diff --git a/docs/index.html b/docs/index.html index 6d0d497..4c81696 100644 --- a/docs/index.html +++ b/docs/index.html @@ -107,11 +107,11 @@

Quick start

opt
);
knncolle::VptreeBuilder
-
nenesub::compute
void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)
Definition nenesub.hpp:86
+
nenesub::compute
void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)
Definition nenesub.hpp:87
nenesub.hpp
Nearest-neighbors subsampling.
nenesub::Options
Options for compute().
Definition nenesub.hpp:24
nenesub::Options::num_neighbors
int num_neighbors
Definition nenesub.hpp:29
-
nenesub::Options::num_threads
int num_threads
Definition nenesub.hpp:41
+
nenesub::Options::num_threads
int num_threads
Definition nenesub.hpp:42
nenesub::Options::min_remaining
int min_remaining
Definition nenesub.hpp:35

Alternatively, we can supply a precomputed list of neighbors:

// Getting the nearest neighbors:
diff --git a/docs/namespacenenesub.html b/docs/namespacenenesub.html index c05c5b8..d4e4ce4 100644 --- a/docs/namespacenenesub.html +++ b/docs/namespacenenesub.html @@ -100,7 +100,7 @@ void compute (Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)   template<typename Index_ , typename Distance_ > -std::vector< Index_ > compute (const knncolle::NeighborList< Index_, Distance_ > &neighbors, const Options &options) +std::vector< Index_ > compute (const knncolle::NeighborList< Index_, Distance_ > &neighbors, const Options &options)   template<typename Dim_ , typename Index_ , typename Float_ > std::vector< Index_ > compute (const knncolle::Prebuilt< Dim_, Index_, Float_ > &prebuilt, const Options &options) @@ -206,7 +206,7 @@

std::vector< Index_ > nenesub::compute ( - const knncolle::NeighborList< Index_, Distance_ > &  + const knncolle::NeighborList< Index_, Distance_ > &  neighbors, diff --git a/docs/nenesub_8hpp.html b/docs/nenesub_8hpp.html index 0206777..f408caf 100644 --- a/docs/nenesub_8hpp.html +++ b/docs/nenesub_8hpp.html @@ -136,7 +136,7 @@ void nenesub::compute (Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)   template<typename Index_ , typename Distance_ > -std::vector< Index_ > nenesub::compute (const knncolle::NeighborList< Index_, Distance_ > &neighbors, const Options &options) +std::vector< Index_ > nenesub::compute (const knncolle::NeighborList< Index_, Distance_ > &neighbors, const Options &options)   template<typename Dim_ , typename Index_ , typename Float_ > std::vector< Index_ > nenesub::compute (const knncolle::Prebuilt< Dim_, Index_, Float_ > &prebuilt, const Options &options) diff --git a/docs/nenesub_8hpp_source.html b/docs/nenesub_8hpp_source.html index 794907e..f473e4e 100644 --- a/docs/nenesub_8hpp_source.html +++ b/docs/nenesub_8hpp_source.html @@ -107,199 +107,173 @@
30
35 int min_remaining = 10;
36
-
41 int num_threads = 10;
-
42};
+
42 int num_threads = 10;
+
43};

