From 541b295ab92df39b2dfa40421343f612568fb019 Mon Sep 17 00:00:00 2001 From: LTLA Date: Sun, 11 Aug 2024 19:58:17 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20knncolle?= =?UTF-8?q?/knncolle=5Fhnsw@02d5c729ad2e60b57819ac4b5982e4e3f106c620=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...nncolle__hnsw_1_1HnswSearcher-members.html | 7 +- docs/classknncolle__hnsw_1_1HnswSearcher.html | 10 + docs/index.html | 5 +- docs/knncolle__hnsw_8hpp.html | 2 + docs/knncolle__hnsw_8hpp_source.html | 516 +++++++++--------- docs/search/all_0.js | 2 +- docs/search/all_1.js | 9 +- docs/search/all_10.js | 10 +- docs/search/all_11.js | 7 +- docs/search/all_12.js | 8 +- docs/search/all_13.js | 6 + docs/search/all_2.js | 13 +- docs/search/all_4.js | 8 +- docs/search/all_7.js | 2 +- docs/search/all_8.js | 2 +- docs/search/all_9.js | 10 +- docs/search/all_a.js | 10 +- docs/search/all_b.js | 9 +- docs/search/all_c.js | 8 +- docs/search/all_d.js | 6 +- docs/search/all_e.js | 3 +- docs/search/all_f.js | 9 +- docs/search/classes_5.js | 7 +- docs/search/classes_6.js | 5 +- docs/search/classes_7.js | 6 +- docs/search/classes_8.js | 7 +- docs/search/classes_9.js | 4 +- docs/search/classes_a.js | 4 + docs/search/files_4.js | 2 +- docs/search/files_5.js | 2 +- docs/search/files_6.js | 2 +- docs/search/files_7.js | 2 +- docs/search/files_8.js | 4 + docs/search/functions_0.js | 2 +- docs/search/functions_1.js | 3 +- docs/search/functions_2.js | 3 +- docs/search/functions_3.js | 3 +- docs/search/functions_4.js | 3 +- docs/search/functions_5.js | 3 +- docs/search/functions_6.js | 2 +- docs/search/functions_7.js | 5 +- docs/search/functions_8.js | 4 +- docs/search/functions_9.js | 4 +- docs/search/functions_a.js | 4 +- docs/search/functions_b.js | 4 + docs/search/functions_c.js | 7 + docs/search/functions_d.js | 4 + docs/search/namespaces_0.js | 2 +- docs/search/searchdata.js | 10 +- docs/search/typedefs_2.js | 2 +- docs/search/typedefs_3.js | 4 + 51 files changed, 420 insertions(+), 356 deletions(-) create mode 100644 docs/search/all_13.js create mode 100644 docs/search/classes_a.js create mode 100644 docs/search/files_8.js create mode 100644 docs/search/functions_b.js create mode 100644 docs/search/functions_c.js create mode 100644 docs/search/functions_d.js create mode 100644 docs/search/typedefs_3.js diff --git a/docs/classknncolle__hnsw_1_1HnswSearcher-members.html b/docs/classknncolle__hnsw_1_1HnswSearcher-members.html index 9348dcf..d8da677 100644 --- a/docs/classknncolle__hnsw_1_1HnswSearcher-members.html +++ b/docs/classknncolle__hnsw_1_1HnswSearcher-members.html @@ -80,8 +80,11 @@

This is the complete list of members for knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >, including all inherited members.

- - + + + + +
search(Index_ i, Index_ k, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) (defined in knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >)knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >inlinevirtual
search(const Float_ *query, Index_ k, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) (defined in knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >)knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >inlinevirtual
can_search_all() constknncolle::Searcher< Index_, Float_ >virtual
search(Index_ i, Index_ k, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) (defined in knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >)knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >inlinevirtual
search(const Float_ *query, Index_ k, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) (defined in knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >)knncolle_hnsw::HnswSearcher< Dim_, Index_, Float_, InternalData_ >inlinevirtual
search_all(Index_ i, Float_ distance, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances)knncolle::Searcher< Index_, Float_ >virtual
search_all(const Float_ *query, Float_ distance, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances)knncolle::Searcher< Index_, Float_ >virtual
diff --git a/docs/classknncolle__hnsw_1_1HnswSearcher.html b/docs/classknncolle__hnsw_1_1HnswSearcher.html index c257230..99928ef 100644 --- a/docs/classknncolle__hnsw_1_1HnswSearcher.html +++ b/docs/classknncolle__hnsw_1_1HnswSearcher.html @@ -101,6 +101,16 @@   void search (const Float_ *query, Index_ k, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances)   +- Public Member Functions inherited from knncolle::Searcher< Index_, Float_ > + +virtual bool can_search_all () const +  + +virtual Index_ search_all (Index_ i, Float_ distance, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) +  + +virtual Index_ search_all (const Float_ *query, Float_ distance, std::vector< Index_ > *output_indices, std::vector< Float_ > *output_distances) + 

Detailed Description

template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
diff --git a/docs/index.html b/docs/index.html index e0704e0..78c5c2f 100644 --- a/docs/index.html +++ b/docs/index.html @@ -90,9 +90,10 @@

auto an_index = an_builder.build_unique(mat);
// Find 10 (approximate) nearest neighbors of every element.
