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<div class="headertitle"><div class="title">vector_ops.hpp</div></div>
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<a href="../../d8/d95/vector__ops_8hpp.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>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="preprocessor">#ifndef VECTOR_OPS_FOR_NN</span></div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="preprocessor">#define VECTOR_OPS_FOR_NN</span></div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> </div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span><span class="preprocessor">#include <algorithm></span></div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="preprocessor">#include <chrono></span></div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span><span class="preprocessor">#include <iostream></span></div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span><span class="preprocessor">#include <random></span></div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span><span class="preprocessor">#include <valarray></span></div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span><span class="preprocessor">#include <vector></span></div>
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span></div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span><span class="keyword">namespace </span><a class="code hl_namespace" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a> {</div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00032" data-start="{" data-end="}">
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a84260cb1be9b63d6e38107000ac4b7e7"> 32</a></span>std::ostream &<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a84260cb1be9b63d6e38107000ac4b7e7">operator<<</a>(std::ostream &out,</div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> std::vector<std::valarray<T>> <span class="keyword">const</span> &A) {</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> out.precision(4);</div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For each row in A</span></div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &x : a) { <span class="comment">// For each element in row</span></div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> std::cout << x << <span class="charliteral">' '</span>; <span class="comment">// print element</span></div>
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<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> }</div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> std::cout << std::endl;</div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> }</div>
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span>}</div>
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</div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span></div>
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00052" data-start="{" data-end="}">
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#af4986b23760039711848155739c31b35"> 52</a></span>std::ostream &<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a84260cb1be9b63d6e38107000ac4b7e7">operator<<</a>(std::ostream &out, <span class="keyword">const</span> std::pair<T, T> &A) {</div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> out.precision(4);</div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> <span class="comment">// printing pair in the form (p, q)</span></div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> std::cout << <span class="stringliteral">"("</span> << A.first << <span class="stringliteral">", "</span> << A.second << <span class="stringliteral">")"</span>;</div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span>}</div>
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</div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span></div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00067" data-start="{" data-end="}">
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a54bf1f3c43271a5fc93101f6ae2e6269"> 67</a></span>std::ostream &<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a84260cb1be9b63d6e38107000ac4b7e7">operator<<</a>(std::ostream &out, <span class="keyword">const</span> std::valarray<T> &A) {</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="comment">// Setting output precision to 4 in case of floating point numbers</span></div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> out.precision(4);</div>
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<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For every element in the vector.</span></div>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> std::cout << a << <span class="charliteral">' '</span>; <span class="comment">// Print element</span></div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> }</div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> std::cout << std::endl;</div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> <span class="keywordflow">return</span> out;</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span>}</div>
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</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span></div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00085" data-start="{" data-end="}">
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd"> 85</a></span>std::valarray<T> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd">insert_element</a>(<span class="keyword">const</span> std::valarray<T> &A, <span class="keyword">const</span> T &ele) {</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> B.resize(A.size() + 1); <span class="comment">// Resizing it accordingly</span></div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every element in A</span></div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> B[i] = A[i]; <span class="comment">// Copy element in B</span></div>
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<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> }</div>
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<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> B[B.size() - 1] = ele; <span class="comment">// Inserting new element in last position</span></div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span>}</div>
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</div>
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span></div>
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00102" data-start="{" data-end="}">
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<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a912cf68863063a38d6e63545be5eb093"> 102</a></span>std::valarray<T> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a912cf68863063a38d6e63545be5eb093">pop_front</a>(<span class="keyword">const</span> std::valarray<T> &A) {</div>
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
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<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> B.resize(A.size() - 1); <span class="comment">// Resizing it accordingly</span></div>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 1; i < A.size();</div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> i++) { <span class="comment">// // For every (except first) element in A</span></div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> B[i - 1] = A[i]; <span class="comment">// Copy element in B with left shifted position</span></div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> }</div>
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<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
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<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span>}</div>
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</div>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span></div>
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00119" data-start="{" data-end="}">
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<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#ae10178b082f0205c326550877d998e5d"> 119</a></span>std::valarray<T> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae10178b082f0205c326550877d998e5d">pop_back</a>(<span class="keyword">const</span> std::valarray<T> &A) {</div>
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<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> std::valarray<T> B; <span class="comment">// New 1D vector to store resultant vector</span></div>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> B.resize(A.size() - 1); <span class="comment">// Resizing it accordingly</span></div>
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<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size() - 1;</div>
