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<p>Implementation of <a href="https://en.wikipedia.org/wiki/Multilayer_perceptron">Multilayer Perceptron</a>.
<a href="#details">More...</a></p>
<div class="textblock"><code>#include &quot;<a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>&quot;</code><br />
<div class="textblock"><code>#include &lt;algorithm&gt;</code><br />
<code>#include &lt;cassert&gt;</code><br />
<code>#include &lt;chrono&gt;</code><br />
<code>#include &lt;cmath&gt;</code><br />
<code>#include &lt;fstream&gt;</code><br />
<code>#include &lt;iostream&gt;</code><br />
<code>#include &lt;sstream&gt;</code><br />
<code>#include &lt;string&gt;</code><br />
<code>#include &lt;valarray&gt;</code><br />
<code>#include &lt;vector&gt;</code><br />
<code>#include &lt;cmath&gt;</code><br />
<code>#include &lt;algorithm&gt;</code><br />
<code>#include &lt;chrono&gt;</code><br />
<code>#include &lt;string&gt;</code><br />
<code>#include &lt;fstream&gt;</code><br />
<code>#include &lt;sstream&gt;</code><br />
<code>#include &lt;cassert&gt;</code><br />
<code>#include &quot;<a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>&quot;</code><br />
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<dl class="section return"><dt>Returns</dt><dd>derivative of relu(x) </dd></dl>
<div class="fragment"><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; <span class="keywordflow">return</span> x &gt;= 0.0 ? 1.0 : 0.0;</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;{ <span class="keywordflow">return</span> x &gt;= 0.0 ? 1.0 : 0.0; }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns derivative of sigmoid(x) </dd></dl>
<div class="fragment"><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; {</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keywordflow">return</span> x * (1 - x);</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;{ <span class="keywordflow">return</span> x * (1 - x); }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns derivative of tanh(x) </dd></dl>
<div class="fragment"><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">return</span> 1 - x * x;</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;{ <span class="keywordflow">return</span> 1 - x * x; }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns x </dd></dl>
<div class="fragment"><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; {</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">return</span> x;</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;{ <span class="keywordflow">return</span> x; }</div>
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<p>Driver Code </p>
<div class="fragment"><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>&#160; {</div>
<div class="line"><a name="l00787"></a><span class="lineno"> 787</span>&#160; <span class="comment">// Testing</span></div>
<div class="line"><a name="l00788"></a><span class="lineno"> 788</span>&#160; <a class="code" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>();</div>
<div class="line"><a name="l00789"></a><span class="lineno"> 789</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00790"></a><span class="lineno"> 790</span>&#160;}</div>
<div class="fragment"><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>&#160; {</div>
<div class="line"><a name="l00831"></a><span class="lineno"> 831</span>&#160; <span class="comment">// Testing</span></div>
<div class="line"><a name="l00832"></a><span class="lineno"> 832</span>&#160; <a class="code" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>();</div>
<div class="line"><a name="l00833"></a><span class="lineno"> 833</span>&#160; <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00834"></a><span class="lineno"> 834</span>&#160;}</div>
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<dl class="section return"><dt>Returns</dt><dd>relu(x) </dd></dl>
<div class="fragment"><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; {</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keywordflow">return</span> <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/max.html">std::max</a>(0.0, x);</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;{ <span class="keywordflow">return</span> <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/max.html">std::max</a>(0.0, x); }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns sigmoid(x) </dd></dl>
<div class="fragment"><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; {</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="keywordflow">return</span> 1.0 / (1.0 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-x));</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;{ <span class="keywordflow">return</span> 1.0 / (1.0 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-x)); }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns x * x </dd></dl>
<div class="fragment"><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; {</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">return</span> x * x;</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160;{ <span class="keywordflow">return</span> x * x; }</div>
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<dl class="section return"><dt>Returns</dt><dd>Returns tanh(x) </dd></dl>
<div class="fragment"><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; {</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="keywordflow">return</span> 2 / (1 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-2 * x)) - 1;</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; }</div>