-
auto results = knncolle::find_nearest_neighbors(*an_index, 10);
+
auto results = knncolle::find_nearest_neighbors(*an_index, 10);
-
knncolle_hnsw::HnswPrebuilt
Prebuilt index for an Hnsw search.
Definition knncolle_hnsw.hpp:218
+
knncolle_hnsw::HnswPrebuilt
Prebuilt index for an Hnsw search.
Definition knncolle_hnsw.hpp:220
+
knncolle::find_nearest_neighbors
NeighborList< Index_, Float_ > find_nearest_neighbors(const Prebuilt< Dim_, Index_, Float_ > &index, int k, int num_threads=1)

We could alternate between exact and approximate searches at run-time:

std::unique_ptr<knncolle::Prebuilt<int, int, double> > ptr;
if (use_exact) {
diff --git a/docs/knncolle__hnsw_8hpp.html b/docs/knncolle__hnsw_8hpp.html index af88188..bf2f634 100644 --- a/docs/knncolle__hnsw_8hpp.html +++ b/docs/knncolle__hnsw_8hpp.html @@ -85,6 +85,8 @@ More...

#include <vector>
#include <type_traits>
+#include <queue>
+#include <algorithm>
#include <memory>
#include "knncolle/knncolle.hpp"
#include "hnswlib/hnswalg.h"
diff --git a/docs/knncolle__hnsw_8hpp_source.html b/docs/knncolle__hnsw_8hpp_source.html index 079c027..4dca30b 100644 --- a/docs/knncolle__hnsw_8hpp_source.html +++ b/docs/knncolle__hnsw_8hpp_source.html @@ -87,281 +87,283 @@
3
4#include <vector>
5#include <type_traits>
-
6#include <memory>
-
7
-
8#include "knncolle/knncolle.hpp"
-
9#include "hnswlib/hnswalg.h"
-
10
-
11#include "distances.hpp"
+
6#include <queue>
+
7#include <algorithm>
+
8#include <memory>
+
9
+
10#include "knncolle/knncolle.hpp"
+
11#include "hnswlib/hnswalg.h"
12
-
23namespace knncolle_hnsw {
-
24
-
35template<typename Dim_ = int, typename InternalData_ = float>
-
-
36struct HnswOptions {
-
42 int num_links = 16;
-
43
-
49 int ef_construction = 200;
-
50
-
56 int ef_search = 10;
-
57
-
61 DistanceOptions<Dim_, InternalData_> distance_options;
-
62};
+
13#include "distances.hpp"
+
14
+
25namespace knncolle_hnsw {
+
26
+
37template<typename Dim_ = int, typename InternalData_ = float>
+
+
38struct HnswOptions {
+
44 int num_links = 16;
+
45
+
51 int ef_construction = 200;
+
52
+
58 int ef_search = 10;
+
59
+
63 DistanceOptions<Dim_, InternalData_> distance_options;
+
64};
-
63
-
64template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
-
65class HnswPrebuilt;
-
66
-
77template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
-
-
78class HnswSearcher : public knncolle::Searcher<Index_, Float_> {
-
79private:
-
80 const HnswPrebuilt<Dim_, Index_, Float_, InternalData_>* my_parent;
-
81
-
82 std::priority_queue<std::pair<InternalData_, hnswlib::labeltype> > my_queue;
-
83 std::vector<InternalData_> my_buffer;
-
84
-
85 static constexpr bool same_internal = std::is_same<Float_, InternalData_>::value;
+
65
+
66template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
+
67class HnswPrebuilt;
+
68
+
79template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
+
+
80class HnswSearcher : public knncolle::Searcher<Index_, Float_> {
+
81private:
+
82 const HnswPrebuilt<Dim_, Index_, Float_, InternalData_>* my_parent;
+
83
+
84 std::priority_queue<std::pair<InternalData_, hnswlib::labeltype> > my_queue;
+
85 std::vector<InternalData_> my_buffer;
86
-
87public:
-
91 HnswSearcher(const HnswPrebuilt<Dim_, Index_, Float_, InternalData_>* parent) : my_parent(parent) {
-
92 if constexpr(!same_internal) {
-
93 my_buffer.resize(my_parent->my_dim);
-
94 }
-
95 }
-
100public:
-
101 void search(Index_ i, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
-
102 my_buffer = my_parent->my_index.template getDataByLabel<InternalData_>(i);
-
103 Index_ kp1 = k + 1;
-
104 my_queue = my_parent->my_index.searchKnn(my_buffer.data(), kp1); // +1, as it forgets to discard 'self'.
