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<a href="../../d5/db0/adaline__learning_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> </div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span><span class="preprocessor">#include &lt;array&gt;</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span><span class="preprocessor">#include &lt;cassert&gt;</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="preprocessor">#include &lt;climits&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="preprocessor">#include &lt;cmath&gt;</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="preprocessor">#include &lt;cstdlib&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span><span class="preprocessor">#include &lt;ctime&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span><span class="preprocessor">#include &lt;numeric&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"><a class="line" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1"> 40</a></span><span class="keyword">constexpr</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="../../d9/d66/group__machine__learning.html#ga5118e5cbc4f0886e27b3a7a2544dded1">MAX_ITER</a> = 500; <span class="comment">// INT_MAX</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a> {</div>
<div class="foldopen" id="foldopen00046" data-start="{" data-end="};">
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="../../d8/df2/classadaline.html"> 46</a></span><span class="keyword">class </span><a class="code hl_function" href="../../d8/df2/classadaline.html#a0acbe32aaab897e7939e5b0454035b8c">adaline</a> {</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">public</span>:</div>
<div class="foldopen" id="foldopen00055" data-start="{" data-end="}">
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a0acbe32aaab897e7939e5b0454035b8c"> 55</a></span> <span class="keyword">explicit</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a0acbe32aaab897e7939e5b0454035b8c">adaline</a>(<span class="keywordtype">int</span> num_features, <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a> = 0.01f,</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a> = 1e-5)</div>
<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> std::cerr &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; std::endl;</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> std::exit(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> = std::vector&lt;double&gt;(</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>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span></div>
<div class="foldopen" id="foldopen00076" data-start="{" data-end="}">
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#ae347040516e995c8fb8ca2e5c0496daa"> 76</a></span> <span class="keyword">friend</span> std::ostream &amp;<a class="code hl_friend" href="../../d6/d30/classmachine__learning_1_1adaline.html#ae347040516e995c8fb8ca2e5c0496daa">operator&lt;&lt;</a>(std::ostream &amp;out, <span class="keyword">const</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a0acbe32aaab897e7939e5b0454035b8c">adaline</a> &amp;ada) {</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>
</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span></div>
<div class="foldopen" id="foldopen00095" data-start="{" data-end="}">
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3"> 95</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>(<span class="keyword">const</span> std::vector&lt;double&gt; &amp;x, <span class="keywordtype">double</span> *out = <span class="keyword">nullptr</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 = std::inner_product(x.begin(), x.end(), <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>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span></div>
<div class="foldopen" id="foldopen00119" data-start="{" data-end="}">
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5"> 119</a></span> <span class="keywordtype">double</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>(<span class="keyword">const</span> std::vector&lt;double&gt; &amp;x, <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;y) {</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.size(); 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.size()] += 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>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keyword">template</span> &lt;<span class="keywordtype">size_t</span> N&gt;</div>
<div class="foldopen" id="foldopen00145" data-start="{" data-end="}">
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a8d61f9ed872eef26bca39388cbda6a91"> 145</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a8d61f9ed872eef26bca39388cbda6a91">fit</a>(std::array&lt;std::vector&lt;double&gt;, N&gt; <span class="keyword">const</span> &amp;X,</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> std::array&lt;int, N&gt; <span class="keyword">const</span> &amp;Y) {</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; N; 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 /= N;</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> std::cout &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; std::endl;</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> std::cout &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; std::endl;</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> std::cout &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; std::endl;</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>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a082f758fb55fe19f22b3df66f89b2325"> 186</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a082f758fb55fe19f22b3df66f89b2325">activation</a>(<span class="keywordtype">double</span> x) { <span class="keywordflow">return</span> x &gt; 0 ? 1 : -1; }</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> </div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <span class="keyword">private</span>:</div>
<div class="foldopen" id="foldopen00196" data-start="{" data-end="}">
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56"> 196</a></span> <span class="keywordtype">bool</span> <a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ac8a9c2aaaa63b0f27ea176857e1e7d56">check_size_match</a>(<span class="keyword">const</span> std::vector&lt;double&gt; &amp;x) {</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> <span class="keywordflow">if</span> (x.size() != (<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> std::cerr &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; std::endl;</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>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> </div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda"> 207</a></span> <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a28160d17e492597a2f112e0d38551cda">eta</a>; </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3"> 208</a></span> <span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#aa23d60262f917f35836ef4b1c1d9f7d3">accuracy</a>; </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"><a class="line" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492"> 209</a></span> std::vector&lt;double&gt; <a class="code hl_variable" href="../../d6/d30/classmachine__learning_1_1adaline.html#a4cd8fe438032fedaa66f93bfd66f5492">weights</a>; </div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span>};</div>
