Refactor coverage report generation
Add some tests to reach 99%
This commit is contained in:
@@ -4,7 +4,7 @@
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<head>
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<title>LCOV - coverage.info - bayesnet/ensembles/Ensemble.cc - functions</title>
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<title>LCOV - BayesNet Coverage Report - bayesnet/ensembles/Ensemble.cc - functions</title>
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<link rel="stylesheet" type="text/css" href="../../gcov.css">
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</head>
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@@ -19,7 +19,7 @@
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<table cellpadding=1 border=0 width="100%">
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<tr>
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<td width="10%" class="headerItem">Current view:</td>
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<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/ensembles</a> - Ensemble.cc<span style="font-size: 80%;"> (<a href="Ensemble.cc.gcov.html">source</a> / functions)</span></td>
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<td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/ensembles</a> - Ensemble.cc<span style="font-size: 80%;"> (<a href="Ensemble.cc.gcov.html">source</a> / functions)</span></td>
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<td width="5%"></td>
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<td width="5%"></td>
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<td width="5%" class="headerCovTableHead">Coverage</td>
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@@ -28,21 +28,29 @@
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</tr>
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<tr>
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<td class="headerItem">Test:</td>
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<td class="headerValue">coverage.info</td>
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<td class="headerValue">BayesNet Coverage Report</td>
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<td></td>
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<td class="headerItem">Lines:</td>
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<td class="headerCovTableEntryHi">98.1 %</td>
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<td class="headerCovTableEntry">154</td>
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<td class="headerCovTableEntry">151</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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<td class="headerCovTableEntry">155</td>
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<td class="headerCovTableEntry">155</td>
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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 20:26:57</td>
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<td class="headerValue">2024-05-06 17:54:04</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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<td class="headerCovTableEntry">25</td>
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<td class="headerCovTableEntry">25</td>
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</tr>
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<tr>
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<td class="headerItem">Legend:</td>
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<td class="headerValueLeg"> Lines:
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<span class="coverLegendCov">hit</span>
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<span class="coverLegendNoCov">not hit</span>
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</td>
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<td></td>
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</tr>
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<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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</table>
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@@ -65,175 +73,175 @@
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L9">bayesnet::Ensemble::Ensemble(bool)</a></td>
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<td class="coverFnHi">156</td>
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<td class="coverFnHi">324</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L31">bayesnet::Ensemble::compute_arg_max(at::Tensor&)</a></td>
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<td class="coverFnHi">212</td>
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<td class="coverFnHi">424</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L22">bayesnet::Ensemble::compute_arg_max(std::vector<std::vector<double, std::allocator<double> >, std::allocator<std::vector<double, std::allocator<double> > > >&)</a></td>
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<td class="coverFnHi">22</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="Ensemble.cc.gcov.html#L204">bayesnet::Ensemble::getNumberOfEdges() const</a></td>
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<td class="coverFnHi">12</td>
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<td class="coverFnHi">28</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L196">bayesnet::Ensemble::getNumberOfNodes() const</a></td>
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<td class="coverFnHi">12</td>
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<td class="coverFnHi">28</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L212">bayesnet::Ensemble::getNumberOfStates() const</a></td>
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<td class="coverFnHi">2</td>
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<td class="coverFnHi">4</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L187">bayesnet::Ensemble::graph(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const</a></td>
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<td class="coverFnHi">6</td>
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<td class="coverFnHi">16</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L77">bayesnet::Ensemble::predict(at::Tensor&)</a></td>
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<td class="coverFnHi">218</td>
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<td class="coverFnHi">436</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L72">bayesnet::Ensemble::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">28</td>
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<td class="coverFnHi">68</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L82">bayesnet::Ensemble::predict_average_proba(at::Tensor&)</a></td>
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<td class="coverFnHi">148</td>
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<td class="coverFnHi">296</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L89">bayesnet::Ensemble::predict_average_proba(at::Tensor&)::{lambda()#1}::operator()() const</a></td>
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<td class="coverFnHi">734</td>
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<td class="coverFnHi">1468</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L102">bayesnet::Ensemble::predict_average_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">18</td>
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<td class="coverFnHi">44</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L109">bayesnet::Ensemble::predict_average_proba(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)::{lambda()#1}::operator()() const</a></td>
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<td class="coverFnHi">122</td>
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<td class="coverFnHi">532</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L117">bayesnet::Ensemble::predict_average_proba(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)::{lambda()#1}::operator()() const::{lambda(double, double)#1}::operator()(double, double) const</a></td>
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<td class="coverFnHi">129960</td>
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<td class="coverFnHi">629712</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L127">bayesnet::Ensemble::predict_average_proba(std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > >&)::{lambda(double)#1}::operator()(double) const</a></td>
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<td class="coverFnHi">16440</td>
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<td class="coverFnHi">43152</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L138">bayesnet::Ensemble::predict_average_voting(at::Tensor&)</a></td>
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<td class="coverFnHi">80</td>
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<td class="coverFnHi">164</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L145">bayesnet::Ensemble::predict_average_voting(at::Tensor&)::{lambda()#1}::operator()() const</a></td>
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<td class="coverFnHi">536</td>
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<td class="coverFnHi">1216</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L131">bayesnet::Ensemble::predict_average_voting(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">14</td>
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<td class="coverFnHi">32</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L65">bayesnet::Ensemble::predict_proba(at::Tensor&)</a></td>
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<td class="coverFnHi">226</td>
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<td class="coverFnHi">452</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L58">bayesnet::Ensemble::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">44</td>
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<td class="coverFnHi">100</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L156">bayesnet::Ensemble::score(at::Tensor&, at::Tensor&)</a></td>
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<td class="coverFnHi">40</td>
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<td class="coverFnHi">80</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L167">bayesnet::Ensemble::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">20</td>
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<td class="coverFnHi">52</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L178">bayesnet::Ensemble::show[abi:cxx11]() const</a></td>
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<td class="coverFnHi">2</td>
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<td class="coverFnHi">4</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L14">bayesnet::Ensemble::trainModel(at::Tensor const&)</a></td>
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<td class="coverFnHi">12</td>
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<td class="coverFnHi">40</td>
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</tr>
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<tr>
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<td class="coverFn"><a href="Ensemble.cc.gcov.html#L36">bayesnet::Ensemble::voting(at::Tensor&)</a></td>
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<td class="coverFnHi">80</td>
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<td class="coverFnHi">164</td>
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</tr>
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