Go linear regression

main.go

package main;

import(
	"fmt"
)

type LinearFunction struct {
	a float64
	b float64
}

func (f *LinearFunction) aPartialDerivative(xi, yi float64) float64 {
    // Compute (xi, yi) derivative of a
	return -2 * xi * (yi - (f.a * xi + f.b));
}

func (f *LinearFunction) bPartialDerivative(xi, yi float64) float64 {
    // Compute (xi, yi) derivative of b
	return -2 * (yi - (f.a * xi + f.b));
}

func (f *LinearFunction) GradientDescent(x, y []float64, alpha float64) {
    // Trying to minimise the mean square error
    // := (1/N)*sum((y_i- a*x_i+b)^2)

	var N int = len(x);
	var aa float64 = 0;
	var bb float64 = 0;

    // Sum of all derivatives
	for i := 0; i < N; i++ {
		aa += f.aPartialDerivative(x[i], y[i]);
		bb += f.bPartialDerivative(x[i], y[i]);
	}

	// Progression Coefficient (step of the grandient descent)
	var progcoef float64 = (alpha/float64(N))
	f.a -= progcoef * aa
	f.b -= progcoef * bb
}

func (f *LinearFunction) Train(x, y []float64, epochs int, alpha float64) {
    // Compute the gradient descent each epoch
	for e := 0; e < epochs; e++ {
		f.GradientDescent(x, y, alpha)
	}
}

func (f *LinearFunction) Predict(v float64) float64 {
    // f(x) = (a*) * x + (b*)
	return f.a * v + f.b;
}

func main() {
	spending := []float64{1, 4, 15, 5, 8, 2, 13, 9, 6, 19};
	sales	 :=	[]float64{1, 2, 10, 5, 9, 16, 4, 13, 8, 7};

	f := &LinearFunction{a:0, b:0};
	f.Train(spending, sales, 5000, .01);

	fmt.Println(f.Predict(8));
}