-
43
-
85template<typename Index_, class GetNeighbors_, class GetIndex_, class GetMaxDistance_>
-
-
86void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options& options, std::vector<Index_>& selected) {
-
87 typedef decltype(get_max_distance(0)) Distance_;
-
88 struct Payload {
-
89 Payload(Index_ identity, Index_ remaining, Distance_ max_distance) : remaining(remaining), identity(identity), max_distance(max_distance) {}
-
90 Index_ remaining;
-
91 Index_ identity;
-
92 Distance_ max_distance;
-
93 };
-
94
-
95 auto cmp = [](const Payload& left, const Payload& right) -> bool {
-
96 if (left.remaining == right.remaining) {
-
97 if (left.max_distance == right.max_distance) {
-
98 return left.identity > right.identity; // smallest identities show up first.
-
99 }
-
100 return left.max_distance > right.max_distance; // smallest distances show up first.
-
101 }
-
102 return left.remaining < right.remaining; // largest remaining show up first.
-
103 };
-
104 std::priority_queue<Payload, std::vector<Payload>, decltype(cmp)> store(
-
105 cmp,
-
106 [&]{
-
107 std::vector<Payload> container;
-
108 container.reserve(num_obs);
-
109 return container;
-
110 }()
-
111 );
-
112
-
113 std::vector<std::vector<Index_> > reverse_map(num_obs);
-
114 std::vector<Index_> remaining(num_obs);
-
115 for (Index_ c = 0; c < num_obs; ++c) {
-
116 const auto& neighbors = get_neighbors(c);
-
117 Index_ nneighbors = neighbors.size();
-
118
-
119 if (nneighbors) { // protect get_max_distance just in case there are no neighbors.
-
120 store.emplace(c, nneighbors, get_max_distance(c));
-
121 for (Index_ n = 0; n < nneighbors; ++n) {
-
122 reverse_map[get_index(neighbors, n)].push_back(c);
-
123 }
-
124 remaining[c] = nneighbors;
-
125 }
-
126 }
-
127
-
128 selected.clear();
-
129 std::vector<uint8_t> tainted(num_obs);
-
130 Index_ min_remaining = options.min_remaining;
-
131 while (!store.empty()) {
-
132 auto payload = store.top();
-
133 store.pop();
-
134 if (tainted[payload.identity]) {
-
135 continue;
-
136 }
-
137
-
138 const auto& neighbors = get_neighbors(payload.identity);
-
139 Index_ new_remaining = remaining[payload.identity];
-
140
-
141 if (new_remaining >= min_remaining) {
-
142 payload.remaining = new_remaining;
-
143 if (!store.empty() && cmp(payload, store.top())) {
-
144 store.push(payload);
-
145 } else {
-
146 selected.push_back(payload.identity);
-
147 tainted[payload.identity] = 1;
-
148 for (auto x : reverse_map[payload.identity]) {
-
149 --remaining[x];
-
150 }
-
151
-
152 Index_ nneighbors = neighbors.size();
-
153 for (Index_ n = 0; n < nneighbors; ++n) {
-
154 auto current = get_index(neighbors, n);
-
155 tainted[current] = 1;
-
156 for (auto x : reverse_map[current]) {
-
157 --remaining[x];
-
158 }
-
159 }
-
160 }
-
161 }
-
162 }
-
163
-
164 std::sort(selected.begin(), selected.end());
-
165}
+
44
+
86template<typename Index_, class GetNeighbors_, class GetIndex_, class GetMaxDistance_>
+
+
87void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options& options, std::vector<Index_>& selected) {
+
88 typedef decltype(get_max_distance(0)) Distance_;
+
89 struct Payload {
+
90 Payload(Index_ identity, Index_ remaining, Distance_ max_distance) : remaining(remaining), identity(identity), max_distance(max_distance) {}
+
91 Index_ remaining;
+
92 Index_ identity;
+
93 Distance_ max_distance;
+
94 };
+
95
+
96 auto cmp = [](const Payload& left, const Payload& right) -> bool {
+
97 if (left.remaining == right.remaining) {
+
98 if (left.max_distance == right.max_distance) {
+
99 return left.identity > right.identity; // smallest identities show up first.
+
100 }
+
101 return left.max_distance > right.max_distance; // smallest distances show up first.
+
102 }
+
103 return left.remaining < right.remaining; // largest remaining show up first.