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<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> i++) { <span class="comment">// For every (except last) element in A</span></div>
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> B[i] = A[i]; <span class="comment">// Copy element in B</span></div>
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<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> }</div>
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<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordflow">return</span> B; <span class="comment">// Return resultant vector</span></div>
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<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span>}</div>
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</div>
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span></div>
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<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00136" data-start="{" data-end="}">
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<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#af801bf30591ca6b2c38ff4fed0ded23f"> 136</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#af801bf30591ca6b2c38ff4fed0ded23f">equal_shuffle</a>(std::vector<std::vector<std::valarray<T>>> &A,</div>
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<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> std::vector<std::vector<std::valarray<T>>> &B) {</div>
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<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="comment">// If two vectors have different sizes</span></div>
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<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> <span class="keywordflow">if</span> (A.size() != B.size()) {</div>
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<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> std::cerr</div>
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<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> << <span class="stringliteral">"Can not equally shuffle two vectors with different sizes: "</span>;</div>
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<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> std::cerr << A.size() << <span class="stringliteral">" and "</span> << B.size() << std::endl;</div>
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<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> }</div>
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<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every element in A and B</span></div>
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<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> <span class="comment">// Genrating random index < size of A and B</span></div>
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<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> std::srand(std::chrono::system_clock::now().time_since_epoch().count());</div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordtype">size_t</span> random_index = std::rand() % A.size();</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> <span class="comment">// Swap elements in both A and B with same random index</span></div>
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<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> std::swap(A[i], A[random_index]);</div>
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<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> std::swap(B[i], B[random_index]);</div>
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<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> }</div>
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<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span>}</div>
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</div>
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<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span></div>
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<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00166" data-start="{" data-end="}">
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<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#abee7b35403af3612222d3b7a53074905"> 166</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#abee7b35403af3612222d3b7a53074905">uniform_random_initialization</a>(std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> <span class="keyword">const</span> std::pair<size_t, size_t> &shape,</div>
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<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">const</span> T &low, <span class="keyword">const</span> T &high) {</div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> <span class="comment">// Uniform distribution in range [low, high]</span></div>
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<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> std::default_random_engine generator(</div>
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<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> std::chrono::system_clock::now().time_since_epoch().count());</div>
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<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> std::uniform_real_distribution<T> distribution(low, high);</div>
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<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) { <span class="comment">// For every row</span></div>
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<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> std::valarray<T></div>
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<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> row.resize(shape.second);</div>
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<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &r : row) { <span class="comment">// For every element in row</span></div>
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<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> r = distribution(generator); <span class="comment">// copy random number</span></div>
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<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> }</div>
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<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> }</div>
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<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span>}</div>
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</div>
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<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span></div>
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<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00193" data-start="{" data-end="}">
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<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4"> 193</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">unit_matrix_initialization</a>(std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> <span class="keyword">const</span> std::pair<size_t, size_t> &shape) {</div>
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<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> std::valarray<T></div>
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<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> row.resize(shape.second);</div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> row[i] = T(1); <span class="comment">// Insert 1 at ith position</span></div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> }</div>
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<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span>}</div>
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</div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span></div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00213" data-start="{" data-end="}">
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e"> 213</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">zeroes_initialization</a>(std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> <span class="keyword">const</span> std::pair<size_t, size_t> &shape) {</div>
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<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> A.clear(); <span class="comment">// Making A empty</span></div>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> std::valarray<T></div>
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<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> row; <span class="comment">// Making empty row which will be inserted in vector</span></div>
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<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> row.resize(shape.second); <span class="comment">// By default all elements are zero</span></div>
