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<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>).
<a href="#details">More...</a></p>
<div class="textblock"><code>#include &lt;algorithm&gt;</code><br />
<code>#include &lt;cassert&gt;</code><br />
<code>#include &lt;cmath&gt;</code><br />
<code>#include &lt;iostream&gt;</code><br />
<code>#include &lt;numeric&gt;</code><br />
<code>#include &lt;unordered_map&gt;</code><br />
<code>#include &lt;vector&gt;</code><br />
</div><div class="textblock"><div class="dynheader">
Include dependency graph for k_nearest_neighbors.cpp:</div>
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<p><a href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html">Go to the source code of this file.</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 id="header-nested-classes" class="groupheader"><a id="nested-classes" name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:machine_5Flearning_3A_3Ak_5Fnearest_5Fneighbors_3A_3AKnn" id="r_machine_5Flearning_3A_3Ak_5Fnearest_5Fneighbors_3A_3AKnn"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html">machine_learning::k_nearest_neighbors::Knn</a></td></tr>
<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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Namespaces</h2></td></tr>
<tr class="memitem:machine_5Flearning" id="r_machine_5Flearning"><td class="memItemLeft" align="right" valign="top">namespace &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a></td></tr>
<tr class="memdesc:d8/d77/namespacemachine__learning"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a href="https://en.wikipedia.org/wiki/A*_search_algorithm" target="_blank">A* search algorithm</a> <br /></td></tr>
<tr class="memitem:k_5Fnearest_5Fneighbors" id="r_k_5Fnearest_5Fneighbors"><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>
<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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Functions</h2></td></tr>
<tr class="memitem:ad6ae16e50bb153ebaa7251d0aaa97b69" id="r_ad6ae16e50bb153ebaa7251d0aaa97b69"><td class="memTemplParams" colspan="2">template&lt;typename T&gt; </td></tr>
<tr class="memitem:ad6ae16e50bb153ebaa7251d0aaa97b69 template"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="#ad6ae16e50bb153ebaa7251d0aaa97b69">machine_learning::k_nearest_neighbors::euclidean_distance</a> (const std::vector&lt; T &gt; &amp;a, const std::vector&lt; T &gt; &amp;b)</td></tr>
<tr class="memdesc:ad6ae16e50bb153ebaa7251d0aaa97b69"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the Euclidean distance between two vectors. <br /></td></tr>
<tr class="memitem:aa8dca7b867074164d5f45b0f3851269d" id="r_aa8dca7b867074164d5f45b0f3851269d"><td class="memItemLeft" align="right" valign="top">static void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="#aa8dca7b867074164d5f45b0f3851269d">test</a> ()</td></tr>
<tr class="memdesc:aa8dca7b867074164d5f45b0f3851269d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Self-test implementations. <br /></td></tr>
<tr class="memitem:ae66f6b31b5ad750f1fe042a706a4e3d4" id="r_ae66f6b31b5ad750f1fe042a706a4e3d4"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a> ()</td></tr>
<tr class="memdesc:ae66f6b31b5ad750f1fe042a706a4e3d4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Main function. <br /></td></tr>
</table>
<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>
</div><a name="doc-func-members" id="doc-func-members"></a><h2 id="header-doc-func-members" class="groupheader">Function Documentation</h2>
<a id="ad6ae16e50bb153ebaa7251d0aaa97b69" name="ad6ae16e50bb153ebaa7251d0aaa97b69"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad6ae16e50bb153ebaa7251d0aaa97b69">&#9670;&#160;</a></span>euclidean_distance()</h2>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename T&gt; </div>
<table class="memname">
<tr>
<td class="memname">double machine_learning::k_nearest_neighbors::euclidean_distance </td>
<td>(</td>
<td class="paramtype">const std::vector&lt; T &gt; &amp;</td> <td class="paramname"><span class="paramname"><em>a</em></span>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::vector&lt; T &gt; &amp;</td> <td class="paramname"><span class="paramname"><em>b</em></span>&#160;)</td>
</tr>
</table>
</div><div class="memdoc">
<p>Compute the Euclidean distance between two vectors. </p>
<dl class="tparams"><dt>Template Parameters</dt><dd>
<table class="tparams">
<tr><td class="paramname">T</td><td>typename of the vector </td></tr>
</table>
</dd>
</dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">a</td><td>first unidimentional vector </td></tr>