+
104 };
+
105 std::priority_queue<Payload, std::vector<Payload>, decltype(cmp)> store(
+
106 cmp,
+
107 [&]{
+
108 std::vector<Payload> container;
+
109 container.reserve(num_obs);
+
110 return container;
+
111 }()
+
112 );
+
113
+
114 std::vector<std::vector<Index_> > reverse_map(num_obs);
+
115 std::vector<Index_> remaining(num_obs);
+
116 for (Index_ c = 0; c < num_obs; ++c) {
+
117 const auto& neighbors = get_neighbors(c);
+
118 Index_ nneighbors = neighbors.size();
+
119
+
120 if (nneighbors) { // protect get_max_distance just in case there are no neighbors.
+
121 store.emplace(c, nneighbors, get_max_distance(c));
+
122 for (Index_ n = 0; n < nneighbors; ++n) {
+
123 reverse_map[get_index(neighbors, n)].push_back(c);
+
124 }
+
125 remaining[c] = nneighbors;
+
126 }
+
127 }
+
128
+
129 selected.clear();
+
130 std::vector<uint8_t> tainted(num_obs);
+
131 Index_ min_remaining = options.min_remaining;
+
132 while (!store.empty()) {
+
133 auto payload = store.top();
+
134 store.pop();
+
135 if (tainted[payload.identity]) {
+
136 continue;
+
137 }
+
138
+
139 const auto& neighbors = get_neighbors(payload.identity);
+
140 Index_ new_remaining = remaining[payload.identity];
+
141
+
142 if (new_remaining >= min_remaining) {
+
143 payload.remaining = new_remaining;
+
144 if (!store.empty() && cmp(payload, store.top())) {
+
145 store.push(payload);
+
146 } else {
+
147 selected.push_back(payload.identity);
+
148 tainted[payload.identity] = 1;
+
149 for (auto x : reverse_map[payload.identity]) {
+
150 --remaining[x];
+
151 }
+
152
+
153 Index_ nneighbors = neighbors.size();
+
154 for (Index_ n = 0; n < nneighbors; ++n) {
+
155 auto current = get_index(neighbors, n);
+
156 tainted[current] = 1;
+
157 for (auto x : reverse_map[current]) {
+
158 --remaining[x];
+
159 }
+
160 }
+
161 }
+
162 }
+
163 }
+
164
+
165 std::sort(selected.begin(), selected.end());
+
166}
-
166
-
182template<typename Index_, typename Distance_>
-
-
183std::vector<Index_> compute(const knncolle::NeighborList<Index_, Distance_>& neighbors, const Options& options) {
-
184 std::vector<Index_> output;
-
185 compute(
-
186 static_cast<Index_>(neighbors.size()),
-
187 [&](size_t i) -> const std::vector<Index_>& { return neighbors[i].first; },
-
188 [](const std::vector<Index_>& x, Index_ n) -> Index_ { return x[n]; },
-
189 [&](size_t i) -> Distance_ { return neighbors[i].second.back(); },
-
190 options,
-
191 output
-
192 );
-
193 return output;
-
194}
+
167
+
183template<typename Index_, typename Distance_>
+
+
184std::vector<Index_> compute(const knncolle::NeighborList<Index_, Distance_>& neighbors, const Options& options) {
+
185 std::vector<Index_> output;
+
186 compute(
+
187 static_cast<Index_>(neighbors.size()),
+
188 [&](size_t i) -> const auto& { return neighbors[i]; },
+
189 [](const auto& x, Index_ n) -> Index_ { return x[n].first; },
+
190 [&](size_t i) -> Distance_ { return neighbors[i].back().second; },
+
191 options,
+
192 output
+
193 );
+
194 return output;
+
195}
-
195
-
208template<typename Dim_, typename Index_, typename Float_>
-
-
209std::vector<Index_> compute(const knncolle::Prebuilt<Dim_, Index_, Float_>& prebuilt, const Options& options) {
-
210 int k = options.num_neighbors;
-
211 if (k < options.min_remaining) {
-
212 throw std::runtime_error("number of neighbors is less than 'min_remaining'");
-
213 }
-
214
-
215 Index_ nobs = prebuilt.num_observations();
-
216 std::vector<std::vector<Index_> > nn_indices(nobs);
-
217 std::vector<Float_> max_distance(nobs);
-
218
-
219#ifndef KNNCOLLE_CUSTOM_PARALLEL
-
220#ifdef _OPENMP
-
221 #pragma omp parallel num_threads(options.num_threads)
-
222 {
-
223 auto sptr = prebuilt.initialize();
-
224 std::vector<Float_> nn_distances;
-
225 #pragma omp for
-
226 for (Index_ i = 0; i < nobs; ++i) {