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<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> A.push_back(row); <span class="comment">// Insert new row in vector</span></div>
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<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> }</div>
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<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span>}</div>
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</div>
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<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span></div>
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<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00232" data-start="{" data-end="}">
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<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35"> 232</a></span>T <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A) {</div>
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<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> T cur_sum = 0; <span class="comment">// Initially sum is zero</span></div>
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<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) { <span class="comment">// For every row in A</span></div>
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<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> cur_sum += a.sum(); <span class="comment">// Add sum of that row to current sum</span></div>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> }</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> <span class="keywordflow">return</span> cur_sum; <span class="comment">// Return sum</span></div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span>}</div>
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</div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span></div>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00247" data-start="{" data-end="}">
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<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078"> 247</a></span>std::pair<size_t, size_t> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A) {</div>
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<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> <span class="keyword">const</span> <span class="keywordtype">size_t</span> sub_size = (*A.begin()).size();</div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> &a : A) {</div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="comment">// If supplied vector don't have same shape in all rows</span></div>
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<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> <span class="keywordflow">if</span> (a.size() != sub_size) {</div>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> std::cerr << <span class="stringliteral">"Supplied vector is not 2D Matrix"</span> << std::endl;</div>
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<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> }</div>
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<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> }</div>
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<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> <span class="keywordflow">return</span> std::make_pair(A.size(), sub_size); <span class="comment">// Return shape as pair</span></div>
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<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span>}</div>
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</div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span></div>
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<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00269" data-start="{" data-end="}">
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#ac332d152078e96311e43ac5e7183ea26"> 269</a></span>std::vector<std::vector<std::valarray<T>>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ac332d152078e96311e43ac5e7183ea26">minmax_scaler</a>(</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <span class="keyword">const</span> std::vector<std::vector<std::valarray<T>>> &A, <span class="keyword">const</span> T &low,</div>
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<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> <span class="keyword">const</span> T &high) {</div>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> std::vector<std::vector<std::valarray<T>>> B =</div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> A; <span class="comment">// Copying into new vector B</span></div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B[0]); <span class="comment">// Storing shape of B's every element</span></div>
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<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="comment">// As this function is used for scaling training data vector should be of</span></div>
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<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> <span class="comment">// shape (1, X)</span></div>
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<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> <span class="keywordflow">if</span> (shape.first != 1) {</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> std::cerr</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> << <span class="stringliteral">"Supplied vector is not supported for minmax scaling, shape: "</span>;</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> std::cerr << shape << std::endl;</div>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> }</div>
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<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.second; i++) {</div>
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<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> T min = B[0][0][i], max = B[0][0][i];</div>
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<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < B.size(); j++) {</div>
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<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> <span class="comment">// Updating minimum and maximum values</span></div>
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<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> min = std::min(min, B[j][0][i]);</div>
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<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> max = std::max(max, B[j][0][i]);</div>
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<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> }</div>
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<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < B.size(); j++) {</div>
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<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="comment">// Applying min-max scaler formula</span></div>
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<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> B[j][0][i] =</div>
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<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> ((B[j][0][i] - min) / (max - min)) * (high - low) + low;</div>
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<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> }</div>
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<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> }</div>
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<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 3D vector</span></div>
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<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span>}</div>
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</div>
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<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span></div>
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<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00307" data-start="{" data-end="}">
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<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec"> 307</a></span><span class="keywordtype">size_t</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">argmax</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A) {</div>
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<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> <span class="comment">// As this function is used on predicted (or target) vector, shape should be</span></div>
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<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> <span class="comment">// (1, X)</span></div>
