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Public Member Functions</h2></td></tr>
<tr class="memitem:a0acbe32aaab897e7939e5b0454035b8c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a0acbe32aaab897e7939e5b0454035b8c">adaline</a> (int num_features, const double <a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>=0.01f, const double <a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>=1e-5)</td></tr>
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<tr class="memitem:ab11242d9ad5b03a75911e29b04f78fd3"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a> (const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt; &amp;x, double *out=nullptr)</td></tr>
<tr class="separator:ab11242d9ad5b03a75911e29b04f78fd3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a74e3c6c037b67895014414c5d75465e5"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a> (const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt; &amp;x, const int &amp;y)</td></tr>
<tr class="separator:a74e3c6c037b67895014414c5d75465e5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8d61f9ed872eef26bca39388cbda6a91"><td class="memTemplParams" colspan="2">template&lt;size_t N&gt; </td></tr>
<tr class="memitem:a8d61f9ed872eef26bca39388cbda6a91"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a8d61f9ed872eef26bca39388cbda6a91">fit</a> (<a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/array.html">std::array</a>&lt; <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt;, N &gt; const &amp;X, <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/array.html">std::array</a>&lt; int, N &gt; const &amp;Y)</td></tr>
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<tr class="memitem:a082f758fb55fe19f22b3df66f89b2325"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a082f758fb55fe19f22b3df66f89b2325">activation</a> (double x)</td></tr>
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Private Member Functions</h2></td></tr>
<tr class="memitem:ac8a9c2aaaa63b0f27ea176857e1e7d56"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a> (const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt; &amp;x)</td></tr>
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Private Attributes</h2></td></tr>
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const double&#160;</td><td class="memItemRight" valign="bottom"><b>eta</b></td></tr>
<tr class="memdesc:a28160d17e492597a2f112e0d38551cda"><td class="mdescLeft">&#160;</td><td class="mdescRight">learning rate of the algorithm <br /></td></tr>
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const double&#160;</td><td class="memItemRight" valign="bottom"><b>accuracy</b></td></tr>
<tr class="memdesc:aa23d60262f917f35836ef4b1c1d9f7d3"><td class="mdescLeft">&#160;</td><td class="mdescRight">model fit convergence accuracy <br /></td></tr>
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<a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>weights</b></td></tr>
<tr class="memdesc:a4cd8fe438032fedaa66f93bfd66f5492"><td class="mdescLeft">&#160;</td><td class="mdescRight">weights of the neural network <br /></td></tr>
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Friends</h2></td></tr>
<tr class="memitem:ae347040516e995c8fb8ca2e5c0496daa"><td class="memItemLeft" align="right" valign="top"><a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::ostream</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#ae347040516e995c8fb8ca2e5c0496daa">operator&lt;&lt;</a> (<a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::ostream</a> &amp;out, const <a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a> &amp;ada)</td></tr>
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<h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a0acbe32aaab897e7939e5b0454035b8c" name="a0acbe32aaab897e7939e5b0454035b8c"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a0acbe32aaab897e7939e5b0454035b8c">&#9670;&nbsp;</a></span>adaline()</h2>
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<td class="memname">machine_learning::adaline::adaline </td>
<td>(</td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>num_features</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>eta</em> = <code>0.01f</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>accuracy</em> = <code>1e-5</code>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">explicit</span></span> </td>
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</div><div class="memdoc">
<p >Default constructor </p><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">num_features</td><td>number of features present </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">eta</td><td>learning rate (optional, default=0.1) </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">convergence</td><td>accuracy (optional, default= \(1\times10^{-5}\)) </td></tr>
</table>
</dd>
</dl>
<div class="fragment"><div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> : <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>(<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>), <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>(<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>) {</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keywordflow">if</span> (<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a> &lt;= 0) {</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> &lt;&lt; <span class="stringliteral">&quot;learning rate should be positive and nonzero&quot;</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</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"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> }</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> </div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a> = <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a>(</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> num_features +</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> 1); <span class="comment">// additional weight is for the constant bias term</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> </div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="comment">// initialize with random weights in the range [-50, 49]</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="keywordflow">for</span> (<span class="keywordtype">double</span> &amp;weight : <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>) weight = 1.f;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> <span class="comment">// weights[i] = (static_cast&lt;double&gt;(std::rand() % 100) - 50);</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</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::cerr</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_a28160d17e492597a2f112e0d38551cda"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">machine_learning::adaline::eta</a></div><div class="ttdeci">const double eta</div><div class="ttdoc">learning rate of the algorithm</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:207</div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_a4cd8fe438032fedaa66f93bfd66f5492"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">machine_learning::adaline::weights</a></div><div class="ttdeci">std::vector&lt; double &gt; weights</div><div class="ttdoc">weights of the neural network</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:209</div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_aa23d60262f917f35836ef4b1c1d9f7d3"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">machine_learning::adaline::accuracy</a></div><div class="ttdeci">const double accuracy</div><div class="ttdoc">model fit convergence accuracy</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:208</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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<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>
</div><!-- fragment --><div class="dynheader">
Here is the call graph for this function:</div>
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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a082f758fb55fe19f22b3df66f89b2325">&#9670;&nbsp;</a></span>activation()</h2>
