59#include <unordered_set>
63#define NANOFLANN_VERSION 0x151
66#if !defined(NOMINMAX) && \
67 (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
88 return static_cast<T
>(3.14159265358979323846);
95template <
typename T,
typename =
int>
106template <
typename T,
typename =
int>
120template <
typename Container>
121inline typename std::enable_if<has_resize<Container>::value,
void>::type
resize(
122 Container& c,
const size_t nElements)
131template <
typename Container>
132inline typename std::enable_if<!has_resize<Container>::value,
void>::type
133 resize(Container& c,
const size_t nElements)
135 if (nElements != c.size())
136 throw std::logic_error(
"Try to change the size of a std::array.");
142template <
typename Container,
typename T>
143inline typename std::enable_if<has_assign<Container>::value,
void>::type
assign(
144 Container& c,
const size_t nElements,
const T& value)
146 c.assign(nElements, value);
152template <
typename Container,
typename T>
153inline typename std::enable_if<!has_assign<Container>::value,
void>::type
154 assign(Container& c,
const size_t nElements,
const T& value)
156 for (
size_t i = 0; i < nElements; i++) c[i] = value;
164 typename _DistanceType,
typename _IndexType = size_t,
165 typename _CountType =
size_t>
169 using DistanceType = _DistanceType;
170 using IndexType = _IndexType;
171 using CountType = _CountType;
181 : indices(
nullptr), dists(
nullptr), capacity(capacity_), count(0)
185 void init(IndexType* indices_, DistanceType* dists_)
191 dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
194 CountType size()
const {
return count; }
195 bool empty()
const {
return count == 0; }
196 bool full()
const {
return count == capacity; }
206 for (i = count; i > 0; --i)
210#ifdef NANOFLANN_FIRST_MATCH
211 if ((dists[i - 1] > dist) ||
212 ((dist == dists[i - 1]) && (indices[i - 1] > index)))
215 if (dists[i - 1] > dist)
220 dists[i] = dists[i - 1];
221 indices[i] = indices[i - 1];
232 if (count < capacity) count++;
238 DistanceType worstDist()
const {
return dists[capacity - 1]; }
243 typename _DistanceType,
typename _IndexType = size_t,
244 typename _CountType =
size_t>
248 using DistanceType = _DistanceType;
249 using IndexType = _IndexType;
250 using CountType = _CountType;
257 DistanceType maximumSearchDistanceSquared;
261 CountType capacity_, DistanceType maximumSearchDistanceSquared_)
266 maximumSearchDistanceSquared(maximumSearchDistanceSquared_)
270 void init(IndexType* indices_, DistanceType* dists_)
276 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;
843 using Offset = uint32_t;
844 using Size = uint32_t;
845 using Dimension = int32_t;
848 void* base_ =
nullptr;
849 void* loc_ =
nullptr;
861 Size wastedMemory = 0;
876 while (base_ !=
nullptr)
879 void* prev = *(
static_cast<void**
>(base_));
896 const Size size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
901 if (size > remaining_)
903 wastedMemory += remaining_;
906 const Size blocksize =
907 (size +
sizeof(
void*) + (WORDSIZE - 1) > BLOCKSIZE)
908 ? size +
sizeof(
void*) + (WORDSIZE - 1)
912 void* m = ::malloc(blocksize);
915 fprintf(stderr,
"Failed to allocate memory.\n");
916 throw std::bad_alloc();
920 static_cast<void**
>(m)[0] = base_;
927 remaining_ = blocksize -
sizeof(
void*) - shift;
928 loc_ = (
static_cast<char*
>(m) +
sizeof(
void*) + shift);
931 loc_ =
static_cast<char*
>(loc_) + size;
946 template <
typename T>
949 T* mem =
static_cast<T*
>(this->malloc(
sizeof(T) * count));
961template <
int32_t DIM,
typename T>
964 using type = std::array<T, DIM>;
970 using type = std::vector<T>;
990 class Derived,
typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
991 typename IndexType = uint32_t>
999 obj.pool_.free_all();
1000 obj.root_node_ =
nullptr;
1001 obj.size_at_index_build_ = 0;
1004 using ElementType =
typename Distance::ElementType;
1005 using DistanceType =
typename Distance::DistanceType;
1012 using Offset =
typename decltype(vAcc_)::size_type;
1013 using Size =
typename decltype(vAcc_)::size_type;
1014 using Dimension = int32_t;
1038 Node *child1 =
nullptr, *child2 =
nullptr;
1046 ElementType low, high;
1051 Size leaf_max_size_ = 0;
1054 Size n_thread_build_ = 1;
1058 Size size_at_index_build_ = 0;
