Return File Library to /lib as it is needed by Local Discretization (factorize)
This commit is contained in:
@@ -37,7 +37,7 @@
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</tr>
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<tr>
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<td class="headerItem">Test Date:</td>
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<td class="headerValue">2024-04-30 13:59:18</td>
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<td class="headerValue">2024-04-30 20:26:57</td>
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<td></td>
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<td class="headerItem">Functions:</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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@@ -65,168 +65,168 @@
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L182">bayesnet::Classifier::dump_cpt[abi:cxx11]() const</a></td>
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<td class="coverFnHi">6</td>
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<td class="coverFnHi">2</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L178">bayesnet::Classifier::topological_order[abi:cxx11]()</a></td>
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<td class="coverFnHi">6</td>
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<td class="coverFnHi">2</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L101">bayesnet::Classifier::predict(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)</a></td>
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<td class="coverFnHi">24</td>
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<td class="coverFnHi">8</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L142">bayesnet::Classifier::score(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&, std::vector<int, std::allocator<int> >&)</a></td>
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<td class="coverFnHi">24</td>
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<td class="coverFnHi">8</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L170">bayesnet::Classifier::getNumberOfStates() const</a></td>
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<td class="coverFnHi">36</td>
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<td class="coverFnHi">12</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L149">bayesnet::Classifier::show[abi:cxx11]() const</a></td>
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<td class="coverFnHi">36</td>
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<td class="coverFnHi">12</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L186">bayesnet::Classifier::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector<unsigned char, std::allocator<unsigned char> >, void> const&)</a></td>
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<td class="coverFnHi">126</td>
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<td class="coverFnHi">42</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L137">bayesnet::Classifier::score(at::Tensor&, at::Tensor&)</a></td>
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<td class="coverFnHi">168</td>
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<td class="coverFnHi">56</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L55">bayesnet::Classifier::fit(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&, std::vector<int, std::allocator<int> >&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
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<td class="coverFnHi">180</td>
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<td class="coverFnHi">60</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L47">bayesnet::Classifier::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
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<td class="coverFnHi">192</td>
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<td class="coverFnHi">64</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L166">bayesnet::Classifier::getNumberOfEdges() const</a></td>
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<td class="coverFnHi">282</td>
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<td class="coverFnHi">94</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L161">bayesnet::Classifier::getNumberOfNodes() const</a></td>
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<td class="coverFnHi">282</td>
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<td class="coverFnHi">94</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L122">bayesnet::Classifier::predict_proba(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)</a></td>
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<td class="coverFnHi">390</td>
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<td class="coverFnHi">130</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L28">bayesnet::Classifier::buildDataset(at::Tensor&)</a></td>
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<td class="coverFnHi">486</td>
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<td class="coverFnHi">162</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L174">bayesnet::Classifier::getClassNumStates() const</a></td>
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<td class="coverFnHi">510</td>
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<td class="coverFnHi">170</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L66">bayesnet::Classifier::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
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<td class="coverFnHi">594</td>
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<td class="coverFnHi">198</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L72">bayesnet::Classifier::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&, at::Tensor const&)</a></td>
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<td class="coverFnHi">990</td>
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<td class="coverFnHi">330</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L153">bayesnet::Classifier::addNodes()</a></td>
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<td class="coverFnHi">1680</td>
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<td class="coverFnHi">560</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L42">bayesnet::Classifier::trainModel(at::Tensor const&)</a></td>
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<td class="coverFnHi">1680</td>
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<td class="coverFnHi">560</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L12">bayesnet::Classifier::build(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&, at::Tensor const&)</a></td>
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<td class="coverFnHi">1932</td>
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<td class="coverFnHi">644</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L77">bayesnet::Classifier::checkFitParameters()</a></td>
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<td class="coverFnHi">1932</td>
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<td class="coverFnHi">644</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L115">bayesnet::Classifier::predict_proba(at::Tensor&)</a></td>
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<td class="coverFnHi">2226</td>
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<td class="coverFnHi">742</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L94">bayesnet::Classifier::predict(at::Tensor&)</a></td>
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<td class="coverFnHi">2550</td>
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<td class="coverFnHi">850</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L10">bayesnet::Classifier::Classifier(bayesnet::Network)</a></td>
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<td class="coverFnHi">2658</td>
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<td class="coverFnHi">886</td>
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</tr>
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@@ -37,7 +37,7 @@
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</tr>
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<tr>
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<td class="headerItem">Test Date:</td>
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<td class="headerValue">2024-04-30 13:59:18</td>
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<td class="headerValue">2024-04-30 20:26:57</td>
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<td></td>
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<td class="headerItem">Functions:</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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@@ -65,168 +65,168 @@
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L10">bayesnet::Classifier::Classifier(bayesnet::Network)</a></td>
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<td class="coverFnHi">2658</td>
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<td class="coverFnHi">886</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L153">bayesnet::Classifier::addNodes()</a></td>
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<td class="coverFnHi">1680</td>
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<td class="coverFnHi">560</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L12">bayesnet::Classifier::build(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&, at::Tensor const&)</a></td>
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<td class="coverFnHi">1932</td>
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<td class="coverFnHi">644</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L28">bayesnet::Classifier::buildDataset(at::Tensor&)</a></td>
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<td class="coverFnHi">486</td>
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<td class="coverFnHi">162</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L77">bayesnet::Classifier::checkFitParameters()</a></td>
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|
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<td class="coverFnHi">1932</td>
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<td class="coverFnHi">644</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L182">bayesnet::Classifier::dump_cpt[abi:cxx11]() const</a></td>
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|
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<td class="coverFnHi">6</td>
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<td class="coverFnHi">2</td>
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|
||||
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</tr>
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||||
<tr>
|
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<td class="coverFn"><a href="Classifier.cc.gcov.html#L47">bayesnet::Classifier::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">192</td>
|
||||
<td class="coverFnHi">64</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L66">bayesnet::Classifier::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">594</td>
|
||||
<td class="coverFnHi">198</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L72">bayesnet::Classifier::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&, at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">990</td>
|
||||
<td class="coverFnHi">330</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L55">bayesnet::Classifier::fit(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&, std::vector<int, std::allocator<int> >&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">180</td>
|
||||
<td class="coverFnHi">60</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L174">bayesnet::Classifier::getClassNumStates() const</a></td>
|
||||
|
||||
<td class="coverFnHi">510</td>
|
||||
<td class="coverFnHi">170</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L166">bayesnet::Classifier::getNumberOfEdges() const</a></td>
|
||||
|
||||
<td class="coverFnHi">282</td>
|
||||
<td class="coverFnHi">94</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L161">bayesnet::Classifier::getNumberOfNodes() const</a></td>
|
||||
|
||||
<td class="coverFnHi">282</td>
|
||||
<td class="coverFnHi">94</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L170">bayesnet::Classifier::getNumberOfStates() const</a></td>
|
||||
|
||||
<td class="coverFnHi">36</td>
|
||||
<td class="coverFnHi">12</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L94">bayesnet::Classifier::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">2550</td>
|
||||
<td class="coverFnHi">850</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L101">bayesnet::Classifier::predict(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L115">bayesnet::Classifier::predict_proba(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">2226</td>
|
||||
<td class="coverFnHi">742</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L122">bayesnet::Classifier::predict_proba(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">390</td>
|
||||
<td class="coverFnHi">130</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L137">bayesnet::Classifier::score(at::Tensor&, at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">168</td>
|
||||
<td class="coverFnHi">56</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L142">bayesnet::Classifier::score(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&, std::vector<int, std::allocator<int> >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L186">bayesnet::Classifier::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector<unsigned char, std::allocator<unsigned char> >, void> const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">126</td>
|
||||
<td class="coverFnHi">42</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L149">bayesnet::Classifier::show[abi:cxx11]() const</a></td>
|
||||
|
||||
<td class="coverFnHi">36</td>
|
||||
<td class="coverFnHi">12</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L178">bayesnet::Classifier::topological_order[abi:cxx11]()</a></td>
|
||||
|
||||
<td class="coverFnHi">6</td>
|
||||
<td class="coverFnHi">2</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.cc.gcov.html#L42">bayesnet::Classifier::trainModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">1680</td>
|
||||
<td class="coverFnHi">560</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -71,188 +71,188 @@
|
||||