<div class="fragment"><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;{ <span class="keywordflow">return</span> 2 / (1 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-2 * x)) - 1; }</div>
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<p>Function to test neural network </p><dl class="section return"><dt>Returns</dt><dd>none </dd></dl>
<div class="fragment"><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>&#160; {</div>
<div class="line"><a name="l00767"></a><span class="lineno"> 767</span>&#160; <span class="comment">// Creating network with 3 layers for &quot;iris.csv&quot;</span></div>
<div class="line"><a name="l00768"></a><span class="lineno"> 768</span>&#160; <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a> myNN =</div>
<div class="line"><a name="l00769"></a><span class="lineno"> 769</span>&#160; <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a>({</div>
<div class="line"><a name="l00770"></a><span class="lineno"> 770</span>&#160; {4, <span class="stringliteral">&quot;none&quot;</span>}, <span class="comment">// First layer with 3 neurons and &quot;none&quot; as activation</span></div>
<div class="line"><a name="l00771"></a><span class="lineno"> 771</span>&#160; {6, <span class="stringliteral">&quot;relu&quot;</span>}, <span class="comment">// Second layer with 6 neurons and &quot;relu&quot; as activation</span></div>
<div class="line"><a name="l00772"></a><span class="lineno"> 772</span>&#160; {3, <span class="stringliteral">&quot;sigmoid&quot;</span>} <span class="comment">// Third layer with 3 neurons and &quot;sigmoid&quot; as activation</span></div>
<div class="line"><a name="l00773"></a><span class="lineno"> 773</span>&#160; });</div>
<div class="line"><a name="l00774"></a><span class="lineno"> 774</span>&#160; <span class="comment">// Printing summary of model</span></div>
<div class="line"><a name="l00775"></a><span class="lineno"> 775</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">summary</a>();</div>
<div class="line"><a name="l00776"></a><span class="lineno"> 776</span>&#160; <span class="comment">// Training Model</span></div>
<div class="line"><a name="l00777"></a><span class="lineno"> 777</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">fit_from_csv</a>(<span class="stringliteral">&quot;iris.csv&quot;</span>, <span class="keyword">true</span>, 100, 0.3, <span class="keyword">false</span>, 2, 32, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00778"></a><span class="lineno"> 778</span>&#160; <span class="comment">// Testing predictions of model</span></div>
<div class="line"><a name="l00779"></a><span class="lineno"> 779</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{5,3.4,1.6,0.4}})) == 0);</div>
<div class="line"><a name="l00780"></a><span class="lineno"> 780</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{6.4,2.9,4.3,1.3}})) == 1);</div>
<div class="line"><a name="l00781"></a><span class="lineno"> 781</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{6.2,3.4,5.4,2.3}})) == 2);</div>
<div class="line"><a name="l00782"></a><span class="lineno"> 782</span>&#160; <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00783"></a><span class="lineno"> 783</span>&#160;}</div>
<div class="fragment"><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>&#160; {</div>
<div class="line"><a name="l00806"></a><span class="lineno"> 806</span>&#160; <span class="comment">// Creating network with 3 layers for &quot;iris.csv&quot;</span></div>
<div class="line"><a name="l00807"></a><span class="lineno"> 807</span>&#160; <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a> myNN =</div>
<div class="line"><a name="l00808"></a><span class="lineno"> 808</span>&#160; <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a>({</div>
<div class="line"><a name="l00809"></a><span class="lineno"> 809</span>&#160; {4, <span class="stringliteral">&quot;none&quot;</span>}, <span class="comment">// First layer with 3 neurons and &quot;none&quot; as activation</span></div>
<div class="line"><a name="l00810"></a><span class="lineno"> 810</span>&#160; {6,</div>
<div class="line"><a name="l00811"></a><span class="lineno"> 811</span>&#160; <span class="stringliteral">&quot;relu&quot;</span>}, <span class="comment">// Second layer with 6 neurons and &quot;relu&quot; as activation</span></div>
<div class="line"><a name="l00812"></a><span class="lineno"> 812</span>&#160; {3, <span class="stringliteral">&quot;sigmoid&quot;</span>} <span class="comment">// Third layer with 3 neurons and &quot;sigmoid&quot; as</span></div>
<div class="line"><a name="l00813"></a><span class="lineno"> 813</span>&#160; <span class="comment">// activation</span></div>
<div class="line"><a name="l00814"></a><span class="lineno"> 814</span>&#160; });</div>
<div class="line"><a name="l00815"></a><span class="lineno"> 815</span>&#160; <span class="comment">// Printing summary of model</span></div>
<div class="line"><a name="l00816"></a><span class="lineno"> 816</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">summary</a>();</div>
<div class="line"><a name="l00817"></a><span class="lineno"> 817</span>&#160; <span class="comment">// Training Model</span></div>
<div class="line"><a name="l00818"></a><span class="lineno"> 818</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">fit_from_csv</a>(<span class="stringliteral">&quot;iris.csv&quot;</span>, <span class="keyword">true</span>, 100, 0.3, <span class="keyword">false</span>, 2, 32, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00819"></a><span class="lineno"> 819</span>&#160; <span class="comment">// Testing predictions of model</span></div>