-
105
-
106 if (output_indices) {
-
107 output_indices->clear();
-
108 output_indices->reserve(kp1);
-
109 }
-
110 if (output_distances) {
-
111 output_distances->clear();
-
112 output_distances->reserve(kp1);
-
113 }
-
114
-
115 bool self_found = false;
-
116 hnswlib::labeltype icopy = i;
-
117 while (!my_queue.empty()) {
-
118 const auto& top = my_queue.top();
-
119 if (!self_found && top.second == icopy) {
-
120 self_found = true;
-
121 } else {
-
122 if (output_indices) {
-
123 output_indices->push_back(top.second);
-
124 }
-
125 if (output_distances) {
-
126 output_distances->push_back(top.first);
-
127 }
-
128 }
-
129 my_queue.pop();
-
130 }
-
131
-
132 if (output_indices) {
-
133 std::reverse(output_indices->begin(), output_indices->end());
-
134 }
-
135 if (output_distances) {
-
136 std::reverse(output_distances->begin(), output_distances->end());
-
137 }
-
138
-
139 // Just in case we're full of ties at duplicate points, such that 'c'
-
140 // is not in the set. Note that, if self_found=false, we must have at
-
141 // least 'K+2' points for 'c' to not be detected as its own neighbor.
-
142 // Thus there is no need to worry whether we are popping off a non-'c'
-
143 // element and then returning fewer elements than expected.
-
144 if (!self_found) {
-
145 if (output_indices) {
-
146 output_indices->pop_back();
-
147 }
-
148 if (output_distances) {
-
149 output_distances->pop_back();
-
150 }
-
151 }
-
152
-
153 if (output_distances && my_parent->my_normalize) {
-
154 for (auto& d : *output_distances) {
-
155 d = my_parent->my_normalize(d);
-
156 }
-
157 }
-
158 }
-
159
-
160private:
-
161 void search_raw(const InternalData_* query, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
-
162 k = std::min(k, my_parent->my_obs);
-
163 my_queue = my_parent->my_index.searchKnn(query, k);
-
164
-
165 if (output_indices) {
-
166 output_indices->resize(k);
-
167 }
-
168 if (output_distances) {
-
169 output_distances->resize(k);
-
170 }
-
171
-
172 size_t position = k;
-
173 while (!my_queue.empty()) {
-
174 const auto& top = my_queue.top();
-
175 --position;
-
176 if (output_indices) {
-
177 (*output_indices)[position] = top.second;
-
178 }
-
179 if (output_distances) {
-
180 (*output_distances)[position] = top.first;
-
181 }
-
182 my_queue.pop();
-
183 }
-
184
-
185 if (output_distances && my_parent->my_normalize) {
-
186 for (auto& d : *output_distances) {
-
187 d = my_parent->my_normalize(d);
-
188 }
-
189 }
-
190 }
-
191
-
192public:
-
193 void search(const Float_* query, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
-
194 if constexpr(same_internal) {
-
195 my_queue = my_parent->my_index.searchKnn(query, k);
-
196 search_raw(query, k, output_indices, output_distances);
-
197 } else {
-
198 std::copy_n(query, my_parent->my_dim, my_buffer.begin());
-
199 search_raw(my_buffer.data(), k, output_indices, output_distances);
-
200 }
-
201 }
-
202};
+
87 static constexpr bool same_internal = std::is_same<Float_, InternalData_>::value;
+
88
+
89public:
+
93 HnswSearcher(const HnswPrebuilt<Dim_, Index_, Float_, InternalData_>* parent) : my_parent(parent) {
+
94 if constexpr(!same_internal) {
+
95 my_buffer.resize(my_parent->my_dim);
+
96 }
+
97 }
+
102public:
+
103 void search(Index_ i, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
+
104 my_buffer = my_parent->my_index.template getDataByLabel<InternalData_>(i);
+
105 Index_ kp1 = k + 1;
+
106 my_queue = my_parent->my_index.searchKnn(my_buffer.data(), kp1); // +1, as it forgets to discard 'self'.