<span id="L9"><span class="lineNum"> 9</span> : #include "Classifier.h"</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> : </span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> : namespace bayesnet {</span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC tlaBgGNC"> 2658 : Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC tlaBgGNC"> 886 : Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> : const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";</span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 1932 : Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 644 : Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : {</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 1932 : this->features = features;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 1932 : this->className = className;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 1932 : this->states = states;</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 1932 : m = dataset.size(1);</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 1932 : n = features.size();</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 1932 : checkFitParameters();</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 1884 : auto n_classes = states.at(className).size();</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 1884 : metrics = Metrics(dataset, features, className, n_classes);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 1884 : model.initialize();</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 1884 : buildModel(weights);</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 1884 : trainModel(weights);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 1860 : fitted = true;</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 1860 : return *this;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 644 : this->features = features;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 644 : this->className = className;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 644 : this->states = states;</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 644 : m = dataset.size(1);</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 644 : n = features.size();</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 644 : checkFitParameters();</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 628 : auto n_classes = states.at(className).size();</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 628 : metrics = Metrics(dataset, features, className, n_classes);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 628 : model.initialize();</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 628 : buildModel(weights);</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 628 : trainModel(weights);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 620 : fitted = true;</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 620 : return *this;</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> : }</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 486 : void Classifier::buildDataset(torch::Tensor& ytmp)</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 162 : void Classifier::buildDataset(torch::Tensor& ytmp)</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> : {</span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> : try {</span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 486 : auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 1506 : dataset = torch::cat({ dataset, yresized }, 0);</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 486 : }</span></span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 24 : catch (const std::exception& e) {</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 24 : std::stringstream oss;</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 24 : oss << "* Error in X and y dimensions *\n";</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 24 : oss << "X dimensions: " << dataset.sizes() << "\n";</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 24 : oss << "y dimensions: " << ytmp.sizes();</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 24 : throw std::runtime_error(oss.str());</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 48 : }</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 972 : }</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 1680 : void Classifier::trainModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 162 : auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 502 : dataset = torch::cat({ dataset, yresized }, 0);</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 162 : }</span></span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 8 : catch (const std::exception& e) {</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 8 : std::stringstream oss;</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 8 : oss << "* Error in X and y dimensions *\n";</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 8 : oss << "X dimensions: " << dataset.sizes() << "\n";</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 8 : oss << "y dimensions: " << ytmp.sizes();</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 8 : throw std::runtime_error(oss.str());</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 16 : }</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 324 : }</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 560 : void Classifier::trainModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> : {</span>
|
||||
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 1680 : model.fit(dataset, weights, features, className, states);</span></span>
|
||||
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 1680 : }</span></span>
|
||||
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 560 : model.fit(dataset, weights, features, className, states);</span></span>
|
||||
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 560 : }</span></span>
|
||||
<span id="L48"><span class="lineNum"> 48</span> : // X is nxm where n is the number of features and m the number of samples</span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 192 : Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 64 : Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L50"><span class="lineNum"> 50</span> : {</span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 192 : dataset = X;</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 192 : buildDataset(y);</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 180 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 312 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 180 : }</span></span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 64 : dataset = X;</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 64 : buildDataset(y);</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 60 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 104 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 60 : }</span></span>
|
||||
<span id="L56"><span class="lineNum"> 56</span> : // X is nxm where n is the number of features and m the number of samples</span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 180 : Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 60 : Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L58"><span class="lineNum"> 58</span> : {</span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 180 : dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);</span></span>
|
||||
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 1254 : for (int i = 0; i < X.size(); ++i) {</span></span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 4296 : dataset.index_put_({ i, "..." }, torch::tensor(X[i], torch::kInt32));</span></span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 60 : dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);</span></span>
|
||||
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 418 : for (int i = 0; i < X.size(); ++i) {</span></span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 1432 : dataset.index_put_({ i, "..." }, torch::tensor(X[i], torch::kInt32));</span></span>
|
||||
<span id="L62"><span class="lineNum"> 62</span> : }</span>
|
||||
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 180 : auto ytmp = torch::tensor(y, torch::kInt32);</span></span>
|
||||
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 180 : buildDataset(ytmp);</span></span>
|
||||
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 168 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 312 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 1278 : }</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 594 : Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 60 : auto ytmp = torch::tensor(y, torch::kInt32);</span></span>
|
||||
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 60 : buildDataset(ytmp);</span></span>
|
||||
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 56 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 104 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 426 : }</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 198 : Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)</span></span>
|
||||
<span id="L69"><span class="lineNum"> 69</span> : {</span>
|
||||
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 594 : this->dataset = dataset;</span></span>
|
||||
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 594 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 1188 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 594 : }</span></span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 990 : Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)</span></span>
|
||||
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 198 : this->dataset = dataset;</span></span>
|
||||
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 198 : const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 396 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 198 : }</span></span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 330 : Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)</span></span>
|
||||
<span id="L75"><span class="lineNum"> 75</span> : {</span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 990 : this->dataset = dataset;</span></span>
|
||||
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 990 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 330 : this->dataset = dataset;</span></span>
|
||||
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 330 : return build(features, className, states, weights);</span></span>
|
||||
<span id="L78"><span class="lineNum"> 78</span> : }</span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 1932 : void Classifier::checkFitParameters()</span></span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 644 : void Classifier::checkFitParameters()</span></span>
|
||||
<span id="L80"><span class="lineNum"> 80</span> : {</span>
|
||||
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 1932 : if (torch::is_floating_point(dataset)) {</span></span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 12 : throw std::invalid_argument("dataset (X, y) must be of type Integer");</span></span>
|
||||
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 644 : if (torch::is_floating_point(dataset)) {</span></span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("dataset (X, y) must be of type Integer");</span></span>
|
||||
<span id="L83"><span class="lineNum"> 83</span> : }</span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 1920 : if (dataset.size(0) - 1 != features.size()) {</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 12 : throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");</span></span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 640 : if (dataset.size(0) - 1 != features.size()) {</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");</span></span>
|
||||
<span id="L86"><span class="lineNum"> 86</span> : }</span>
|
||||
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 1908 : if (states.find(className) == states.end()) {</span></span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 12 : throw std::invalid_argument("class name not found in states");</span></span>
|
||||
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 636 : if (states.find(className) == states.end()) {</span></span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("class name not found in states");</span></span>
|
||||
<span id="L89"><span class="lineNum"> 89</span> : }</span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 42624 : for (auto feature : features) {</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 40740 : if (states.find(feature) == states.end()) {</span></span>
|
||||
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 12 : throw std::invalid_argument("feature [" + feature + "] not found in states");</span></span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 14208 : for (auto feature : features) {</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 13580 : if (states.find(feature) == states.end()) {</span></span>
|
||||
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("feature [" + feature + "] not found in states");</span></span>
|
||||
<span id="L93"><span class="lineNum"> 93</span> : }</span>
|
||||
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 40740 : }</span></span>
|
||||
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 1884 : }</span></span>
|
||||
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 2550 : torch::Tensor Classifier::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 13580 : }</span></span>
|
||||
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 628 : }</span></span>
|
||||
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 850 : torch::Tensor Classifier::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L97"><span class="lineNum"> 97</span> : {</span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 2550 : if (!fitted) {</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 24 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 850 : if (!fitted) {</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 8 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L100"><span class="lineNum"> 100</span> : }</span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 2526 : return model.predict(X);</span></span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 842 : return model.predict(X);</span></span>
|
||||
<span id="L102"><span class="lineNum"> 102</span> : }</span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 24 : std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)</span></span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 8 : std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)</span></span>
|
||||
<span id="L104"><span class="lineNum"> 104</span> : {</span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 24 : if (!fitted) {</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 12 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 8 : if (!fitted) {</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 4 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L107"><span class="lineNum"> 107</span> : }</span>
|
||||
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 12 : auto m_ = X[0].size();</span></span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 12 : auto n_ = X.size();</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 12 : std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</span></span>
|
||||
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 60 : for (auto i = 0; i < n_; i++) {</span></span>
|
||||
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 96 : Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</span></span>
|
||||
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 4 : auto m_ = X[0].size();</span></span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 4 : auto n_ = X.size();</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 4 : std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</span></span>
|
||||
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 20 : for (auto i = 0; i < n_; i++) {</span></span>
|
||||
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 32 : Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</span></span>
|
||||
<span id="L113"><span class="lineNum"> 113</span> : }</span>
|
||||
