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<div id="projectname">BayesNet<span id="projectnumber"> 1.0.5</span>
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<div id="projectbrief">Bayesian Network Classifiers using libtorch from scratch</div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="comment">// ***************************************************************</span></div>
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="comment">// ***************************************************************</span></div>
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span> </div>
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="preprocessor">#include <thread></span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="preprocessor">#include <mutex></span></div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="preprocessor">#include <sstream></span></div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="preprocessor">#include "Network.h"</span></div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="preprocessor">#include "bayesnet/utils/bayesnetUtils.h"</span></div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="keyword">namespace </span>bayesnet {</div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }</div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> {</div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> }</div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> Network::Network(<span class="keywordtype">float</span> maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }</div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> {</div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</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="l00020" name="l00020"></a><span class="lineno"> 20</span> Network::Network(<span class="keyword">const</span> Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),</div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)</div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> {</div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> <span class="keywordflow">if</span> (samples.defined())</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> samples = samples.clone();</div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& node : other.nodes) {</div>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> nodes[node.first] = std::make_unique<Node>(*node.second);</div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> }</div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> }</div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="keywordtype">void</span> Network::initialize()</div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> {</div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> features.clear();</div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> className = <span class="stringliteral">""</span>;</div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> classNumStates = 0;</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> fitted = <span class="keyword">false</span>;</div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> nodes.clear();</div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> samples = torch::Tensor();</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> }</div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> <span class="keywordtype">float</span> Network::getMaxThreads()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> <span class="keywordflow">return</span> maxThreads;</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> torch::Tensor& Network::getSamples()</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 class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> <span class="keywordflow">return</span> samples;</div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> }</div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> <span class="keywordtype">void</span> Network::addNode(<span class="keyword">const</span> std::string& name)</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> {</div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="keywordflow">if</span> (name == <span class="stringliteral">""</span>) {</div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Node name cannot be empty"</span>);</div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> }</div>
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="keywordflow">if</span> (nodes.find(name) != nodes.end()) {</div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keywordflow">return</span>;</div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> }</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="keywordflow">if</span> (find(features.begin(), features.end(), name) == features.end()) {</div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> features.push_back(name);</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> }</div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> nodes[name] = std::make_unique<Node>(name);</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 class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> std::vector<std::string> Network::getFeatures()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <span class="keywordflow">return</span> features;</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> <span class="keywordtype">int</span> Network::getClassNumStates()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keywordflow">return</span> classNumStates;</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> }</div>
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> <span class="keywordtype">int</span> Network::getStates()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="keywordtype">int</span> result = 0;</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">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> result += node.second->getNumStates();</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> <span class="keywordflow">return</span> result;</div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> }</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> std::string Network::getClassName()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="keywordflow">return</span> className;</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> <span class="keywordtype">bool</span> Network::isCyclic(<span class="keyword">const</span> std::string& nodeId, std::unordered_set<std::string>& visited, std::unordered_set<std::string>& recStack)</div>
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> {</div>
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<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keywordflow">if</span> (visited.find(nodeId) == visited.end()) <span class="comment">// if node hasn't been visited yet</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> visited.insert(nodeId);</div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> recStack.insert(nodeId);</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">for</span> (Node* child : nodes[nodeId]->getChildren()) {</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keywordflow">if</span> (visited.find(child->getName()) == visited.end() && isCyclic(child->getName(), visited, recStack))</div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keywordflow">if</span> (recStack.find(child->getName()) != recStack.end())</div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="keywordflow">return</span> <span class="keyword">true</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> }</div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> recStack.erase(nodeId); <span class="comment">// remove node from recursion stack before function ends</span></div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</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="l00095" name="l00095"></a><span class="lineno"> 95</span> <span class="keywordtype">void</span> Network::addEdge(<span class="keyword">const</span> std::string& parent, <span class="keyword">const</span> std::string& child)</div>
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<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> {</div>
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<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> <span class="keywordflow">if</span> (nodes.find(parent) == nodes.end()) {</div>
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<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Parent node "</span> + parent + <span class="stringliteral">" does not exist"</span>);</div>
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<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> }</div>
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<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keywordflow">if</span> (nodes.find(child) == nodes.end()) {</div>