</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> </div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span>} <span class="comment">// namespace machine_learning</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span><span class="keyword">using </span><a class="code hl_class" href="../../d6/d30/classmachine__learning_1_1adaline.html">machine_learning::adaline</a>;</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span></div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span></div>
<div class="foldopen" id="foldopen00224" data-start="{" data-end="}">
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"><a class="line" href="../../d5/db0/adaline__learning_8cpp.html#a52053d88ea1bcbbed9aca67ab4eeb499"> 224</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d3/dae/dsu__path__compression_8cpp.html#ae7880ce913f3058a35ff106d5be9e243">test1</a>(<span class="keywordtype">double</span> eta = 0.01) {</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> <a class="code hl_class" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a> ada(2, eta); <span class="comment">// 2 features</span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> </div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = 10; <span class="comment">// number of sample points</span></div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span> std::array&lt;std::vector&lt;double&gt;, N&gt; X = {</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> std::vector&lt;double&gt;({0, 1}), std::vector&lt;double&gt;({1, -2}),</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> std::vector&lt;double&gt;({2, 3}), std::vector&lt;double&gt;({3, -1}),</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> std::vector&lt;double&gt;({4, 1}), std::vector&lt;double&gt;({6, -5}),</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> std::vector&lt;double&gt;({-7, -3}), std::vector&lt;double&gt;({-8, 5}),</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> std::vector&lt;double&gt;({-9, 2}), std::vector&lt;double&gt;({-10, -15})};</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> std::array&lt;int, N&gt; y = {1, -1, 1, -1, -1,</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> -1, 1, 1, 1, -1}; <span class="comment">// corresponding y-values</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> </div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 1 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model before fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> </div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>&lt;N&gt;(X, y);</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model after fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> </div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> <span class="keywordtype">int</span> predict = ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>({5, -3});</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Predict for x=(5,-3): &quot;</span> &lt;&lt; predict;</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> assert(predict == -1);</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> std::cout &lt;&lt; <span class="stringliteral">&quot; ...passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> </div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> predict = ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>({5, 8});</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Predict for x=(5,8): &quot;</span> &lt;&lt; predict;</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> assert(predict == 1);</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> std::cout &lt;&lt; <span class="stringliteral">&quot; ...passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span>}</div>
</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span></div>
<div class="foldopen" id="foldopen00262" data-start="{" data-end="}">
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"><a class="line" href="../../d5/db0/adaline__learning_8cpp.html#a379f7488a305f2571f2932b319931f82"> 262</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d3/dae/dsu__path__compression_8cpp.html#a45d94ead4cf4e1ff9f87c38bc99f59ae">test2</a>(<span class="keywordtype">double</span> eta = 0.01) {</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> <a class="code hl_class" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a> ada(2, eta); <span class="comment">// 2 features</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> </div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = 50; <span class="comment">// number of sample points</span></div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> </div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> std::array&lt;std::vector&lt;double&gt;, N&gt; X;</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> std::array&lt;int, N&gt; Y{}; <span class="comment">// corresponding y-values</span></div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> </div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <span class="comment">// generate sample points in the interval</span></div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> <span class="comment">// [-range2/100 , (range2-1)/100]</span></div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> <span class="keywordtype">int</span> range = 500; <span class="comment">// sample points full-range</span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <span class="keywordtype">int</span> range2 = range &gt;&gt; 1; <span class="comment">// sample points half-range</span></div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N; i++) {</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="keywordtype">double</span> x0 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> <span class="keywordtype">double</span> x1 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> X[i] = std::vector&lt;double&gt;({x0, x1});</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> Y[i] = (x0 + 3. * x1) &gt; -1 ? 