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 12 : auto yp = model.predict(Xd);</span></span>
|
||||
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 24 : return yp;</span></span>
|
||||
<span id="L116"><span class="lineNum"> 116</span> <span class="tlaGNC"> 12 : }</span></span>
|
||||
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 2226 : torch::Tensor Classifier::predict_proba(torch::Tensor& X)</span></span>
|
||||
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 4 : auto yp = model.predict(Xd);</span></span>
|
||||
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 8 : return yp;</span></span>
|
||||
<span id="L116"><span class="lineNum"> 116</span> <span class="tlaGNC"> 4 : }</span></span>
|
||||
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 742 : torch::Tensor Classifier::predict_proba(torch::Tensor& X)</span></span>
|
||||
<span id="L118"><span class="lineNum"> 118</span> : {</span>
|
||||
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 2226 : if (!fitted) {</span></span>
|
||||
<span id="L120"><span class="lineNum"> 120</span> <span class="tlaGNC"> 12 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 742 : if (!fitted) {</span></span>
|
||||
<span id="L120"><span class="lineNum"> 120</span> <span class="tlaGNC"> 4 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L121"><span class="lineNum"> 121</span> : }</span>
|
||||
<span id="L122"><span class="lineNum"> 122</span> <span class="tlaGNC"> 2214 : return model.predict_proba(X);</span></span>
|
||||
<span id="L122"><span class="lineNum"> 122</span> <span class="tlaGNC"> 738 : return model.predict_proba(X);</span></span>
|
||||
<span id="L123"><span class="lineNum"> 123</span> : }</span>
|
||||
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 390 : std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)</span></span>
|
||||
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 130 : std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)</span></span>
|
||||
<span id="L125"><span class="lineNum"> 125</span> : {</span>
|
||||
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 390 : if (!fitted) {</span></span>
|
||||
<span id="L127"><span class="lineNum"> 127</span> <span class="tlaGNC"> 12 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 130 : if (!fitted) {</span></span>
|
||||
<span id="L127"><span class="lineNum"> 127</span> <span class="tlaGNC"> 4 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L128"><span class="lineNum"> 128</span> : }</span>
|
||||
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 378 : auto m_ = X[0].size();</span></span>
|
||||
<span id="L130"><span class="lineNum"> 130</span> <span class="tlaGNC"> 378 : auto n_ = X.size();</span></span>
|
||||
<span id="L131"><span class="lineNum"> 131</span> <span class="tlaGNC"> 378 : std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</span></span>
|
||||
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 126 : auto m_ = X[0].size();</span></span>
|
||||
<span id="L130"><span class="lineNum"> 130</span> <span class="tlaGNC"> 126 : auto n_ = X.size();</span></span>
|
||||
<span id="L131"><span class="lineNum"> 131</span> <span class="tlaGNC"> 126 : std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</span></span>
|
||||
<span id="L132"><span class="lineNum"> 132</span> : // Convert to nxm vector</span>
|
||||
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 3240 : for (auto i = 0; i < n_; i++) {</span></span>
|
||||
<span id="L134"><span class="lineNum"> 134</span> <span class="tlaGNC"> 5724 : Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</span></span>
|
||||
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 1080 : for (auto i = 0; i < n_; i++) {</span></span>
|
||||
<span id="L134"><span class="lineNum"> 134</span> <span class="tlaGNC"> 1908 : Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</span></span>
|
||||
<span id="L135"><span class="lineNum"> 135</span> : }</span>
|
||||
<span id="L136"><span class="lineNum"> 136</span> <span class="tlaGNC"> 378 : auto yp = model.predict_proba(Xd);</span></span>
|
||||
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaGNC"> 756 : return yp;</span></span>
|
||||
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC"> 378 : }</span></span>
|
||||
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 168 : float Classifier::score(torch::Tensor& X, torch::Tensor& y)</span></span>
|
||||
<span id="L136"><span class="lineNum"> 136</span> <span class="tlaGNC"> 126 : auto yp = model.predict_proba(Xd);</span></span>
|
||||
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaGNC"> 252 : return yp;</span></span>
|
||||
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC"> 126 : }</span></span>
|
||||
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 56 : float Classifier::score(torch::Tensor& X, torch::Tensor& y)</span></span>
|
||||
<span id="L140"><span class="lineNum"> 140</span> : {</span>
|
||||
<span id="L141"><span class="lineNum"> 141</span> <span class="tlaGNC"> 168 : torch::Tensor y_pred = predict(X);</span></span>
|
||||
<span id="L142"><span class="lineNum"> 142</span> <span class="tlaGNC"> 312 : return (y_pred == y).sum().item<float>() / y.size(0);</span></span>
|
||||
<span id="L143"><span class="lineNum"> 143</span> <span class="tlaGNC"> 156 : }</span></span>
|
||||
<span id="L144"><span class="lineNum"> 144</span> <span class="tlaGNC"> 24 : float Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)</span></span>
|
||||
<span id="L141"><span class="lineNum"> 141</span> <span class="tlaGNC"> 56 : torch::Tensor y_pred = predict(X);</span></span>
|
||||
<span id="L142"><span class="lineNum"> 142</span> <span class="tlaGNC"> 104 : return (y_pred == y).sum().item<float>() / y.size(0);</span></span>
|
||||
<span id="L143"><span class="lineNum"> 143</span> <span class="tlaGNC"> 52 : }</span></span>
|
||||
<span id="L144"><span class="lineNum"> 144</span> <span class="tlaGNC"> 8 : float Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)</span></span>
|
||||
<span id="L145"><span class="lineNum"> 145</span> : {</span>
|
||||
<span id="L146"><span class="lineNum"> 146</span> <span class="tlaGNC"> 24 : if (!fitted) {</span></span>
|
||||
<span id="L147"><span class="lineNum"> 147</span> <span class="tlaGNC"> 12 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L146"><span class="lineNum"> 146</span> <span class="tlaGNC"> 8 : if (!fitted) {</span></span>
|
||||
<span id="L147"><span class="lineNum"> 147</span> <span class="tlaGNC"> 4 : throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
|
||||
<span id="L148"><span class="lineNum"> 148</span> : }</span>
|
||||
<span id="L149"><span class="lineNum"> 149</span> <span class="tlaGNC"> 12 : return model.score(X, y);</span></span>
|
||||
<span id="L149"><span class="lineNum"> 149</span> <span class="tlaGNC"> 4 : return model.score(X, y);</span></span>
|
||||
<span id="L150"><span class="lineNum"> 150</span> : }</span>
|
||||
<span id="L151"><span class="lineNum"> 151</span> <span class="tlaGNC"> 36 : std::vector<std::string> Classifier::show() const</span></span>
|
||||
<span id="L151"><span class="lineNum"> 151</span> <span class="tlaGNC"> 12 : std::vector<std::string> Classifier::show() const</span></span>
|
||||
<span id="L152"><span class="lineNum"> 152</span> : {</span>
|
||||
<span id="L153"><span class="lineNum"> 153</span> <span class="tlaGNC"> 36 : return model.show();</span></span>
|
||||
<span id="L153"><span class="lineNum"> 153</span> <span class="tlaGNC"> 12 : return model.show();</span></span>
|
||||
<span id="L154"><span class="lineNum"> 154</span> : }</span>
|
||||
<span id="L155"><span class="lineNum"> 155</span> <span class="tlaGNC"> 1680 : void Classifier::addNodes()</span></span>
|
||||
<span id="L155"><span class="lineNum"> 155</span> <span class="tlaGNC"> 560 : void Classifier::addNodes()</span></span>
|
||||
<span id="L156"><span class="lineNum"> 156</span> : {</span>
|
||||
<span id="L157"><span class="lineNum"> 157</span> : // Add all nodes to the network</span>
|
||||
<span id="L158"><span class="lineNum"> 158</span> <span class="tlaGNC"> 39648 : for (const auto& feature : features) {</span></span>
|
||||
<span id="L159"><span class="lineNum"> 159</span> <span class="tlaGNC"> 37968 : model.addNode(feature);</span></span>
|
||||
<span id="L158"><span class="lineNum"> 158</span> <span class="tlaGNC"> 13216 : for (const auto& feature : features) {</span></span>
|
||||
<span id="L159"><span class="lineNum"> 159</span> <span class="tlaGNC"> 12656 : model.addNode(feature);</span></span>
|
||||
<span id="L160"><span class="lineNum"> 160</span> : }</span>
|
||||
<span id="L161"><span class="lineNum"> 161</span> <span class="tlaGNC"> 1680 : model.addNode(className);</span></span>
|
||||
<span id="L162"><span class="lineNum"> 162</span> <span class="tlaGNC"> 1680 : }</span></span>
|
||||
<span id="L163"><span class="lineNum"> 163</span> <span class="tlaGNC"> 282 : int Classifier::getNumberOfNodes() const</span></span>
|
||||
<span id="L161"><span class="lineNum"> 161</span> <span class="tlaGNC"> 560 : model.addNode(className);</span></span>
|
||||
<span id="L162"><span class="lineNum"> 162</span> <span class="tlaGNC"> 560 : }</span></span>
|
||||
<span id="L163"><span class="lineNum"> 163</span> <span class="tlaGNC"> 94 : int Classifier::getNumberOfNodes() const</span></span>
|
||||
<span id="L164"><span class="lineNum"> 164</span> : {</span>
|
||||
<span id="L165"><span class="lineNum"> 165</span> : // Features does not include class</span>
|
||||
<span id="L166"><span class="lineNum"> 166</span> <span class="tlaGNC"> 282 : return fitted ? model.getFeatures().size() : 0;</span></span>
|
||||
<span id="L166"><span class="lineNum"> 166</span> <span class="tlaGNC"> 94 : return fitted ? model.getFeatures().size() : 0;</span></span>
|
||||
<span id="L167"><span class="lineNum"> 167</span> : }</span>
|
||||
<span id="L168"><span class="lineNum"> 168</span> <span class="tlaGNC"> 282 : int Classifier::getNumberOfEdges() const</span></span>
|
||||
<span id="L168"><span class="lineNum"> 168</span> <span class="tlaGNC"> 94 : int Classifier::getNumberOfEdges() const</span></span>
|
||||
<span id="L169"><span class="lineNum"> 169</span> : {</span>
|
||||
<span id="L170"><span class="lineNum"> 170</span> <span class="tlaGNC"> 282 : return fitted ? model.getNumEdges() : 0;</span></span>
|
||||
<span id="L170"><span class="lineNum"> 170</span> <span class="tlaGNC"> 94 : return fitted ? model.getNumEdges() : 0;</span></span>
|
||||
<span id="L171"><span class="lineNum"> 171</span> : }</span>
|
||||
<span id="L172"><span class="lineNum"> 172</span> <span class="tlaGNC"> 36 : int Classifier::getNumberOfStates() const</span></span>
|
||||
<span id="L172"><span class="lineNum"> 172</span> <span class="tlaGNC"> 12 : int Classifier::getNumberOfStates() const</span></span>
|
||||
<span id="L173"><span class="lineNum"> 173</span> : {</span>
|
||||
<span id="L174"><span class="lineNum"> 174</span> <span class="tlaGNC"> 36 : return fitted ? model.getStates() : 0;</span></span>
|
||||
<span id="L174"><span class="lineNum"> 174</span> <span class="tlaGNC"> 12 : return fitted ? model.getStates() : 0;</span></span>
|
||||
<span id="L175"><span class="lineNum"> 175</span> : }</span>
|
||||
<span id="L176"><span class="lineNum"> 176</span> <span class="tlaGNC"> 510 : int Classifier::getClassNumStates() const</span></span>
|
||||
<span id="L176"><span class="lineNum"> 176</span> <span class="tlaGNC"> 170 : int Classifier::getClassNumStates() const</span></span>
|
||||
<span id="L177"><span class="lineNum"> 177</span> : {</span>
|
||||
<span id="L178"><span class="lineNum"> 178</span> <span class="tlaGNC"> 510 : return fitted ? model.getClassNumStates() : 0;</span></span>
|
||||
<span id="L178"><span class="lineNum"> 178</span> <span class="tlaGNC"> 170 : return fitted ? model.getClassNumStates() : 0;</span></span>
|
||||
<span id="L179"><span class="lineNum"> 179</span> : }</span>
|
||||
<span id="L180"><span class="lineNum"> 180</span> <span class="tlaGNC"> 6 : std::vector<std::string> Classifier::topological_order()</span></span>
|
||||
<span id="L180"><span class="lineNum"> 180</span> <span class="tlaGNC"> 2 : std::vector<std::string> Classifier::topological_order()</span></span>
|
||||
<span id="L181"><span class="lineNum"> 181</span> : {</span>
|
||||
<span id="L182"><span class="lineNum"> 182</span> <span class="tlaGNC"> 6 : return model.topological_sort();</span></span>
|
||||
<span id="L182"><span class="lineNum"> 182</span> <span class="tlaGNC"> 2 : return model.topological_sort();</span></span>
|
||||
<span id="L183"><span class="lineNum"> 183</span> : }</span>
|
||||
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 6 : std::string Classifier::dump_cpt() const</span></span>
|
||||
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 2 : std::string Classifier::dump_cpt() const</span></span>
|
||||
<span id="L185"><span class="lineNum"> 185</span> : {</span>
|
||||
<span id="L186"><span class="lineNum"> 186</span> <span class="tlaGNC"> 6 : return model.dump_cpt();</span></span>
|
||||
<span id="L186"><span class="lineNum"> 186</span> <span class="tlaGNC"> 2 : return model.dump_cpt();</span></span>
|
||||
<span id="L187"><span class="lineNum"> 187</span> : }</span>
|
||||
<span id="L188"><span class="lineNum"> 188</span> <span class="tlaGNC"> 126 : void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)</span></span>
|
||||
<span id="L188"><span class="lineNum"> 188</span> <span class="tlaGNC"> 42 : void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)</span></span>
|
||||
<span id="L189"><span class="lineNum"> 189</span> : {</span>
|
||||
<span id="L190"><span class="lineNum"> 190</span> <span class="tlaGNC"> 126 : if (!hyperparameters.empty()) {</span></span>
|
||||
<span id="L191"><span class="lineNum"> 191</span> <span class="tlaGNC"> 12 : throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());</span></span>
|
||||
<span id="L190"><span class="lineNum"> 190</span> <span class="tlaGNC"> 42 : if (!hyperparameters.empty()) {</span></span>
|
||||
<span id="L191"><span class="lineNum"> 191</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());</span></span>
|
||||
<span id="L192"><span class="lineNum"> 192</span> : }</span>
|
||||
<span id="L193"><span class="lineNum"> 193</span> <span class="tlaGNC"> 114 : }</span></span>
|
||||
<span id="L193"><span class="lineNum"> 193</span> <span class="tlaGNC"> 38 : }</span></span>
|
||||
<span id="L194"><span class="lineNum"> 194</span> : }</span>
|
||||
</pre>
|
||||
</td>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L31">bayesnet::Classifier::getVersion[abi:cxx11]()</a></td>
|
||||
|
||||
<td class="coverFnHi">48</td>
|
||||
<td class="coverFnHi">16</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L36">bayesnet::Classifier::getNotes[abi:cxx11]() const</a></td>
|
||||
|
||||
<td class="coverFnHi">114</td>
|
||||
<td class="coverFnHi">38</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L30">bayesnet::Classifier::getStatus() const</a></td>
|
||||
|
||||
<td class="coverFnHi">192</td>
|
||||
<td class="coverFnHi">64</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L16">bayesnet::Classifier::~Classifier()</a></td>
|
||||
|
||||
<td class="coverFnHi">1818</td>
|
||||
<td class="coverFnHi">606</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L36">bayesnet::Classifier::getNotes[abi:cxx11]() const</a></td>