1082 Size
size(
const Derived& obj)
const {
return obj.size_; }
1085 Size
veclen(
const Derived& obj) {
return DIM > 0 ? DIM : obj.dim; }
1089 const Derived& obj, IndexType element, Dimension component)
const
1091 return obj.dataset_.kdtree_get_pt(element, component);
1100 return obj.pool_.usedMemory + obj.pool_.wastedMemory +
1101 obj.dataset_.kdtree_get_point_count() *
1106 const Derived& obj, Offset ind, Size count, Dimension element,
1107 ElementType& min_elem, ElementType& max_elem)
1109 min_elem = dataset_get(obj, vAcc_[ind], element);
1110 max_elem = min_elem;
1111 for (Offset i = 1; i < count; ++i)
1113 ElementType val = dataset_get(obj, vAcc_[ind + i], element);
1114 if (val < min_elem) min_elem = val;
1115 if (val > max_elem) max_elem = val;
1127 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox)
1129 NodePtr node = obj.pool_.template allocate<Node>();
1130 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1133 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1135 node->
child1 = node->child2 =
nullptr;
1140 for (Dimension i = 0; i < dims; ++i)
1142 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1143 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1145 for (Offset k = left + 1; k < right; ++k)
1147 for (Dimension i = 0; i < dims; ++i)
1149 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1150 if (bbox[i].low > val) bbox[i].low = val;
1151 if (bbox[i].high < val) bbox[i].high = val;
1159 DistanceType cutval;
1160 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1165 left_bbox[cutfeat].high = cutval;
1166 node->
child1 = this->divideTree(obj, left, left + idx, left_bbox);
1169 right_bbox[cutfeat].low = cutval;
1170 node->child2 = this->divideTree(obj, left + idx, right, right_bbox);
1172 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1173 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1175 for (Dimension i = 0; i < dims; ++i)
1177 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1178 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1196 Derived& obj,
const Offset left,
const Offset right,
BoundingBox& bbox,
1197 std::atomic<unsigned int>& thread_count, std::mutex& mutex)
1199 std::unique_lock<std::mutex> lock(mutex);
1200 NodePtr node = obj.pool_.template allocate<Node>();
1203 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1206 if ((right - left) <=
static_cast<Offset
>(obj.leaf_max_size_))
1208 node->
child1 = node->child2 =
nullptr;
1213 for (Dimension i = 0; i < dims; ++i)
1215 bbox[i].low = dataset_get(obj, obj.vAcc_[left], i);
1216 bbox[i].high = dataset_get(obj, obj.vAcc_[left], i);
1218 for (Offset k = left + 1; k < right; ++k)
1220 for (Dimension i = 0; i < dims; ++i)
1222 const auto val = dataset_get(obj, obj.vAcc_[k], i);
1223 if (bbox[i].low > val) bbox[i].low = val;
1224 if (bbox[i].high < val) bbox[i].high = val;
1232 DistanceType cutval;
1233 middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
1237 std::future<NodePtr> left_future, right_future;
1240 left_bbox[cutfeat].high = cutval;
1241 if (++thread_count < n_thread_build_)
1243 left_future = std::async(
1244 std::launch::async, &KDTreeBaseClass::divideTreeConcurrent,
1245 this, std::ref(obj), left, left + idx, std::ref(left_bbox),
1246 std::ref(thread_count), std::ref(mutex));
1251 node->
child1 = this->divideTreeConcurrent(
1252 obj, left, left + idx, left_bbox, thread_count, mutex);
1256 right_bbox[cutfeat].low = cutval;
1257 if (++thread_count < n_thread_build_)
1259 right_future = std::async(
1260 std::launch::async, &KDTreeBaseClass::divideTreeConcurrent,
1261 this, std::ref(obj), left + idx, right,
1262 std::ref(right_bbox), std::ref(thread_count),
1268 node->child2 = this->divideTreeConcurrent(
1269 obj, left + idx, right, right_bbox, thread_count, mutex);
1272 if (left_future.valid())
1274 node->
child1 = left_future.get();
1277 if (right_future.valid())
1279 node->child2 = right_future.get();
1283 node->
node_type.sub.divlow = left_bbox[cutfeat].high;
1284 node->
node_type.sub.divhigh = right_bbox[cutfeat].low;
1286 for (Dimension i = 0; i < dims; ++i)
1288 bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
1289 bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
