pmlpp/hypothesis_testing/hypothesis_testing.cpp

45 lines
2.6 KiB
C++

/*************************************************************************/
/* hypothesis_testing.cpp */
/*************************************************************************/
/* 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. */
/*************************************************************************/
#include "hypothesis_testing.h"
std::tuple<bool, real_t> MLPPHypothesisTesting::chiSquareTest(std::vector<real_t> observed, std::vector<real_t> expected) {
//real_t df = observed.size() - 1; // These are our degrees of freedom
//real_t sum = 0;
//for (uint32_t i = 0; i < observed.size(); i++) {
// sum += (observed[i] - expected[i]) * (observed[i] - expected[i]) / expected[i];
//}
return std::tuple<bool, real_t>();
}
void MLPPHypothesisTesting::_bind_methods() {
}