|
||||
|
||||
<td class="coverFnHi">114</td>
|
||||
<td class="coverFnHi">38</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L30">bayesnet::Classifier::getStatus() const</a></td>
|
||||
|
||||
<td class="coverFnHi">192</td>
|
||||
<td class="coverFnHi">64</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L31">bayesnet::Classifier::getVersion[abi:cxx11]()</a></td>
|
||||
|
||||
<td class="coverFnHi">48</td>
|
||||
<td class="coverFnHi">16</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Classifier.h.gcov.html#L16">bayesnet::Classifier::~Classifier()</a></td>
|
||||
|
||||
<td class="coverFnHi">1818</td>
|
||||
<td class="coverFnHi">606</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -77,7 +77,7 @@
|
||||
<span id="L15"><span class="lineNum"> 15</span> : class Classifier : public BaseClassifier {</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : public:</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : Classifier(Network model);</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC tlaBgGNC"> 1818 : virtual ~Classifier() = default;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC tlaBgGNC"> 606 : virtual ~Classifier() = default;</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;</span>
|
||||
@@ -91,13 +91,13 @@
|
||||
<span id="L29"><span class="lineNum"> 29</span> : std::vector<int> predict(std::vector<std::vector<int>>& X) override;</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> : torch::Tensor predict_proba(torch::Tensor& X) override;</span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> : std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;</span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 192 : status_t getStatus() const override { return status; }</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 144 : std::string getVersion() override { return { project_version.begin(), project_version.end() }; };</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 64 : status_t getStatus() const override { return status; }</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 48 : std::string getVersion() override { return { project_version.begin(), project_version.end() }; };</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> : float score(torch::Tensor& X, torch::Tensor& y) override;</span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> : float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;</span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> : std::vector<std::string> show() const override;</span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> : std::vector<std::string> topological_order() override;</span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 114 : std::vector<std::string> getNotes() const override { return notes; }</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 38 : std::vector<std::string> getNotes() const override { return notes; }</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> : std::string dump_cpt() const override;</span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> : void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters</span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> : protected:</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,35 +65,35 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L101">bayesnet::KDB::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L13">bayesnet::KDB::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector<unsigned char, std::allocator<unsigned char> >, void> const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">18</td>
|
||||
<td class="coverFnHi">6</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L26">bayesnet::KDB::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">78</td>
|
||||
<td class="coverFnHi">26</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
|
||||
|
||||
<td class="coverFnHi">222</td>
|
||||
<td class="coverFnHi">74</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L77">bayesnet::KDB::add_m_edges(int, std::vector<int, std::allocator<int> >&, at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">516</td>
|
||||
<td class="coverFnHi">172</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,35 +65,35 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
|
||||
|
||||
<td class="coverFnHi">222</td>
|
||||
<td class="coverFnHi">74</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L77">bayesnet::KDB::add_m_edges(int, std::vector<int, std::allocator<int> >&, at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">516</td>
|
||||
<td class="coverFnHi">172</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L26">bayesnet::KDB::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">78</td>
|
||||
<td class="coverFnHi">26</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L101">bayesnet::KDB::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.cc.gcov.html#L13">bayesnet::KDB::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector<unsigned char, std::allocator<unsigned char> >, void> const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">18</td>
|
||||
<td class="coverFnHi">6</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -69,25 +69,25 @@
|
||||
<span id="L7"><span class="lineNum"> 7</span> : #include "KDB.h"</span>
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 222 : KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)</span></span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 74 : KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> : {</span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 666 : validHyperparameters = { "k", "theta" };</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 222 : validHyperparameters = { "k", "theta" };</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> : </span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 666 : }</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 18 : void KDB::setHyperparameters(const nlohmann::json& hyperparameters_)</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 222 : }</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 6 : void KDB::setHyperparameters(const nlohmann::json& hyperparameters_)</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : {</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 18 : auto hyperparameters = hyperparameters_;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 18 : if (hyperparameters.contains("k")) {</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 6 : k = hyperparameters["k"];</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 6 : hyperparameters.erase("k");</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 6 : auto hyperparameters = hyperparameters_;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 6 : if (hyperparameters.contains("k")) {</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 2 : k = hyperparameters["k"];</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 2 : hyperparameters.erase("k");</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : }</span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 18 : if (hyperparameters.contains("theta")) {</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 6 : theta = hyperparameters["theta"];</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 6 : hyperparameters.erase("theta");</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 6 : if (hyperparameters.contains("theta")) {</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 2 : theta = hyperparameters["theta"];</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 2 : hyperparameters.erase("theta");</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> : }</span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 18 : Classifier::setHyperparameters(hyperparameters);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 18 : }</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 78 : void KDB::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 6 : Classifier::setHyperparameters(hyperparameters);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 6 : }</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 26 : void KDB::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> : {</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> : /*</span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> : 1. For each feature Xi, compute mutual information, I(X;C),</span>
|
||||
@@ -110,66 +110,66 @@
|
||||
<span id="L48"><span class="lineNum"> 48</span> : */</span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> : // 1. For each feature Xi, compute mutual information, I(X;C),</span>
|
||||
<span id="L50"><span class="lineNum"> 50</span> : // where C is the class.</span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 78 : addNodes();</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 234 : const torch::Tensor& y = dataset.index({ -1, "..." });</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 78 : std::vector<double> mi;</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 594 : for (auto i = 0; i < features.size(); i++) {</span></span>
|
||||
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 1548 : torch::Tensor firstFeature = dataset.index({ i, "..." });</span></span>
|
||||
<span id="L56"><span class="lineNum"> 56</span> <span class="tlaGNC"> 516 : mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</span></span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 516 : }</span></span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 26 : addNodes();</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 78 : const torch::Tensor& y = dataset.index({ -1, "..." });</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 26 : std::vector<double> mi;</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 198 : for (auto i = 0; i < features.size(); i++) {</span></span>
|
||||
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 516 : torch::Tensor firstFeature = dataset.index({ i, "..." });</span></span>
|
||||
<span id="L56"><span class="lineNum"> 56</span> <span class="tlaGNC"> 172 : mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</span></span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 172 : }</span></span>
|
||||
<span id="L58"><span class="lineNum"> 58</span> : // 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 78 : auto conditionalEdgeWeights = metrics.conditionalEdge(weights);</span></span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 26 : auto conditionalEdgeWeights = metrics.conditionalEdge(weights);</span></span>
|
||||
<span id="L60"><span class="lineNum"> 60</span> : // 3. Let the used variable list, S, be empty.</span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 78 : std::vector<int> S;</span></span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 26 : std::vector<int> S;</span></span>
|
||||
<span id="L62"><span class="lineNum"> 62</span> : // 4. Let the DAG network being constructed, BN, begin with a single</span>
|
||||
<span id="L63"><span class="lineNum"> 63</span> : // class node, C.</span>
|
||||
<span id="L64"><span class="lineNum"> 64</span> : // 5. Repeat until S includes all domain features</span>
|
||||
<span id="L65"><span class="lineNum"> 65</span> : // 5.1. Select feature Xmax which is not in S and has the largest value</span>
|
||||
<span id="L66"><span class="lineNum"> 66</span> : // I(Xmax;C).</span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 78 : auto order = argsort(mi);</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 594 : for (auto idx : order) {</span></span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 26 : auto order = argsort(mi);</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 198 : for (auto idx : order) {</span></span>
|
||||
<span id="L69"><span class="lineNum"> 69</span> : // 5.2. Add a node to BN representing Xmax.</span>
|
||||
<span id="L70"><span class="lineNum"> 70</span> : // 5.3. Add an arc from C to Xmax in BN.</span>
|
||||
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 516 : model.addEdge(className, features[idx]);</span></span>
|
||||
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 172 : model.addEdge(className, features[idx]);</span></span>
|
||||
<span id="L72"><span class="lineNum"> 72</span> : // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span>
|
||||
<span id="L73"><span class="lineNum"> 73</span> : // the highest value for I(Xmax;X,jC).</span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 516 : add_m_edges(idx, S, conditionalEdgeWeights);</span></span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 172 : add_m_edges(idx, S, conditionalEdgeWeights);</span></span>
|
||||
<span id="L75"><span class="lineNum"> 75</span> : // 5.5. Add Xmax to S.</span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 516 : S.push_back(idx);</span></span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 172 : S.push_back(idx);</span></span>
|
||||
<span id="L77"><span class="lineNum"> 77</span> : }</span>
|
||||
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 672 : }</span></span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 516 : void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights)</span></span>
|
||||
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 224 : }</span></span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 172 : void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights)</span></span>
|
||||
<span id="L80"><span class="lineNum"> 80</span> : {</span>
|
||||
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 516 : auto n_edges = std::min(k, static_cast<int>(S.size()));</span></span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 516 : auto cond_w = clone(weights);</span></span>
|
||||
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 516 : bool exit_cond = k == 0;</span></span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 516 : int num = 0;</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 1506 : while (!exit_cond) {</span></span>
|
||||
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 3960 : auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();</span></span>
|
||||
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 990 : auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();</span></span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 2646 : if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {</span></span>
|
||||
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 172 : auto n_edges = std::min(k, static_cast<int>(S.size()));</span></span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 172 : auto cond_w = clone(weights);</span></span>
|
||||
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 172 : bool exit_cond = k == 0;</span></span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 172 : int num = 0;</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 502 : while (!exit_cond) {</span></span>
|
||||
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 1320 : auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();</span></span>
|
||||
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 330 : auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();</span></span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 882 : if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {</span></span>
|
||||