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<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> <span class="keywordflow">if</span> (shape.first != 1) {</div>
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<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> std::cerr << <span class="stringliteral">"Supplied vector is ineligible for argmax"</span> << std::endl;</div>
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<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> }</div>
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="comment">// Return distance of max element from first element (i.e. index)</span></div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> <span class="keywordflow">return</span> std::distance(std::begin(A[0]),</div>
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> std::max_element(std::begin(A[0]), std::end(A[0])));</div>
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<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span>}</div>
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</div>
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span></div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00329" data-start="{" data-end="}">
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<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb"> 329</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">apply_function</a>(</div>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &A, T (*func)(<span class="keyword">const</span> T &)) {</div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> b = b.apply(func); <span class="comment">// Apply function to that row</span></div>
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<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> }</div>
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<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span>}</div>
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</div>
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<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span></div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00347" data-start="{" data-end="}">
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<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a16f34574b7e0dd51bc3b3fda37446695"> 347</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a16f34574b7e0dd51bc3b3fda37446695">operator*</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> <span class="keyword">const</span> T &val) {</div>
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<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> b = b * val; <span class="comment">// Multiply row with scaler</span></div>
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<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> }</div>
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<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span>}</div>
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</div>
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<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span></div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00365" data-start="{" data-end="}">
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<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#ae6ec42318d172b97fbdf45638d09d7b5"> 365</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae6ec42318d172b97fbdf45638d09d7b5">operator/</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> <span class="keyword">const</span> T &val) {</div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> std::vector<std::valarray<double>> B =</div>
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<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> A; <span class="comment">// New vector to store resultant vector</span></div>
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<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> &b : B) { <span class="comment">// For every row in vector</span></div>
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<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> b = b / val; <span class="comment">// Divide row with scaler</span></div>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> }</div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span>}</div>
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</div>
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<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span></div>
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<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00382" data-start="{" data-end="}">
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<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a"> 382</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">transpose</a>(</div>
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<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &A) {</div>
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<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A); <span class="comment">// Current shape of vector</span></div>
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<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> std::vector<std::valarray<T>> B; <span class="comment">// New vector to store result</span></div>
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<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> <span class="comment">// Storing transpose values of A in B</span></div>
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<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < shape.second; j++) {</div>
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> std::valarray<T> row;</div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> row.resize(shape.first);</div>
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<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape.first; i++) {</div>
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<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> row[i] = A[i][j];</div>
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<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> }</div>
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<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> B.push_back(row);</div>
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<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> }</div>
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<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> <span class="keywordflow">return</span> B; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span>}</div>
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</div>
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<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span></div>
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<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00406" data-start="{" data-end="}">
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<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a2466857dab977a49f117029835b3b6d2"> 406</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a2466857dab977a49f117029835b3b6d2">operator+</a>(</div>
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<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &B) {</div>
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<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> <span class="comment">// If vectors don't have equal shape</span></div>
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<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> std::cerr << <span class="stringliteral">"Supplied vectors have different shapes "</span>;</div>
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<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> }</div>
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<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> std::vector<std::valarray<T>> C;</div>
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<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every row</span></div>
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<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> C.push_back(A[i] + B[i]); <span class="comment">// Elementwise addition</span></div>
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<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> }</div>
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<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span>}</div>
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</div>
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<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span></div>
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<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00433" data-start="{" data-end="}">
