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BayesNet/html/bayesnet/ensembles/AODELd.cc.gcov.html

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LCOV - code coverage report
Current view: top level - bayesnet/ensembles - AODELd.cc (source / functions) Coverage Total Hit
Test: coverage.info Lines: 100.0 % 24 24
Test Date: 2024-04-30 20:26:57 Functions: 100.0 % 5 5 

            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 "AODELd.h"
       8              : 
       9              : namespace bayesnet {
      10           34 :     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
      11              :     {
      12           34 :     }
      13           10 :     AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
      14              :     {
      15           10 :         checkInput(X_, y_);
      16           10 :         features = features_;
      17           10 :         className = className_;
      18           10 :         Xf = X_;
      19           10 :         y = y_;
      20              :         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
      21           10 :         states = fit_local_discretization(y);
      22              :         // We have discretized the input data
      23              :         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
      24           10 :         Ensemble::fit(dataset, features, className, states);
      25           10 :         return *this;
      26              : 
      27              :     }
      28           10 :     void AODELd::buildModel(const torch::Tensor& weights)
      29              :     {
      30           10 :         models.clear();
      31           84 :         for (int i = 0; i < features.size(); ++i) {
      32           74 :             models.push_back(std::make_unique<SPODELd>(i));
      33              :         }
      34           10 :         n_models = models.size();
      35           10 :         significanceModels = std::vector<double>(n_models, 1.0);
      36           10 :     }
      37           10 :     void AODELd::trainModel(const torch::Tensor& weights)
      38              :     {
      39           84 :         for (const auto& model : models) {
      40           74 :             model->fit(Xf, y, features, className, states);
      41              :         }
      42           10 :     }
      43            2 :     std::vector<std::string> AODELd::graph(const std::string& name) const
      44              :     {
      45            2 :         return Ensemble::graph(name);
      46              :     }
      47              : }

Generated by: LCOV version 2.0-1

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