<span id="L89"><span class="lineNum"> 89</span> : try {</span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 480 : model.addEdge(features[max_minfo], features[idx]);</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 480 : num++;</span></span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 160 : model.addEdge(features[max_minfo], features[idx]);</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 160 : num++;</span></span>
|
||||
<span id="L92"><span class="lineNum"> 92</span> : }</span>
|
||||
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaUNC tlaBgUNC"> 0 : catch (const std::invalid_argument& e) {</span></span>
|
||||
<span id="L94"><span class="lineNum"> 94</span> : // Loops are not allowed</span>
|
||||
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaUNC"> 0 : }</span></span>
|
||||
<span id="L96"><span class="lineNum"> 96</span> : }</span>
|
||||
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC tlaBgGNC"> 3960 : cond_w.index_put_({ idx, max_minfo }, -1);</span></span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 2970 : auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 990 : auto candidates = candidates_mask.nonzero();</span></span>
|
||||
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 990 : exit_cond = num == n_edges || candidates.size(0) == 0;</span></span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 990 : }</span></span>
|
||||
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 4038 : }</span></span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 12 : std::vector<std::string> KDB::graph(const std::string& title) const</span></span>
|
||||
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC tlaBgGNC"> 1320 : cond_w.index_put_({ idx, max_minfo }, -1);</span></span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 990 : auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 330 : auto candidates = candidates_mask.nonzero();</span></span>
|
||||
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 330 : exit_cond = num == n_edges || candidates.size(0) == 0;</span></span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 330 : }</span></span>
|
||||
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 1346 : }</span></span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 4 : std::vector<std::string> KDB::graph(const std::string& title) const</span></span>
|
||||
<span id="L104"><span class="lineNum"> 104</span> : {</span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 12 : std::string header{ title };</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 12 : if (title == "KDB") {</span></span>
|
||||
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 12 : header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")";</span></span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 4 : std::string header{ title };</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 4 : if (title == "KDB") {</span></span>
|
||||
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 4 : header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")";</span></span>
|
||||
<span id="L108"><span class="lineNum"> 108</span> : }</span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 24 : return model.graph(header);</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 12 : }</span></span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 8 : return model.graph(header);</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 4 : }</span></span>
|
||||
<span id="L111"><span class="lineNum"> 111</span> : }</span>
|
||||
</pre>
|
||||
</td>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
|
||||
|
||||
<td class="coverFnHi">66</td>
|
||||
<td class="coverFnHi">22</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
|
||||
|
||||
<td class="coverFnHi">66</td>
|
||||
<td class="coverFnHi">22</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -81,7 +81,7 @@
|
||||
<span id="L19"><span class="lineNum"> 19</span> : void buildModel(const torch::Tensor& weights) override;</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : public:</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : explicit KDB(int k, float theta = 0.03);</span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC tlaBgGNC"> 66 : virtual ~KDB() = default;</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC tlaBgGNC"> 22 : virtual ~KDB() = default;</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> : void setHyperparameters(const nlohmann::json& hyperparameters_) override;</span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> : std::vector<std::string> graph(const std::string& name = "KDB") const override;</span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> : };</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">bayesnet::KDBLd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">6</td>
|
||||
<td class="coverFnHi">2</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">bayesnet::KDBLd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">bayesnet::KDBLd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">bayesnet::KDBLd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">6</td>
|
||||
<td class="coverFnHi">2</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -69,30 +69,30 @@
|
||||
<span id="L7"><span class="lineNum"> 7</span> : #include "KDBLd.h"</span>
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 102 : KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 30 : KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 34 : KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 10 : KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> : {</span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 30 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 30 : features = features_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 30 : className = className_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 30 : Xf = X_;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 30 : y = y_;</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 10 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 10 : features = features_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 10 : className = className_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 10 : Xf = X_;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 10 : y = y_;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 30 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 10 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : // We have discretized the input data</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network</span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 30 : KDB::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 30 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 30 : return *this;</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 10 : KDB::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 10 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 10 : return *this;</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> : }</span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 24 : torch::Tensor KDBLd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 8 : torch::Tensor KDBLd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> : {</span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 24 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 48 : return KDB::predict(Xt);</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 24 : }</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 6 : std::vector<std::string> KDBLd::graph(const std::string& name) const</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 8 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 16 : return KDB::predict(Xt);</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 8 : }</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 2 : std::vector<std::string> KDBLd::graph(const std::string& name) const</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> : {</span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 6 : return KDB::graph(name);</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 2 : return KDB::graph(name);</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> : }</span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> : }</span>
|
||||
</pre>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -76,7 +76,7 @@
|
||||
<span id="L14"><span class="lineNum"> 14</span> : private:</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : public:</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : explicit KDBLd(int k);</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 30 : virtual ~KDBLd() = default;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 10 : virtual ~KDBLd() = default;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : std::vector<std::string> graph(const std::string& name = "KDB") const override;</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : torch::Tensor predict(torch::Tensor& X) override;</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,56 +65,56 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">252</td>
|
||||
<td class="coverFnHi">84</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
|
||||
|
||||
<td class="coverFnHi">300</td>
|
||||
<td class="coverFnHi">100</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L25">bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)</a></td>
|
||||
|
||||
<td class="coverFnHi">318</td>
|
||||
<td class="coverFnHi">106</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L16">bayesnet::Proposal::checkInput(at::Tensor const&, at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">342</td>
|
||||
<td class="coverFnHi">114</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L77">bayesnet::Proposal::fit_local_discretization[abi:cxx11](at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">348</td>
|
||||
<td class="coverFnHi">116</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L9">bayesnet::Proposal::Proposal(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">636</td>
|
||||
<td class="coverFnHi">212</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L47">auto bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)::{lambda(auto:1 const&)#2}::operator()<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">2058</td>
|
||||
<td class="coverFnHi">686</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L41">auto bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)::{lambda(auto:1 const&)#1}::operator()<bayesnet::Node*>(bayesnet::Node* const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">4044</td>
|
||||
<td class="coverFnHi">1348</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,56 +65,56 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L41">auto bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)::{lambda(auto:1 const&)#1}::operator()<bayesnet::Node*>(bayesnet::Node* const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">4044</td>
|
||||
<td class="coverFnHi">1348</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L47">auto bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)::{lambda(auto:1 const&)#2}::operator()<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">2058</td>
|
||||
<td class="coverFnHi">686</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L9">bayesnet::Proposal::Proposal(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">636</td>
|
||||
<td class="coverFnHi">212</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L16">bayesnet::Proposal::checkInput(at::Tensor const&, at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">342</td>
|
||||
<td class="coverFnHi">114</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L77">bayesnet::Proposal::fit_local_discretization[abi:cxx11](at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">348</td>
|
||||
<td class="coverFnHi">116</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L25">bayesnet::Proposal::localDiscretizationProposal(std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > > const&, bayesnet::Network&)</a></td>
|
||||
|
||||
<td class="coverFnHi">318</td>
|
||||
<td class="coverFnHi">106</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">252</td>
|
||||
<td class="coverFnHi">84</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
|
||||
|
||||
<td class="coverFnHi">300</td>
|
||||
<td class="coverFnHi">100</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -70,111 +70,111 @@
|
||||
<span id="L8"><span class="lineNum"> 8</span> : #include "Proposal.h"</span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : </span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> : namespace bayesnet {</span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 636 : Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 300 : Proposal::~Proposal()</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 212 : Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 100 : Proposal::~Proposal()</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> : {</span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 2844 : for (auto& [key, value] : discretizers) {</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 2544 : delete value;</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 948 : for (auto& [key, value] : discretizers) {</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 848 : delete value;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : }</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 300 : }</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 342 : void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 100 : }</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 114 : void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : {</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 342 : if (!torch::is_floating_point(X)) {</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 114 : if (!torch::is_floating_point(X)) {</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaUNC tlaBgUNC"> 0 : throw std::invalid_argument("X must be a floating point tensor");</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> : }</span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC tlaBgGNC"> 342 : if (torch::is_floating_point(y)) {</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC tlaBgGNC"> 114 : if (torch::is_floating_point(y)) {</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaUNC tlaBgUNC"> 0 : throw std::invalid_argument("y must be an integer tensor");</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> : }</span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC tlaBgGNC"> 342 : }</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 318 : map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(const map<std::string, std::vector<int>>& oldStates, Network& model)</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC tlaBgGNC"> 114 : }</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 106 : map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(const map<std::string, std::vector<int>>& oldStates, Network& model)</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> : {</span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> : // order of local discretization is important. no good 0, 1, 2...