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<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a0cc29566568e0383dd7d374068cbe6b3"> 433</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a0cc29566568e0383dd7d374068cbe6b3">operator-</a>(</div>
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<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &B) {</div>
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<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> <span class="comment">// If vectors don't have equal shape</span></div>
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<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> std::cerr << <span class="stringliteral">"Supplied vectors have different shapes "</span>;</div>
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<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> }</div>
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<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"> 446</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) { <span class="comment">// For every row</span></div>
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<div class="line"><a id="l00447" name="l00447"></a><span class="lineno"> 447</span> C.push_back(A[i] - B[i]); <span class="comment">// Elementwise substraction</span></div>
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<div class="line"><a id="l00448" name="l00448"></a><span class="lineno"> 448</span> }</div>
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<div class="line"><a id="l00449" name="l00449"></a><span class="lineno"> 449</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"> 450</span>}</div>
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</div>
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<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"> 451</span></div>
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<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00460" data-start="{" data-end="}">
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<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9"> 460</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">multiply</a>(<span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &B) {</div>
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<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> <span class="comment">// If vectors are not eligible for multiplication</span></div>
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<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> <span class="keywordflow">if</span> (shape_a.second != shape_b.first) {</div>
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<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> std::cerr << <span class="stringliteral">"Vectors are not eligible for multiplication "</span>;</div>
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<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> }</div>
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<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> <span class="comment">// Normal matrix multiplication</span></div>
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<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < shape_a.first; i++) {</div>
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<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> std::valarray<T> row;</div>
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<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> row.resize(shape_b.second);</div>
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<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < shape_b.second; j++) {</div>
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<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k < shape_a.second; k++) {</div>
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<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> row[j] += A[i][k] * B[k][j];</div>
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<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> }</div>
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<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> }</div>
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<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> C.push_back(row);</div>
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<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> }</div>
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<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span>}</div>
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</div>
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<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span></div>
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<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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<div class="foldopen" id="foldopen00494" data-start="{" data-end="}">
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<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#acafa3e62b686aebdbad81c4f89913f43"> 494</a></span>std::vector<std::valarray<T>> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#acafa3e62b686aebdbad81c4f89913f43">hadamard_product</a>(</div>
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<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &A,</div>
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<div class="line"><a id="l00496" name="l00496"></a><span class="lineno"> 496</span> <span class="keyword">const</span> std::vector<std::valarray<T>> &B) {</div>
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<div class="line"><a id="l00497" name="l00497"></a><span class="lineno"> 497</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_a = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(A);</div>
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<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"> 498</span> <span class="keyword">const</span> <span class="keyword">auto</span> shape_b = <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">get_shape</a>(B);</div>
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<div class="line"><a id="l00499" name="l00499"></a><span class="lineno"> 499</span> <span class="comment">// If vectors are not eligible for hadamard product</span></div>
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<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"> 500</span> <span class="keywordflow">if</span> (shape_a.first != shape_b.first || shape_a.second != shape_b.second) {</div>
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<div class="line"><a id="l00501" name="l00501"></a><span class="lineno"> 501</span> std::cerr << <span class="stringliteral">"ERROR ("</span> << __func__ << <span class="stringliteral">") : "</span>;</div>
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<div class="line"><a id="l00502" name="l00502"></a><span class="lineno"> 502</span> std::cerr << <span class="stringliteral">"Vectors have different shapes "</span>;</div>
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<div class="line"><a id="l00503" name="l00503"></a><span class="lineno"> 503</span> std::cerr << shape_a << <span class="stringliteral">" and "</span> << shape_b << std::endl;</div>
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<div class="line"><a id="l00504" name="l00504"></a><span class="lineno"> 504</span> std::exit(EXIT_FAILURE);</div>
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<div class="line"><a id="l00505" name="l00505"></a><span class="lineno"> 505</span> }</div>
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<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"> 506</span> std::vector<std::valarray<T>> C; <span class="comment">// Vector to store result</span></div>
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<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"> 507</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < A.size(); i++) {</div>
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<div class="line"><a id="l00508" name="l00508"></a><span class="lineno"> 508</span> C.push_back(A[i] * B[i]); <span class="comment">// Elementwise multiplication</span></div>
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<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"> 509</span> }</div>
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<div class="line"><a id="l00510" name="l00510"></a><span class="lineno"> 510</span> <span class="keywordflow">return</span> C; <span class="comment">// Return new resultant 2D vector</span></div>
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<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span>}</div>