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Child node "</span> + child + <span class="stringliteral">" does not exist"</span>);</div>
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<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> }</div>
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> <span class="comment">// Temporarily add edge to check for cycles</span></div>
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<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> nodes[parent]->addChild(nodes[child].get());</div>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> nodes[child]->addParent(nodes[parent].get());</div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> std::unordered_set<std::string> visited;</div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> std::unordered_set<std::string> recStack;</div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> <span class="keywordflow">if</span> (isCyclic(nodes[child]->getName(), visited, recStack)) <span class="comment">// if adding this edge forms a cycle</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="comment">// remove problematic edge</span></div>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> nodes[parent]->removeChild(nodes[child].get());</div>
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<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> nodes[child]->removeParent(nodes[parent].get());</div>
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<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Adding this edge forms a cycle in the graph."</span>);</div>
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<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> }</div>
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> }</div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> std::map<std::string, std::unique_ptr<Node>>& Network::getNodes()</div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> {</div>
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> <span class="keywordflow">return</span> nodes;</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="keywordtype">void</span> Network::checkFitData(<span class="keywordtype">int</span> n_samples, <span class="keywordtype">int</span> n_features, <span class="keywordtype">int</span> n_samples_y, <span class="keyword">const</span> std::vector<std::string>& featureNames, <span class="keyword">const</span> std::string& className, <span class="keyword">const</span> std::map<std::string, std::vector<int>>& states, <span class="keyword">const</span> torch::Tensor& weights)</div>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> {</div>
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<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> <span class="keywordflow">if</span> (weights.size(0) != n_samples) {</div>
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|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Weights ("</span> + std::to_string(weights.size(0)) + <span class="stringliteral">") must have the same number of elements as samples ("</span> + std::to_string(n_samples) + <span class="stringliteral">") in Network::fit"</span>);</div>
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> }</div>
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<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keywordflow">if</span> (n_samples != n_samples_y) {</div>
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|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X and y must have the same number of samples in Network::fit ("</span> + std::to_string(n_samples) + <span class="stringliteral">" != "</span> + std::to_string(n_samples_y) + <span class="stringliteral">")"</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 class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="keywordflow">if</span> (n_features != featureNames.size()) {</div>
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|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X and features must have the same number of features in Network::fit ("</span> + std::to_string(n_features) + <span class="stringliteral">" != "</span> + std::to_string(featureNames.size()) + <span class="stringliteral">")"</span>);</div>
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<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> }</div>
|
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<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">if</span> (features.size() == 0) {</div>
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|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"The network has not been initialized. You must call addNode() before calling fit()"</span>);</div>
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|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> }</div>
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<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> <span class="keywordflow">if</span> (n_features != features.size() - 1) {</div>
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|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X and local features must have the same number of features in Network::fit ("</span> + std::to_string(n_features) + <span class="stringliteral">" != "</span> + std::to_string(features.size() - 1) + <span class="stringliteral">")"</span>);</div>
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<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> }</div>
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<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> <span class="keywordflow">if</span> (find(features.begin(), features.end(), className) == features.end()) {</div>
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|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Class Name not found in Network::features"</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 class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& feature : featureNames) {</div>
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|
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> <span class="keywordflow">if</span> (find(features.begin(), features.end(), feature) == features.end()) {</div>
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<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Feature "</span> + feature + <span class="stringliteral">" not found in Network::features"</span>);</div>
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|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> }</div>
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<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordflow">if</span> (states.find(feature) == states.end()) {</div>
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<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Feature "</span> + feature + <span class="stringliteral">" not found in states"</span>);</div>
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<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> }</div>
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<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> }</div>
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<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> }</div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordtype">void</span> Network::setStates(<span class="keyword">const</span> std::map<std::string, std::vector<int>>& states)</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> {</div>
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<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> <span class="comment">// Set states to every Node in the network</span></div>
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<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> for_each(features.begin(), features.end(), [<span class="keyword">this</span>, &states](<span class="keyword">const</span> std::string& feature) {</div>
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<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> nodes.at(feature)->setNumStates(states.at(feature).size());</div>
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<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> });</div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> classNumStates = nodes.at(className)->getNumStates();</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="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="comment">// X comes in nxm, where n is the number of features and m the number of samples</span></div>
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<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> torch::Tensor& X, <span class="keyword">const</span> torch::Tensor& y, <span class="keyword">const</span> torch::Tensor& weights, <span class="keyword">const</span> std::vector<std::string>& featureNames, <span class="keyword">const</span> std::string& className, <span class="keyword">const</span> std::map<std::string, std::vector<int>>& states)</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> checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);</div>
