📈 Simple machine learning, learning by simple humans
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/* @authors: Yann & Sam' */
#include <iostream>
#include <random>
#include "../Modules/Graphics/graphics.hpp"
using namespace std;
using namespace Eigen;
#define FIRSTALPHA 0.01
#define NBEXAMPLES 500
#define NBITERATIONS 1000
#define INTERVALWIDTH 20
void getRandomCoordinates(long double[], long double[], Matrix<long double, 1, 2>);
void getGradient(const long double[], const long double[], const Matrix<long double, 1, 2>, Matrix<long double, 1, 2> &);
int main(int argc, char const *argv[])
{
(void)argc;
(void)argv;
/* We fix our coefficients here */
Matrix<long double, 1, 2> LEADINGCOEFFICIENTS = {1.0, 2.0};
long double x[NBEXAMPLES];
long double y[NBEXAMPLES];
/* Coordinates generation */
getRandomCoordinates(x, y, LEADINGCOEFFICIENTS);
long double alpha(FIRSTALPHA);
Matrix<long double, 1, 2> theta;
theta.setZero();
Matrix<long double, 1, 2> gradOld;
getGradient(x, y, theta, gradOld);
Matrix<long double, 1, 2> grad;
Matrix<long double, 1, 2> deltaGrad;
/* Training ! */
int i;
for(i = 0; i < NBITERATIONS; i++)
{
theta -= alpha * gradOld;
getGradient(x, y, theta, grad);
if(grad != gradOld)
{
deltaGrad = grad - gradOld;
alpha = -alpha * gradOld.dot(deltaGrad) / deltaGrad.dot(deltaGrad);
}
if(grad.isZero())
{
i++;
break;
}
gradOld = grad;
}
cout << "Theta: " << theta << endl;
cout << "| Done in " << i << " iterations. |" << endl;
/* This function will display the input coordinates,
and the computed regression, and will be waiting while the window stay open */
sfmlDisplay(string("Affine Regression (C++)"), Matrix<long double, 1, NBEXAMPLES>(x), Matrix<long double, 1, NBEXAMPLES>(y), theta);
return 0;
}
void getRandomCoordinates(long double x[], long double y[], Matrix<long double, 1, 2> leadingCoefficients)
{
random_device rd;
mt19937 mt(rd());
uniform_real_distribution<double> random_bias(-INTERVALWIDTH, INTERVALWIDTH);
for(int i(0); i < NBEXAMPLES; i++)
{
x[i] = i;
y[i] = (i * leadingCoefficients(0, 1)) + leadingCoefficients(0, 0) + random_bias(mt);
}
}
void getGradient(const long double x[], const long double y[], const Matrix<long double, 1, 2> theta, Matrix<long double, 1, 2> &gradient)
{
gradient.setZero();
for(int i(0); i < NBEXAMPLES; i++)
{
gradient(0, 0) += (theta(0, 1) * x[i] + theta(0, 0)) - y[i];
gradient(0, 1) += ((theta(0, 1) * x[i] + theta(0, 0)) - y[i]) * x[i];
}
gradient /= NBEXAMPLES;
}