BayesNet/TANLd.cc.gcov.html at 80043d5181355739b80e88097fc1d9f2f8ea8ba0

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Add some tests to reach 99%

2024-05-06 17:56:00 +02:00

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LCOV - code coverage report

Current view:	top level - bayesnet/classifiers - TANLd.cc (source / functions)		Coverage	Total	Hit
Test:	BayesNet Coverage Report	Lines:	100.0 %	17	17
Test Date:	2024-05-06 17:54:04	Functions:	100.0 %	4	4
Legend:	Lines: hit not hit

            Line data    Source code

       1              : // ***************************************************************
       2              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
       3              : // SPDX-FileType: SOURCE
       4              : // SPDX-License-Identifier: MIT
       5              : // ***************************************************************
       6              : 
       7              : #include "TANLd.h"
       8              : 
       9              : namespace bayesnet {
      10           68 :     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
      11           20 :     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_)
      12              :     {
      13           20 :         checkInput(X_, y_);
      14           20 :         features = features_;
      15           20 :         className = className_;
      16           20 :         Xf = X_;
      17           20 :         y = y_;
      18              :         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
      19           20 :         states = fit_local_discretization(y);
      20              :         // We have discretized the input data
      21              :         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
      22           20 :         TAN::fit(dataset, features, className, states);
      23           20 :         states = localDiscretizationProposal(states, model);
      24           20 :         return *this;
      25              : 
      26              :     }
      27           16 :     torch::Tensor TANLd::predict(torch::Tensor& X)
      28              :     {
      29           16 :         auto Xt = prepareX(X);
      30           32 :         return TAN::predict(Xt);
      31           16 :     }
      32            4 :     std::vector<std::string> TANLd::graph(const std::string& name) const
      33              :     {
      34            4 :         return TAN::graph(name);
      35              :     }
      36              : }

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7.8 KiB Raw Blame History

7.8 KiB

Raw Blame History