1297 const Derived& obj,
const Offset ind,
const Size count, Offset& index,
1298 Dimension& cutfeat, DistanceType& cutval,
const BoundingBox& bbox)
1300 const auto dims = (DIM > 0 ? DIM : obj.dim_);
1301 const auto EPS =
static_cast<DistanceType
>(0.00001);
1302 ElementType max_span = bbox[0].high - bbox[0].low;
1303 for (Dimension i = 1; i < dims; ++i)
1305 ElementType span = bbox[i].high - bbox[i].low;
1306 if (span > max_span) { max_span = span; }
1308 ElementType max_spread = -1;
1310 ElementType min_elem = 0, max_elem = 0;
1311 for (Dimension i = 0; i < dims; ++i)
1313 ElementType span = bbox[i].high - bbox[i].low;
1314 if (span > (1 - EPS) * max_span)
1316 ElementType min_elem_, max_elem_;
1317 computeMinMax(obj, ind, count, i, min_elem_, max_elem_);
1318 ElementType spread = max_elem_ - min_elem_;
1319 if (spread > max_spread)
1322 max_spread = spread;
1323 min_elem = min_elem_;
1324 max_elem = max_elem_;
1329 DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
1331 if (split_val < min_elem)
1333 else if (split_val > max_elem)
1339 planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
1341 if (lim1 > count / 2)
1343 else if (lim2 < count / 2)
1359 const Derived& obj,
const Offset ind,
const Size count,
1360 const Dimension cutfeat,
const DistanceType& cutval, Offset& lim1,
1365 Offset right = count - 1;
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]);
1387 while (left <= right &&
1388 dataset_get(obj, vAcc_[ind + left], cutfeat) <= cutval)
1390 while (right && left <= right &&
1391 dataset_get(obj, vAcc_[ind + right], cutfeat) > cutval)
1393 if (left > right || !right)
1395 std::swap(vAcc_[ind + left], vAcc_[ind + right]);
1402 DistanceType computeInitialDistances(
1403 const Derived& obj,
const ElementType* vec,
1404 distance_vector_t& dists)
const
1407 DistanceType dist = DistanceType();
1409 for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim_); ++i)
1411 if (vec[i] < obj.root_bbox_[i].low)
1414 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].low, i);
1417 if (vec[i] > obj.root_bbox_[i].high)
1420 obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i);
1427 static void save_tree(
1428 const Derived& obj, std::ostream& stream,
const NodeConstPtr tree)
1430 save_value(stream, *tree);
1431 if (tree->child1 !=
nullptr) { save_tree(obj, stream, tree->child1); }
1432 if (tree->child2 !=
nullptr) { save_tree(obj, stream, tree->child2); }
1435 static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree)
1437 tree = obj.pool_.template allocate<Node>();
1438 load_value(stream, *tree);
1439 if (tree->child1 !=
nullptr) { load_tree(obj, stream, tree->child1); }
1440 if (tree->child2 !=
nullptr) { load_tree(obj, stream, tree->child2); }
1448 void saveIndex(
const Derived& obj, std::ostream& stream)
const
1450 save_value(stream, obj.size_);
1451 save_value(stream, obj.dim_);
1452 save_value(stream, obj.root_bbox_);
1453 save_value(stream, obj.leaf_max_size_);
1454 save_value(stream, obj.vAcc_);
1455 if (obj.root_node_) save_tree(obj, stream, obj.root_node_);
1465 load_value(stream, obj.size_);
1466 load_value(stream, obj.dim_);
1467 load_value(stream, obj.root_bbox_);
1468 load_value(stream, obj.leaf_max_size_);
1469 load_value(stream, obj.vAcc_);
1470 load_tree(obj, stream, obj.root_node_);
1516 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
1517 typename IndexType = uint32_t>
1520 KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>,
1521 Distance, DatasetAdaptor, DIM, IndexType>
1527 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
1538 Distance, DatasetAdaptor, DIM, IndexType>,
1539 Distance, DatasetAdaptor, DIM, IndexType>;
1541 using Offset =
typename Base::Offset;
1542 using Size =
typename Base::Size;
1543 using Dimension =
typename Base::Dimension;
1545 using ElementType =
typename Base::ElementType;
1546 using DistanceType =
typename Base::DistanceType;
1548 using Node =
typename Base::Node;
1549 using NodePtr = Node*;
1551 using Interval =
typename Base::Interval;
1581 template <
class... Args>
1583 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1585 : dataset_(inputData),
1586 indexParams(params),
1587 distance_(inputData, std::forward<Args>(args)...)