+
107
+
108 if (output_indices) {
+
109 output_indices->clear();
+
110 output_indices->reserve(kp1);
+
111 }
+
112 if (output_distances) {
+
113 output_distances->clear();
+
114 output_distances->reserve(kp1);
+
115 }
+
116
+
117 bool self_found = false;
+
118 hnswlib::labeltype icopy = i;
+
119 while (!my_queue.empty()) {
+
120 const auto& top = my_queue.top();
+
121 if (!self_found && top.second == icopy) {
+
122 self_found = true;
+
123 } else {
+
124 if (output_indices) {
+
125 output_indices->push_back(top.second);
+
126 }
+
127 if (output_distances) {
+
128 output_distances->push_back(top.first);
+
129 }
+
130 }
+
131 my_queue.pop();
+
132 }
+
133
+
134 if (output_indices) {
+
135 std::reverse(output_indices->begin(), output_indices->end());
+
136 }
+
137 if (output_distances) {
+
138 std::reverse(output_distances->begin(), output_distances->end());
+
139 }
+
140
+
141 // Just in case we're full of ties at duplicate points, such that 'c'
+
142 // is not in the set. Note that, if self_found=false, we must have at
+
143 // least 'K+2' points for 'c' to not be detected as its own neighbor.
+
144 // Thus there is no need to worry whether we are popping off a non-'c'
+
145 // element and then returning fewer elements than expected.
+
146 if (!self_found) {
+
147 if (output_indices) {
+
148 output_indices->pop_back();
+
149 }
+
150 if (output_distances) {
+
151 output_distances->pop_back();
+
152 }
+
153 }
+
154
+
155 if (output_distances && my_parent->my_normalize) {
+
156 for (auto& d : *output_distances) {
+
157 d = my_parent->my_normalize(d);
+
158 }
+
159 }
+
160 }
+
161
+
162private:
+
163 void search_raw(const InternalData_* query, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
+
164 k = std::min(k, my_parent->my_obs);
+
165 my_queue = my_parent->my_index.searchKnn(query, k);
+
166
+
167 if (output_indices) {
+
168 output_indices->resize(k);
+
169 }
+
170 if (output_distances) {
+
171 output_distances->resize(k);
+
172 }
+
173
+
174 size_t position = k;
+
175 while (!my_queue.empty()) {
+
176 const auto& top = my_queue.top();
+
177 --position;
+
178 if (output_indices) {
+
179 (*output_indices)[position] = top.second;
+
180 }
+
181 if (output_distances) {
+
182 (*output_distances)[position] = top.first;
+
183 }
+
184 my_queue.pop();
+
185 }
+
186
+
187 if (output_distances && my_parent->my_normalize) {
+
188 for (auto& d : *output_distances) {
+
189 d = my_parent->my_normalize(d);
+
190 }
+
191 }
+
192 }
+
193
+
194public:
+
195 void search(const Float_* query, Index_ k, std::vector<Index_>* output_indices, std::vector<Float_>* output_distances) {
+
196 if constexpr(same_internal) {
+
197 my_queue = my_parent->my_index.searchKnn(query, k);
+
198 search_raw(query, k, output_indices, output_distances);
+
199 } else {
+
200 std::copy_n(query, my_parent->my_dim, my_buffer.begin());
+
201 search_raw(my_buffer.data(), k, output_indices, output_distances);
+
202 }
+
203 }
+
204};
-
203
-
217template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
-
-
218class HnswPrebuilt : public knncolle::Prebuilt<Dim_, Index_, Float_> {
-
219public:
-
223 template<class Matrix_>
-
224 HnswPrebuilt(const Matrix_& data, const HnswOptions<Dim_, InternalData_>& options) :
-
225 my_dim(data.num_dimensions()),
-
226 my_obs(data.num_observations()),
-
227 my_space([&]() {
-
228 if (options.distance_options.create) {
-
229 return options.distance_options.create(my_dim);
-
230 } else if constexpr(std::is_same<InternalData_, float>::value) {
-
231 return static_cast<hnswlib::SpaceInterface<InternalData_>*>(new hnswlib::L2Space(my_dim));
-
232 } else {
-
233 return static_cast<hnswlib::SpaceInterface<InternalData_>*>(new SquaredEuclideanDistance<InternalData_>(my_dim));
-
234 }
-
