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@@ -170,7 +170,7 @@ Here is the call graph for this function:</div>
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="../../dir_074119ce3a874b57120c49a0cc4bb5ad.html">range_queries</a></li><li class="navelem"><a class="el" href="../../d6/d2e/fenwick__tree_8cpp.html">fenwick_tree.cpp</a></li>
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@@ -195,21 +195,21 @@ Friends</h2></td></tr>
</table>
</dd>
</dl>
<div class="fragment"><div class="line"><a name="l00053"></a><span class="lineno"> 53</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="l00054"></a><span class="lineno"> 54</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="l00055"></a><span class="lineno"> 55</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="l00056"></a><span class="lineno"> 56</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="l00057"></a><span class="lineno"> 57</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="l00058"></a><span class="lineno"> 58</span>&#160; }</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</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="l00061"></a><span class="lineno"> 61</span>&#160; num_features +</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; 1); <span class="comment">// additional weight is for the constant bias term</span></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; <span class="comment">// initialize with random weights in the range [-50, 49]</span></div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; <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++) <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i] = 1.<a class="code" href="../../d4/d32/fibonacci__fast_8cpp.html#a3ba232425d45f9e9c0b87a8cf7ab69d9">f</a>;</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="comment">// weights[i] = (static_cast&lt;double&gt;(std::rand() % 100) - 50);</span></div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00055"></a><span class="lineno"> 55</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="l00056"></a><span class="lineno"> 56</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="l00057"></a><span class="lineno"> 57</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="l00058"></a><span class="lineno"> 58</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="l00059"></a><span class="lineno"> 59</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="l00060"></a><span class="lineno"> 60</span>&#160; }</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; </div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</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="l00063"></a><span class="lineno"> 63</span>&#160; num_features +</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; 1); <span class="comment">// additional weight is for the constant bias term</span></div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; </div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="comment">// initialize with random weights in the range [-50, 49]</span></div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; <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++) <a class="code" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>[i] = 1.<a class="code" href="../../d4/d32/fibonacci__fast_8cpp.html#a3ba232425d45f9e9c0b87a8cf7ab69d9">f</a>;</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// weights[i] = (static_cast&lt;double&gt;(std::rand() % 100) - 50);</span></div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; }</div>
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@@ -252,16 +252,16 @@ Here is the call graph for this function:</div>
<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="l00174"></a><span class="lineno"> 174</span>&#160; {</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</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="l00176"></a><span class="lineno"> 176</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="l00177"></a><span class="lineno"> 177</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="l00178"></a><span class="lineno"> 178</span>&#160; <span class="stringliteral">&quot;dimension in model!&quot;</span></div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</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="l00180"></a><span class="lineno"> 180</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; }</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; {</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</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="l00178"></a><span class="lineno"> 178</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="l00179"></a><span class="lineno"> 179</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="l00180"></a><span class="lineno"> 180</span>&#160; <span class="stringliteral">&quot;dimension in model!&quot;</span></div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</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="l00182"></a><span class="lineno"> 182</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; }</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; }</div>
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@@ -312,23 +312,23 @@ Here is the call graph for this function:</div>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>correction factor </dd></dl>
<div class="fragment"><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; {</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</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="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; </div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="comment">/* output of the model with current weights */</span></div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</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="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keywordtype">int</span> prediction_error = y - p; <span class="comment">// error in estimation</span></div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</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="l00118"></a><span class="lineno"> 118</span>&#160; </div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="comment">/* update each weight, the last weight is the bias term */</span></div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</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="l00121"></a><span class="lineno"> 121</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="l00122"></a><span class="lineno"> 122</span>&#160; }</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</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="l00124"></a><span class="lineno"> 124</span>&#160; </div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordflow">return</span> correction_factor;</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; {</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</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="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; </div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">/* output of the model with current weights */</span></div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</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="l00118"></a><span class="lineno"> 118</span>&#160; <span class="keywordtype">int</span> prediction_error = y - p; <span class="comment">// error in estimation</span></div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</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="l00120"></a><span class="lineno"> 120</span>&#160; </div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="comment">/* update each weight, the last weight is the bias term */</span></div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</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="l00123"></a><span class="lineno"> 123</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="l00124"></a><span class="lineno"> 124</span>&#160; }</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</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="l00126"></a><span class="lineno"> 126</span>&#160; </div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">return</span> correction_factor;</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; }</div>
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@@ -380,35 +380,35 @@ template&lt;int N&gt; </div>
</table>
</dd>
</dl>
<div class="fragment"><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="keywordtype">double</span> avg_pred_error = 1.f;</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; </div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keywordtype">int</span> iter;</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="keywordflow">for</span> (iter = 0; (iter &lt; MAX_ITER) &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="l00139"></a><span class="lineno"> 139</span>&#160; iter++) {</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; avg_pred_error = 0.f;</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; </div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="comment">// perform fit for each sample</span></div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N; i++) {</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</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="l00145"></a><span class="lineno"> 145</span>&#160; avg_pred_error += std::abs(err);</div>
