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<div class="headertitle"><div class="title">kohonen_som_trace.cpp</div></div>
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<div class="contents">
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<a href="../../d9/d49/kohonen__som__trace_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span></div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span><span class="preprocessor">#define _USE_MATH_DEFINES </span><span class="comment">// required for MS Visual C++</span></div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span><span class="preprocessor">#include <algorithm></span></div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span><span class="preprocessor">#include <array></span></div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span><span class="preprocessor">#include <cmath></span></div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span><span class="preprocessor">#include <cstdlib></span></div>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span><span class="preprocessor">#include <ctime></span></div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span><span class="preprocessor">#include <fstream></span></div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span><span class="preprocessor">#include <iostream></span></div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span><span class="preprocessor">#include <valarray></span></div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span><span class="preprocessor">#include <vector></span></div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="preprocessor">#ifdef _OPENMP </span><span class="comment">// check if OpenMP based parallellization is available</span></div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="preprocessor">#include <omp.h></span></div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span></div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(<span class="keywordtype">double</span> a, <span class="keywordtype">double</span> b) {</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="keywordflow">return</span> ((b - a) * (std::rand() % 100) / 100.f) + a;</div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span>}</div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span></div>
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<div class="foldopen" id="foldopen00058" data-start="{" data-end="}">
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67"> 58</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *fname,</div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> <span class="keyword">const</span> std::vector<std::valarray<double>> &X) {</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> <span class="keywordtype">size_t</span> num_points = X.size(); <span class="comment">// number of rows</span></div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <span class="keywordtype">size_t</span> num_features = X[0].size(); <span class="comment">// number of columns</span></div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> </div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> std::ofstream fp;</div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> fp.open(fname);</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keywordflow">if</span> (!fp.is_open()) {</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="comment">// error with opening file to write</span></div>
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> std::cerr << <span class="stringliteral">"Error opening file "</span> << fname << <span class="stringliteral">"\n"</span>;</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="keywordflow">return</span> -1;</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> }</div>
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<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> </div>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> <span class="comment">// for each point in the array</span></div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < num_points; i++) {</div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> <span class="comment">// for each feature in the array</span></div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < num_features; j++) {</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> fp << X[i][j]; <span class="comment">// print the feature value</span></div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> <span class="keywordflow">if</span> (j < num_features - 1) { <span class="comment">// if not the last feature</span></div>
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<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> fp << <span class="stringliteral">","</span>; <span class="comment">// suffix comma</span></div>
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<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> }</div>
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keywordflow">if</span> (i < num_points - 1) { <span class="comment">// if not the last row</span></div>
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<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> fp << <span class="stringliteral">"\n"</span>; <span class="comment">// start a new line</span></div>
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<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> }</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> }</div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> </div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> fp.close();</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keywordflow">return</span> 0;</div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span>}</div>
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</div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span></div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</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="l00093" name="l00093"></a><span class="lineno"> 93</span></div>
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<div class="foldopen" id="foldopen00103" data-start="{" data-end="}">
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#aa6aac06ccf128b0a9c55c9ee1a8e5631"> 103</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a>(<span class="keyword">const</span> std::valarray<double> &x,</div>
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<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> std::vector<std::valarray<double>> *W,</div>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> std::valarray<double> *D, <span class="keywordtype">double</span> alpha, <span class="keywordtype">int</span> R) {</div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keywordtype">int</span> j = 0;</div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> <span class="keywordtype">int</span> num_out = W->size(); <span class="comment">// number of SOM output nodes</span></div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> <span class="comment">// int num_features = x.size(); // number of data features</span></div>
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<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> </div>
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<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <span class="comment">// step 1: for each output point</span></div>
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<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> <span class="keywordflow">for</span> (j = 0; j < num_out; j++) {</div>
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> <span class="comment">// compute Euclidian distance of each output</span></div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> <span class="comment">// point from the current sample</span></div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> (*D)[j] = (((*W)[j] - x) * ((*W)[j] - x)).<a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a6f1c98c016ad34ff3d9f39372161bd35">sum</a>();</div>
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> }</div>
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<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> </div>
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<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="comment">// step 2: get closest node i.e., node with snallest Euclidian distance to</span></div>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> <span class="comment">// the current pattern</span></div>
