98 lines
3.5 KiB
C
98 lines
3.5 KiB
C
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#pragma once
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/*
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* Copyright (C) 2024 Brett Terpstra
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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#ifndef IMAGE_GP_6_SLR_H
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#define IMAGE_GP_6_SLR_H
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template<typename T, blt::size_t sample_size>
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float mean(const std::array<T, sample_size>& data)
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{
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T x = 0;
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for (blt::size_t n = 0; n < sample_size; n++)
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{
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x = x + data[n];
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}
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x = x / sample_size;
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return x;
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}
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// https://github.com/georgemaier/simple-linear-regression/blob/master/slr.cpp
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template<typename T, blt::size_t sample_size>
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class slr
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{
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private:
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T WN1 = 0, WN2 = 0, WN3 = 0, WN4 = 0, Sy = 0, Sx = 0;
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public:
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T r = 0, rsquared = 0, alpha = 0, beta = 0, x = 0, y = 0;
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T yhat = 0, ybar = 0, xbar = 0;
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T SSR = 0, SSE = 0, SST = 0;
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T residualSE = 0, residualmax = 0, residualmin = 0, residualmean = 0, t = 0;
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T SEBeta = 0, sample = 0, residuals[sample_size]{};
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slr(const std::array<T, sample_size>& datax, const std::array<T, sample_size>& datay)
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{
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//This is the main regression function that is called when a new SLR object is created.
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//Calculate means
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sample = sample_size;
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xbar = mean(datax);
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ybar = mean(datay);
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//Calculate r correlation
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for (blt::size_t n = 0; n < sample_size; ++n)
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{
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WN1 += (datax[n] - xbar) * (datay[n] - ybar);
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WN2 += pow((datax[n] - xbar), 2);
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WN3 += pow((datay[n] - ybar), 2);
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}
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WN4 = WN2 * WN3;
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r = WN1 / (std::sqrt(WN4));
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//Calculate alpha and beta
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Sy = std::sqrt(WN3 / (sample_size - 1));
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Sx = std::sqrt(WN2 / (sample_size - 1));
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beta = r * (Sy / Sx);
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alpha = ybar - beta * xbar;
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//Calculate SSR, SSE, R-Squared, residuals
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for (blt::size_t n = 0; n < sample_size; n++)
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{
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yhat = alpha + (beta * datax[n]);
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SSE += std::pow((yhat - ybar), 2);
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SSR += std::pow((datay[n] - yhat), 2);
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residuals[n] = (datay[n] - yhat);
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if (residuals[n] > residualmax)
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residualmax = residuals[n];
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if (residuals[n] < residualmin)
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residualmin = residuals[n];
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residualmean += std::fabs(residuals[n]);
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}
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residualmean = (residualmean / sample_size);
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SST = SSR + SSE;
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rsquared = SSE / SST; //Can also be obtained by r ^ 2 for simple regression (i.e. 1 independent variable)
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//Calculate T-test for Beta
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residualSE = std::sqrt(SSR / (sample_size - 2));
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SEBeta = (residualSE / (Sx * std::sqrt(sample_size - 1)));
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t = beta / SEBeta;
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}
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};
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#endif //IMAGE_GP_6_SLR_H
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