1 : -1;</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> }</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> </div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 2 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model before fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> </div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>(X, Y);</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model after fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> </div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> <span class="keywordtype">int</span> N_test_cases = 5;</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N_test_cases; i++) {</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <span class="keywordtype">double</span> x0 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <span class="keywordtype">double</span> x1 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> </div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="keywordtype">int</span> predict = ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>({x0, x1});</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> </div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Predict for x=(&quot;</span> &lt;&lt; x0 &lt;&lt; <span class="stringliteral">&quot;,&quot;</span> &lt;&lt; x1 &lt;&lt; <span class="stringliteral">&quot;): &quot;</span> &lt;&lt; predict;</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> </div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> <span class="keywordtype">int</span> expected_val = (x0 + 3. * x1) &gt; -1 ? 1 : -1;</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> assert(predict == expected_val);</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> std::cout &lt;&lt; <span class="stringliteral">&quot; ...passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> }</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span>}</div>
</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span></div>
<div class="foldopen" id="foldopen00313" data-start="{" data-end="}">
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"><a class="line" href="../../d5/db0/adaline__learning_8cpp.html#a992bdf1fdb0b9d414bcf7981d2d87aa9"> 313</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../dd/d0c/hamiltons__cycle_8cpp.html#a0cc94918b6831f308d4fe4fa27f08299">test3</a>(<span class="keywordtype">double</span> eta = 0.01) {</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> <a class="code hl_class" href="../../d6/d30/classmachine__learning_1_1adaline.html">adaline</a> ada(6, eta); <span class="comment">// 2 features</span></div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> </div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = 100; <span class="comment">// number of sample points</span></div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> </div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> std::array&lt;std::vector&lt;double&gt;, N&gt; X;</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> std::array&lt;int, N&gt; Y{}; <span class="comment">// corresponding y-values</span></div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> </div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> <span class="comment">// generate sample points in the interval</span></div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> <span class="comment">// [-range2/100 , (range2-1)/100]</span></div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> <span class="keywordtype">int</span> range = 200; <span class="comment">// sample points full-range</span></div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> <span class="keywordtype">int</span> range2 = range &gt;&gt; 1; <span class="comment">// sample points half-range</span></div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N; i++) {</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> <span class="keywordtype">double</span> x0 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordtype">double</span> x1 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> <span class="keywordtype">double</span> x2 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> X[i] = std::vector&lt;double&gt;({x0, x1, x2, x0 * x0, x1 * x1, x2 * x2});</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> Y[i] = ((x0 * x0) + (x1 * x1) + (x2 * x2)) &lt;= 1.f ? 1 : -1;</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> }</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> </div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> std::cout &lt;&lt; <span class="stringliteral">&quot;------- Test 3 -------&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model before fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> </div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#a74e3c6c037b67895014414c5d75465e5">fit</a>(X, Y);</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Model after fit: &quot;</span> &lt;&lt; ada &lt;&lt; std::endl;</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> </div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> <span class="keywordtype">int</span> N_test_cases = 5;</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; N_test_cases; i++) {</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> <span class="keywordtype">double</span> x0 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keywordtype">double</span> x1 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <span class="keywordtype">double</span> x2 = (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(std::rand() % range) - range2) / 100.f;</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> </div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> <span class="keywordtype">int</span> predict = ada.<a class="code hl_function" href="../../d6/d30/classmachine__learning_1_1adaline.html#ab11242d9ad5b03a75911e29b04f78fd3">predict</a>({x0, x1, x2, x0 * x0, x1 * x1, x2 * x2});</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> </div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Predict for x=(&quot;</span> &lt;&lt; x0 &lt;&lt; <span class="stringliteral">&quot;,&quot;</span> &lt;&lt; x1 &lt;&lt; <span class="stringliteral">&quot;,&quot;</span> &lt;&lt; x2</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> &lt;&lt; <span class="stringliteral">&quot;): &quot;</span> &lt;&lt; predict;</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> </div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> <span class="keywordtype">int</span> expected_val = ((x0 * x0) + (x1 * x1) + (x2 * x2)) &lt;= 1.f ? 1 : -1;</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> assert(predict == expected_val);</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> std::cout &lt;&lt; <span class="stringliteral">&quot; ...passed&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> }</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span>}</div>
</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span></div>
<div class="foldopen" id="foldopen00357" data-start="{" data-end="}">
