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BayesNet/html/bayesnet/classifiers/KDBLd.cc.gcov.html

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LCOV - code coverage report
Current view: top level - bayesnet/classifiers - KDBLd.cc (source / functions) Coverage Total Hit
Test: coverage.info Lines: 100.0 % 17 17
Test Date: 2024-04-21 16:43:29 Functions: 100.0 % 4 4 

            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 "KDBLd.h"
       8              : 
       9              : namespace bayesnet {
      10           17 :     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
      11            5 :     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_)
      12              :     {
      13            5 :         checkInput(X_, y_);
      14            5 :         features = features_;
      15            5 :         className = className_;
      16            5 :         Xf = X_;
      17            5 :         y = y_;
      18              :         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
      19            5 :         states = fit_local_discretization(y);
      20              :         // We have discretized the input data
      21              :         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
      22            5 :         KDB::fit(dataset, features, className, states);
      23            5 :         states = localDiscretizationProposal(states, model);
      24            5 :         return *this;
      25              :     }
      26            4 :     torch::Tensor KDBLd::predict(torch::Tensor& X)
      27              :     {
      28            4 :         auto Xt = prepareX(X);
      29            8 :         return KDB::predict(Xt);
      30            4 :     }
      31            1 :     std::vector<std::string> KDBLd::graph(const std::string& name) const
      32              :     {
      33            1 :         return KDB::graph(name);
      34              :     }
      35              : }

Generated by: LCOV version 2.0-1

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