<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; avg_pred_error /= N;</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="comment">// Print updates every 200th iteration</span></div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="comment">// if (iter % 100 == 0)</span></div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</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="l00152"></a><span class="lineno"> 152</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="l00153"></a><span class="lineno"> 153</span>&#160; }</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; </div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keywordflow">if</span> (iter &lt; MAX_ITER)</div>
<div class="fragment"><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; {</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keywordtype">double</span> avg_pred_error = 1.f;</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="keywordtype">int</span> iter;</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <span class="keywordflow">for</span> (iter = 0; (iter &lt; MAX_ITER) &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="l00141"></a><span class="lineno"> 141</span>&#160; iter++) {</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; avg_pred_error = 0.f;</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; </div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="comment">// perform fit for each sample</span></div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N; i++) {</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</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="l00147"></a><span class="lineno"> 147</span>&#160; avg_pred_error += std::abs(err);</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; avg_pred_error /= N;</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; </div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="comment">// Print updates every 200th iteration</span></div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="comment">// if (iter % 100 == 0)</span></div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</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="l00154"></a><span class="lineno"> 154</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="l00155"></a><span class="lineno"> 155</span>&#160; }</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; </div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</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="l00158"></a><span class="lineno"> 158</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="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keywordflow">else</span></div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</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="l00161"></a><span class="lineno"> 161</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="l00162"></a><span class="lineno"> 162</span>&#160; }</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; <span class="keywordflow">if</span> (iter &lt; MAX_ITER)</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; <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="l00160"></a><span class="lineno"> 160</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="l00161"></a><span class="lineno"> 161</span>&#160; <span class="keywordflow">else</span></div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</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="l00163"></a><span class="lineno"> 163</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="l00164"></a><span class="lineno"> 164</span>&#160; }</div>
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@@ -453,20 +453,20 @@ template&lt;int N&gt; </div>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>model prediction output </dd></dl>
<div class="fragment"><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; {</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</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="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</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="fragment"><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; {</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</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="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keywordflow">return</span> 0;</div>
<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="comment">// for (int i = 0; i &lt; x.size(); i++) y += x[i] * weights[i];</span></div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</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="l00098"></a><span class="lineno"> 98</span>&#160; </div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</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="l00100"></a><span class="lineno"> 100</span>&#160; *out = y;</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="keywordflow">return</span> activation(y); <span class="comment">// quantizer: apply ADALINE threshold function</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; }</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</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="l00097"></a><span class="lineno"> 97</span>&#160; </div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</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="l00099"></a><span class="lineno"> 99</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="l00100"></a><span class="lineno"> 100</span>&#160; </div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</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="l00102"></a><span class="lineno"> 102</span>&#160; *out = y;</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keywordflow">return</span> activation(y); <span class="comment">// quantizer: apply ADALINE threshold function</span></div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; }</div>
</div><!-- fragment --><div class="dynheader">
Here is the call graph for this function:</div>
<div class="dyncontent">
@@ -511,16 +511,16 @@ Here is the call graph for this function:</div>
</table>
</div><div class="memdoc">
<p>Operator to print the weights of the model </p>
<div class="fragment"><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; {</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&lt;&quot;</span>;</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</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="l00075"></a><span class="lineno"> 75</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="l00076"></a><span class="lineno"> 76</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="l00077"></a><span class="lineno"> 77</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;, &quot;</span>;</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; }</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&gt;&quot;</span>;</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keywordflow">return</span> out;</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; {</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&lt;&quot;</span>;</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</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="l00077"></a><span class="lineno"> 77</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="l00078"></a><span class="lineno"> 78</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="l00079"></a><span class="lineno"> 79</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;, &quot;</span>;</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; out &lt;&lt; <span class="stringliteral">&quot;&gt;&quot;</span>;</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="keywordflow">return</span> out;</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div>
</div><!-- fragment -->
</div>
</div>
@@ -533,23 +533,23 @@ Here is the call graph for this function:</div>
<div class="ttc" id="afibonacci__fast_8cpp_html_a3ba232425d45f9e9c0b87a8cf7ab69d9"><div class="ttname"><a href="../../d4/d32/fibonacci__fast_8cpp.html#a3ba232425d45f9e9c0b87a8cf7ab69d9">f</a></div><div class="ttdeci">uint64_t f[MAX]</div><div class="ttdef"><b>Definition:</b> fibonacci_fast.cpp:27</div></div>
<div class="ttc" id="avector_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector.html">std::vector&lt; double &gt;</a></div></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>
<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:110</div></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:112</div></div>
<div class="ttc" id="aback_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/container/vector/back.html">std::vector::back</a></div><div class="ttdeci">T back(T... args)</div></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:90</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:174</div></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:92</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:176</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>
<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:185</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:187</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:187</div></div>
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