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|
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> this->className = className;</div>
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<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> torch::Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);</div>
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<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> samples = torch::cat({ X , ytmp }, 0);</div>
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<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < featureNames.size(); ++i) {</div>
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<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <span class="keyword">auto</span> row_feature = X.index({ i, <span class="stringliteral">"..."</span> });</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> completeFit(states, weights);</div>
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<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> }</div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> torch::Tensor& samples, <span class="keyword">const</span> torch::Tensor& weights, <span class="keyword">const</span> std::vector<std::string>& featureNames, <span class="keyword">const</span> std::string& className, <span class="keyword">const</span> std::map<std::string, std::vector<int>>& states)</div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> {</div>
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<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);</div>
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<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> this->className = className;</div>
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<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> this->samples = samples;</div>
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<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> completeFit(states, weights);</div>
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<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> }</div>
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|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> <span class="comment">// input_data comes in nxm, where n is the number of features and m the number of samples</span></div>
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<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> std::vector<std::vector<int>>& input_data, <span class="keyword">const</span> std::vector<int>& labels, <span class="keyword">const</span> std::vector<double>& weights_, <span class="keyword">const</span> std::vector<std::string>& featureNames, <span class="keyword">const</span> std::string& className, <span class="keyword">const</span> std::map<std::string, std::vector<int>>& states)</div>
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<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> {</div>
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<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> <span class="keyword">const</span> torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);</div>
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<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);</div>
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|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> this->className = className;</div>
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<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> <span class="comment">// Build tensor of samples (nxm) (n+1 because of the class)</span></div>
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|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> samples = torch::zeros({ <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(input_data.size() + 1), <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(input_data[0].size()) }, torch::kInt32);</div>
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|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < featureNames.size(); ++i) {</div>
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<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> samples.index_put_({ i, <span class="stringliteral">"..."</span> }, torch::tensor(input_data[i], torch::kInt32));</div>
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<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> }</div>
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<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> samples.index_put_({ -1, <span class="stringliteral">"..."</span> }, torch::tensor(labels, torch::kInt32));</div>
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|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> completeFit(states, weights);</div>
|
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<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> }</div>
|
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> <span class="keywordtype">void</span> Network::completeFit(<span class="keyword">const</span> std::map<std::string, std::vector<int>>& states, <span class="keyword">const</span> torch::Tensor& weights)</div>
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|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> {</div>
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<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> setStates(states);</div>
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|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> laplaceSmoothing = 1.0 / samples.size(1); <span class="comment">// To use in CPT computation</span></div>
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<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> std::vector<std::thread> threads;</div>
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<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> threads.emplace_back([<span class="keyword">this</span>, &node, &weights]() {</div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> node.second->computeCPT(samples, features, laplaceSmoothing, weights);</div>
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<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> });</div>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> }</div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& thread : threads) {</div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> thread.join();</div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> }</div>
|
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> fitted = <span class="keyword">true</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 class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> torch::Tensor Network::predict_tensor(<span class="keyword">const</span> torch::Tensor& samples, <span class="keyword">const</span> <span class="keywordtype">bool</span> proba)</div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> {</div>
|
|
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> <span class="keywordflow">if</span> (!fitted) {</div>
|
|
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">"You must call fit() before calling predict()"</span>);</div>
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|
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> }</div>
|
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> torch::Tensor result;</div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);</div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < samples.size(1); ++i) {</div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> <span class="keyword">const</span> torch::Tensor sample = samples.index({ <span class="stringliteral">"..."</span>, i });</div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> <span class="keyword">auto</span> psample = predict_sample(sample);</div>
|
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<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> <span class="keyword">auto</span> temp = torch::tensor(psample, torch::kFloat64);</div>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> <span class="comment">// result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));</span></div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> result.index_put_({ i, <span class="stringliteral">"..."</span> }, temp);</div>
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<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> }</div>
|
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<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> <span class="keywordflow">if</span> (proba)</div>
|
