2054 distance_(inputData)
2057 Base::size_at_index_build_ = 0;
2058 for (
auto& v : Base::root_bbox_) v = {};
2059 Base::dim_ = dimensionality;
2060 if (DIM > 0) Base::dim_ = DIM;
2061 Base::leaf_max_size_ = params.leaf_max_size;
2062 if (params.n_thread_build > 0)
2064 Base::n_thread_build_ = params.n_thread_build;
2068 Base::n_thread_build_ =
2069 std::max(std::thread::hardware_concurrency(), 1u);
2082 std::swap(Base::vAcc_, tmp.Base::vAcc_);
2083 std::swap(Base::leaf_max_size_, tmp.Base::leaf_max_size_);
2084 std::swap(index_params_, tmp.index_params_);
2085 std::swap(treeIndex_, tmp.treeIndex_);
2086 std::swap(Base::size_, tmp.Base::size_);
2087 std::swap(Base::size_at_index_build_, tmp.Base::size_at_index_build_);
2088 std::swap(Base::root_node_, tmp.Base::root_node_);
2089 std::swap(Base::root_bbox_, tmp.Base::root_bbox_);
2090 std::swap(Base::pool_, tmp.Base::pool_);
2099 Base::size_ = Base::vAcc_.size();
2100 this->freeIndex(*
this);
2101 Base::size_at_index_build_ = Base::size_;
2102 if (Base::size_ == 0)
return;
2103 computeBoundingBox(Base::root_bbox_);
2105 if (Base::n_thread_build_ == 1)
2108 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
2112#ifndef NANOFLANN_NO_THREADS
2113 std::atomic<unsigned int> thread_count(0u);
2115 Base::root_node_ = this->divideTreeConcurrent(
2116 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
2118 throw std::runtime_error(
"Multithreading is disabled");
2146 template <
typename RESULTSET>
2148 RESULTSET& result,
const ElementType* vec,
2152 if (this->size(*
this) == 0)
return false;
2153 if (!Base::root_node_)
return false;
2154 float epsError = 1 + searchParams.eps;
2157 distance_vector_t dists;
2160 dists, (DIM > 0 ? DIM : Base::dim_),
2161 static_cast<typename distance_vector_t::value_type>(0));
2162 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
2163 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
2164 return result.full();
2182 const ElementType* query_point,
const Size num_closest,
2183 IndexType* out_indices, DistanceType* out_distances,
2187 resultSet.init(out_indices, out_distances);
2188 findNeighbors(resultSet, query_point, searchParams);
2189 return resultSet.size();
2212 const ElementType* query_point,
const DistanceType& radius,
2217 radius, IndicesDists);
2218 const size_t nFound =
2219 radiusSearchCustomCallback(query_point, resultSet, searchParams);
2228 template <
class SEARCH_CALLBACK>
2230 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
2233 findNeighbors(resultSet, query_point, searchParams);
2234 return resultSet.size();
2240 void computeBoundingBox(BoundingBox& bbox)
2242 const auto dims = (DIM > 0 ? DIM : Base::dim_);
2245 if (dataset_.kdtree_get_bbox(bbox))
2251 const Size N = Base::size_;
2253 throw std::runtime_error(
2254 "[nanoflann] computeBoundingBox() called but "
2255 "no data points found.");
2256 for (Dimension i = 0; i < dims; ++i)
2258 bbox[i].low = bbox[i].high =
2259 this->dataset_get(*
this, Base::vAcc_[0], i);
2261 for (Offset k = 1; k < N; ++k)
2263 for (Dimension i = 0; i < dims; ++i)
2266 this->dataset_get(*
this, Base::vAcc_[k], i);
2267 if (val < bbox[i].low) bbox[i].low = val;
2268 if (val > bbox[i].high) bbox[i].high = val;
2278 template <
class RESULTSET>
2280 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
2282 const float epsError)
const
2285 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
2287 DistanceType worst_dist = result_set.worstDist();
2288 for (Offset i = node->node_type.lr.left;
2289 i < node->node_type.lr.right; ++i)
2291 const IndexType index = Base::vAcc_[i];
2292 if (treeIndex_[index] == -1)
continue;
2293 DistanceType dist = distance_.evalMetric(
2294 vec, index, (DIM > 0 ? DIM : Base::dim_));
2295 if (dist < worst_dist)
2297 if (!result_set.addPoint(
2298 static_cast<typename RESULTSET::DistanceType
>(dist),
2299 static_cast<typename RESULTSET::IndexType
>(
2312 Dimension idx = node->node_type.sub.divfeat;
2313 ElementType val = vec[idx];
2314 DistanceType diff1 = val - node->node_type.sub.divlow;
2315 DistanceType diff2 = val - node->node_type.sub.divhigh;
2319 DistanceType cut_dist;
2320 if ((diff1 + diff2) < 0)
2322 bestChild = node->child1;
2323 otherChild = node->child2;