1589 init(dimensionality, params);
1593 const Dimension dimensionality,
const DatasetAdaptor& inputData,
1595 : dataset_(inputData), indexParams(params), distance_(inputData)
1597 init(dimensionality, params);
1602 const Dimension dimensionality,
1603 const KDTreeSingleIndexAdaptorParams& params)
1605 Base::size_ = dataset_.kdtree_get_point_count();
1606 Base::size_at_index_build_ = Base::size_;
1607 Base::dim_ = dimensionality;
1608 if (DIM > 0) Base::dim_ = DIM;
1609 Base::leaf_max_size_ = params.leaf_max_size;
1610 if (params.n_thread_build > 0)
1612 Base::n_thread_build_ = params.n_thread_build;
1616 Base::n_thread_build_ =
1617 std::max(std::thread::hardware_concurrency(), 1u);
1620 if (!(params.flags &
1621 KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex))
1634 Base::size_ = dataset_.kdtree_get_point_count();
1635 Base::size_at_index_build_ = Base::size_;
1637 this->freeIndex(*
this);
1638 Base::size_at_index_build_ = Base::size_;
1639 if (Base::size_ == 0)
return;
1640 computeBoundingBox(Base::root_bbox_);
1642 if (Base::n_thread_build_ == 1)
1645 this->divideTree(*
this, 0, Base::size_, Base::root_bbox_);
1649 std::atomic<unsigned int> thread_count(0u);
1651 Base::root_node_ = this->divideTreeConcurrent(
1652 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
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);
1695 return result.full();
1714 const ElementType* query_point,
const Size num_closest,
1715 IndexType* out_indices, DistanceType* out_distances)
const
1718 resultSet.init(out_indices, out_distances);
1719 findNeighbors(resultSet, query_point);
1720 return resultSet.size();
1743 const ElementType* query_point,
const DistanceType& radius,
1748 radius, IndicesDists);
1750 radiusSearchCustomCallback(query_point, resultSet, searchParams);
1751 if (searchParams.sorted)
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 std::atomic<unsigned int> thread_count(0u);
2114 Base::root_node_ = this->divideTreeConcurrent(
2115 *
this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
2142 template <
typename RESULTSET>
2144 RESULTSET& result,
const ElementType* vec,
2148 if (this->size(*
this) == 0)
return false;
2149 if (!Base::root_node_)
return false;
2150 float epsError = 1 + searchParams.eps;
2153 distance_vector_t dists;
2156 dists, (DIM > 0 ? DIM : Base::dim_),
2157 static_cast<typename distance_vector_t::value_type>(0));
2158 DistanceType dist = this->computeInitialDistances(*
this, vec, dists);
2159 searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
2160 return result.full();
2178 const ElementType* query_point,
const Size num_closest,
2179 IndexType* out_indices, DistanceType* out_distances,
2183 resultSet.init(out_indices, out_distances);
2184 findNeighbors(resultSet, query_point, searchParams);
2185 return resultSet.size();
2208 const ElementType* query_point,
const DistanceType& radius,
2213 radius, IndicesDists);
2214 const size_t nFound =
2215 radiusSearchCustomCallback(query_point, resultSet, searchParams);
2216 if (searchParams.sorted)
2227 template <
class SEARCH_CALLBACK>
2229 const ElementType* query_point, SEARCH_CALLBACK& resultSet,
2232 findNeighbors(resultSet, query_point, searchParams);
2233 return resultSet.size();
2239 void computeBoundingBox(BoundingBox& bbox)
2241 const auto dims = (DIM > 0 ? DIM : Base::dim_);
2244 if (dataset_.kdtree_get_bbox(bbox))
2250 const Size N = Base::size_;
2252 throw std::runtime_error(
2253 "[nanoflann] computeBoundingBox() called but "
2254 "no data points found.");
2255 for (Dimension i = 0; i < dims; ++i)
2257 bbox[i].low = bbox[i].high =
2258 this->dataset_get(*
this, Base::vAcc_[0], i);
2260 for (Offset k = 1; k < N; ++k)
2262 for (Dimension i = 0; i < dims; ++i)
2265 this->dataset_get(*
this, Base::vAcc_[k], i);
2266 if (val < bbox[i].low) bbox[i].low = val;
2267 if (val > bbox[i].high) bbox[i].high = val;
2277 template <
class RESULTSET>
2279 RESULTSET& result_set,