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<td class="memname">int machine_learning::adaline::activation </td>
<td>(</td>
<td class="paramtype">double&#160;</td>
<td class="paramname"><em>x</em></td><td>)</td>
<td></td>
</tr>
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<p >Defines activation function as Heaviside's step function. </p><p class="formulaDsp">
\[ f(x) = \begin{cases} -1 &amp; \forall x \le 0\\ 1 &amp; \forall x &gt; 0 \end{cases} \]
</p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">x</td><td>input value to apply activation on </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>activation output </dd></dl>
<div class="fragment"><div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span>{ <span class="keywordflow">return</span> x &gt; 0 ? 1 : -1; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac8a9c2aaaa63b0f27ea176857e1e7d56">&#9670;&nbsp;</a></span>check_size_match()</h2>
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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; double &gt; &amp;&#160;</td>
<td class="paramname"><em>x</em></td><td>)</td>
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<p >convenient function to check if input feature vector size matches the model weights size </p><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">x</td><td>fecture vector to check </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd><code>true</code> size matches </dd>
<dd>
<code>false</code> size does not match </dd></dl>
<div class="fragment"><div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> {</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> <span class="keywordflow">if</span> (x.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() != (<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.size() - 1)) {</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> <a class="code hl_classRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/basic_ostream.html">std::cerr</a> &lt;&lt; __func__ &lt;&lt; <span class="stringliteral">&quot;: &quot;</span></div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> &lt;&lt; <span class="stringliteral">&quot;Number of features in x does not match the feature &quot;</span></div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="stringliteral">&quot;dimension in model!&quot;</span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</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"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> }</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> }</div>
<div class="ttc" id="asize_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/size.html">std::vector::size</a></div><div class="ttdeci">T size(T... args)</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a74e3c6c037b67895014414c5d75465e5">&#9670;&nbsp;</a></span>fit() <span class="overload">[1/2]</span></h2>
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<td class="memname">double machine_learning::adaline::fit </td>
<td>(</td>
<td class="paramtype">const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt; &amp;&#160;</td>
<td class="paramname"><em>x</em>, </td>
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<td class="paramtype">const int &amp;&#160;</td>
<td class="paramname"><em>y</em>&#160;</td>
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<p >Update the weights of the model using supervised learning for one feature vector </p><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">x</td><td>feature vector </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">y</td><td>known output value </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>correction factor </dd></dl>
<div class="fragment"><div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> {</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a>(x)) {</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> }</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> <span class="comment">/* output of the model with current weights */</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keywordtype">int</span> p = <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>(x);</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordtype">int</span> prediction_error = y - p; <span class="comment">// error in estimation</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordtype">double</span> correction_factor = <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a> * prediction_error;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> <span class="comment">/* update each weight, the last weight is the bias term */</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; x.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i] += correction_factor * x[i];</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> }</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[x.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>()] += correction_factor; <span class="comment">// update bias</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordflow">return</span> correction_factor;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> }</div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_ab11242d9ad5b03a75911e29b04f78fd3"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">machine_learning::adaline::predict</a></div><div class="ttdeci">int predict(const std::vector&lt; double &gt; &amp;x, double *out=nullptr)</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:95</div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_ac8a9c2aaaa63b0f27ea176857e1e7d56"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">machine_learning::adaline::check_size_match</a></div><div class="ttdeci">bool check_size_match(const std::vector&lt; double &gt; &amp;x)</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:196</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a8d61f9ed872eef26bca39388cbda6a91">&#9670;&nbsp;</a></span>fit() <span class="overload">[2/2]</span></h2>
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template&lt;size_t N&gt; </div>
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<td class="memname">void machine_learning::adaline::fit </td>
<td>(</td>
<td class="paramtype"><a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/array.html">std::array</a>&lt; <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt;, N &gt; const &amp;&#160;</td>
<td class="paramname"><em>X</em>, </td>
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<td></td>
<td class="paramtype"><a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/array.html">std::array</a>&lt; int, N &gt; const &amp;&#160;</td>
<td class="paramname"><em>Y</em>&#160;</td>
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<td>)</td>
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<p >Update the weights of the model using supervised learning for an array of vectors. </p><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">X</td><td>array of feature vector </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">y</td><td>known output value for each feature vector </td></tr>
</table>
</dd>
</dl>
