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<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">K-Nearest Neighbors (<a class="el" href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html" title="K-Nearest Neighbors (Knn) class using Euclidean distance as distance metric.">Knn</a>) class using Euclidean distance as distance metric. <a href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">namespace &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d7/d4c/namespacek__nearest__neighbors.html">k_nearest_neighbors</a></td></tr>
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<tr class="memdesc:d7/d4c/namespacek__nearest__neighbors"><td class="mdescLeft">&#160;</td><td class="mdescRight">Functions for the [K-Nearest Neighbors algorithm] (<a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm</a>) implementation. <br /></td></tr>
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<tr class="memdesc:ad6ae16e50bb153ebaa7251d0aaa97b69"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the Euclidean distance between two vectors. <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<a name="details" id="details"></a><h2 id="header-details" class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Implementation of [K-Nearest Neighbors algorithm] (<a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm</a>). </p>
<dl class="section author"><dt>Author</dt><dd><a href="https://github.com/luizcarloscf" target="_blank">Luiz Carlos Cosmi Filho</a></dd></dl>
<p>K-nearest neighbors algorithm, also known as KNN or k-NN, is a supervised learning classifier, which uses proximity to make classifications. This implementantion uses the Euclidean Distance as distance metric to find the K-nearest neighbors. </p>
<p class="definition">Definition in file <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html">k_nearest_neighbors.cpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ad6ae16e50bb153ebaa7251d0aaa97b69">&#9670;&#160;</a></span>euclidean_distance()</h2>
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