</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> : // although we rediscretize features after the local discretization of every feature</span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 318 : auto order = model.topological_sort();</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 318 : auto& nodes = model.getNodes();</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 318 : map<std::string, std::vector<int>> states = oldStates;</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 318 : std::vector<int> indicesToReDiscretize;</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 318 : bool upgrade = false; // Flag to check if we need to upgrade the model</span></span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 2664 : for (auto feature : order) {</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 2346 : auto nodeParents = nodes[feature]->getParents();</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 2346 : if (nodeParents.size() < 2) continue; // Only has class as parent</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 1986 : upgrade = true;</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 1986 : int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 1986 : indicesToReDiscretize.push_back(index); // We need to re-discretize this feature</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 1986 : std::vector<std::string> parents;</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 6030 : transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 106 : auto order = model.topological_sort();</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 106 : auto& nodes = model.getNodes();</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 106 : map<std::string, std::vector<int>> states = oldStates;</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 106 : std::vector<int> indicesToReDiscretize;</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 106 : bool upgrade = false; // Flag to check if we need to upgrade the model</span></span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 888 : for (auto feature : order) {</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 782 : auto nodeParents = nodes[feature]->getParents();</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 782 : if (nodeParents.size() < 2) continue; // Only has class as parent</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 662 : upgrade = true;</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 662 : int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 662 : indicesToReDiscretize.push_back(index); // We need to re-discretize this feature</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 662 : std::vector<std::string> parents;</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 2010 : transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> : // Remove class as parent as it will be added later</span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 1986 : parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</span></span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 662 : parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</span></span>
|
||||
<span id="L46"><span class="lineNum"> 46</span> : // Get the indices of the parents</span>
|
||||
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 1986 : std::vector<int> indices;</span></span>
|
||||
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 1986 : indices.push_back(-1); // Add class index</span></span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 4044 : transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</span></span>
|
||||
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 662 : std::vector<int> indices;</span></span>
|
||||
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 662 : indices.push_back(-1); // Add class index</span></span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 1348 : transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</span></span>
|
||||
<span id="L50"><span class="lineNum"> 50</span> : // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)</span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 1986 : std::vector<std::string> yJoinParents(Xf.size(1));</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 6030 : for (auto idx : indices) {</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 1437960 : for (int i = 0; i < Xf.size(1); ++i) {</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 4301748 : yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());</span></span>
|
||||
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 662 : std::vector<std::string> yJoinParents(Xf.size(1));</span></span>
|
||||
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 2010 : for (auto idx : indices) {</span></span>
|
||||
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 479320 : for (int i = 0; i < Xf.size(1); ++i) {</span></span>
|
||||
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 1433916 : yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());</span></span>
|
||||
<span id="L55"><span class="lineNum"> 55</span> : }</span>
|
||||
<span id="L56"><span class="lineNum"> 56</span> : }</span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 1986 : auto arff = ArffFiles();</span></span>
|
||||
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 1986 : auto yxv = arff.factorize(yJoinParents);</span></span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 3972 : auto xvf_ptr = Xf.index({ index }).data_ptr<float>();</span></span>
|
||||
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 1986 : auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));</span></span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 1986 : discretizers[feature]->fit(xvf, yxv);</span></span>
|
||||
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 2706 : }</span></span>
|
||||
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 318 : if (upgrade) {</span></span>
|
||||
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 662 : auto arff = ArffFiles();</span></span>
|
||||
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 662 : auto yxv = arff.factorize(yJoinParents);</span></span>
|
||||
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 1324 : auto xvf_ptr = Xf.index({ index }).data_ptr<float>();</span></span>
|
||||
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 662 : auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));</span></span>
|
||||
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 662 : discretizers[feature]->fit(xvf, yxv);</span></span>
|
||||
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 902 : }</span></span>
|
||||
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 106 : if (upgrade) {</span></span>
|
||||
<span id="L64"><span class="lineNum"> 64</span> : // Discretize again X (only the affected indices) with the new fitted discretizers</span>
|
||||
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 2304 : for (auto index : indicesToReDiscretize) {</span></span>
|
||||
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 3972 : auto Xt_ptr = Xf.index({ index }).data_ptr<float>();</span></span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 1986 : auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 7944 : pDataset.index_put_({ index, "..." }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));</span></span>
|
||||
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 1986 : auto xStates = std::vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);</span></span>
|
||||
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 1986 : iota(xStates.begin(), xStates.end(), 0);</span></span>
|
||||
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 768 : for (auto index : indicesToReDiscretize) {</span></span>
|
||||
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 1324 : auto Xt_ptr = Xf.index({ index }).data_ptr<float>();</span></span>
|
||||
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 662 : auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
|
||||
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 2648 : pDataset.index_put_({ index, "..." }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));</span></span>
|
||||
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 662 : auto xStates = std::vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);</span></span>
|
||||
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 662 : iota(xStates.begin(), xStates.end(), 0);</span></span>
|
||||
<span id="L71"><span class="lineNum"> 71</span> : //Update new states of the feature/node</span>
|
||||
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 1986 : states[pFeatures[index]] = xStates;</span></span>
|
||||
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 1986 : }</span></span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 318 : const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 318 : model.fit(pDataset, weights, pFeatures, pClassName, states);</span></span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 318 : }</span></span>
|
||||
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 636 : return states;</span></span>
|
||||
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 1440192 : }</span></span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 348 : map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)</span></span>
|
||||
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 662 : states[pFeatures[index]] = xStates;</span></span>
|
||||
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 662 : }</span></span>
|
||||
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 106 : const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</span></span>
|
||||
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 106 : model.fit(pDataset, weights, pFeatures, pClassName, states);</span></span>
|
||||
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 106 : }</span></span>
|
||||
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 212 : return states;</span></span>
|
||||
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 480064 : }</span></span>
|
||||
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 116 : map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)</span></span>
|
||||
<span id="L80"><span class="lineNum"> 80</span> : {</span>
|
||||
<span id="L81"><span class="lineNum"> 81</span> : // Discretize the continuous input data and build pDataset (Classifier::dataset)</span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 348 : int m = Xf.size(1);</span></span>
|
||||
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 348 : int n = Xf.size(0);</span></span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 348 : map<std::string, std::vector<int>> states;</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 348 : pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</span></span>
|
||||
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 348 : auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));</span></span>
|
||||
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 116 : int m = Xf.size(1);</span></span>
|
||||
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 116 : int n = Xf.size(0);</span></span>
|
||||
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 116 : map<std::string, std::vector<int>> states;</span></span>
|
||||
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 116 : pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</span></span>
|
||||
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 116 : auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));</span></span>
|
||||
<span id="L87"><span class="lineNum"> 87</span> : // discretize input data by feature(row)</span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 2916 : for (auto i = 0; i < pFeatures.size(); ++i) {</span></span>
|
||||
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 2568 : auto* discretizer = new mdlp::CPPFImdlp();</span></span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 5136 : auto Xt_ptr = Xf.index({ i }).data_ptr<float>();</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 2568 : auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
|
||||
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 2568 : discretizer->fit(Xt, yv);</span></span>
|
||||
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 10272 : pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));</span></span>
|
||||
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 2568 : auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);</span></span>
|
||||
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 2568 : iota(xStates.begin(), xStates.end(), 0);</span></span>
|
||||
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 2568 : states[pFeatures[i]] = xStates;</span></span>
|
||||
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 2568 : discretizers[pFeatures[i]] = discretizer;</span></span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 2568 : }</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 348 : int n_classes = torch::max(y).item<int>() + 1;</span></span>
|
||||
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 348 : auto yStates = std::vector<int>(n_classes);</span></span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 348 : iota(yStates.begin(), yStates.end(), 0);</span></span>
|
||||
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 348 : states[pClassName] = yStates;</span></span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 1044 : pDataset.index_put_({ n, "..." }, y);</span></span>
|
||||
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 696 : return states;</span></span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 5832 : }</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 252 : torch::Tensor Proposal::prepareX(torch::Tensor& X)</span></span>
|
||||
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 972 : for (auto i = 0; i < pFeatures.size(); ++i) {</span></span>
|
||||
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 856 : auto* discretizer = new mdlp::CPPFImdlp();</span></span>
|
||||
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 1712 : auto Xt_ptr = Xf.index({ i }).data_ptr<float>();</span></span>
|
||||
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 856 : auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
|
||||
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 856 : discretizer->fit(Xt, yv);</span></span>
|
||||
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 3424 : pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));</span></span>
|
||||
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 856 : auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);</span></span>
|
||||
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 856 : iota(xStates.begin(), xStates.end(), 0);</span></span>
|
||||
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 856 : states[pFeatures[i]] = xStates;</span></span>
|
||||
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 856 : discretizers[pFeatures[i]] = discretizer;</span></span>
|
||||
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 856 : }</span></span>
|
||||
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 116 : int n_classes = torch::max(y).item<int>() + 1;</span></span>
|
||||
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 116 : auto yStates = std::vector<int>(n_classes);</span></span>
|
||||
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 116 : iota(yStates.begin(), yStates.end(), 0);</span></span>
|
||||
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 116 : states[pClassName] = yStates;</span></span>
|
||||