const ElementType* vec,
const NodePtr node,
2281 const float epsError)
const
2284 if ((node->child1 ==
nullptr) && (node->child2 ==
nullptr))
2286 DistanceType worst_dist = result_set.worstDist();
2287 for (Offset i = node->node_type.lr.left;
2288 i < node->node_type.lr.right; ++i)
2290 const IndexType index = Base::vAcc_[i];
2291 if (treeIndex_[index] == -1)
continue;
2292 DistanceType dist = distance_.evalMetric(
2293 vec, index, (DIM > 0 ? DIM : Base::dim_));
2294 if (dist < worst_dist)
2296 if (!result_set.addPoint(
2297 static_cast<typename RESULTSET::DistanceType
>(dist),
2298 static_cast<typename RESULTSET::IndexType
>(
2311 Dimension idx = node->node_type.sub.divfeat;
2312 ElementType val = vec[idx];
2313 DistanceType diff1 = val - node->node_type.sub.divlow;
2314 DistanceType diff2 = val - node->node_type.sub.divhigh;
2318 DistanceType cut_dist;
2319 if ((diff1 + diff2) < 0)
2321 bestChild = node->child1;
2322 otherChild = node->child2;
2324 distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
2328 bestChild = node->child2;
2329 otherChild = node->child1;
2331 distance_.accum_dist(val, node->node_type.sub.divlow, idx);
2335 searchLevel(result_set, vec, bestChild, mindist, dists, epsError);
2337 DistanceType dst = dists[idx];
2338 mindist = mindist + cut_dist - dst;
2339 dists[idx] = cut_dist;
2340 if (mindist * epsError <= result_set.worstDist())
2342 searchLevel(result_set, vec, otherChild, mindist, dists, epsError);
2378 typename Distance,
class DatasetAdaptor, int32_t DIM = -1,
2379 typename IndexType = uint32_t>
2383 using ElementType =
typename Distance::ElementType;
2384 using DistanceType =
typename Distance::DistanceType;
2387 Distance, DatasetAdaptor, DIM>::Offset;
2389 Distance, DatasetAdaptor, DIM>::Size;
2391 Distance, DatasetAdaptor, DIM>::Dimension;
2394 Size leaf_max_size_;
2406 std::unordered_set<int> removedPoints_;
2413 Distance, DatasetAdaptor, DIM, IndexType>;
2414 std::vector<index_container_t> index_;
2426 int First0Bit(IndexType num)
2440 using my_kd_tree_t = KDTreeSingleIndexDynamicAdaptor_<
2441 Distance, DatasetAdaptor, DIM, IndexType>;
2442 std::vector<my_kd_tree_t> index(
2444 my_kd_tree_t(dim_ , dataset_, treeIndex_, index_params_));
2467 const int dimensionality,
const DatasetAdaptor& inputData,
2470 const size_t maximumPointCount = 1000000000U)
2471 : dataset_(inputData), index_params_(params), distance_(inputData)
2473 treeCount_ =
static_cast<size_t>(std::log2(maximumPointCount)) + 1;
2475 dim_ = dimensionality;
2477 if (DIM > 0) dim_ = DIM;
2478 leaf_max_size_ = params.leaf_max_size;
2480 const size_t num_initial_points = dataset_.kdtree_get_point_count();
2481 if (num_initial_points > 0) { addPoints(0, num_initial_points - 1); }
2487 Distance, DatasetAdaptor, DIM, IndexType>&) =
delete;
2492 const Size count = end - start + 1;
2494 treeIndex_.resize(treeIndex_.size() + count);
2495 for (IndexType idx = start; idx <= end; idx++)
2497 const int pos = First0Bit(pointCount_);
2498 maxIndex = std::max(pos, maxIndex);
2499 treeIndex_[pointCount_] = pos;
2501 const auto it = removedPoints_.find(idx);
2502 if (it != removedPoints_.end())
2504 removedPoints_.erase(it);
2505 treeIndex_[idx] = pos;
2508 for (
int i = 0; i < pos; i++)
2510 for (
int j = 0; j < static_cast<int>(index_[i].vAcc_.size());
2513 index_[pos].vAcc_.push_back(index_[i].vAcc_[j]);
2514 if (treeIndex_[index_[i].vAcc_[j]] != -1)
2515 treeIndex_[index_[i].vAcc_[j]] = pos;
2517 index_[i].vAcc_.clear();
2519 index_[pos].vAcc_.push_back(idx);
2523 for (
int i = 0; i <= maxIndex; ++i)
2525 index_[i].freeIndex(index_[i]);
2526 if (!index_[i].vAcc_.empty()) index_[i].buildIndex();
2533 if (idx >= pointCount_)
return;
2534 removedPoints_.insert(idx);
2535 treeIndex_[idx] = -1;
2554 template <
typename RESULTSET>
2556 RESULTSET& result,
const ElementType* vec,
2559 for (
size_t i = 0; i < treeCount_; i++)
2561 index_[i].findNeighbors(result, &vec[0], searchParams);
