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121 lines
4.9 KiB
C++
121 lines
4.9 KiB
C++
/*************************************************************************/
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/* uni_lin_reg.cpp */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2022-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "uni_lin_reg.h"
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#include "../stat/stat.h"
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// General Multivariate Linear Regression Model
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// ŷ = b0 + b1x1 + b2x2 + ... + bkxk
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// Univariate Linear Regression Model
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// ŷ = b0 + b1x1
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Ref<MLPPVector> MLPPUniLinReg::get_input_set() const {
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return _input_set;
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}
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void MLPPUniLinReg::set_input_set(const Ref<MLPPVector> &val) {
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_input_set = val;
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}
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Ref<MLPPVector> MLPPUniLinReg::get_output_set() const {
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return _output_set;
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}
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void MLPPUniLinReg::set_output_set(const Ref<MLPPVector> &val) {
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_output_set = val;
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}
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real_t MLPPUniLinReg::get_b0() const {
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return _b0;
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}
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void MLPPUniLinReg::set_b0(const real_t val) {
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_b0 = val;
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}
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real_t MLPPUniLinReg::get_b1() const {
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return _b1;
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}
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void MLPPUniLinReg::set_b1(const real_t val) {
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_b1 = val;
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}
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void MLPPUniLinReg::train() {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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MLPPStat estimator;
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_b1 = estimator.b1_estimation(_input_set, _output_set);
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_b0 = estimator.b0_estimation(_input_set, _output_set);
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}
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Ref<MLPPVector> MLPPUniLinReg::model_set_test(const Ref<MLPPVector> &x) {
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return x->scalar_multiplyn(_b1)->scalar_addn(_b0);
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}
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real_t MLPPUniLinReg::model_test(real_t x) {
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return _b0 + _b1 * x;
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}
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MLPPUniLinReg::MLPPUniLinReg(const Ref<MLPPVector> &p_input_set, const Ref<MLPPVector> &p_output_set) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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train();
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}
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MLPPUniLinReg::MLPPUniLinReg() {
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_b0 = 0;
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_b1 = 0;
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}
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MLPPUniLinReg::~MLPPUniLinReg() {
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}
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void MLPPUniLinReg::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPUniLinReg::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPUniLinReg::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPUniLinReg::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPUniLinReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_b0"), &MLPPUniLinReg::get_b0);
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ClassDB::bind_method(D_METHOD("set_b0", "val"), &MLPPUniLinReg::set_b0);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "b0"), "set_b0", "get_b0");
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ClassDB::bind_method(D_METHOD("get_b1"), &MLPPUniLinReg::get_b1);
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ClassDB::bind_method(D_METHOD("set_b1", "val"), &MLPPUniLinReg::set_b1);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "b1"), "set_b1", "get_b1");
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ClassDB::bind_method(D_METHOD("train"), &MLPPUniLinReg::train);
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ClassDB::bind_method(D_METHOD("model_set_test", "x"), &MLPPUniLinReg::model_set_test);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPUniLinReg::model_test);
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}
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