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@@ -114,7 +114,7 @@ <h1><a class="anchor" id="autotoc_md2"></a>
 <div class="line"><span class="comment">// Find 10 nearest neighbors of every element.</span></div>
 <div class="line"><span class="keyword">auto</span> results = <a class="code hl_functionRef" href="https://knncolle.github.io/knncolle/namespaceknncolle.html#a2c6d8b116464bab254bda34216338c3c">knncolle::find_nearest_neighbors</a>(*kindex, 10); </div>
 <div class="ttc" id="aclassknncolle_1_1SimpleMatrix_html"><div class="ttname"><a href="https://knncolle.github.io/knncolle/classknncolle_1_1SimpleMatrix.html">knncolle::SimpleMatrix</a></div></div>
-<div class="ttc" id="aclassknncolle__kmknn_1_1KmknnBuilder_html"><div class="ttname"><a href="classknncolle__kmknn_1_1KmknnBuilder.html">knncolle_kmknn::KmknnBuilder</a></div><div class="ttdoc">Perform a nearest neighbor search based on k-means clustering.</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:659</div></div>
+<div class="ttc" id="aclassknncolle__kmknn_1_1KmknnBuilder_html"><div class="ttname"><a href="classknncolle__kmknn_1_1KmknnBuilder.html">knncolle_kmknn::KmknnBuilder</a></div><div class="ttdoc">Perform a nearest neighbor search based on k-means clustering.</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:660</div></div>
 <div class="ttc" id="anamespaceknncolle_html_a2c6d8b116464bab254bda34216338c3c"><div class="ttname"><a href="https://knncolle.github.io/knncolle/namespaceknncolle.html#a2c6d8b116464bab254bda34216338c3c">knncolle::find_nearest_neighbors</a></div><div class="ttdeci">NeighborList&lt; Index_, Distance_ &gt; find_nearest_neighbors(const Prebuilt&lt; Index_, Data_, Distance_ &gt; &amp;index, int k, int num_threads=1)</div></div>
 </div><!-- fragment --><p>Check out the <a href="https://knncolle.github.io/knncolle_kmknn/">reference documentation</a> for more details.</p>
 <h1><a class="anchor" id="autotoc_md3"></a>
@@ -131,8 +131,8 @@ <h1><a class="anchor" id="autotoc_md3"></a>
 <div class="line"><a class="code hl_class" href="classknncolle__kmknn_1_1KmknnBuilder.html">knncolle_kmknn::KmknnBuilder&lt;int, double, double&gt;</a> kbuilder(dist, new_options);</div>
 <div class="ttc" id="aclasskmeans_1_1InitializeRandom_html"><div class="ttname"><a href="https://ltla.github.io/CppKmeans/classkmeans_1_1InitializeRandom.html">kmeans::InitializeRandom</a></div></div>
 <div class="ttc" id="aclasskmeans_1_1RefineLloyd_html"><div class="ttname"><a href="https://ltla.github.io/CppKmeans/classkmeans_1_1RefineLloyd.html">kmeans::RefineLloyd</a></div></div>
-<div class="ttc" id="astructknncolle__kmknn_1_1KmknnOptions_html"><div class="ttname"><a href="structknncolle__kmknn_1_1KmknnOptions.html">knncolle_kmknn::KmknnOptions</a></div><div class="ttdoc">Options for KmknnBuilder construction.</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:183</div></div>
-<div class="ttc" id="astructknncolle__kmknn_1_1KmknnOptions_html_a84b020741aac4160fed42a291d054216"><div class="ttname"><a href="structknncolle__kmknn_1_1KmknnOptions.html#a84b020741aac4160fed42a291d054216">knncolle_kmknn::KmknnOptions::power</a></div><div class="ttdeci">double power</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:188</div></div>
+<div class="ttc" id="astructknncolle__kmknn_1_1KmknnOptions_html"><div class="ttname"><a href="structknncolle__kmknn_1_1KmknnOptions.html">knncolle_kmknn::KmknnOptions</a></div><div class="ttdoc">Options for KmknnBuilder construction.</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:184</div></div>
+<div class="ttc" id="astructknncolle__kmknn_1_1KmknnOptions_html_a84b020741aac4160fed42a291d054216"><div class="ttname"><a href="structknncolle__kmknn_1_1KmknnOptions.html#a84b020741aac4160fed42a291d054216">knncolle_kmknn::KmknnOptions::power</a></div><div class="ttdeci">double power</div><div class="ttdef"><b>Definition</b> knncolle_kmknn.hpp:189</div></div>
 </div><!-- fragment --><p>We can also pass in a different distance metric, if so desired. KMKNN works most naturally with the Euclidean distance as k-means aims to minimize the Euclidean distance between data points and the centroids. But a different distance metric will still give the correct results:</p>
 <div class="fragment"><div class="line"><span class="keyword">auto</span> man_dist = std::make_shared&lt;knncolle_kmknn::ManhattanDistance&lt;double, double&gt; &gt;();</div>
 <div class="line"><a class="code hl_class" href="classknncolle__kmknn_1_1KmknnBuilder.html">knncolle_kmknn::KmknnBuilder&lt;int, double, double&gt;</a> man_kbuilder(man_dist);</div>

docs/knncolle__kmknn_8hpp.html

@@ -108,6 +108,7 @@
 <code>#include &lt;limits&gt;</code><br />
 <code>#include &lt;cmath&gt;</code><br />
 <code>#include &lt;cstddef&gt;</code><br />
+<code>#include &lt;type_traits&gt;</code><br />
 </div>
 <p><a href="knncolle__kmknn_8hpp_source.html">Go to the source code of this file.</a></p>
 <table class="memberdecls">