2563 return result.full();
2594 bool row_major =
true>
2599 using num_t =
typename MatrixType::Scalar;
2600 using IndexType =
typename MatrixType::Index;
2601 using metric_t =
typename Distance::template traits<
2602 num_t,
self_t, IndexType>::distance_t;
2606 row_major ? MatrixType::ColsAtCompileTime
2607 : MatrixType::RowsAtCompileTime,
2614 using Size =
typename index_t::Size;
2615 using Dimension =
typename index_t::Dimension;
2619 const Dimension dimensionality,
2620 const std::reference_wrapper<const MatrixType>& mat,
2621 const int leaf_max_size = 10)
2622 : m_data_matrix(mat)
2624 const auto dims = row_major ? mat.get().cols() : mat.get().rows();
2625 if (
static_cast<Dimension
>(dims) != dimensionality)
2626 throw std::runtime_error(
2627 "Error: 'dimensionality' must match column count in data "
2629 if (DIM > 0 &&
static_cast<int32_t
>(dims) != DIM)
2630 throw std::runtime_error(
2631 "Data set dimensionality does not match the 'DIM' template "
2644 const std::reference_wrapper<const MatrixType> m_data_matrix;
2655 const num_t* query_point,
const Size num_closest,
2656 IndexType* out_indices, num_t* out_distances)
const
2659 resultSet.init(out_indices, out_distances);
2666 const self_t& derived()
const {
return *
this; }
2667 self_t& derived() {
return *
this; }
2670 Size kdtree_get_point_count()
const
2673 return m_data_matrix.get().rows();
2675 return m_data_matrix.get().cols();
2679 num_t kdtree_get_pt(
const IndexType idx,
size_t dim)
const
2682 return m_data_matrix.get().coeff(idx, IndexType(dim));
2684 return m_data_matrix.get().coeff(IndexType(dim), idx);
2692 template <
class BBOX>
2693 bool kdtree_get_bbox(BBOX& )
const
// end of grouping
Definition nanoflann.hpp:993
Size usedMemory(Derived &obj)
Definition nanoflann.hpp:1098
NodePtr divideTreeConcurrent(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox, std::atomic< unsigned int > &thread_count, std::mutex &mutex)
Definition nanoflann.hpp:1195
void planeSplit(const Derived &obj, const Offset ind, const Size count, const Dimension cutfeat, const DistanceType &cutval, Offset &lim1, Offset &lim2)
Definition nanoflann.hpp:1358
typename array_or_vector< DIM, DistanceType >::type distance_vector_t
Definition nanoflann.hpp:1067
std::vector< IndexType > vAcc_
Definition nanoflann.hpp:1010
Size size(const Derived &obj) const
Definition nanoflann.hpp:1082
void freeIndex(Derived &obj)
Definition nanoflann.hpp:997
void loadIndex(Derived &obj, std::istream &stream)
Definition nanoflann.hpp:1463
typename array_or_vector< DIM, Interval >::type BoundingBox
Definition nanoflann.hpp:1063
BoundingBox root_bbox_
Definition nanoflann.hpp:1070
void saveIndex(const Derived &obj, std::ostream &stream) const
Definition nanoflann.hpp:1448
PooledAllocator pool_
Definition nanoflann.hpp:1079
NodePtr divideTree(Derived &obj, const Offset left, const Offset right, BoundingBox &bbox)
Definition nanoflann.hpp:1126
ElementType dataset_get(const Derived &obj, IndexType element, Dimension component) const
Helper accessor to the dataset points:
Definition nanoflann.hpp:1088
Size veclen(const Derived &obj)
Definition nanoflann.hpp:1085
Definition nanoflann.hpp:1522
Size radiusSearchCustomCallback(const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1763
Size rknnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const DistanceType &radius) const
Definition nanoflann.hpp:1787
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
KDTreeSingleIndexAdaptor(const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms, Args &&... args)
Definition nanoflann.hpp:1582
Size radiusSearch(const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1742
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:1559
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:1945
typename Base::BoundingBox BoundingBox
Definition nanoflann.hpp:1555
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances) const