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 348 : pDataset.index_put_({ n, "..." }, y);</span></span>
|
||||
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 232 : return states;</span></span>
|
||||
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 1944 : }</span></span>
|
||||
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 84 : torch::Tensor Proposal::prepareX(torch::Tensor& X)</span></span>
|
||||
<span id="L107"><span class="lineNum"> 107</span> : {</span>
|
||||
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 252 : auto Xtd = torch::zeros_like(X, torch::kInt32);</span></span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 2064 : for (int i = 0; i < X.size(0); ++i) {</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 1812 : auto Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));</span></span>
|
||||
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 1812 : auto Xd = discretizers[pFeatures[i]]->transform(Xt);</span></span>
|
||||
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 5436 : Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</span></span>
|
||||
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaGNC"> 1812 : }</span></span>
|
||||
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 252 : return Xtd;</span></span>
|
||||
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 1812 : }</span></span>
|
||||
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 84 : auto Xtd = torch::zeros_like(X, torch::kInt32);</span></span>
|
||||
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 688 : for (int i = 0; i < X.size(0); ++i) {</span></span>
|
||||
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 604 : auto Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));</span></span>
|
||||
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 604 : auto Xd = discretizers[pFeatures[i]]->transform(Xt);</span></span>
|
||||
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 1812 : Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</span></span>
|
||||
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaGNC"> 604 : }</span></span>
|
||||
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 84 : return Xtd;</span></span>
|
||||
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 604 : }</span></span>
|
||||
<span id="L116"><span class="lineNum"> 116</span> : }</span>
|
||||
</pre>
|
||||
</td>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,21 +65,21 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L24">bayesnet::SPODE::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">1524</td>
|
||||
<td class="coverFnHi">508</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L9">bayesnet::SPODE::SPODE(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">1686</td>
|
||||
<td class="coverFnHi">562</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,21 +65,21 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L9">bayesnet::SPODE::SPODE(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">1686</td>
|
||||
<td class="coverFnHi">562</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">1524</td>
|
||||
<td class="coverFnHi">508</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.cc.gcov.html#L24">bayesnet::SPODE::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -70,24 +70,24 @@
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> : </span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 1686 : SPODE::SPODE(int root) : Classifier(Network()), root(root) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 562 : SPODE::SPODE(int root) : Classifier(Network()), root(root) {}</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> : </span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 1524 : void SPODE::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 508 : void SPODE::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> : {</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : // 0. Add all nodes to the model</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 1524 : addNodes();</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 508 : addNodes();</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : // 1. Add edges from the class node to all other nodes</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : // 2. Add edges from the root node to all other nodes</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 38520 : for (int i = 0; i < static_cast<int>(features.size()); ++i) {</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 36996 : model.addEdge(className, features[i]);</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 36996 : if (i != root) {</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 35472 : model.addEdge(features[root], features[i]);</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 12840 : for (int i = 0; i < static_cast<int>(features.size()); ++i) {</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 12332 : model.addEdge(className, features[i]);</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 12332 : if (i != root) {</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 11824 : model.addEdge(features[root], features[i]);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> : }</span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> : }</span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 1524 : }</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 102 : std::vector<std::string> SPODE::graph(const std::string& name) const</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 508 : }</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 34 : std::vector<std::string> SPODE::graph(const std::string& name) const</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> : {</span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 102 : return model.graph(name);</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 34 : return model.graph(name);</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> : }</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> : </span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> : }</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.h.gcov.html#L17">bayesnet::SPODE::~SPODE()</a></td>
|
||||
|
||||
<td class="coverFnHi">2754</td>
|
||||
<td class="coverFnHi">918</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODE.h.gcov.html#L17">bayesnet::SPODE::~SPODE()</a></td>
|
||||
|
||||
<td class="coverFnHi">2754</td>
|
||||
<td class="coverFnHi">918</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -78,7 +78,7 @@
|
||||
<span id="L16"><span class="lineNum"> 16</span> : void buildModel(const torch::Tensor& weights) override;</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : public:</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : explicit SPODE(int root);</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC tlaBgGNC"> 2754 : virtual ~SPODE() = default;</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC tlaBgGNC"> 918 : virtual ~SPODE() = default;</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : std::vector<std::string> graph(const std::string& name = "SPODE") const override;</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : };</span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> : }</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,42 +65,42 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">bayesnet::SPODELd::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">bayesnet::SPODELd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">54</td>
|
||||
<td class="coverFnHi">18</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">bayesnet::SPODELd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">204</td>
|
||||
<td class="coverFnHi">68</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">bayesnet::SPODELd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">252</td>
|
||||
<td class="coverFnHi">84</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">bayesnet::SPODELd::commonFit(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">258</td>
|
||||
<td class="coverFnHi">86</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">bayesnet::SPODELd::SPODELd(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">330</td>
|
||||
<td class="coverFnHi">110</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,42 +65,42 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">bayesnet::SPODELd::SPODELd(int)</a></td>
|
||||
|
||||
<td class="coverFnHi">330</td>
|
||||
<td class="coverFnHi">110</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">bayesnet::SPODELd::commonFit(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">258</td>
|
||||
<td class="coverFnHi">86</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">bayesnet::SPODELd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">252</td>
|
||||
<td class="coverFnHi">84</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">bayesnet::SPODELd::fit(at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">bayesnet::SPODELd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">54</td>
|
||||
<td class="coverFnHi">18</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">bayesnet::SPODELd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">204</td>
|
||||
<td class="coverFnHi">68</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -69,45 +69,45 @@
|
||||
<span id="L7"><span class="lineNum"> 7</span> : #include "SPODELd.h"</span>
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 330 : SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 252 : SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 110 : SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 84 : SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> : {</span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 252 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 252 : Xf = X_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 252 : y = y_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 252 : return commonFit(features_, className_, states_);</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 84 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 84 : Xf = X_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 84 : y = y_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 84 : return commonFit(features_, className_, states_);</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : }</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : </span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 12 : SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 4 : SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : {</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 12 : if (!torch::is_floating_point(dataset)) {</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 6 : throw std::runtime_error("Dataset must be a floating point tensor");</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 4 : if (!torch::is_floating_point(dataset)) {</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 2 : throw std::runtime_error("Dataset must be a floating point tensor");</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> : }</span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 24 : Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 18 : y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 6 : return commonFit(features_, className_, states_);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 18 : }</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 8 : Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 6 : y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 2 : return commonFit(features_, className_, states_);</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 6 : }</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> : </span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 258 : SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 86 : SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> : {</span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 258 : features = features_;</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 258 : className = className_;</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 86 : features = features_;</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 86 : className = className_;</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 258 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 86 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> : // We have discretized the input data</span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> : // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network</span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 258 : SPODE::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 258 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 258 : return *this;</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 86 : SPODE::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 86 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 86 : return *this;</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> : }</span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 204 : torch::Tensor SPODELd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 68 : torch::Tensor SPODELd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> : {</span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 204 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 408 : return SPODE::predict(Xt);</span></span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 204 : }</span></span>
|
||||
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 54 : std::vector<std::string> SPODELd::graph(const std::string& name) const</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 68 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 136 : return SPODE::predict(Xt);</span></span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 68 : }</span></span>
|
||||
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 18 : std::vector<std::string> SPODELd::graph(const std::string& name) const</span></span>
|
||||
<span id="L47"><span class="lineNum"> 47</span> : {</span>
|
||||
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 54 : return SPODE::graph(name);</span></span>
|
||||
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 18 : return SPODE::graph(name);</span></span>
|
||||
<span id="L49"><span class="lineNum"> 49</span> : }</span>
|
||||
<span id="L50"><span class="lineNum"> 50</span> : }</span>
|
||||
</pre>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.h.gcov.html#L14">bayesnet::SPODELd::~SPODELd()</a></td>
|
||||
|
||||
<td class="coverFnHi">480</td>
|
||||
<td class="coverFnHi">160</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="SPODELd.h.gcov.html#L14">bayesnet::SPODELd::~SPODELd()</a></td>
|
||||
|
||||
<td class="coverFnHi">480</td>
|
||||
<td class="coverFnHi">160</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -75,7 +75,7 @@
|
||||
<span id="L13"><span class="lineNum"> 13</span> : class SPODELd : public SPODE, public Proposal {</span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> : public:</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : explicit SPODELd(int root);</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC tlaBgGNC"> 480 : virtual ~SPODELd() = default;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC tlaBgGNC"> 160 : virtual ~SPODELd() = default;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L39">bayesnet::TAN::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L10">bayesnet::TAN::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">78</td>