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"><a class="line" href="../../d5/db0/adaline__learning_8cpp.html#a3c04138a5bfe5d72780bb7e82a18e627"> 357</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="../../dd/d1e/generate__parentheses_8cpp.html#gae66f6b31b5ad750f1fe042a706a4e3d4">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv) {</div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> std::srand(std::time(<span class="keyword">nullptr</span>)); <span class="comment">// initialize random number generator</span></div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> </div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <span class="keywordtype">double</span> eta = 0.1; <span class="comment">// default value of eta</span></div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> <span class="keywordflow">if</span> (argc == 2) { <span class="comment">// read eta value from commandline argument if present</span></div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> eta = strtof(argv[1], <span class="keyword">nullptr</span>);</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> }</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> </div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <a class="code hl_function" href="../../d3/dae/dsu__path__compression_8cpp.html#ae7880ce913f3058a35ff106d5be9e243">test1</a>(eta);</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> </div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Press ENTER to continue...&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> std::cin.get();</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> </div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> <a class="code hl_function" href="../../d3/dae/dsu__path__compression_8cpp.html#a45d94ead4cf4e1ff9f87c38bc99f59ae">test2</a>(eta);</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> </div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> std::cout &lt;&lt; <span class="stringliteral">&quot;Press ENTER to continue...&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> std::cin.get();</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> </div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> <a class="code hl_function" href="../../dd/d0c/hamiltons__cycle_8cpp.html#a0cc94918b6831f308d4fe4fa27f08299">test3</a>(eta);</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> </div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> <span class="keywordflow">return</span> 0;</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span>}</div>
</div>
<div class="ttc" id="aclassadaline_html_a0acbe32aaab897e7939e5b0454035b8c"><div class="ttname"><a href="../../d8/df2/classadaline.html#a0acbe32aaab897e7939e5b0454035b8c">adaline::adaline</a></div><div class="ttdeci">adaline(int num_features, const double eta=0.01f, const double accuracy=1e-5)</div><div class="ttdef"><b>Definition</b> <a href="#l00055">adaline_learning.cpp:55</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html">machine_learning::adaline</a></div><div class="ttdef"><b>Definition</b> <a href="#l00046">adaline_learning.cpp:46</a></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> <a href="#l00186">adaline_learning.cpp:186</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_a0acbe32aaab897e7939e5b0454035b8c"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a0acbe32aaab897e7939e5b0454035b8c">machine_learning::adaline::adaline</a></div><div class="ttdeci">adaline(int num_features, const double eta=0.01f, const double accuracy=1e-5)</div><div class="ttdef"><b>Definition</b> <a href="#l00055">adaline_learning.cpp:55</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> <a href="#l00207">adaline_learning.cpp:207</a></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> <a href="#l00209">adaline_learning.cpp:209</a></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> <a href="#l00119">adaline_learning.cpp:119</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_a8d61f9ed872eef26bca39388cbda6a91"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#a8d61f9ed872eef26bca39388cbda6a91">machine_learning::adaline::fit</a></div><div class="ttdeci">void fit(std::array&lt; std::vector&lt; double &gt;, N &gt; const &amp;X, std::array&lt; int, N &gt; const &amp;Y)</div><div class="ttdef"><b>Definition</b> <a href="#l00145">adaline_learning.cpp:145</a></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> <a href="#l00208">adaline_learning.cpp:208</a></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> <a href="#l00095">adaline_learning.cpp:95</a></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> <a href="#l00196">adaline_learning.cpp:196</a></div></div>
<div class="ttc" id="aclassmachine__learning_1_1adaline_html_ae347040516e995c8fb8ca2e5c0496daa"><div class="ttname"><a href="../../d6/d30/classmachine__learning_1_1adaline.html#ae347040516e995c8fb8ca2e5c0496daa">machine_learning::adaline::operator&lt;&lt;</a></div><div class="ttdeci">friend std::ostream &amp; operator&lt;&lt;(std::ostream &amp;out, const adaline &amp;ada)</div><div class="ttdef"><b>Definition</b> <a href="#l00076">adaline_learning.cpp:76</a></div></div>
<div class="ttc" id="adsu__path__compression_8cpp_html_a45d94ead4cf4e1ff9f87c38bc99f59ae"><div class="ttname"><a href="../../d3/dae/dsu__path__compression_8cpp.html#a45d94ead4cf4e1ff9f87c38bc99f59ae">test2</a></div><div class="ttdeci">static void test2()</div><div class="ttdoc">Self-implementations, 2nd test.</div><div class="ttdef"><b>Definition</b> <a href="../../d3/dae/dsu__path__compression_8cpp_source.html#l00187">dsu_path_compression.cpp:187</a></div></div>
<div class="ttc" id="adsu__path__compression_8cpp_html_ae7880ce913f3058a35ff106d5be9e243"><div class="ttname"><a href="../../d3/dae/dsu__path__compression_8cpp.html#ae7880ce913f3058a35ff106d5be9e243">test1</a></div><div class="ttdeci">static void test1()</div><div class="ttdoc">Self-test implementations, 1st test.</div><div class="ttdef"><b>Definition</b> <a href="../../d3/dae/dsu__path__compression_8cpp_source.html#l00170">dsu_path_compression.cpp:170</a></div></div>
<div class="ttc" id="agenerate__parentheses_8cpp_html_gae66f6b31b5ad750f1fe042a706a4e3d4"><div class="ttname"><a href="../../dd/d1e/generate__parentheses_8cpp.html#gae66f6b31b5ad750f1fe042a706a4e3d4">main</a></div><div class="ttdeci">int main()</div><div class="ttdoc">Main function.</div><div class="ttdef"><b>Definition</b> <a href="../../dd/d1e/generate__parentheses_8cpp_source.html#l00110">generate_parentheses.cpp:110</a></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> <a href="#l00040">adaline_learning.cpp:40</a></div></div>
<div class="ttc" id="ahamiltons__cycle_8cpp_html_a0cc94918b6831f308d4fe4fa27f08299"><div class="ttname"><a href="../../dd/d0c/hamiltons__cycle_8cpp.html#a0cc94918b6831f308d4fe4fa27f08299">test3</a></div><div class="ttdeci">static void test3()</div><div class="ttdef"><b>Definition</b> <a href="../../dd/d0c/hamiltons__cycle_8cpp_source.html#l00122">hamiltons_cycle.cpp:122</a></div></div>
<div class="ttc" id="anamespacemachine__learning_html"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html">machine_learning</a></div><div class="ttdoc">A* search algorithm</div></div>
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