Gradient descent converging towards the wrong value - c++

Question

I'm trying to implement a gradient descent algorithm in C++. Here's the code I have so far :
#include <iostream>
double X[] {163,169,158,158,161,172,156,161,154,145};
double Y[] {52, 68, 49, 73, 71, 99, 50, 82, 56, 46 };
double m, p;
int n = sizeof(X)/sizeof(X[0]);
int main(void) {
double alpha = 0.00004; // 0.00007;
m = (Y[1] - Y[0]) / (X[1] - X[0]);
p = Y[0] - m * X[0];
for (int i = 1; i <= 8; i++) {
gradientStep(alpha);
}
return 0;
}
double Loss_function(void) {
double res = 0;
double tmp;
for (int i = 0; i < n; i++) {
tmp = Y[i] - m * X[i] - p;
res += tmp * tmp;
}
return res / 2.0 / (double)n;
}
void gradientStep(double alpha) {
double pg = 0, mg = 0;
for (int i = 0; i < n; i++) {
pg += Y[i] - m * X[i] - p;
mg += X[i] * (Y[i] - m * X[i] - p);
}
p += alpha * pg / n;
m += alpha * mg / n;
}
This code converges towards m = 2.79822, p = -382.666, and an error of 102.88. But if I use my calculator to find out the correct linear regression model, I find that the correct values of m and p should respectively be 1.601 and -191.1.
I also noticed that the algorithm won't converge for alpha > 0.00007, which seems quite low, and the value of p barely changes during the 8 iterations (or even after 2000 iterations).
What's wrong with my code?
Here's a good overview of the algorithm I'm trying to implement. The values of theta0 and theta1 are called p and m in my program.
Other implementation in python
More about the algorithm

Answer 1

This link gives a comprehensive view of the algorithm; it turns out I was following a completely wrong approach.
The following code does not work properly (and I have no plans to work on it further), but should put on track anyone who's confronted to the same problem as me :
#include <vector>
#include <iostream>
typedef std::vector<double> vect;
std::vector<double> y, omega(2, 0), omega2(2, 0);;
std::vector<std::vector<double>> X;
int n = 10;
int main(void) {
/* Initialize x so that each members contains (1, x_i) */
/* Initialize x so that each members contains y_i */
double alpha = 0.00001;
display();
for (int i = 1; i <= 8; i++) {
gradientStep(alpha);
display();
}
return 0;
}
double f_function(const std::vector<double> &x) {
double c;
for (unsigned int i = 0; i < omega.size(); i++) {
c += omega[i] * x[i];
}
return c;
}
void gradientStep(double alpha) {
for (int i = 0; i < n; i++) {
for (unsigned int j = 0; j < X[0].size(); j++) {
omega2[j] -= alpha/(double)n * (f_function(X[i]) - y[i]) * X[i][j];
}
}
omega = omega2;
}
void display(void) {
double res = 0, tmp = 0;
for (int i = 0; i < n; i++) {
tmp = y[i] - f_function(X[i]);
res += tmp * tmp; // Loss functionn
}
std::cout << "omega = ";
for (unsigned int i = 0; i < omega.size(); i++) {
std::cout << "[" << omega[i] << "] ";
}
std::cout << "\tError : " << res * .5/(double)n << std::endl;
}