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<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> <span class="keyword">auto</span> result = std::min_element(std::begin(*D), std::end(*D));</div>
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<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="comment">// double d_min = *result;</span></div>
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> <span class="keywordtype">int</span> d_min_idx = std::distance(std::begin(*D), result);</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="comment">// step 3a: get the neighborhood range</span></div>
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<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordtype">int</span> from_node = std::max(0, d_min_idx - R);</div>
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="keywordtype">int</span> to_node = std::min(num_out, d_min_idx + R + 1);</div>
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<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> </div>
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<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="comment">// step 3b: update the weights of nodes in the</span></div>
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<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="comment">// neighborhood</span></div>
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<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordflow">for</span> (j = from_node; j < to_node; j++) {</div>
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<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> <span class="comment">// update weights of nodes in the neighborhood</span></div>
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<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> (*W)[j] += alpha * (x - (*W)[j]);</div>
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<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> }</div>
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<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span>}</div>
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</div>
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<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span></div>
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<div class="foldopen" id="foldopen00149" data-start="{" data-end="}">
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"><a class="line" href="../../d8/d77/namespacemachine__learning.html#a042f435bca0839e721fc1574a61e8da3"> 149</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(<span class="keyword">const</span> std::vector<std::valarray<double>> &X,</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> std::vector<std::valarray<double>> *W,</div>
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<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> <span class="keywordtype">double</span> alpha_min) {</div>
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<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> <span class="keywordtype">int</span> num_samples = X.size(); <span class="comment">// number of rows</span></div>
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<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> <span class="comment">// int num_features = X[0].size(); // number of columns</span></div>
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<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="keywordtype">int</span> num_out = W->size(); <span class="comment">// number of rows</span></div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keywordtype">int</span> R = num_out >> 2, iter = 0;</div>
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<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <span class="keywordtype">double</span> alpha = 1.f;</div>
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<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> </div>
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<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> std::valarray<double> D(num_out);</div>
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<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> </div>
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<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> <span class="comment">// Loop alpha from 1 to slpha_min</span></div>
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<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> <span class="keywordflow">do</span> {</div>
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<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="comment">// Loop for each sample pattern in the data set</span></div>
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<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sample = 0; sample < num_samples; sample++) {</div>
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<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> <span class="comment">// update weights for the current input pattern sample</span></div>
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<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a>(X[sample], W, &D, alpha, R);</div>
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<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> }</div>
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<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> </div>
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<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> <span class="comment">// every 10th iteration, reduce the neighborhood range</span></div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> <span class="keywordflow">if</span> (iter % 10 == 0 && R > 1) {</div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> R--;</div>
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<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> }</div>
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<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> </div>
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<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> alpha -= 0.01;</div>
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<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> iter++;</div>
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<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> } <span class="keywordflow">while</span> (alpha > alpha_min);</div>
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<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span>}</div>
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</div>
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<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> </div>
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<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span>} <span class="comment">// namespace machine_learning</span></div>
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<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span></div>
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<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> </div>
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<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span><span class="keyword">using </span><a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#a042f435bca0839e721fc1574a61e8da3">machine_learning::kohonen_som_tracer</a>;</div>
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<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span></div>
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<div class="foldopen" id="foldopen00196" data-start="{" data-end="}">
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#ae571600aa42a81bc14a4a602ea5ff00d"> 196</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a>(std::vector<std::valarray<double>> *<a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>) {</div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> <span class="keyword">const</span> <span class="keywordtype">double</span> R = 0.75, dr = 0.3;</div>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> <span class="keywordtype">double</span> a_t = 0., b_t = 2.f * M_PI; <span class="comment">// theta random between 0 and 2*pi</span></div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="keywordtype">double</span> a_r = R - dr, b_r = R + dr; <span class="comment">// radius random between R-dr and R+dr</span></div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> <span class="keywordtype">int</span> i = 0;</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="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> <span class="keywordtype">double</span> r = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(a_r, b_r); <span class="comment">// random radius</span></div>