<div class="fragment"><div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> {</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> <span class="keywordtype">double</span> avg_pred_error = 1.f;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> </div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordtype">int</span> iter = 0;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> <span class="keywordflow">for</span> (iter = 0; (iter &lt; <a class="code hl_variable" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a>) &amp;&amp; (avg_pred_error &gt; <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> iter++) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> avg_pred_error = 0.f;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="comment">// perform fit for each sample</span></div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; <a class="code hl_variable" href="../../d6/d42/data__structures_2sparse__table_8cpp.html#a10f3ffb3f6f7e1b83d556b9c8de89a5d">N</a>; i++) {</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <span class="keywordtype">double</span> err = <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>(X[i], Y[i]);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> avg_pred_error += std::abs(err);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> }</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> avg_pred_error /= <a class="code hl_variable" href="../../d6/d42/data__structures_2sparse__table_8cpp.html#a10f3ffb3f6f7e1b83d556b9c8de89a5d">N</a>;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> </div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> <span class="comment">// Print updates every 200th iteration</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="comment">// if (iter % 100 == 0)</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</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;\tIter &quot;</span> &lt;&lt; iter &lt;&lt; <span class="stringliteral">&quot;: Training weights: &quot;</span> &lt;&lt; *<span class="keyword">this</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> &lt;&lt; <span class="stringliteral">&quot;\tAvg error: &quot;</span> &lt;&lt; avg_pred_error &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"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> }</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> </div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> <span class="keywordflow">if</span> (iter &lt; <a class="code hl_variable" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a>) {</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</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;Converged after &quot;</span> &lt;&lt; iter &lt;&lt; <span class="stringliteral">&quot; iterations.&quot;</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</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"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00171" name="l00171"></a><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;Did not converge after &quot;</span> &lt;&lt; iter &lt;&lt; <span class="stringliteral">&quot; iterations.&quot;</span></div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</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"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> }</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> }</div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_a74e3c6c037b67895014414c5d75465e5"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">machine_learning::adaline::fit</a></div><div class="ttdeci">double fit(const std::vector&lt; double &gt; &amp;x, const int &amp;y)</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:119</div></div>
<div class="ttc" id="adata__structures_2sparse__table_8cpp_html_a10f3ffb3f6f7e1b83d556b9c8de89a5d"><div class="ttname"><a href="../../d6/d42/data__structures_2sparse__table_8cpp.html#a10f3ffb3f6f7e1b83d556b9c8de89a5d">data_structures::sparse_table::N</a></div><div class="ttdeci">constexpr uint32_t N</div><div class="ttdoc">A struct to represent sparse table for min() as their invariant function, for the given array A....</div><div class="ttdef"><b>Definition:</b> sparse_table.cpp:47</div></div>
<div class="ttc" id="agroup__machine__learning_html_ga5118e5cbc4f0886e27b3a7a2544dded1"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a></div><div class="ttdeci">constexpr int MAX_ITER</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:40</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ab11242d9ad5b03a75911e29b04f78fd3">&#9670;&nbsp;</a></span>predict()</h2>
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<td class="memname">int machine_learning::adaline::predict </td>
<td>(</td>
<td class="paramtype">const <a class="elRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector</a>&lt; double &gt; &amp;&#160;</td>
<td class="paramname"><em>x</em>, </td>
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<p >predict the output of the model for given set of features </p><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">x</td><td>input vector </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">out</td><td>optional argument to return neuron output before applying activation function (optional, <code>nullptr</code> to ignore) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>model prediction output </dd></dl>
<div class="fragment"><div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> <span class="keywordflow">if</span> (!<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a>(x)) {</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> }</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keywordtype">double</span> y = <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.back(); <span class="comment">// assign bias value</span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> </div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="comment">// for (int i = 0; i &lt; x.size(); i++) y += x[i] * weights[i];</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> y = <a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/inner_product.html">std::inner_product</a>(x.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>(), x.<a class="code hl_functionRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/end.html">end</a>(), <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.begin(), y);</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> </div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> <span class="keywordflow">if</span> (out != <span class="keyword">nullptr</span>) { <span class="comment">// if out variable is provided</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> *out = y;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> }</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> </div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keywordflow">return</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a082f758fb55fe19f22b3df66f89b2325">activation</a>(y); <span class="comment">// quantizer: apply ADALINE threshold function</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> }</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="aclassmachine__learning_1_1adaline_html_a082f758fb55fe19f22b3df66f89b2325"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a082f758fb55fe19f22b3df66f89b2325">machine_learning::adaline::activation</a></div><div class="ttdeci">int activation(double x)</div><div class="ttdef"><b>Definition:</b> adaline_learning.cpp:186</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="ainner_product_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/inner_product.html">std::inner_product</a></div><div class="ttdeci">T inner_product(T... args)</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae347040516e995c8fb8ca2e5c0496daa">&#9670;&nbsp;</a></span>operator&lt;&lt;</h2>
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<p >Operator to print the weights of the model </p>
<div class="fragment"><div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> {</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> out &lt;&lt; <span class="stringliteral">&quot;&lt;&quot;</span>;</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ada.<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.size(); i++) {</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> out &lt;&lt; ada.<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i];</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keywordflow">if</span> (i &lt; ada.<a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.size() - 1) {</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> out &lt;&lt; <span class="stringliteral">&quot;, &quot;</span>;</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> }</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> }</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> out &lt;&lt; <span class="stringliteral">&quot;&gt;&quot;</span>;</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">return</span> out;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> }</div>
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