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<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keywordflow">return</span> result;</div>
|
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<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> <span class="keywordflow">return</span> result.argmax(1);</div>
|
|
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> }</div>
|
|
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> <span class="comment">// Return mxn tensor of probabilities</span></div>
|
|
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> torch::Tensor Network::predict_proba(<span class="keyword">const</span> torch::Tensor& samples)</div>
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<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> {</div>
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<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> <span class="keywordflow">return</span> predict_tensor(samples, <span class="keyword">true</span>);</div>
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|
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> }</div>
|
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<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> </div>
|
|
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span> <span class="comment">// Return mxn tensor of probabilities</span></div>
|
|
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> torch::Tensor Network::predict(<span class="keyword">const</span> torch::Tensor& samples)</div>
|
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<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> {</div>
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|
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> <span class="keywordflow">return</span> predict_tensor(samples, <span class="keyword">false</span>);</div>
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<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> }</div>
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<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> </div>
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|
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> <span class="comment">// Return mx1 std::vector of predictions</span></div>
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|
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> <span class="comment">// tsamples is nxm std::vector of samples</span></div>
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|
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> std::vector<int> Network::predict(<span class="keyword">const</span> std::vector<std::vector<int>>& tsamples)</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 class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> <span class="keywordflow">if</span> (!fitted) {</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">"You must call fit() before calling predict()"</span>);</div>
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<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> }</div>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> std::vector<int> predictions;</div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> std::vector<int> sample;</div>
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<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < tsamples[0].size(); ++row) {</div>
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<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> sample.clear();</div>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = 0; col < tsamples.size(); ++col) {</div>
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<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> sample.push_back(tsamples[col][row]);</div>
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<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> }</div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> std::vector<double> classProbabilities = predict_sample(sample);</div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="comment">// Find the class with the maximum posterior probability</span></div>
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<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> <span class="keyword">auto</span> maxElem = max_element(classProbabilities.begin(), classProbabilities.end());</div>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> <span class="keywordtype">int</span> predictedClass = distance(classProbabilities.begin(), maxElem);</div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> predictions.push_back(predictedClass);</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> <span class="keywordflow">return</span> predictions;</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="comment">// Return mxn std::vector of probabilities</span></div>
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|
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> <span class="comment">// tsamples is nxm std::vector of samples</span></div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> std::vector<std::vector<double>> Network::predict_proba(<span class="keyword">const</span> std::vector<std::vector<int>>& tsamples)</div>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> {</div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> <span class="keywordflow">if</span> (!fitted) {</div>
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|
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">"You must call fit() before calling predict_proba()"</span>);</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="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> std::vector<std::vector<double>> predictions;</div>
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<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> std::vector<int> sample;</div>
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<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < tsamples[0].size(); ++row) {</div>
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<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> sample.clear();</div>
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<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = 0; col < tsamples.size(); ++col) {</div>
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> sample.push_back(tsamples[col][row]);</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> }</div>
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<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> predictions.push_back(predict_sample(sample));</div>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> }</div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <span class="keywordflow">return</span> predictions;</div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> }</div>
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<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="keywordtype">double</span> Network::score(<span class="keyword">const</span> std::vector<std::vector<int>>& tsamples, <span class="keyword">const</span> std::vector<int>& labels)</div>
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<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> {</div>
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<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> std::vector<int> y_pred = predict(tsamples);</div>
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<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> <span class="keywordtype">int</span> correct = 0;</div>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_pred.size(); ++i) {</div>
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<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> <span class="keywordflow">if</span> (y_pred[i] == labels[i]) {</div>
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<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> correct++;</div>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> }</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">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size();</div>
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|
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> }</div>
|
|
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> <span class="comment">// Return 1xn std::vector of probabilities</span></div>
|
|
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> std::vector<double> Network::predict_sample(<span class="keyword">const</span> std::vector<int>& sample)</div>
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|
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> {</div>
|
|
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <span class="comment">// Ensure the sample size is equal to the number of features</span></div>
|
|
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <span class="keywordflow">if</span> (sample.size() != features.size() - 1) {</div>
