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108 lines
2.4 KiB
108 lines
2.4 KiB
/* @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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using namespace Eigen;
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#define FIRSTALPHA 0.01
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#define NBEXAMPLES 500
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#define NBITERATIONS 1000
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#define INTERVALWIDTH 20
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void getRandomCoordinates(long double[], long double[], Matrix<long double, 1, 2>);
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void getGradient(const long double[], const long double[], const Matrix<long double, 1, 2>, Matrix<long double, 1, 2> &);
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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 fix our coefficients here */
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Matrix<long double, 1, 2> LEADINGCOEFFICIENTS = {1.0, 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, LEADINGCOEFFICIENTS);
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long double alpha(FIRSTALPHA);
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Matrix<long double, 1, 2> theta;
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theta.setZero();
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Matrix<long double, 1, 2> gradOld;
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getGradient(x, y, theta, gradOld);
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Matrix<long double, 1, 2> grad;
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Matrix<long double, 1, 2> deltaGrad;
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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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getGradient(x, y, theta, grad);
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if(grad != gradOld)
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{
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deltaGrad = grad - gradOld;
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alpha = -alpha * gradOld.dot(deltaGrad) / deltaGrad.dot(deltaGrad);
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}
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if(grad.isZero())
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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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/* This function will display the input coordinates,
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and the computed regression, and will be waiting while the window stay open */
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sfmlDisplay(string("Affine Regression (C++)"), Matrix<long double, 1, NBEXAMPLES>(x), Matrix<long double, 1, NBEXAMPLES>(y), theta);
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return 0;
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}
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void getRandomCoordinates(long double x[], long double y[], Matrix<long double, 1, 2> leadingCoefficients)
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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 * leadingCoefficients(0, 1)) + leadingCoefficients(0, 0) + random_bias(mt);
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}
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}
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void getGradient(const long double x[], const long double y[], const Matrix<long double, 1, 2> theta, Matrix<long double, 1, 2> &gradient)
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{
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gradient.setZero();
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for(int i(0); i < NBEXAMPLES; i++)
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{
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gradient(0, 0) += (theta(0, 1) * x[i] + theta(0, 0)) - y[i];
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gradient(0, 1) += ((theta(0, 1) * x[i] + theta(0, 0)) - y[i]) * x[i];
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}
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gradient /= NBEXAMPLES;
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}
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