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<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> <span class="keywordtype">double</span> theta = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(a_t, b_t); <span class="comment">// random theta</span></div>
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<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = r * cos(theta); <span class="comment">// convert from polar to cartesian</span></div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = r * sin(theta);</div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> }</div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span>}</div>
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</div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span></div>
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<div class="foldopen" id="foldopen00233" data-start="{" data-end="}">
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<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a1440a7779ac56f47a3f355ce4a8c7da0"> 233</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a>() {</div>
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<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> <span class="keywordtype">int</span> j = 0, N = 500;</div>
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<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> <span class="keywordtype">int</span> features = 2;</div>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> <span class="keywordtype">int</span> num_out = 50;</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> X[i] = std::valarray<double>(features);</div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> }</div>
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<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> </div>
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<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> }</div>
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<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> }</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> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a>(&X); <span class="comment">// create test data around circumference of a circle</span></div>
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<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test1.csv"</span>, X); <span class="comment">// save test data points</span></div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w11.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.1); <span class="comment">// train the SOM</span></div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w12.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span>}</div>
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</div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span></div>
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<div class="foldopen" id="foldopen00277" data-start="{" data-end="}">
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<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a53082f2e5bacec40266499da4547309a"> 277</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a53082f2e5bacec40266499da4547309a">test_lamniscate</a>(std::vector<std::valarray<double>> *<a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>) {</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dr = 0.2;</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> </div>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> <span class="keywordtype">double</span> dx = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-dr, dr); <span class="comment">// random change in x</span></div>
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<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> <span class="keywordtype">double</span> dy = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-dr, dr); <span class="comment">// random change in y</span></div>
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<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> <span class="keywordtype">double</span> theta = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(0, M_PI); <span class="comment">// random theta</span></div>
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<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = dx + cos(theta); <span class="comment">// convert from polar to cartesian</span></div>
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<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = dy + sin(2. * theta) / 2.f;</div>
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<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> }</div>
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<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span>}</div>
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</div>
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<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span></div>
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<div class="foldopen" id="foldopen00315" data-start="{" data-end="}">
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<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a0283886819c7c140a023582b7269e2d0"> 315</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a0283886819c7c140a023582b7269e2d0">test2</a>() {</div>
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="keywordtype">int</span> j = 0, N = 500;</div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> <span class="keywordtype">int</span> features = 2;</div>
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> <span class="keywordtype">int</span> num_out = 20;</div>
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<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> X[i] = std::valarray<double>(features);</div>
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<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> }</div>
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<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> </div>
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<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</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> }</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 class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> </div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a53082f2e5bacec40266499da4547309a">test_lamniscate</a>(&X); <span class="comment">// create test data around the lamniscate</span></div>
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<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test2.csv"</span>, X); <span class="comment">// save test data points</span></div>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w21.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.01); <span class="comment">// train the SOM</span></div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w22.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span>}</div>
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</div>
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<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span></div>
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<div class="foldopen" id="foldopen00359" data-start="{" data-end="}">
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<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a7154fe319e6033485a8a6cd6f0d8932d"> 359</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a>(std::vector<std::valarray<double>> *<a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>) {</div>
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<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> <span class="keyword">const</span> <span class="keywordtype">double</span> R = 0.1; <span class="comment">// radius of cluster</span></div>