235 }()),
-
236 my_normalize([&]() {
-
237 if (options.distance_options.normalize) {
-
238 return options.distance_options.normalize;
-
239 } else if (options.distance_options.create) {
-
240 return std::function<InternalData_(InternalData_)>();
-
241 } else {
-
242 return std::function<InternalData_(InternalData_)>([](InternalData_ x) -> InternalData_ { return std::sqrt(x); });
-
243 }
-
244 }()),
-
245 my_index(my_space.get(), my_obs, options.num_links, options.ef_construction)
-
246 {
-
247 typedef typename Matrix_::data_type Data_;
-
248 auto work = data.create_workspace();
-
249 if constexpr(std::is_same<Data_, InternalData_>::value) {
-
250 for (Index_ i = 0; i < my_obs; ++i) {
-
251 auto ptr = data.get_observation(work);
-
252 my_index.addPoint(ptr, i);
-
253 }
-
254 } else {
-
255 std::vector<InternalData_> incoming(my_dim);
-
256 for (Index_ i = 0; i < my_obs; ++i) {
-
257 auto ptr = data.get_observation(work);
-
258 std::copy_n(ptr, my_dim, incoming.begin());
-
259 my_index.addPoint(incoming.data(), i);
-
260 }
-
261 }
-
262
-
263 my_index.setEf(options.ef_search);
-
264 return;
-
265 }
-
270private:
-
271 Dim_ my_dim;
-
272 Index_ my_obs;
-
273
-
274 // The following must be a pointer for polymorphism, but also so that
-
275 // references to the object in my_index are still valid after copying.
-
276 std::shared_ptr<hnswlib::SpaceInterface<InternalData_> > my_space;
-
277
-
278 std::function<InternalData_(InternalData_)> my_normalize;
-
279 hnswlib::HierarchicalNSW<InternalData_> my_index;
-
280
-
281 friend class HnswSearcher<Dim_, Index_, Float_, InternalData_>;
+
205
+
219template<typename Dim_, typename Index_, typename Float_, typename InternalData_>
+
+
220class HnswPrebuilt : public knncolle::Prebuilt<Dim_, Index_, Float_> {
+
221public:
+
225 template<class Matrix_>
+
226 HnswPrebuilt(const Matrix_& data, const HnswOptions<Dim_, InternalData_>& options) :
+
227 my_dim(data.num_dimensions()),
+
228 my_obs(data.num_observations()),
+
229 my_space([&]() {
+
230 if (options.distance_options.create) {
+
231 return options.distance_options.create(my_dim);
+
232 } else if constexpr(std::is_same<InternalData_, float>::value) {
+
233 return static_cast<hnswlib::SpaceInterface<InternalData_>*>(new hnswlib::L2Space(my_dim));
+
234 } else {
+
235 return static_cast<hnswlib::SpaceInterface<InternalData_>*>(new SquaredEuclideanDistance<InternalData_>(my_dim));
+
236 }
+
237 }()),
+
238 my_normalize([&]() {
+
239 if (options.distance_options.normalize) {
+
240 return options.distance_options.normalize;
+
241 } else if (options.distance_options.create) {
+
242 return std::function<InternalData_(InternalData_)>();
+
243 } else {
+
244 return std::function<InternalData_(InternalData_)>([](InternalData_ x) -> InternalData_ { return std::sqrt(x); });
+
245 }
+
246 }()),
+
247 my_index(my_space.get(), my_obs, options.num_links, options.ef_construction)
+
248 {
+
249 typedef typename Matrix_::data_type Data_;
+
250 auto work = data.create_workspace();
+
251 if constexpr(std::is_same<Data_, InternalData_>::value) {
+
252 for (Index_ i = 0; i < my_obs; ++i) {
+
253 auto ptr = data.get_observation(work);
+
254 my_index.addPoint(ptr, i);
+
255 }
+
256 } else {
+
257 std::vector<InternalData_> incoming(my_dim);
+
258 for (Index_ i = 0; i < my_obs; ++i) {
+
259 auto ptr = data.get_observation(work);
+
260 std::copy_n(ptr, my_dim, incoming.begin());
+
261 my_index.addPoint(incoming.data(), i);
+
262 }
+
263 }
+
264
+
265 my_index.setEf(options.ef_search);
+
266 return;
+
267 }
+
272private:
+
273 Dim_ my_dim;
+
274 Index_ my_obs;
+
275
+
276 // The following must be a pointer for polymorphism, but also so that
+
277 // references to the object in my_index are still valid after copying.
