mirror of https://github.com/Relintai/pmlpp.git
87 lines
3.7 KiB
C++
87 lines
3.7 KiB
C++
/*************************************************************************/
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/* gauss_markov_checker.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) 2023-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 "gauss_markov_checker.h"
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#include "../stat/stat.h"
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#include <iostream>
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/*
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void MLPPGaussMarkovChecker::checkGMConditions(std::vector<real_t> eps) {
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bool condition1 = arithmeticMean(eps);
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bool condition2 = homoscedasticity(eps);
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bool condition3 = exogeneity(eps);
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if (condition1 && condition2 && condition3) {
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std::cout << "Gauss-Markov conditions were not violated. You may use OLS to obtain a BLUE estimator" << std::endl;
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} else {
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std::cout << "A test of the expected value of 0 of the error terms returned " << std::boolalpha << condition1 << ", a test of homoscedasticity has returned " << std::boolalpha << condition2 << ", and a test of exogenity has returned " << std::boolalpha << "." << std::endl;
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}
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}
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bool MLPPGaussMarkovChecker::arithmeticMean(std::vector<real_t> eps) {
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MLPPStat stat;
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if (stat.mean(eps) == 0) {
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return true;
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} else {
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return false;
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}
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}
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bool MLPPGaussMarkovChecker::homoscedasticity(std::vector<real_t> eps) {
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MLPPStat stat;
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real_t currentVar = (eps[0] - stat.mean(eps)) * (eps[0] - stat.mean(eps)) / eps.size();
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for (uint32_t i = 0; i < eps.size(); i++) {
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if (currentVar != (eps[i] - stat.mean(eps)) * (eps[i] - stat.mean(eps)) / eps.size()) {
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return false;
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}
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}
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return true;
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}
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bool MLPPGaussMarkovChecker::exogeneity(std::vector<real_t> eps) {
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MLPPStat stat;
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for (uint32_t i = 0; i < eps.size(); i++) {
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for (uint32_t j = 0; j < eps.size(); j++) {
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if (i != j) {
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if ((eps[i] - stat.mean(eps)) * (eps[j] - stat.mean(eps)) / eps.size() != 0) {
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return false;
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}
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}
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}
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}
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return true;
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}
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*/
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void MLPPGaussMarkovChecker::_bind_methods() {
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}
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