BayesNet/AODELd.cc.gcov.html at ae469b8146c2e68b1c1e06acc7d267e255389c45

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move test libraries to test folder

2024-04-30 00:52:09 +02:00

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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:	2024-04-29 20:48:03	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          187 :     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
      11              :     {
      12          187 :     }
      13           55 :     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           55 :         checkInput(X_, y_);
      16           55 :         features = features_;
      17           55 :         className = className_;
      18           55 :         Xf = X_;
      19           55 :         y = y_;
      20              :         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
      21           55 :         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           55 :         Ensemble::fit(dataset, features, className, states);
      25           55 :         return *this;
      26              : 
      27              :     }
      28           55 :     void AODELd::buildModel(const torch::Tensor& weights)
      29              :     {
      30           55 :         models.clear();
      31          462 :         for (int i = 0; i < features.size(); ++i) {
      32          407 :             models.push_back(std::make_unique<SPODELd>(i));
      33              :         }
      34           55 :         n_models = models.size();
      35           55 :         significanceModels = std::vector<double>(n_models, 1.0);
      36           55 :     }
      37           55 :     void AODELd::trainModel(const torch::Tensor& weights)
      38              :     {
      39          462 :         for (const auto& model : models) {
      40          407 :             model->fit(Xf, y, features, className, states);
      41              :         }
      42           55 :     }
      43           11 :     std::vector<std::string> AODELd::graph(const std::string& name) const
      44              :     {
      45           11 :         return Ensemble::graph(name);
      46              :     }
      47              : }

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8.9 KiB Raw Blame History

8.9 KiB

Raw Blame History