<tr><td class="paramname">b</td><td>second unidimentional vector </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>double scalar representing the Euclidean distance between provided vectors </dd></dl>
<p class="definition">Definition at line <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html#l00043">43</a> of file <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html">k_nearest_neighbors.cpp</a>.</p>
<div class="fragment"><div class="line"><span class="lineno"> 43</span> {</div>
<div class="line"><span class="lineno"> 44</span> std::vector&lt;double&gt; aux;</div>
<div class="line"><span class="lineno"> 45</span> std::transform(a.begin(), a.end(), b.begin(), std::back_inserter(aux),</div>
<div class="line"><span class="lineno"> 46</span> [](T x1, T x2) { return std::pow((x1 - x2), 2); });</div>
<div class="line"><span class="lineno"> 47</span> aux.shrink_to_fit();</div>
<div class="line"><span class="lineno"> 48</span> <span class="keywordflow">return</span> std::sqrt(std::accumulate(aux.begin(), aux.end(), 0.0));</div>
<div class="line"><span class="lineno"> 49</span>}</div>
</div><!-- fragment -->
</div>
</div>
<a id="ae66f6b31b5ad750f1fe042a706a4e3d4" name="ae66f6b31b5ad750f1fe042a706a4e3d4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae66f6b31b5ad750f1fe042a706a4e3d4">&#9670;&#160;</a></span>main()</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">int main </td>
<td>(</td>
<td class="paramtype">void</td> <td class="paramname"><span class="paramname"><em></em></span></td><td>)</td>
<td></td>
</tr>
</table>
</div><div class="memdoc">
<p>Main function. </p>
<dl class="section return"><dt>Returns</dt><dd>int 0 on exit </dd></dl>
<p class="definition">Definition at line <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html#l00191">191</a> of file <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html">k_nearest_neighbors.cpp</a>.</p>
<div class="fragment"><div class="line"><span class="lineno"> 191</span> {</div>
<div class="line"><span class="lineno"> 192</span> <a class="code hl_function" href="#aa8dca7b867074164d5f45b0f3851269d">test</a>(); <span class="comment">// run self-test implementations</span></div>
<div class="line"><span class="lineno"> 193</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><span class="lineno"> 194</span>}</div>
<div class="ttc" id="ak__nearest__neighbors_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdoc">Self-test implementations.</div><div class="ttdef"><b>Definition</b> <a href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html#l00140">k_nearest_neighbors.cpp:140</a></div></div>
</div><!-- fragment -->
</div>
</div>
<a id="aa8dca7b867074164d5f45b0f3851269d" name="aa8dca7b867074164d5f45b0f3851269d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa8dca7b867074164d5f45b0f3851269d">&#9670;&#160;</a></span>test()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">void test </td>
<td>(</td>
<td class="paramname"><span class="paramname"><em></em></span></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel static">static</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Self-test implementations. </p>
<dl class="section return"><dt>Returns</dt><dd>void </dd></dl>
<p class="definition">Definition at line <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html#l00140">140</a> of file <a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html">k_nearest_neighbors.cpp</a>.</p>
<div class="fragment"><div class="line"><span class="lineno"> 140</span> {</div>
<div class="line"><span class="lineno"> 141</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 1 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 142</span> std::vector&lt;std::vector&lt;double&gt;&gt; X1 = {{0.0, 0.0}, {0.25, 0.25},</div>
<div class="line"><span class="lineno"> 143</span> {0.0, 0.5}, {0.5, 0.5},</div>
<div class="line"><span class="lineno"> 144</span> {1.0, 0.5}, {1.0, 1.0}};</div>
<div class="line"><span class="lineno"> 145</span> std::vector&lt;int&gt; Y1 = {1, 1, 1, 1, 2, 2};</div>
<div class="line"><span class="lineno"> 146</span> <span class="keyword">auto</span> model1 = <a class="code hl_class" href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html">machine_learning::k_nearest_neighbors::Knn</a>(X1, Y1);</div>
<div class="line"><span class="lineno"> 147</span> std::vector&lt;double&gt; sample1 = {1.2, 1.2};</div>
<div class="line"><span class="lineno"> 148</span> std::vector&lt;double&gt; sample2 = {0.1, 0.1};</div>