-
227#else
-
228 auto sptr = prebuilt.initialize();
-
229 std::vector<Float_> nn_distances;
-
230 for (Index_ i = 0; i < nobs; ++i) {
-
231#endif
-
232#else
-
233 KNNCOLLE_CUSTOM_PARALLEL(nobs, options.num_threads, [&](Index_ start, Index_ length) -> void {
-
234 auto sptr = prebuilt.initialize();
-
235 std::vector<Float_> nn_distances;
-
236 for (Index_ i = start, end = start + length; i < end; ++i) {
-
237#endif
-
238
-
239 sptr->search(i, k, &(nn_indices[i]), &nn_distances);
-
240 max_distance[i] = (k ? 0 : nn_distances.back());
-
241
-
242#ifndef KNNCOLLE_CUSTOM_PARALLEL
-
243#ifdef _OPENMP
-
244 }
-
245 }
-
246#else
-
247 }
-
248#endif
-
249#else
-
250 }
-
251 });
-
252#endif
-
253
-
254 std::vector<Index_> output;
-
255 compute(
-
256 nobs,
-
257 [&](size_t i) -> const std::vector<Index_>& { return nn_indices[i]; },
-
258 [](const std::vector<Index_>& x, Index_ n) -> Index_ { return x[n]; },
-
259 [&](size_t i) -> Float_ { return max_distance[i]; },
-
260 options,
-
261 output
-
262 );
-
263 return output;
-
264}
+
196
+
209template<typename Dim_, typename Index_, typename Float_>
+
+
210std::vector<Index_> compute(const knncolle::Prebuilt<Dim_, Index_, Float_>& prebuilt, const Options& options) {
+
211 int k = options.num_neighbors;
+
212 if (k < options.min_remaining) {
+
213 throw std::runtime_error("number of neighbors is less than 'min_remaining'");
+
214 }
+
215
+
216 Index_ nobs = prebuilt.num_observations();
+
217 std::vector<std::vector<Index_> > nn_indices(nobs);
+
218 std::vector<Float_> max_distance(nobs);
+
219
+
220 knncolle::parallelize(options.num_threads, nobs, [&](int, Index_ start, Index_ length) -> void {
+
221 auto sptr = prebuilt.initialize();
+
222 std::vector<Float_> nn_distances;
+
223 for (Index_ i = start, end = start + length; i < end; ++i) {
+
224 sptr->search(i, k, &(nn_indices[i]), &nn_distances);
+
225 max_distance[i] = (k ? 0 : nn_distances.back());
+
226 }
+
227 });
+
228
+
229 std::vector<Index_> output;
+
230 compute(
+
231 nobs,
+
232 [&](size_t i) -> const std::vector<Index_>& { return nn_indices[i]; },
+
233 [](const std::vector<Index_>& x, Index_ n) -> Index_ { return x[n]; },
+
234 [&](size_t i) -> Float_ { return max_distance[i]; },
+
235 options,
+
236 output
+
237 );
+
238 return output;
+
239}
-
265
-
282template<typename Dim_, typename Index_, typename Value_, typename Float_>
-
-
283std::vector<Index_> compute(
-
284 Dim_ num_dims,
-
285 Index_ num_obs,
-
286 const Value_* data,
-
287 const knncolle::Builder<knncolle::SimpleMatrix<Dim_, Index_, Value_>, Float_>& knn_method,
-
288 const Options& options)
-
289{
-
290 auto prebuilt = knn_method.build_unique(knncolle::SimpleMatrix<Dim_, Index_, Value_>(num_dims, num_obs, data));
-
291 return compute(*prebuilt, options);
-
292}
+
240
+
257template<typename Dim_, typename Index_, typename Value_, typename Float_>
+
+
258std::vector<Index_> compute(
+
259 Dim_ num_dims,
+
260 Index_ num_obs,
+
261 const Value_* data,
+
262 const knncolle::Builder<knncolle::SimpleMatrix<Dim_, Index_, Value_>, Float_>& knn_method,
+
263 const Options& options)
+
264{
+
265 auto prebuilt = knn_method.build_unique(knncolle::SimpleMatrix<Dim_, Index_, Value_>(num_dims, num_obs, data));
+
266 return compute(*prebuilt, options);
+
267}
-
293
-
294}
-
295
-
296#endif
+
268
+
269}
+
270
+
271#endif
knncolle::Prebuilt::num_observations
virtual Index_ num_observations() const=0
-
knncolle::Prebuilt::initialize
virtual std::unique_ptr< Searcher< Index_, Float_ > > initialize() const=0
-
knncolle::NeighborList
std::vector< std::pair< std::vector< Index_ >, std::vector< Float_ > > > NeighborList
+
knncolle::NeighborList
std::vector< std::vector< std::pair< Index_, Float_ > > > NeighborList
+
knncolle::parallelize