Definition nanoflann.hpp:1713
void init_vind()
Definition nanoflann.hpp:1804
const DatasetAdaptor & dataset_
Definition nanoflann.hpp:1530
void saveIndex(std::ostream &stream) const
Definition nanoflann.hpp:1935
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:1676
void buildIndex()
Definition nanoflann.hpp:1632
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
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:2207
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:2228
KDTreeSingleIndexDynamicAdaptor_(const KDTreeSingleIndexDynamicAdaptor_ &rhs)=default
void buildIndex()
Definition nanoflann.hpp:2097
void saveIndex(std::ostream &stream)
Definition nanoflann.hpp:2353
typename Base::distance_vector_t distance_vector_t
Definition nanoflann.hpp:2029
void loadIndex(std::istream &stream)
Definition nanoflann.hpp:2360
Size knnSearch(const ElementType *query_point, const Size num_closest, IndexType *out_indices, DistanceType *out_distances, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2177
void searchLevel(RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const
Definition nanoflann.hpp:2278
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2143
KDTreeSingleIndexDynamicAdaptor_ operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs)
Definition nanoflann.hpp:2078
Definition nanoflann.hpp:2381
bool findNeighbors(RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const
Definition nanoflann.hpp:2555
const DatasetAdaptor & dataset_
The source of our data.
Definition nanoflann.hpp:2401
void removePoint(size_t idx)
Definition nanoflann.hpp:2531
void addPoints(IndexType start, IndexType end)
Definition nanoflann.hpp:2490
KDTreeSingleIndexDynamicAdaptor(const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=KDTreeSingleIndexAdaptorParams(), const size_t maximumPointCount=1000000000U)
Definition nanoflann.hpp:2466
std::vector< int > treeIndex_
Definition nanoflann.hpp:2405
const std::vector< index_container_t > & getAllIndices() const
Definition nanoflann.hpp:2419
Dimension dim_
Dimensionality of each data point.
Definition nanoflann.hpp:2410
KDTreeSingleIndexDynamicAdaptor(const KDTreeSingleIndexDynamicAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
Definition nanoflann.hpp:167
bool addPoint(DistanceType dist, IndexType index)
Definition nanoflann.hpp:203
Definition nanoflann.hpp:833
~PooledAllocator()
Definition nanoflann.hpp:871
void free_all()
Definition nanoflann.hpp:874
void * malloc(const size_t req_size)
Definition nanoflann.hpp:890
T * allocate(const size_t count=1)
Definition nanoflann.hpp:947
PooledAllocator()
Definition nanoflann.hpp:866
Definition nanoflann.hpp:246
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:143
T pi_const()
Definition nanoflann.hpp:86
std::enable_if< has_resize< Container >::value, void >::type resize(Container &c, const size_t nElements)
Definition nanoflann.hpp:121
Definition nanoflann.hpp:328
bool operator()(const PairType &p1, const PairType &p2) const
Definition nanoflann.hpp:331
Definition nanoflann.hpp:1045
Definition nanoflann.hpp:1020
DistanceType divlow
The values used for subdivision.
Definition nanoflann.hpp:1033
Node * child1
Definition nanoflann.hpp:1038
Dimension divfeat
Definition nanoflann.hpp:1031
Offset right
Indices of points in leaf node.
Definition nanoflann.hpp:1027
union nanoflann::KDTreeBaseClass::Node::@0 node_type
Definition nanoflann.hpp:2596
void query(const num_t *query_point, const Size num_closest, IndexType *out_indices, num_t *out_distances) const
Definition nanoflann.hpp:2654
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:2618
KDTreeEigenMatrixAdaptor(const self_t &)=delete
typename index_t::Offset Offset
Definition nanoflann.hpp:2613
Definition nanoflann.hpp:784
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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
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