#ifndef MLPP_BERNOULLI_NB_H #define MLPP_BERNOULLI_NB_H /*************************************************************************/ /* bernoulli_nb.h */ /*************************************************************************/ /* This file is part of: */ /* PMLPP Machine Learning Library */ /* https://github.com/Relintai/pmlpp */ /*************************************************************************/ /* Copyright (c) 2023-present Péter Magyar. */ /* Copyright (c) 2022-2023 Marc Melikyan */ /* */ /* Permission is hereby granted, free of charge, to any person obtaining */ /* a copy of this software and associated documentation files (the */ /* "Software"), to deal in the Software without restriction, including */ /* without limitation the rights to use, copy, modify, merge, publish, */ /* distribute, sublicense, and/or sell copies of the Software, and to */ /* permit persons to whom the Software is furnished to do so, subject to */ /* the following conditions: */ /* */ /* The above copyright notice and this permission notice shall be */ /* included in all copies or substantial portions of the Software. */ /* */ /* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */ /* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */ /* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/ /* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */ /* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */ /* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */ /* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /*************************************************************************/ #ifdef USING_SFW #include "sfw.h" #else #include "core/containers/hash_map.h" #include "core/containers/vector.h" #include "core/math/math_defs.h" #include "core/object/reference.h" #endif #include "../lin_alg/mlpp_matrix.h" #include "../lin_alg/mlpp_vector.h" class MLPPBernoulliNB : public Reference { GDCLASS(MLPPBernoulliNB, Reference); public: Ref model_set_test(const Ref &X); real_t model_test(const Ref &x); real_t score(); MLPPBernoulliNB(const Ref &p_input_set, const Ref &p_output_set); MLPPBernoulliNB(); ~MLPPBernoulliNB(); protected: void compute_vocab(); void compute_theta(); void evaluate(); static void _bind_methods(); // Model Params real_t _prior_1; real_t _prior_0; Vector> _theta; Ref _vocab; int _class_num; // Datasets Ref _input_set; Ref _output_set; Ref _y_hat; }; #endif /* BernoulliNB_hpp */