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<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes = 8;</div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> <span class="keyword">const</span> std::array<const std::array<double, 3>, num_classes> centres = {</div>
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<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <span class="comment">// centres of each class cluster</span></div>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> std::array<double, 3>({.5, .5, .5}), <span class="comment">// centre of class 0</span></div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> std::array<double, 3>({.5, .5, -.5}), <span class="comment">// centre of class 1</span></div>
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<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> std::array<double, 3>({.5, -.5, .5}), <span class="comment">// centre of class 2</span></div>
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<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> std::array<double, 3>({.5, -.5, -.5}), <span class="comment">// centre of class 3</span></div>
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<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> std::array<double, 3>({-.5, .5, .5}), <span class="comment">// centre of class 4</span></div>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> std::array<double, 3>({-.5, .5, -.5}), <span class="comment">// centre of class 5</span></div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> std::array<double, 3>({-.5, -.5, .5}), <span class="comment">// centre of class 6</span></div>
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<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> std::array<double, 3>({-.5, -.5, -.5}) <span class="comment">// centre of class 7</span></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="l00375" name="l00375"></a><span class="lineno"> 375</span> </div>
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<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> <span class="keywordtype">int</span> cls =</div>
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<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> std::rand() % num_classes; <span class="comment">// select a random class for the point</span></div>
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<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"> 382</span> </div>
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<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> <span class="comment">// create random coordinates (x,y,z) around the centre of the class</span></div>
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<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][0] - R, centres[cls][0] + R);</div>
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<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][1] - R, centres[cls][1] + R);</div>
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<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][2] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][2] - R, centres[cls][2] + R);</div>
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<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> </div>
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> <span class="comment">/* The follosing can also be used</span></div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span><span class="comment"> for (int j = 0; j < 3; j++)</span></div>
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<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span><span class="comment"> data[0][i][j] = _random(centres[cls][j] - R, centres[cls][j] + R);</span></div>
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<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span><span class="comment"> */</span></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>}</div>
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</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="foldopen" id="foldopen00414" data-start="{" data-end="}">
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<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a6d0455dd5c30adda100e95f0423c786e"> 414</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a6d0455dd5c30adda100e95f0423c786e">test3</a>() {</div>
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<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> <span class="keywordtype">int</span> j = 0, N = 200;</div>
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<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> <span class="keywordtype">int</span> features = 3;</div>
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<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> <span class="keywordtype">int</span> num_out = 20;</div>
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<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> X[i] = std::valarray<double>(features);</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="l00425" name="l00425"></a><span class="lineno"> 425</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> </div>
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<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</div>
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<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> }</div>
|
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<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> }</div>
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<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> }</div>
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<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> </div>
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<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a>(&X); <span class="comment">// create test data around the lamniscate</span></div>
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|
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test3.csv"</span>, X); <span class="comment">// save test data points</span></div>
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|
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w31.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.01); <span class="comment">// train the SOM</span></div>
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<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w32.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span>}</div>
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</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="foldopen" id="foldopen00452" data-start="{" data-end="}">
|
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<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908"> 452</a></span><span class="keywordtype">double</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(clock_t start_t, clock_t end_t) {</div>
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<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(end_t - start_t) / CLOCKS_PER_SEC;</div>
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<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span>}</div>
|
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</div>
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<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span></div>
|
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<div class="foldopen" id="foldopen00457" data-start="{" data-end="}">
|
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<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"><a class="line" href="../../d9/d49/kohonen__som__trace_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4"> 457</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>() {</div>
|
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<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span> std::cout << <span class="stringliteral">"Using OpenMP based parallelization\n"</span>;</div>
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<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"> 460</span><span class="preprocessor">#else</span></div>
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<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> std::cout << <span class="stringliteral">"NOT using OpenMP based parallelization\n"</span>;</div>
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<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span><span class="preprocessor">#endif</span></div>
