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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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Classes</h2></td></tr>
<tr class="memitem:"><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>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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Namespaces</h2></td></tr>
<tr class="memitem:d8/d77/namespacemachine__learning"><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="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:d7/d4c/namespacek__nearest__neighbors"><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"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:ad6ae16e50bb153ebaa7251d0aaa97b69"><td class="memTemplItemLeft" align="right" valign="top">double&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp.html#ad6ae16e50bb153ebaa7251d0aaa97b69">machine_learning::k_nearest_neighbors::euclidean_distance</a> (const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; T &gt; &amp;a, const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&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="separator:ad6ae16e50bb153ebaa7251d0aaa97b69"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa8dca7b867074164d5f45b0f3851269d"><td class="memItemLeft" align="right" valign="top">static void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp.html#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="separator:aa8dca7b867074164d5f45b0f3851269d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0ddf1224851353fc92bfbff6f499fa97"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d4/d3e/k__nearest__neighbors_8cpp.html#a0ddf1224851353fc92bfbff6f499fa97">main</a> (int argc, char *argv[])</td></tr>
<tr class="memdesc:a0ddf1224851353fc92bfbff6f499fa97"><td class="mdescLeft">&#160;</td><td class="mdescRight">Main function. <br /></td></tr>
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<a name="details" id="details"></a><h2 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>
</div><h2 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>
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template&lt;typename T &gt; </div>
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<td class="memname">double machine_learning::k_nearest_neighbors::euclidean_distance </td>
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<td class="paramtype">const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; T &gt; &amp;&#160;</td>
<td class="paramname"><em>a</em>, </td>
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<td class="paramtype">const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; T &gt; &amp;&#160;</td>
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<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>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">a</td><td>first unidimentional vector </td></tr>
<tr><td class="paramname">b</td><td>second unidimentional vector </td></tr>
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</dd>
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<dl class="section return"><dt>Returns</dt><dd>double scalar representing the Euclidean distance between provided vectors </dd></dl>
<div class="fragment"><div class="line"><span class="lineno"> 43</span> {</div>
<div class="line"><span class="lineno"> 44</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> aux;</div>
<div class="line"><span class="lineno"> 45</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/transform.html">std::transform</a>(a.begin(), a.end(), b.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>(), <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/iterator/back_inserter.html">std::back_inserter</a>(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.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/shrink_to_fit.html">shrink_to_fit</a>();</div>
<div class="line"><span class="lineno"> 48</span> <span class="keywordflow">return</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/sqrt.html">std::sqrt</a>(<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/accumulate.html">std::accumulate</a>(aux.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>(), aux.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/end.html">end</a>(), 0.0));</div>
<div class="line"><span class="lineno"> 49</span>}</div>
<div class="ttc" id="aaccumulate_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/accumulate.html">std::accumulate</a></div><div class="ttdeci">T accumulate(T... args)</div></div>
<div class="ttc" id="aback_inserter_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/iterator/back_inserter.html">std::back_inserter</a></div><div class="ttdeci">T back_inserter(T... args)</div></div>
<div class="ttc" id="abegin_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/begin.html">std::vector::begin</a></div><div class="ttdeci">T begin(T... args)</div></div>
<div class="ttc" id="aend_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/end.html">std::vector::end</a></div><div class="ttdeci">T end(T... args)</div></div>
<div class="ttc" id="ashrink_to_fit_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/shrink_to_fit.html">std::vector::shrink_to_fit</a></div><div class="ttdeci">T shrink_to_fit(T... args)</div></div>
<div class="ttc" id="asqrt_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/numeric/math/sqrt.html">std::sqrt</a></div><div class="ttdeci">T sqrt(T... args)</div></div>
<div class="ttc" id="atransform_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/transform.html">std::transform</a></div><div class="ttdeci">T transform(T... args)</div></div>
<div class="ttc" id="avector_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0ddf1224851353fc92bfbff6f499fa97">&#9670;&#160;</a></span>main()</h2>
<div class="memitem">
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<td class="memname">int main </td>
<td>(</td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>argc</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">char *&#160;</td>
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<p>Main function. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">argc</td><td>commandline argument count (ignored) </td></tr>
<tr><td class="paramname">argv</td><td>commandline array of arguments (ignored) </td></tr>
</table>
</dd>
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<dl class="section return"><dt>Returns</dt><dd>int 0 on exit </dd></dl>
<div class="fragment"><div class="line"><span class="lineno"> 193</span> {</div>
<div class="line"><span class="lineno"> 194</span> <a class="code hl_function" href="../../d4/d3e/k__nearest__neighbors_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>(); <span class="comment">// run self-test implementations</span></div>
<div class="line"><span class="lineno"> 195</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><span class="lineno"> 196</span>}</div>
<div class="ttc" id="ak__nearest__neighbors_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="../../d4/d3e/k__nearest__neighbors_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdoc">Self-test implementations.</div><div class="ttdef"><b>Definition:</b> k_nearest_neighbors.cpp:140</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa8dca7b867074164d5f45b0f3851269d">&#9670;&#160;</a></span>test()</h2>
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<td class="memname">static void test </td>
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<p>Self-test implementations. </p>
<dl class="section return"><dt>Returns</dt><dd>void </dd></dl>
<div class="fragment"><div class="line"><span class="lineno"> 140</span> {</div>
<div class="line"><span class="lineno"> 141</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;------- Test 1 -------&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 142</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;std::vector&lt;double&gt;</a>&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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;int&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample1 = {1.2, 1.2};</div>
<div class="line"><span class="lineno"> 148</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample2 = {0.1, 0.1};</div>
<div class="line"><span class="lineno"> 149</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample3 = {0.1, 0.5};</div>
<div class="line"><span class="lineno"> 150</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 156</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;------- Test 2 -------&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 157</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;std::vector&lt;double&gt;</a>&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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;int&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample5 = {1.2, 1.2, 0.0};</div>
<div class="line"><span class="lineno"> 163</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample6 = {0.1, 0.1, 0.0};</div>
<div class="line"><span class="lineno"> 164</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample7 = {0.1, 0.5, 0.0};</div>
<div class="line"><span class="lineno"> 165</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 171</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;------- Test 3 -------&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 172</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;std::vector&lt;double&gt;</a>&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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;int&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample9 = {0.5};</div>
<div class="line"><span class="lineno"> 177</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample10 = {2.9};</div>
<div class="line"><span class="lineno"> 178</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> sample11 = {5.5};</div>
<div class="line"><span class="lineno"> 179</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a> 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> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a> &lt;&lt; <span class="stringliteral">&quot;... Passed&quot;</span> &lt;&lt; <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><span class="lineno"> 185</span>}</div>
<div class="ttc" id="abasic_ostream_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cout</a></div></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> k_nearest_neighbors.cpp:55</div></div>
<div class="ttc" id="aendl_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a></div><div class="ttdeci">T endl(T... args)</div></div>
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