<div class="line"><a name="l00820"></a><span class="lineno"> 820</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(</div>
<div class="line"><a name="l00821"></a><span class="lineno"> 821</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ac130322a5abb1ff763b7c1a55405a35e">single_predict</a>({{5, 3.4, 1.6, 0.4}})) == 0);</div>
<div class="line"><a name="l00822"></a><span class="lineno"> 822</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(</div>
<div class="line"><a name="l00823"></a><span class="lineno"> 823</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ac130322a5abb1ff763b7c1a55405a35e">single_predict</a>({{6.4, 2.9, 4.3, 1.3}})) == 1);</div>
<div class="line"><a name="l00824"></a><span class="lineno"> 824</span>&#160; assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(</div>
<div class="line"><a name="l00825"></a><span class="lineno"> 825</span>&#160; myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ac130322a5abb1ff763b7c1a55405a35e">single_predict</a>({{6.2, 3.4, 5.4, 2.3}})) == 2);</div>
<div class="line"><a name="l00826"></a><span class="lineno"> 826</span>&#160; <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00827"></a><span class="lineno"> 827</span>&#160;}</div>
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<div class="ttc" id="anamespacemachine__learning_html_a1b42d24ad7bedbfa8e5b59fe96987a44"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a></div><div class="ttdeci">size_t argmax(const std::vector&lt; std::valarray&lt; T &gt;&gt; &amp;A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:296</div></div>
<div class="ttc" id="aneural__network_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:766</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a26680e7a28b3925f83b984d2dfa52256"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">machine_learning::neural_network::NeuralNetwork::single_predict</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; double &gt; &gt; single_predict(const std::vector&lt; std::valarray&lt; double &gt;&gt; &amp;X)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:453</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a5172a6791b9bd24f4232bab8d6b81fff"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">machine_learning::neural_network::NeuralNetwork::fit_from_csv</a></div><div class="ttdeci">void fit_from_csv(const std::string &amp;file_name, const bool &amp;last_label, const int &amp;epochs, const double &amp;learning_rate, const bool &amp;normalize, const int &amp;slip_lines=1, const size_t &amp;batch_size=32, const bool &amp;shuffle=true)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:574</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a></div><div class="ttdef"><b>Definition:</b> neural_network.cpp:261</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a61d30113d13304c664057118b92a5931"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">machine_learning::neural_network::NeuralNetwork::summary</a></div><div class="ttdeci">void summary()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:742</div></div>
<div class="ttc" id="anamespacemachine__learning_html_a1b42d24ad7bedbfa8e5b59fe96987a44"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a></div><div class="ttdeci">size_t argmax(const std::vector&lt; std::valarray&lt; T &gt;&gt; &amp;A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:307</div></div>
<div class="ttc" id="aneural__network_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:805</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a5172a6791b9bd24f4232bab8d6b81fff"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">machine_learning::neural_network::NeuralNetwork::fit_from_csv</a></div><div class="ttdeci">void fit_from_csv(const std::string &amp;file_name, const bool &amp;last_label, const int &amp;epochs, const double &amp;learning_rate, const bool &amp;normalize, const int &amp;slip_lines=1, const size_t &amp;batch_size=32, const bool &amp;shuffle=true)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:587</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a></div><div class="ttdef"><b>Definition:</b> neural_network.cpp:247</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a61d30113d13304c664057118b92a5931"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">machine_learning::neural_network::NeuralNetwork::summary</a></div><div class="ttdeci">void summary()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:773</div></div>
<div class="ttc" id="aexp_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a></div><div class="ttdeci">T exp(T... args)</div></div>
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_ac130322a5abb1ff763b7c1a55405a35e"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ac130322a5abb1ff763b7c1a55405a35e">machine_learning::neural_network::NeuralNetwork::single_predict</a></div><div class="ttdeci">std::vector&lt; std::valarray&lt; double &gt; &gt; single_predict(const std::vector&lt; std::valarray&lt; double &gt;&gt; &amp;X)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:451</div></div>
<div class="ttc" id="amax_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/max.html">std::max</a></div><div class="ttdeci">T max(T... args)</div></div>
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