|
|
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Sample size ("</span> + std::to_string(sample.size()) +</div>
|
|
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="stringliteral">") does not match the number of features ("</span> + std::to_string(features.size() - 1) + <span class="stringliteral">")"</span>);</div>
|
|
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> }</div>
|
|
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> std::map<std::string, int> evidence;</div>
|
|
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < sample.size(); ++i) {</div>
|
|
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> evidence[features[i]] = sample[i];</div>
|
|
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> }</div>
|
|
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> <span class="keywordflow">return</span> exactInference(evidence);</div>
|
|
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> }</div>
|
|
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> <span class="comment">// Return 1xn std::vector of probabilities</span></div>
|
|
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> std::vector<double> Network::predict_sample(<span class="keyword">const</span> torch::Tensor& sample)</div>
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|
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> {</div>
|
|
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> <span class="comment">// Ensure the sample size is equal to the number of features</span></div>
|
|
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> <span class="keywordflow">if</span> (sample.size(0) != features.size() - 1) {</div>
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|
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Sample size ("</span> + std::to_string(sample.size(0)) +</div>
|
|
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> <span class="stringliteral">") does not match the number of features ("</span> + std::to_string(features.size() - 1) + <span class="stringliteral">")"</span>);</div>
|
|
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> }</div>
|
|
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> std::map<std::string, int> evidence;</div>
|
|
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < sample.size(0); ++i) {</div>
|
|
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> evidence[features[i]] = sample[i].item<<span class="keywordtype">int</span>>();</div>
|
|
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> }</div>
|
|
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> <span class="keywordflow">return</span> exactInference(evidence);</div>
|
|
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> }</div>
|
|
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> <span class="keywordtype">double</span> Network::computeFactor(std::map<std::string, int>& completeEvidence)</div>
|
|
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> {</div>
|
|
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> <span class="keywordtype">double</span> result = 1.0;</div>
|
|
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& node : getNodes()) {</div>
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|
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> result *= node.second->getFactorValue(completeEvidence);</div>
|
|
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> }</div>
|
|
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> <span class="keywordflow">return</span> result;</div>
|
|
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> }</div>
|
|
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)</div>
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|
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> {</div>
|
|
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> std::vector<double> result(classNumStates, 0.0);</div>
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|
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> std::vector<std::thread> threads;</div>
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|
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> std::mutex mtx;</div>
|
|
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < classNumStates; ++i) {</div>
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|
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> threads.emplace_back([<span class="keyword">this</span>, &result, &evidence, i, &mtx]() {</div>
|
|
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> <span class="keyword">auto</span> completeEvidence = std::map<std::string, int>(evidence);</div>
|
|
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> completeEvidence[getClassName()] = i;</div>
|
|
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="keywordtype">double</span> factor = computeFactor(completeEvidence);</div>
|
|
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> std::lock_guard<std::mutex> lock(mtx);</div>
|
|
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> result[i] = factor;</div>
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|
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> });</div>
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|
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> }</div>
|
|
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& thread : threads) {</div>
|
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<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> thread.join();</div>
|
|
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> }</div>
|
|
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> <span class="comment">// Normalize result</span></div>
|
|
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> <span class="keywordtype">double</span> sum = accumulate(result.begin(), result.end(), 0.0);</div>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> transform(result.begin(), result.end(), result.begin(), [sum](<span class="keyword">const</span> <span class="keywordtype">double</span>& value) { return value / sum; });</div>
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|
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keywordflow">return</span> result;</div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> }</div>
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<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> std::vector<std::string> Network::show()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> std::vector<std::string> result;</div>
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<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> <span class="comment">// Draw the network</span></div>
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<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> std::string line = node.first + <span class="stringliteral">" -> "</span>;</div>
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<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> child : node.second->getChildren()) {</div>
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<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> line += child->getName() + <span class="stringliteral">", "</span>;</div>
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<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> }</div>
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|
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> result.push_back(line);</div>
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<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> }</div>
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<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> <span class="keywordflow">return</span> result;</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="l00357" name="l00357"></a><span class="lineno"> 357</span> std::vector<std::string> Network::graph(<span class="keyword">const</span> std::string& title)<span class="keyword"> const</span></div>
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<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> <span class="keyword">auto</span> output = std::vector<std::string>();</div>
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|
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <span class="keyword">auto</span> prefix = <span class="stringliteral">"digraph BayesNet {\nlabel=<BayesNet "</span>;</div>
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|
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> <span class="keyword">auto</span> suffix = <span class="stringliteral">">\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n"</span>;</div>
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<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> std::string header = prefix + title + suffix;</div>
