You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
<divclass="textblock"><divclass="compoundTemplParams">template<typename Index_, typename Data_, typename Distance_, class AnnoyDistance_, typename AnnoyIndex_ = Index_, typename AnnoyData_ = float, class AnnoyRng_ = Annoy::Kiss64Random, class AnnoyThreadPolicy_ = Annoy::AnnoyIndexSingleThreadedBuildPolicy, class Matrix_ = knncolle::Matrix<Index_, Data_>><br/>
149
149
class knncolle_annoy::AnnoyBuilder< Index_, Data_, Distance_, AnnoyDistance_, AnnoyIndex_, AnnoyData_, AnnoyRng_, AnnoyThreadPolicy_, Matrix_ ></div><p>Perform an approximate nearest neighbor search with Annoy. </p>
150
-
<ul>
151
-
<li>In the Approximate Nearest Neighbors Oh Yeah (Annoy) algorithm, a tree is constructed where a random hyperplane splits the points into two subsets at each internal node. * Leaf nodes are defined when the number of points in a subset falls below a threshold (close to twice the number of dimensions for the settings used here). * Multiple trees are constructed in this manner, each of which is different due to the random choice of hyperplanes. * For a given query point, each tree is searched to identify the subset of all points in the same leaf node as the query point. The union of these subsets across all trees is exhaustively searched to identify the actual nearest neighbors to the query.</li>
152
-
</ul>
150
+
<p>In the Approximate Nearest Neighbors Oh Yeah (Annoy) algorithm, a tree is constructed where a random hyperplane splits the points into two subsets at each internal node. Leaf nodes are defined when the number of points in a subset falls below a threshold (close to twice the number of dimensions for the settings used here). Multiple trees are constructed in this manner, each of which is different due to the random choice of hyperplanes. For a given query point, each tree is searched to identify the subset of all points in the same leaf node as the query point. The union of these subsets across all trees is exhaustively searched to identify the actual nearest neighbors to the query.</p>
153
151
<dlclass="section see"><dt>See also</dt><dd>Bernhardsson E (2018). Annoy. <ahref="https://github.com/spotify/annoy">https://github.com/spotify/annoy</a></dd></dl>
<dlclass="section return"><dt>Returns</dt><dd>Options to the Annoy algorithm, to be modified prior to calling <code>build_raw()</code> and friends. </dd></dl>
337
+
<dlclass="section return"><dt>Returns</dt><dd>Options to the Annoy algorithm, to be modified prior to calling <code><aclass="elRef" href="https://knncolle.github.io/knncolle/classknncolle_1_1Builder.html#acd18a78af362f948ede3ddd8d5f6e88c">build_raw()</a></code> and friends. </dd></dl>
369
338
370
339
</div>
371
340
</div>
372
341
<hr/>The documentation for this class was generated from the following file:<ul>
<trid="row_0_0_0_" class="even"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="knncolle__annoy_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="knncolle__annoy_8hpp.html" target="_self">knncolle_annoy.hpp</a></td><tdclass="desc">Approximate nearest neighbor search with Annoy </td></tr>
97
-
<trid="row_0_0_1_" class="odd"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="load__annoy__prebuilt_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="load__annoy__prebuilt_8hpp.html" target="_self">load_annoy_prebuilt.hpp</a></td><tdclass="desc">Load a prebuilt Annoy index </td></tr>
98
-
<trid="row_0_0_2_" class="even"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="utils_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="utils_8hpp.html" target="_self">utils.hpp</a></td><tdclass="desc">Utilities for the Annoy wrappers </td></tr>
<trid="row_0_0_1_" class="odd"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="knncolle__annoy_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="knncolle__annoy_8hpp.html" target="_self">knncolle_annoy.hpp</a></td><tdclass="desc">Approximate nearest neighbor search with Annoy </td></tr>
98
+
<trid="row_0_0_2_" class="even"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="load__annoy__prebuilt_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="load__annoy__prebuilt_8hpp.html" target="_self">load_annoy_prebuilt.hpp</a></td><tdclass="desc">Load a prebuilt Annoy index </td></tr>
99
+
<trid="row_0_0_3_" class="odd"><tdclass="entry"><spanstyle="width:48px;display:inline-block;"> </span><ahref="utils_8hpp_source.html"><spanclass="icondoc"></span></a><aclass="el" href="utils_8hpp.html" target="_self">utils.hpp</a></td><tdclass="desc">Utilities for the Annoy wrappers </td></tr>
0 commit comments