void parallelize(int num_workers, Task_ num_tasks, Run_ run_task_range)
nenesub
Nearest-neighbors subsampling.
-
nenesub::compute
void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)
Definition nenesub.hpp:86
+
nenesub::compute
void compute(Index_ num_obs, GetNeighbors_ get_neighbors, GetIndex_ get_index, GetMaxDistance_ get_max_distance, const Options &options, std::vector< Index_ > &selected)
Definition nenesub.hpp:87
nenesub::Options
Options for compute().
Definition nenesub.hpp:24
nenesub::Options::num_neighbors
int num_neighbors
Definition nenesub.hpp:29
-
nenesub::Options::num_threads
int num_threads
Definition nenesub.hpp:41
+
nenesub::Options::num_threads
int num_threads
Definition nenesub.hpp:42
nenesub::Options::min_remaining
int min_remaining
Definition nenesub.hpp:35
diff --git a/docs/search/all_0.js b/docs/search/all_0.js index 0407c48..8249621 100644 --- a/docs/search/all_0.js +++ b/docs/search/all_0.js @@ -4,7 +4,7 @@ var searchData= ['bruteforcebuilder_1',['BruteforceBuilder',['https://knncolle.github.io/knncolle/classknncolle_1_1BruteforceBuilder.html',1,'knncolle']]], ['bruteforceprebuilt_2',['BruteforcePrebuilt',['https://knncolle.github.io/knncolle/classknncolle_1_1BruteforcePrebuilt.html',1,'knncolle']]], ['bruteforcesearcher_3',['BruteforceSearcher',['https://knncolle.github.io/knncolle/classknncolle_1_1BruteforceSearcher.html',1,'knncolle']]], - ['build_5fraw_4',['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_1VptreeBuilder.html#a0c25ca5b2d7a812032d7cf1d42e3ad3a',1,'knncolle::VptreeBuilder::build_raw()']]], + ['build_5fraw_4',['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_5',['build_shared',['https://knncolle.github.io/knncolle/classknncolle_1_1Builder.html#afe899198735bd5f9d1921283fa65ff3a',1,'knncolle::Builder']]], 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['simplematrix_2',['SimpleMatrix',['https://knncolle.github.io/knncolle/classknncolle_1_1SimpleMatrix.html#a5ff473e3597354a19a09456f6580121a',1,'knncolle::SimpleMatrix']]] +]; diff --git a/docs/search/functions_c.js b/docs/search/functions_c.js new file mode 100644 index 0000000..4e393da --- /dev/null +++ b/docs/search/functions_c.js @@ -0,0 +1,4 @@ +var searchData= +[ + ['vptreeprebuilt_0',['VptreePrebuilt',['https://knncolle.github.io/knncolle/classknncolle_1_1VptreePrebuilt.html#a5391976fe1e6b957181229a8590a6ca3',1,'knncolle::VptreePrebuilt']]] +]; diff --git a/docs/search/searchdata.js b/docs/search/searchdata.js index 799e39d..17bc1d9 100644 --- a/docs/search/searchdata.js +++ b/docs/search/searchdata.js @@ -1,10 +1,10 @@ var indexSectionsWithContent = { - 0: "bcdefgikmnoprsvw", - 1: "bekmopsvw", + 0: "bcdefgiklmnoprsvw", + 1: "beklmopsvw", 2: "kn", - 3: "bdfkmnpsv", - 4: "bcdfgiknrsv", + 3: "bdfklmnpsv", + 4: "bcdfgiklnprsv", 5: "imnpr", 6: "dino", 7: "ns" diff --git a/docs/search/typedefs_2.js b/docs/search/typedefs_2.js index d0800fd..2755f39 100644 --- a/docs/search/typedefs_2.js +++ b/docs/search/typedefs_2.js @@ -1,4 +1,4 @@ var searchData= [ - ['neighborlist_0',['NeighborList',['https://knncolle.github.io/knncolle/namespaceknncolle.html#a3faddeb80b10f56cb982a4e83ad3aca9',1,'knncolle']]] + ['neighborlist_0',['NeighborList',['https://knncolle.github.io/knncolle/namespaceknncolle.html#a25d7a950cf7745b99bc834b79e064c09',1,'knncolle']]] ]; diff --git a/docs/structnenesub_1_1Options.html b/docs/structnenesub_1_1Options.html index 3f93ce4..38a0d38 100644 --- a/docs/structnenesub_1_1Options.html +++ b/docs/structnenesub_1_1Options.html @@ -147,7 +147,7 @@

-

The number of threads to use. Only relevant for the compute() overloads without pre-computed neighbors.

+

The number of threads to use. This uses the parallelization scheme defined by knncolle::parallelize(). Only relevant for the compute() overloads that perform a neighbor search.