+
278 std::shared_ptr<hnswlib::SpaceInterface<InternalData_> > my_space;
+
279
+
280 std::function<InternalData_(InternalData_)> my_normalize;
+
281 hnswlib::HierarchicalNSW<InternalData_> my_index;
282
-
283public:
-
284 Dim_ num_dimensions() const {
-
285 return my_dim;
-
286 }
-
287
-
288 Index_ num_observations() const {
-
289 return my_obs;
-
290 }
-
291
-
292 std::unique_ptr<knncolle::Searcher<Index_, Float_> > initialize() const {
-
293 return std::make_unique<HnswSearcher<Dim_, Index_, Float_, InternalData_> >(this);
-
294 }
-
295};
+
283 friend class HnswSearcher<Dim_, Index_, Float_, InternalData_>;
+
284
+
285public:
+
286 Dim_ num_dimensions() const {
+
287 return my_dim;
+
288 }
+
289
+
290 Index_ num_observations() const {
+
291 return my_obs;
+
292 }
+
293
+
294 std::unique_ptr<knncolle::Searcher<Index_, Float_> > initialize() const {
+
295 return std::make_unique<HnswSearcher<Dim_, Index_, Float_, InternalData_> >(this);
+
296 }
+
297};
-
296
-
320template<
-
321 class Matrix_ = knncolle::SimpleMatrix<int, int, double>,
-
322 typename Float_ = double,
-
323 typename InternalData_ = float>
-
-
324class HnswBuilder : public knncolle::Builder<Matrix_, Float_> {
-
325public:
-
329 typedef HnswOptions<typename Matrix_::dimension_type, InternalData_> Options;
-
330
-
331private:
-
332 Options my_options;
-
333
-
334public:
-
338 HnswBuilder(Options options) : my_options(std::move(options)) {}
-
339
-
343 HnswBuilder() = default;
-
344
-
-
348 Options& get_options() {
-
349 return my_options;
-
350 }
+
298
+
322template<
+
323 class Matrix_ = knncolle::SimpleMatrix<int, int, double>,
+
324 typename Float_ = double,
+
325 typename InternalData_ = float>
+
+
326class HnswBuilder : public knncolle::Builder<Matrix_, Float_> {
+
327public:
+
331 typedef HnswOptions<typename Matrix_::dimension_type, InternalData_> Options;
+
332
+
333private:
+
334 Options my_options;
+
335
+
336public:
+
340 HnswBuilder(Options options) : my_options(std::move(options)) {}
+
341
+
345 HnswBuilder() = default;
+
346
+
+
350 Options& get_options() {
+
351 return my_options;
+
352 }
-
351
-
352public:
-
353 knncolle::Prebuilt<typename Matrix_::dimension_type, typename Matrix_::index_type, Float_>* build_raw(const Matrix_& data) const {
-
354 return new HnswPrebuilt<typename Matrix_::dimension_type, typename Matrix_::index_type, Float_, InternalData_>(data, my_options);
-
355 }
-
356};
+
353
+
354public:
+
355 knncolle::Prebuilt<typename Matrix_::dimension_type, typename Matrix_::index_type, Float_>* build_raw(const Matrix_& data) const {
+
356 return new HnswPrebuilt<typename Matrix_::dimension_type, typename Matrix_::index_type, Float_, InternalData_>(data, my_options);
+
357 }
+
358};
-
357
-
358}
359
-
360#endif
+
360}
+
361
+
362#endif
-
knncolle_hnsw::HnswBuilder
Perform an approximate nearest neighbor search with HNSW.
