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<div id="projectbrief">Set of algorithms implemented in C++.</div>
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<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>
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<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>
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<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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<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"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a></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"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a></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"><a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a></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:a1d821a24e1503d468c95d4acedca58b3"><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#a1d821a24e1503d468c95d4acedca58b3">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"></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>
</tr>
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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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<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 name="l00057"></a><span class="lineno"> 57</span>&#160; : <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>(<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>), <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>(<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>) {</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a> &lt;= 0) {</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <a class="codeRef" 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 name="l00060"></a><span class="lineno"> 60</span>&#160; &lt;&lt; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/utility/program/exit.html">std::exit</a>(EXIT_FAILURE);</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; }</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a> = <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt;double&gt;</a>(</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; num_features +</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; 1); <span class="comment">// additional weight is for the constant bias term</span></div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; </div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// initialize with random weights in the range [-50, 49]</span></div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">double</span> &amp;weight : <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>) weight = 1.f;</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="comment">// weights[i] = (static_cast&lt;double&gt;(std::rand() % 100) - 50);</span></div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; }</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>
<a id="a082f758fb55fe19f22b3df66f89b2325"></a>
<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>
</table>
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<span class="mlabels"><span class="mlabel">inline</span></span> </td>
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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 name="l00186"></a><span class="lineno"> 186</span>&#160;{ <span class="keywordflow">return</span> x &gt; 0 ? 1 : -1; }</div>
</div><!-- fragment -->
</div>
</div>
<a id="ac8a9c2aaaa63b0f27ea176857e1e7d56"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac8a9c2aaaa63b0f27ea176857e1e7d56">&#9670;&nbsp;</a></span>check_size_match()</h2>
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<td class="memname">bool machine_learning::adaline::check_size_match </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><td>)</td>
<td></td>
</tr>
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<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">private</span></span> </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 name="l00196"></a><span class="lineno"> 196</span>&#160; {</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; <span class="keywordflow">if</span> (x.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() != (<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() - 1)) {</div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <a class="codeRef" 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 name="l00199"></a><span class="lineno"> 199</span>&#160; &lt;&lt; <span class="stringliteral">&quot;Number of features in x does not match the feature &quot;</span></div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="stringliteral">&quot;dimension in model!&quot;</span></div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; &lt;&lt; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; }</div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; }</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>
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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 name="l00119"></a><span class="lineno"> 119</span>&#160; {</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a>(x)) {</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; }</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; </div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <span class="comment">/* output of the model with current weights */</span></div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordtype">int</span> p = <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>(x);</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keywordtype">int</span> prediction_error = y - p; <span class="comment">// error in estimation</span></div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordtype">double</span> correction_factor = <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a> * prediction_error;</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">/* update each weight, the last weight is the bias term */</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; x.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i] += correction_factor * x[i];</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; }</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[x.<a class="codeRef" 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 name="l00134"></a><span class="lineno"> 134</span>&#160; </div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">return</span> correction_factor;</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; }</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>
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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 name="l00146"></a><span class="lineno"> 146</span>&#160; {</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="keywordtype">double</span> avg_pred_error = 1.f;</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; </div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keywordtype">int</span> iter = 0;</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keywordflow">for</span> (iter = 0; (iter &lt; <a class="code" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a>) &amp;&amp; (avg_pred_error &gt; <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>);</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; iter++) {</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; avg_pred_error = 0.f;</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; </div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="comment">// perform fit for each sample</span></div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N; i++) {</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keywordtype">double</span> err = <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>(X[i], Y[i]);</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; avg_pred_error += std::abs(err);</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; }</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; avg_pred_error /= N;</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="comment">// Print updates every 200th iteration</span></div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="comment">// if (iter % 100 == 0)</span></div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="codeRef" 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 name="l00164"></a><span class="lineno"> 164</span>&#160; &lt;&lt; <span class="stringliteral">&quot;\tAvg error: &quot;</span> &lt;&lt; avg_pred_error &lt;&lt; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; }</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; </div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <span class="keywordflow">if</span> (iter &lt; <a class="code" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a>) {</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <a class="codeRef" 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 name="l00169"></a><span class="lineno"> 169</span>&#160; &lt;&lt; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <a class="codeRef" 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 name="l00172"></a><span class="lineno"> 172</span>&#160; &lt;&lt; <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a>;</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; }</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; }</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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<td class="paramtype">double *&#160;</td>
<td class="paramname"><em>out</em> = <code>nullptr</code>&#160;</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 name="l00095"></a><span class="lineno"> 95</span>&#160; {</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a>(x)) {</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; }</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; </div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="keywordtype">double</span> y = <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/back.html">back</a>(); <span class="comment">// assign bias value</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; </div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="comment">// for (int i = 0; i &lt; x.size(); i++) y += x[i] * weights[i];</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; y = <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/inner_product.html">std::inner_product</a>(x.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>(), x.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/end.html">end</a>(), <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/begin.html">begin</a>(), y);</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <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 name="l00106"></a><span class="lineno"> 106</span>&#160; *out = y;</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; }</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keywordflow">return</span> <a class="code" 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 name="l00110"></a><span class="lineno"> 110</span>&#160; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1d821a24e1503d468c95d4acedca58b3">&#9670;&nbsp;</a></span>operator&lt;&lt;</h2>
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<td class="paramtype">const <a class="el" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a> &amp;&#160;</td>
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<p>Operator to print the weights of the model </p>
<div class="fragment"><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; {</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&lt;&quot;</span>;</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ada.<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>(); i++) {</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; out &lt;&lt; ada.<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i];</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keywordflow">if</span> (i &lt; ada.<a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>.<a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/container/vector/size.html">size</a>() - 1) {</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;, &quot;</span>;</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; }</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&gt;&quot;</span>;</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="keywordflow">return</span> out;</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; }</div>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<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>
<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>
<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>
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<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>
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