Samuel FORESTIER
97 lines
1.9 KiB
C++
97 lines
1.9 KiB
C++
/* @authors: Yann & Sam' */
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#include <iostream>
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#include <random>
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#include "../Modules/Graphics/graphics.hpp"
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using namespace std;
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#define FIRSTALPHA 0.01
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#define NBEXAMPLES 1000
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#define NBITERATIONS 1000
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#define INTERVALWIDTH 20
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void getRandomCoordinates(long double[], long double[], const long double);
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long double getGradient(const long double[], const long double[], const long double);
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int main(int argc, char const *argv[])
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{
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(void)argc;
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(void)argv;
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/* We set the coefficient here */
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const long double LEADINGCOEFFICIENT = 2.0;
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long double x[NBEXAMPLES];
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long double y[NBEXAMPLES];
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/* Coordinates generation */
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getRandomCoordinates(x, y, LEADINGCOEFFICIENT);
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long double theta(0.0);
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long double alpha(FIRSTALPHA);
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long double gradOld(getGradient(x, y, theta));
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long double grad;
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/* Training ! */
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int i;
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for(i = 0; i < NBITERATIONS; i++)
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{
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theta -= alpha * gradOld;
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grad = getGradient(x, y, theta);
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if(grad != gradOld)
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{
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alpha = -alpha * gradOld / (grad - gradOld);
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}
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if(grad == 0.0)
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{
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i++;
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break;
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}
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gradOld = grad;
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}
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cout << "Theta: " << theta << endl;
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cout << "| Done in " << i << " iterations. |" << endl;
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sfmlDisplay("Linear Regression (C++)", Matrix<long double, 1, NBEXAMPLES>(x), Matrix<long double, 1, NBEXAMPLES>(y), Matrix<long double, 1, 1>(theta));
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return 0;
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}
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void getRandomCoordinates(long double x[], long double y[], const long double leadingCoefficient)
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{
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random_device rd;
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mt19937 mt(rd());
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uniform_real_distribution<double> random_bias(-INTERVALWIDTH, INTERVALWIDTH);
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for(int i(0); i < NBEXAMPLES; i++)
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{
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x[i] = i;
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y[i] = (i * leadingCoefficient) + random_bias(mt);
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}
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}
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long double getGradient(const long double x[], const long double y[], const long double theta)
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{
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long double value = 0.0;
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for(int i(0); i < NBEXAMPLES; i++)
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{
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value += (theta * x[i] - y[i]) * x[i];
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}
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return value / NBEXAMPLES;
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}
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