2325 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
2329 bestChild = node->child2;
2330 otherChild = node->child1;
2332 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
2336 searchLevel(result_set, vec, bestChild, mindist, dists, epsError);
2338 DistanceType dst = dists[idx];
2339 mindist = mindist + cut_dist - dst;
2340 dists[idx] = cut_dist;
2341 if (mindist * epsError <= result_set.worstDist())
2343 searchLevel(result_set, vec, otherChild, mindist, dists, epsError);
2379 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
2380 typename IndexType = uint32_t>
2384 using ElementType =
typename Distance::ElementType;
2385 using DistanceType =
typename Distance::DistanceType;
2388 Distance, DatasetAdaptor, DIM>::Offset;
2390 Distance, DatasetAdaptor, DIM>::Size;
2392 Distance, DatasetAdaptor, DIM>::Dimension;
2395 Size leaf_max_size_;
2407 std::unordered_set<int> removedPoints_;
2414 Distance, DatasetAdaptor, DIM, IndexType>;
2415 std::vector<index_container_t> index_;
2427 int First0Bit(IndexType num)
2441 using my_kd_tree_t = KDTreeSingleIndexDynamicAdaptor_<
2442 Distance, DatasetAdaptor, DIM, IndexType>;
2443 std::vector<my_kd_tree_t> index(
2445 my_kd_tree_t(dim_ , dataset_, treeIndex_, index_params_));
2468 const int dimensionality,
const DatasetAdaptor& inputData,
2471 const size_t maximumPointCount = 1000000000U)
2472 : dataset_(inputData), index_params_(params), distance_(inputData)
2474 treeCount_ =
static_cast<size_t>(std::log2(maximumPointCount)) + 1;
2476 dim_ = dimensionality;
2478 if (DIM > 0) dim_ = DIM;
2479 leaf_max_size_ = params.leaf_max_size;
2481 const size_t num_initial_points = dataset_.kdtree_get_point_count();
2482 if (num_initial_points > 0) { addPoints(0, num_initial_points - 1); }
2488 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
2493 const Size count = end - start + 1;
2495 treeIndex_.resize(treeIndex_.size() + count);
2496 for (IndexType idx = start; idx <= end; idx++)
2498 const int pos = First0Bit(pointCount_);
2499 maxIndex = std::max(pos, maxIndex);
2500 treeIndex_[pointCount_] = pos;
2502 const auto it = removedPoints_.find(idx);
2503 if (it != removedPoints_.end())
2505 removedPoints_.erase(it);
2506 treeIndex_[idx] = pos;
2509 for (
int i = 0; i < pos; i++)
2511 for (
int j = 0; j < static_cast<int>(index_[i].vAcc_.size());
2514 index_[pos].vAcc_.push_back(index_[i].vAcc_[j]);
2515 if (treeIndex_[index_[i].vAcc_[j]] != -1)
2516 treeIndex_[index_[i].vAcc_[j]] = pos;
2518 index_[i].vAcc_.clear();
2520 index_[pos].vAcc_.push_back(idx);
2524 for (
int i = 0; i <= maxIndex; ++i)
2526 index_[i].freeIndex(index_[i]);
2527 if (!index_[i].vAcc_.empty()) index_[i].buildIndex();
2534 if (idx >= pointCount_)
return;
2535 removedPoints_.insert(idx);
2536 treeIndex_[idx] = -1;
2555 template <
typename RESULTSET>
2557 RESULTSET& result,
const ElementType* vec,
2560 for (
size_t i = 0; i < treeCount_; i++)
2562 index_[i].findNeighbors(result, &vec[0], searchParams);
2564 return result.full();
2595 bool row_major =
true>
2600 using num_t =
typename MatrixType::Scalar;
2601 using IndexType =
typename MatrixType::Index;
2602 using metric_t =
typename Distance::template traits<
2603 num_t,
self_t, IndexType>::distance_t;
2607 row_major ? MatrixType::ColsAtCompileTime
2608 : MatrixType::RowsAtCompileTime,
2615 using Size =
typename index_t::Size;
2616 using Dimension =
typename index_t::Dimension;
2620 const Dimension dimensionality,
2621 const std::reference_wrapper<const MatrixType>& mat,
2622 const int leaf_max_size = 10,
const unsigned int n_thread_build = 1)
2623 : m_data_matrix(mat)
2625 const auto dims = row_major ? mat.get().cols() : mat.get().rows();
2626 if (
static_cast<Dimension
>(dims) != dimensionality)
2627 throw std::runtime_error(
2628 "Error: 'dimensionality' must match column count in data "
2630 if (DIM > 0 &&
static_cast<int32_t
>(dims) != DIM)
2631 throw std::runtime_error(
2632 "Data set dimensionality does not match the 'DIM' template "
2637 leaf_max_size, nanoflann::KDTreeSingleIndexAdaptorFlags::None,
2647 const std::reference_wrapper<const MatrixType> m_data_matrix;
2658 const num_t* query_point,
const Size num_closest,
2659 IndexType* out_indices, num_t* out_distances)
const