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<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> output.push_back(header);</div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <span class="keyword">auto</span> result = node.second->graph(className);</div>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> output.insert(output.end(), result.begin(), result.end());</div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> }</div>
|
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<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> output.push_back(<span class="stringliteral">"}\n"</span>);</div>
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|
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> <span class="keywordflow">return</span> output;</div>
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<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> }</div>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> std::vector<std::pair<std::string, std::string>> Network::getEdges()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> <span class="keyword">auto</span> edges = std::vector<std::pair<std::string, std::string>>();</div>
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<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> <span class="keyword">auto</span> head = node.first;</div>
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<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& child : node.second->getChildren()) {</div>
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<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> <span class="keyword">auto</span> tail = child->getName();</div>
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<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span> edges.push_back({ head, tail });</div>
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<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> }</div>
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<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> }</div>
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<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> <span class="keywordflow">return</span> edges;</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="keywordtype">int</span> Network::getNumEdges()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> <span class="keywordflow">return</span> getEdges().size();</div>
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<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> }</div>
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<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> std::vector<std::string> Network::topological_sort()</div>
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> {</div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> <span class="comment">/* Check if al the fathers of every node are before the node */</span></div>
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<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">auto</span> result = features;</div>
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<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> result.erase(remove(result.begin(), result.end(), className), result.end());</div>
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<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> <span class="keywordtype">bool</span> ending{ <span class="keyword">false</span> };</div>
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<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> <span class="keywordflow">while</span> (!ending) {</div>
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<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> ending = <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : features) {</div>
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<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> <span class="keyword">auto</span> fathers = nodes[feature]->getParents();</div>
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<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& father : fathers) {</div>
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<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> <span class="keyword">auto</span> fatherName = father->getName();</div>
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<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> <span class="keywordflow">if</span> (fatherName == className) {</div>
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<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> <span class="keywordflow">continue</span>;</div>
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<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> }</div>
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<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> <span class="comment">// Check if father is placed before the actual feature</span></div>
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<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> <span class="keyword">auto</span> it = find(result.begin(), result.end(), fatherName);</div>
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<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> <span class="keywordflow">if</span> (it != result.end()) {</div>
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<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> <span class="keyword">auto</span> it2 = find(result.begin(), result.end(), feature);</div>
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<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> <span class="keywordflow">if</span> (it2 != result.end()) {</div>
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<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> <span class="keywordflow">if</span> (distance(it, it2) < 0) {</div>
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<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> <span class="comment">// if it is not, insert it before the feature</span></div>
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<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> result.erase(remove(result.begin(), result.end(), fatherName), result.end());</div>
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<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> result.insert(it2, fatherName);</div>
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<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> ending = <span class="keyword">false</span>;</div>
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<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> }</div>
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<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> }</div>
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<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> }</div>
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<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> }</div>
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<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> }</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> <span class="keywordflow">return</span> result;</div>
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<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> }</div>
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<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> std::string Network::dump_cpt()<span class="keyword"> const</span></div>
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<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> std::stringstream oss;</div>
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<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& node : nodes) {</div>
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<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> oss << <span class="stringliteral">"* "</span> << node.first << <span class="stringliteral">": ("</span> << node.second->getNumStates() << <span class="stringliteral">") : "</span> << node.second->getCPT().sizes() << std::endl;</div>
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<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> oss << node.second->getCPT() << std::endl;</div>
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<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> }</div>
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<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> <span class="keywordflow">return</span> oss.str();</div>
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<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span> }</div>
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|
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span>}</div>
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