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</div>
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<div class="line"><a id="l00512" name="l00512"></a><span class="lineno"> 512</span>} <span class="comment">// namespace machine_learning</span></div>
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<div class="line"><a id="l00513" name="l00513"></a><span class="lineno"> 513</span> </div>
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<div class="line"><a id="l00514" name="l00514"></a><span class="lineno"> 514</span><span class="preprocessor">#endif</span></div>
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<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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<div class="ttc" id="anamespacemachine__learning_html_a0cc29566568e0383dd7d374068cbe6b3"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a0cc29566568e0383dd7d374068cbe6b3">machine_learning::operator-</a></div><div class="ttdeci">std::vector< std::valarray< T > > operator-(const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</div><div class="ttdef"><b>Definition</b> <a href="#l00433">vector_ops.hpp:433</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a16f34574b7e0dd51bc3b3fda37446695"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a16f34574b7e0dd51bc3b3fda37446695">machine_learning::operator*</a></div><div class="ttdeci">std::vector< std::valarray< T > > operator*(const std::vector< std::valarray< T > > &A, const T &val)</div><div class="ttdef"><b>Definition</b> <a href="#l00347">vector_ops.hpp:347</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a2466857dab977a49f117029835b3b6d2"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a2466857dab977a49f117029835b3b6d2">machine_learning::operator+</a></div><div class="ttdeci">std::vector< std::valarray< T > > operator+(const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</div><div class="ttdef"><b>Definition</b> <a href="#l00406">vector_ops.hpp:406</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a496302e3371aa7b478cb7d5917904bdd"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a496302e3371aa7b478cb7d5917904bdd">machine_learning::insert_element</a></div><div class="ttdeci">std::valarray< T > insert_element(const std::valarray< T > &A, const T &ele)</div><div class="ttdef"><b>Definition</b> <a href="#l00085">vector_ops.hpp:85</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a50480fccfb39de20ca47f1bf51ecb6ec"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a50480fccfb39de20ca47f1bf51ecb6ec">machine_learning::argmax</a></div><div class="ttdeci">size_t argmax(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00307">vector_ops.hpp:307</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a5342906d42b80fc6b6b3ad17bf00fcb9"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a5342906d42b80fc6b6b3ad17bf00fcb9">machine_learning::multiply</a></div><div class="ttdeci">std::vector< std::valarray< T > > multiply(const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</div><div class="ttdef"><b>Definition</b> <a href="#l00460">vector_ops.hpp:460</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a6f1c98c016ad34ff3d9f39372161bd35"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">machine_learning::sum</a></div><div class="ttdeci">T sum(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00232">vector_ops.hpp:232</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a84260cb1be9b63d6e38107000ac4b7e7"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a84260cb1be9b63d6e38107000ac4b7e7">machine_learning::operator<<</a></div><div class="ttdeci">std::ostream & operator<<(std::ostream &out, std::vector< std::valarray< T > > const &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00032">vector_ops.hpp:32</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a89fde571b38f9483576594f66572958a"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a89fde571b38f9483576594f66572958a">machine_learning::transpose</a></div><div class="ttdeci">std::vector< std::valarray< T > > transpose(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00382">vector_ops.hpp:382</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a8dd3f1ffbc2f26a3c88da1b1f8b7e9c4">machine_learning::unit_matrix_initialization</a></div><div class="ttdeci">void unit_matrix_initialization(std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape)</div><div class="ttdef"><b>Definition</b> <a href="#l00193">vector_ops.hpp:193</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a912cf68863063a38d6e63545be5eb093"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a912cf68863063a38d6e63545be5eb093">machine_learning::pop_front</a></div><div class="ttdeci">std::valarray< T > pop_front(const std::valarray< T > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00102">vector_ops.hpp:102</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_aa4bbf61e65f8cd297255fa94b983d078"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#aa4bbf61e65f8cd297255fa94b983d078">machine_learning::get_shape</a></div><div class="ttdeci">std::pair< size_t, size_t > get_shape(const std::vector< std::valarray< T > > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00247">vector_ops.hpp:247</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_abee7b35403af3612222d3b7a53074905"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#abee7b35403af3612222d3b7a53074905">machine_learning::uniform_random_initialization</a></div><div class="ttdeci">void uniform_random_initialization(std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape, const T &low, const T &high)</div><div class="ttdef"><b>Definition</b> <a href="#l00166">vector_ops.hpp:166</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ac1bdaa2a724b4ce6a6bb371a5dbe2e7e"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ac1bdaa2a724b4ce6a6bb371a5dbe2e7e">machine_learning::zeroes_initialization</a></div><div class="ttdeci">void zeroes_initialization(std::vector< std::valarray< T > > &A, const std::pair< size_t, size_t > &shape)</div><div class="ttdef"><b>Definition</b> <a href="#l00213">vector_ops.hpp:213</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ac332d152078e96311e43ac5e7183ea26"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ac332d152078e96311e43ac5e7183ea26">machine_learning::minmax_scaler</a></div><div class="ttdeci">std::vector< std::vector< std::valarray< T > > > minmax_scaler(const std::vector< std::vector< std::valarray< T > > > &A, const T &low, const T &high)</div><div class="ttdef"><b>Definition</b> <a href="#l00269">vector_ops.hpp:269</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_acafa3e62b686aebdbad81c4f89913f43"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#acafa3e62b686aebdbad81c4f89913f43">machine_learning::hadamard_product</a></div><div class="ttdeci">std::vector< std::valarray< T > > hadamard_product(const std::vector< std::valarray< T > > &A, const std::vector< std::valarray< T > > &B)</div><div class="ttdef"><b>Definition</b> <a href="#l00494">vector_ops.hpp:494</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ad0bdc88e5f1be47c46c0f0c8ebf754bb"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ad0bdc88e5f1be47c46c0f0c8ebf754bb">machine_learning::apply_function</a></div><div class="ttdeci">std::vector< std::valarray< T > > apply_function(const std::vector< std::valarray< T > > &A, T(*func)(const T &))</div><div class="ttdef"><b>Definition</b> <a href="#l00329">vector_ops.hpp:329</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ae10178b082f0205c326550877d998e5d"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ae10178b082f0205c326550877d998e5d">machine_learning::pop_back</a></div><div class="ttdeci">std::valarray< T > pop_back(const std::valarray< T > &A)</div><div class="ttdef"><b>Definition</b> <a href="#l00119">vector_ops.hpp:119</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ae6ec42318d172b97fbdf45638d09d7b5"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ae6ec42318d172b97fbdf45638d09d7b5">machine_learning::operator/</a></div><div class="ttdeci">std::vector< std::valarray< T > > operator/(const std::vector< std::valarray< T > > &A, const T &val)</div><div class="ttdef"><b>Definition</b> <a href="#l00365">vector_ops.hpp:365</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_af801bf30591ca6b2c38ff4fed0ded23f"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#af801bf30591ca6b2c38ff4fed0ded23f">machine_learning::equal_shuffle</a></div><div class="ttdeci">void equal_shuffle(std::vector< std::vector< std::valarray< T > > > &A, std::vector< std::vector< std::valarray< T > > > &B)</div><div class="ttdef"><b>Definition</b> <a href="#l00136">vector_ops.hpp:136</a></div></div>
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