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<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> </div>
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<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> std::srand(std::time(<span class="keyword">nullptr</span>));</div>
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<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> </div>
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<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> std::clock_t start_clk = std::clock();</div>
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<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a>();</div>
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<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> <span class="keyword">auto</span> end_clk = std::clock();</div>
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<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> std::cout << <span class="stringliteral">"Test 1 completed in "</span> << <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> </div>
|
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<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> start_clk = std::clock();</div>
|
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<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a0283886819c7c140a023582b7269e2d0">test2</a>();</div>
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<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> end_clk = std::clock();</div>
|
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<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> std::cout << <span class="stringliteral">"Test 2 completed in "</span> << <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> </div>
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<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> start_clk = std::clock();</div>
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<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a6d0455dd5c30adda100e95f0423c786e">test3</a>();</div>
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<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> end_clk = std::clock();</div>
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<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> std::cout << <span class="stringliteral">"Test 3 completed in "</span> << <a class="code hl_function" href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> </div>
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<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> std::cout</div>
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<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span> << <span class="stringliteral">"(Note: Calculated times include: creating test sets, training "</span></div>
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<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> <span class="stringliteral">"model and writing files to disk.)\n\n"</span>;</div>
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<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> <span class="keywordflow">return</span> 0;</div>
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<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span>}</div>
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</div>
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<div class="ttc" id="agroup__machine__learning_html_gae0208548f8b393528e5db01717e88e67"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a></div><div class="ttdeci">int save_nd_data(const char *fname, const std::vector< std::valarray< double > > &X)</div><div class="ttdef"><b>Definition</b> <a href="#l00058">kohonen_som_trace.cpp:58</a></div></div>
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<div class="ttc" id="agroup__machine__learning_html_gaf5ce14f026d6d231bef29161bac2b485"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a></div><div class="ttdeci">double _random(double a, double b)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00053">kohonen_som_topology.cpp:53</a></div></div>
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<div class="ttc" id="ahash__search_8cpp_html_a6e1a77282bc65ad359d753d25df23243"><div class="ttname"><a href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a></div><div class="ttdeci">int data[MAX]</div><div class="ttdoc">test data</div><div class="ttdef"><b>Definition</b> <a href="../../d1/df3/hash__search_8cpp_source.html#l00024">hash_search.cpp:24</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a0283886819c7c140a023582b7269e2d0"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a0283886819c7c140a023582b7269e2d0">test2</a></div><div class="ttdeci">void test2()</div><div class="ttdef"><b>Definition</b> <a href="#l00315">kohonen_som_trace.cpp:315</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a042f435bca0839e721fc1574a61e8da3"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a></div><div class="ttdeci">void kohonen_som_tracer(const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</div><div class="ttdef"><b>Definition</b> <a href="#l00149">kohonen_som_trace.cpp:149</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a1440a7779ac56f47a3f355ce4a8c7da0"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a></div><div class="ttdeci">void test1()</div><div class="ttdef"><b>Definition</b> <a href="#l00233">kohonen_som_trace.cpp:233</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a2256c10b16edba377b64a44b6c656908"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a></div><div class="ttdeci">double get_clock_diff(clock_t start_t, clock_t end_t)</div><div class="ttdef"><b>Definition</b> <a href="#l00452">kohonen_som_trace.cpp:452</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a53082f2e5bacec40266499da4547309a"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a53082f2e5bacec40266499da4547309a">test_lamniscate</a></div><div class="ttdeci">void test_lamniscate(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="#l00277">kohonen_som_trace.cpp:277</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a6d0455dd5c30adda100e95f0423c786e"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a6d0455dd5c30adda100e95f0423c786e">test3</a></div><div class="ttdeci">void test3()</div><div class="ttdef"><b>Definition</b> <a href="#l00414">kohonen_som_trace.cpp:414</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a7154fe319e6033485a8a6cd6f0d8932d"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a></div><div class="ttdeci">void test_3d_classes(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="#l00359">kohonen_som_trace.cpp:359</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_ae571600aa42a81bc14a4a602ea5ff00d"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a></div><div class="ttdeci">void test_circle(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="#l00196">kohonen_som_trace.cpp:196</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_ae66f6b31b5ad750f1fe042a706a4e3d4"><div class="ttname"><a href="../../d9/d49/kohonen__som__trace_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a></div><div class="ttdeci">int main()</div><div class="ttdef"><b>Definition</b> <a href="#l00457">kohonen_som_trace.cpp:457</a></div></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_a042f435bca0839e721fc1574a61e8da3"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a042f435bca0839e721fc1574a61e8da3">machine_learning::kohonen_som_tracer</a></div><div class="ttdeci">void kohonen_som_tracer(const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</div><div class="ttdef"><b>Definition</b> <a href="#l00149">kohonen_som_trace.cpp:149</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="../../d8/d95/vector__ops_8hpp_source.html#l00232">vector_ops.hpp:232</a></div></div>
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<div class="ttc" id="anamespacemachine__learning_html_ae868ad43698a1d69ba46ea3827d7d2c3"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ae868ad43698a1d69ba46ea3827d7d2c3">machine_learning::update_weights</a></div><div class="ttdeci">double update_weights(const std::valarray< double > &X, std::vector< std::vector< std::valarray< double > > > *W, std::vector< std::valarray< double > > *D, double alpha, int R)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00200">kohonen_som_topology.cpp:200</a></div></div>
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