<div class="line"><span class="lineno"> 149</span> std::vector&lt;double&gt; sample3 = {0.1, 0.5};</div>
<div class="line"><span class="lineno"> 150</span> std::vector&lt;double&gt; sample4 = {1.0, 0.75};</div>
<div class="line"><span class="lineno"> 151</span> assert(model1.predict(sample1, 2) == 2);</div>
<div class="line"><span class="lineno"> 152</span> assert(model1.predict(sample2, 2) == 1);</div>
<div class="line"><span class="lineno"> 153</span> assert(model1.predict(sample3, 2) == 1);</div>
<div class="line"><span class="lineno"> 154</span> assert(model1.predict(sample4, 2) == 2);</div>
<div class="line"><span class="lineno"> 155</span> std::cout &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 156</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 2 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 157</span> std::vector&lt;std::vector&lt;double&gt;&gt; X2 = {</div>
<div class="line"><span class="lineno"> 158</span> {0.0, 0.0, 0.0}, {0.25, 0.25, 0.0}, {0.0, 0.5, 0.0}, {0.5, 0.5, 0.0},</div>
<div class="line"><span class="lineno"> 159</span> {1.0, 0.5, 0.0}, {1.0, 1.0, 0.0}, {1.0, 1.0, 1.0}, {1.5, 1.5, 1.0}};</div>
<div class="line"><span class="lineno"> 160</span> std::vector&lt;int&gt; Y2 = {1, 1, 1, 1, 2, 2, 3, 3};</div>
<div class="line"><span class="lineno"> 161</span> <span class="keyword">auto</span> model2 = <a class="code hl_class" href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html">machine_learning::k_nearest_neighbors::Knn</a>(X2, Y2);</div>
<div class="line"><span class="lineno"> 162</span> std::vector&lt;double&gt; sample5 = {1.2, 1.2, 0.0};</div>
<div class="line"><span class="lineno"> 163</span> std::vector&lt;double&gt; sample6 = {0.1, 0.1, 0.0};</div>
<div class="line"><span class="lineno"> 164</span> std::vector&lt;double&gt; sample7 = {0.1, 0.5, 0.0};</div>
<div class="line"><span class="lineno"> 165</span> std::vector&lt;double&gt; sample8 = {1.0, 0.75, 1.0};</div>
<div class="line"><span class="lineno"> 166</span> assert(model2.predict(sample5, 2) == 2);</div>
<div class="line"><span class="lineno"> 167</span> assert(model2.predict(sample6, 2) == 1);</div>
<div class="line"><span class="lineno"> 168</span> assert(model2.predict(sample7, 2) == 1);</div>
<div class="line"><span class="lineno"> 169</span> assert(model2.predict(sample8, 2) == 3);</div>
<div class="line"><span class="lineno"> 170</span> std::cout &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 171</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 3 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 172</span> std::vector&lt;std::vector&lt;double&gt;&gt; X3 = {{0.0}, {1.0}, {2.0}, {3.0},</div>
<div class="line"><span class="lineno"> 173</span> {4.0}, {5.0}, {6.0}, {7.0}};</div>
<div class="line"><span class="lineno"> 174</span> std::vector&lt;int&gt; Y3 = {1, 1, 1, 1, 2, 2, 2, 2};</div>
<div class="line"><span class="lineno"> 175</span> <span class="keyword">auto</span> model3 = <a class="code hl_class" href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html">machine_learning::k_nearest_neighbors::Knn</a>(X3, Y3);</div>
<div class="line"><span class="lineno"> 176</span> std::vector&lt;double&gt; sample9 = {0.5};</div>
<div class="line"><span class="lineno"> 177</span> std::vector&lt;double&gt; sample10 = {2.9};</div>
<div class="line"><span class="lineno"> 178</span> std::vector&lt;double&gt; sample11 = {5.5};</div>
<div class="line"><span class="lineno"> 179</span> std::vector&lt;double&gt; sample12 = {7.5};</div>
<div class="line"><span class="lineno"> 180</span> assert(model3.predict(sample9, 3) == 1);</div>
<div class="line"><span class="lineno"> 181</span> assert(model3.predict(sample10, 3) == 1);</div>
<div class="line"><span class="lineno"> 182</span> assert(model3.predict(sample11, 3) == 2);</div>
<div class="line"><span class="lineno"> 183</span> assert(model3.predict(sample12, 3) == 2);</div>
<div class="line"><span class="lineno"> 184</span> std::cout &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><span class="lineno"> 185</span>}</div>
<div class="ttc" id="aclassmachine__learning_1_1k__nearest__neighbors_1_1_knn_html"><div class="ttname"><a href="../../da/d94/classmachine__learning_1_1k__nearest__neighbors_1_1_knn.html">machine_learning::k_nearest_neighbors::Knn</a></div><div class="ttdoc">K-Nearest Neighbors (Knn) class using Euclidean distance as distance metric.</div><div class="ttdef"><b>Definition</b> <a href="../../d4/d3e/k__nearest__neighbors_8cpp_source.html#l00055">k_nearest_neighbors.cpp:55</a></div></div>
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