Definition knncolle_hnsw.hpp:324
-
knncolle_hnsw::HnswBuilder::Options
HnswOptions< typename Matrix_::dimension_type, InternalData_ > Options
Definition knncolle_hnsw.hpp:329
-
knncolle_hnsw::HnswBuilder::get_options
Options & get_options()
Definition knncolle_hnsw.hpp:348
-
knncolle_hnsw::HnswBuilder::HnswBuilder
HnswBuilder(Options options)
Definition knncolle_hnsw.hpp:338
+
knncolle_hnsw::HnswBuilder
Perform an approximate nearest neighbor search with HNSW.
Definition knncolle_hnsw.hpp:326
+
knncolle_hnsw::HnswBuilder::Options
HnswOptions< typename Matrix_::dimension_type, InternalData_ > Options
Definition knncolle_hnsw.hpp:331
+
knncolle_hnsw::HnswBuilder::get_options
Options & get_options()
Definition knncolle_hnsw.hpp:350
+
knncolle_hnsw::HnswBuilder::HnswBuilder
HnswBuilder(Options options)
Definition knncolle_hnsw.hpp:340
knncolle_hnsw::HnswBuilder::HnswBuilder
HnswBuilder()=default
-
knncolle_hnsw::HnswPrebuilt
Prebuilt index for an Hnsw search.
Definition knncolle_hnsw.hpp:218
-
knncolle_hnsw::HnswSearcher
Searcher on an Hnsw index.
Definition knncolle_hnsw.hpp:78
+
knncolle_hnsw::HnswPrebuilt
Prebuilt index for an Hnsw search.
Definition knncolle_hnsw.hpp:220
+
knncolle_hnsw::HnswSearcher
Searcher on an Hnsw index.
Definition knncolle_hnsw.hpp:80
knncolle_hnsw
knncolle bindings for HNSW search.
Definition distances.hpp:12
knncolle_hnsw::DistanceOptions
Distance options for the HNSW index.
Definition distances.hpp:21
-
knncolle_hnsw::HnswOptions
Options for HnswBuilder and HnswPrebuilt.
Definition knncolle_hnsw.hpp:36
-
knncolle_hnsw::HnswOptions::distance_options
DistanceOptions< Dim_, InternalData_ > distance_options
Definition knncolle_hnsw.hpp:61
-
knncolle_hnsw::HnswOptions::ef_construction
int ef_construction
Definition knncolle_hnsw.hpp:49
-
knncolle_hnsw::HnswOptions::ef_search
int ef_search
Definition knncolle_hnsw.hpp:56
-
knncolle_hnsw::HnswOptions::num_links
int num_links
Definition knncolle_hnsw.hpp:42
+
knncolle_hnsw::HnswOptions
Options for HnswBuilder and HnswPrebuilt.
Definition knncolle_hnsw.hpp:38
+
knncolle_hnsw::HnswOptions::distance_options
DistanceOptions< Dim_, InternalData_ > distance_options
Definition knncolle_hnsw.hpp:63
+
knncolle_hnsw::HnswOptions::ef_construction
int ef_construction
Definition knncolle_hnsw.hpp:51
+
knncolle_hnsw::HnswOptions::ef_search
int ef_search
Definition knncolle_hnsw.hpp:58
+
knncolle_hnsw::HnswOptions::num_links
int num_links
Definition knncolle_hnsw.hpp:44
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['build_5fraw_5',['build_raw',['https://knncolle.github.io/knncolle/classknncolle_1_1BruteforceBuilder.html#a491f8b75d24452615248da7c236b7759',1,'knncolle::BruteforceBuilder::build_raw()'],['https://knncolle.github.io/knncolle/classknncolle_1_1Builder.html#aa1c84ac7bd4120f5539be1320b74f5f4',1,'knncolle::Builder::build_raw()'],['https://knncolle.github.io/knncolle/classknncolle_1_1KmknnBuilder.html#aafe27a514becc203a33ec54584bf6562',1,'knncolle::KmknnBuilder::build_raw()'],['https://knncolle.github.io/knncolle/classknncolle_1_1L2NormalizedBuilder.html#a9747f9c49d62c8d0c47c017586e0b297',1,'knncolle::L2NormalizedBuilder::build_raw()'],['https://knncolle.github.io/knncolle/classknncolle_1_1VptreeBuilder.html#a0c25ca5b2d7a812032d7cf1d42e3ad3a',1,'knncolle::VptreeBuilder::build_raw()']]], ['build_5fshared_6',['build_shared',['https://knncolle.github.io/knncolle/classknncolle_1_1Builder.html#afe899198735bd5f9d1921283fa65ff3a',1,'knncolle::Builder']]], 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