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docs/Kmknn_8hpp_source.html

@@ -94,6 +94,7 @@
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 <tr id="row_0_0_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html" target="_self">KmknnBuilder</a></td><td class="desc">Perform a nearest neighbor search based on k-means clustering </td></tr>
 <tr id="row_0_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structknncolle__kmknn_1_1KmknnOptions.html" target="_self">KmknnOptions</a></td><td class="desc">Options for <code><a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html" title="Perform a nearest neighbor search based on k-means clustering.">KmknnBuilder</a></code> construction </td></tr>
+<tr id="row_0_2_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html" target="_self">KmknnPrebuiltTypes</a></td><td class="desc">Template types of a saved KMKNN index </td></tr>
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docs/annotated.html

@@ -93,7 +93,7 @@
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+<dd><a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html">KmknnBuilder</a> (<a class="el" href="namespaceknncolle__kmknn.html">knncolle_kmknn</a>)</dd><dd><a class="el" href="structknncolle__kmknn_1_1KmknnOptions.html">KmknnOptions</a> (<a class="el" href="namespaceknncolle__kmknn.html">knncolle_kmknn</a>)</dd><dd><a class="el" href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html">KmknnPrebuiltTypes</a> (<a class="el" href="namespaceknncolle__kmknn.html">knncolle_kmknn</a>)</dd></dl>
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docs/classes.html

@@ -228,10 +228,11 @@ <h2 class="memtitle"><span class="permalink"><a href="#a8d95fab3e38f2acd0c0ae33a
   <table class="params">
     <tr><td class="paramname">metric_data</td><td>Pointer to a distance metric (e.g., <code><a class="elRef" href="https://knncolle.github.io/knncolle/classknncolle_1_1EuclideanDistance.html">knncolle::EuclideanDistance</a></code>) for computing distances between observations. </td></tr>
     <tr><td class="paramname">metric_center</td><td>Pointer to a distance metric (e.g., <code><a class="elRef" href="https://knncolle.github.io/knncolle/classknncolle_1_1EuclideanDistance.html">knncolle::EuclideanDistance</a></code>) for computing distances between observations and cluster centroids. </td></tr>
-    <tr><td class="paramname">options</td><td>Further options for the KMKNN algorithm. </td></tr>
+    <tr><td class="paramname">options</td><td>Further options for the KMKNN algorithm.</td></tr>
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   </dd>
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+<p><code>metric_center</code> and <code>metric_data</code> should compute the same distance metric. Specifically, the distance computed by <code>metric_data</code> between two <code>Data_</code> arrays should be the same as the distance computed by <code>metric_center</code> between two <code>KmeansFloat_</code> arrays with the same contents, assuming that the contents can be losslessly converted from <code>Data_</code> to <code>KmeansFloat_</code>. We only accept two separate arguments here to support use cases where <code>Data_</code> and <code>KmeansFloat_</code> are different types (e.g., integer <code>Data_</code> and double-precision <code>KmeansFloat_</code>), where a more efficient calculation of the same distance may be possible with one of the types. </p>
 
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docs/classknncolle__kmknn_1_1KmknnBuilder.html

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+<a href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html"/>
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@@ -41,6 +45,7 @@
 <a href="functions_vars.html"/>
 <a href="functions_vars.html"/>
 <a href="functions_vars.html"/>
+<a href="functions_vars.html"/>
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+<a href="load__kmknn__prebuilt_8hpp.html#a2e91a868a2c55f67bb0bf52f82f3966b"/>
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+<a href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html"/>
+<a href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html#a6c7162a3f4b401f1ebe6da39940caf54"/>
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docs/doxygen_crawl.html

@@ -92,6 +92,7 @@
 <li>build_known_unique()&#160;:&#160;<a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html#a59c9c836c77549822cd3c069f5e38714">knncolle_kmknn::KmknnBuilder&lt; Index_, Data_, Distance_, Matrix_, DistanceMetricData_, KmeansIndex_, KmeansData_, KmeansCluster_, KmeansFloat_, KmeansMatrix_, DistanceMetricCenter_ &gt;</a></li>
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+<li>kmeansfloat&#160;:&#160;<a class="el" href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html#a6c7162a3f4b401f1ebe6da39940caf54">knncolle_kmknn::KmknnPrebuiltTypes</a></li>
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 <li>Options&#160;:&#160;<a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html#a8956bd3195d022eb5b4c0b69e5dcc45f">knncolle_kmknn::KmknnBuilder&lt; Index_, Data_, Distance_, Matrix_, DistanceMetricData_, KmeansIndex_, KmeansData_, KmeansCluster_, KmeansFloat_, KmeansMatrix_, DistanceMetricCenter_ &gt;</a></li>
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docs/functions.html

@@ -88,6 +88,7 @@
 <div class="contents">
 <div class="textblock">Here is a list of all documented variables with links to the class documentation for each member:</div><ul>
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+<li>kmeansfloat&#160;:&#160;<a class="el" href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html#a6c7162a3f4b401f1ebe6da39940caf54">knncolle_kmknn::KmknnPrebuiltTypes</a></li>
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docs/functions_vars.html

@@ -95,6 +95,7 @@
 <tr id="row_0_0_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html" target="_self">knncolle_kmknn::KmknnBuilder&lt; Index_, Data_, Distance_, Matrix_, DistanceMetricData_, KmeansIndex_, KmeansData_, KmeansCluster_, KmeansFloat_, KmeansMatrix_, DistanceMetricCenter_ &gt;</a></td><td class="desc">Perform a nearest neighbor search based on k-means clustering </td></tr>
 <tr id="row_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structknncolle__kmknn_1_1KmknnOptions.html" target="_self">knncolle_kmknn::KmknnOptions&lt; Index_, Data_, Distance_, KmeansIndex_, KmeansData_, KmeansCluster_, KmeansFloat_, KmeansMatrix_ &gt;</a></td><td class="desc">Options for <code><a class="el" href="classknncolle__kmknn_1_1KmknnBuilder.html" title="Perform a nearest neighbor search based on k-means clustering.">KmknnBuilder</a></code> construction </td></tr>
 <tr id="row_2_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structknncolle__kmknn_1_1KmknnOptions.html" target="_self">knncolle_kmknn::KmknnOptions&lt; Index_, Data_, Distance_, Index_, Data_, Index_, Distance_, kmeans::SimpleMatrix&lt; Index_, Data_ &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structknncolle__kmknn_1_1KmknnPrebuiltTypes.html" target="_self">knncolle_kmknn::KmknnPrebuiltTypes</a></td><td class="desc">Template types of a saved KMKNN index </td></tr>
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 </div><!-- directory -->
 </div><!-- contents -->