2662 resultSet.init(out_indices, out_distances);
2669 const self_t& derived()
const {
return *
this; }
2670 self_t& derived() {
return *
this; }
2673 Size kdtree_get_point_count()
const
2676 return m_data_matrix.get().rows();
2678 return m_data_matrix.get().cols();
2682 num_t kdtree_get_pt(
const IndexType idx,
size_t dim)
const
2685 return m_data_matrix.get().coeff(idx, IndexType(dim));
2687 return m_data_matrix.get().coeff(IndexType(dim), idx);
2695 template <
class BBOX>
2696 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:1443
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:1353
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:1204
void loadIndex(Derived &obj, std::istream &stream)
Definition nanoflann.hpp:1458
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:1517
bool searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:1852
void saveIndex(std::ostream &stream) const
Definition nanoflann.hpp:1935
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1745
void init_vind()
Definition nanoflann.hpp:1804
void buildIndex()
Definition nanoflann.hpp:1628
const DatasetAdaptor & dataset_
Definition nanoflann.hpp:1525
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t > &)=delete
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1676
Size rknnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const DistanceType &radius) const
Definition nanoflann.hpp:1787
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1763
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:1555
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:1945
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:1551
KDTreeSingleIndexAdaptor(const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms, Args &&... args)
Definition nanoflann.hpp:1578
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances) const
Definition nanoflann.hpp:1716
Definition nanoflann.hpp:1994
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2211
KDTreeSingleIndexDynamicAdaptor_(const Dimension dimensionality, const DatasetAdaptor &inputData, std::vector< int > &treeIndex, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams())
Definition nanoflann.hpp:2046
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:2025
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:1999
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2229
KDTreeSingleIndexDynamicAdaptor_(const KDTreeSingleIndexDynamicAdaptor_ &rhs)=default
void buildIndex()
Definition nanoflann.hpp:2097
void saveIndex(std::ostream &stream)
Definition nanoflann.hpp:2354
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:2029
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:2361
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2181
void searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:2279
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2147
KDTreeSingleIndexDynamicAdaptor_ operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs)
Definition nanoflann.hpp:2078
Definition nanoflann.hpp:2382
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2556
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:2402
void removePoint(size_t idx)
Definition nanoflann.hpp:2532
void addPoints(IndexType start, IndexType end)
Definition nanoflann.hpp:2491
KDTreeSingleIndexDynamicAdaptor(const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams(), const size_t maximumPointCount=1000000000U)
Definition nanoflann.hpp:2467
std::vector< int > treeIndex_
Definition nanoflann.hpp:2406
const std::vector< index_container_t > & getAllIndices() const
Definition nanoflann.hpp:2420
Dimension dim_
Dimensionality of each data point.
Definition nanoflann.hpp:2411
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:2597
void query(const num_t *query_point, const Size num_closest, IndexType *out_indices, num_t *out_distances) const
Definition nanoflann.hpp:2657
KDTreeEigenMatrixAdaptor(const self_t &)=delete
typename index_t::Offset Offset
Definition nanoflann.hpp:2614
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:2619
Definition nanoflann.hpp:800
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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
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