|
||||
<td class="coverFnHi">26</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L8">bayesnet::TAN::TAN()</a></td>
|
||||
|
||||
<td class="coverFnHi">282</td>
|
||||
<td class="coverFnHi">94</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L23">auto bayesnet::TAN::buildModel(at::Tensor const&)::{lambda(auto:1 const&, auto:2 const&)#1}::operator()<std::pair<int, float>, std::pair<int, float> >(std::pair<int, float> const&, std::pair<int, float> const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">972</td>
|
||||
<td class="coverFnHi">324</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L23">auto bayesnet::TAN::buildModel(at::Tensor const&)::{lambda(auto:1 const&, auto:2 const&)#1}::operator()<std::pair<int, float>, std::pair<int, float> >(std::pair<int, float> const&, std::pair<int, float> const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">972</td>
|
||||
<td class="coverFnHi">324</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L8">bayesnet::TAN::TAN()</a></td>
|
||||
|
||||
<td class="coverFnHi">282</td>
|
||||
<td class="coverFnHi">94</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L10">bayesnet::TAN::buildModel(at::Tensor const&)</a></td>
|
||||
|
||||
<td class="coverFnHi">78</td>
|
||||
<td class="coverFnHi">26</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.cc.gcov.html#L39">bayesnet::TAN::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">12</td>
|
||||
<td class="coverFnHi">4</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -69,40 +69,40 @@
|
||||
<span id="L7"><span class="lineNum"> 7</span> : #include "TAN.h"</span>
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 282 : TAN::TAN() : Classifier(Network()) {}</span></span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 94 : TAN::TAN() : Classifier(Network()) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> : </span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 78 : void TAN::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 26 : void TAN::buildModel(const torch::Tensor& weights)</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> : {</span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> : // 0. Add all nodes to the model</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 78 : addNodes();</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 26 : addNodes();</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : // 1. Compute mutual information between each feature and the class and set the root node</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> : // as the highest mutual information with the class</span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 78 : auto mi = std::vector <std::pair<int, float >>();</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 234 : torch::Tensor class_dataset = dataset.index({ -1, "..." });</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 534 : for (int i = 0; i < static_cast<int>(features.size()); ++i) {</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 1368 : torch::Tensor feature_dataset = dataset.index({ i, "..." });</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 456 : auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 456 : mi.push_back({ i, mi_value });</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 456 : }</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 1050 : sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 78 : auto root = mi[mi.size() - 1].first;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 26 : auto mi = std::vector <std::pair<int, float >>();</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 78 : torch::Tensor class_dataset = dataset.index({ -1, "..." });</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 178 : for (int i = 0; i < static_cast<int>(features.size()); ++i) {</span></span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 456 : torch::Tensor feature_dataset = dataset.index({ i, "..." });</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 152 : auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 152 : mi.push_back({ i, mi_value });</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 152 : }</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 350 : sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});</span></span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 26 : auto root = mi[mi.size() - 1].first;</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> : // 2. Compute mutual information between each feature and the class</span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 78 : auto weights_matrix = metrics.conditionalEdge(weights);</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 26 : auto weights_matrix = metrics.conditionalEdge(weights);</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> : // 3. Compute the maximum spanning tree</span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 78 : auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 26 : auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> : // 4. Add edges from the maximum spanning tree to the model</span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 456 : for (auto i = 0; i < mst.size(); ++i) {</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 378 : auto [from, to] = mst[i];</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 378 : model.addEdge(features[from], features[to]);</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 152 : for (auto i = 0; i < mst.size(); ++i) {</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 126 : auto [from, to] = mst[i];</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 126 : model.addEdge(features[from], features[to]);</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> : }</span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> : // 5. Add edges from the class to all features</span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 534 : for (auto feature : features) {</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 456 : model.addEdge(className, feature);</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 456 : }</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 612 : }</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 12 : std::vector<std::string> TAN::graph(const std::string& title) const</span></span>
|
||||
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 178 : for (auto feature : features) {</span></span>
|
||||
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 152 : model.addEdge(className, feature);</span></span>
|
||||
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 152 : }</span></span>
|
||||
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 204 : }</span></span>
|
||||
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 4 : std::vector<std::string> TAN::graph(const std::string& title) const</span></span>
|
||||
<span id="L42"><span class="lineNum"> 42</span> : {</span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 12 : return model.graph(title);</span></span>
|
||||
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 4 : return model.graph(title);</span></span>
|
||||
<span id="L44"><span class="lineNum"> 44</span> : }</span>
|
||||
<span id="L45"><span class="lineNum"> 45</span> : }</span>
|
||||
</pre>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.h.gcov.html#L15">bayesnet::TAN::~TAN()</a></td>
|
||||
|
||||
<td class="coverFnHi">114</td>
|
||||
<td class="coverFnHi">38</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TAN.h.gcov.html#L15">bayesnet::TAN::~TAN()</a></td>
|
||||
|
||||
<td class="coverFnHi">114</td>
|
||||
<td class="coverFnHi">38</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -76,7 +76,7 @@
|
||||
<span id="L14"><span class="lineNum"> 14</span> : void buildModel(const torch::Tensor& weights) override;</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : public:</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : TAN();</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 114 : virtual ~TAN() = default;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 38 : virtual ~TAN() = default;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : std::vector<std::string> graph(const std::string& name = "TAN") const override;</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : };</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : }</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L30">bayesnet::TANLd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">6</td>
|
||||
<td class="coverFnHi">2</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L25">bayesnet::TANLd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L9">bayesnet::TANLd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L8">bayesnet::TANLd::TANLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,28 +65,28 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L8">bayesnet::TANLd::TANLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">102</td>
|
||||
<td class="coverFnHi">34</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L9">bayesnet::TANLd::fit(at::Tensor&, at::Tensor&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<int, std::allocator<int> >, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::vector<int, std::allocator<int> > > > >&)</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L30">bayesnet::TANLd::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
|
||||
|
||||
<td class="coverFnHi">6</td>
|
||||
<td class="coverFnHi">2</td>
|
||||
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.cc.gcov.html#L25">bayesnet::TANLd::predict(at::Tensor&)</a></td>
|
||||
|
||||
<td class="coverFnHi">24</td>
|
||||
<td class="coverFnHi">8</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -69,31 +69,31 @@
|
||||
<span id="L7"><span class="lineNum"> 7</span> : #include "TANLd.h"</span>
|
||||
<span id="L8"><span class="lineNum"> 8</span> : </span>
|
||||
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 102 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 30 : TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 34 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
|
||||
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 10 : TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
|
||||
<span id="L12"><span class="lineNum"> 12</span> : {</span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 30 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 30 : features = features_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 30 : className = className_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 30 : Xf = X_;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 30 : y = y_;</span></span>
|
||||
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 10 : checkInput(X_, y_);</span></span>
|
||||
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 10 : features = features_;</span></span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 10 : className = className_;</span></span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 10 : Xf = X_;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 10 : y = y_;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 30 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 10 : states = fit_local_discretization(y);</span></span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : // We have discretized the input data</span>
|
||||
<span id="L21"><span class="lineNum"> 21</span> : // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network</span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 30 : TAN::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 30 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 30 : return *this;</span></span>
|
||||
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 10 : TAN::fit(dataset, features, className, states);</span></span>
|
||||
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 10 : states = localDiscretizationProposal(states, model);</span></span>
|
||||
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 10 : return *this;</span></span>
|
||||
<span id="L25"><span class="lineNum"> 25</span> : </span>
|
||||
<span id="L26"><span class="lineNum"> 26</span> : }</span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 24 : torch::Tensor TANLd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 8 : torch::Tensor TANLd::predict(torch::Tensor& X)</span></span>
|
||||
<span id="L28"><span class="lineNum"> 28</span> : {</span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 24 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 48 : return TAN::predict(Xt);</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 24 : }</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 6 : std::vector<std::string> TANLd::graph(const std::string& name) const</span></span>
|
||||
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 8 : auto Xt = prepareX(X);</span></span>
|
||||
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 16 : return TAN::predict(Xt);</span></span>
|
||||
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 8 : }</span></span>
|
||||
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 2 : std::vector<std::string> TANLd::graph(const std::string& name) const</span></span>
|
||||
<span id="L33"><span class="lineNum"> 33</span> : {</span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 6 : return TAN::graph(name);</span></span>
|
||||
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 2 : return TAN::graph(name);</span></span>
|
||||
<span id="L35"><span class="lineNum"> 35</span> : }</span>
|
||||
<span id="L36"><span class="lineNum"> 36</span> : }</span>
|
||||
</pre>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.h.gcov.html#L15">bayesnet::TANLd::~TANLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -65,7 +65,7 @@
|
||||
<tr>
|
||||
<td class="coverFn"><a href="TANLd.h.gcov.html#L15">bayesnet::TANLd::~TANLd()</a></td>
|
||||
|
||||
<td class="coverFnHi">30</td>
|
||||
<td class="coverFnHi">10</td>
|
||||
|
||||
|
||||
</tr>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
@@ -76,7 +76,7 @@
|
||||
<span id="L14"><span class="lineNum"> 14</span> : private:</span>
|
||||
<span id="L15"><span class="lineNum"> 15</span> : public:</span>
|
||||
<span id="L16"><span class="lineNum"> 16</span> : TANLd();</span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 30 : virtual ~TANLd() = default;</span></span>
|
||||
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 10 : virtual ~TANLd() = default;</span></span>
|
||||
<span id="L18"><span class="lineNum"> 18</span> : TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;</span>
|
||||
<span id="L19"><span class="lineNum"> 19</span> : std::vector<std::string> graph(const std::string& name = "TAN") const override;</span>
|
||||
<span id="L20"><span class="lineNum"> 20</span> : torch::Tensor predict(torch::Tensor& X) override;</span>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
|
||||
@@ -37,7 +37,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="headerItem">Test Date:</td>
|
||||
<td class="headerValue">2024-04-30 13:59:18</td>
|
||||
<td class="headerValue">2024-04-30 20:26:57</td>
|
||||
<td></td>
|
||||
<td class="headerItem">Functions:</td>
|
||||
<td class="headerCovTableEntryHi">100.0 %</td>
|
||||
|
||||
Reference in New Issue
Block a user