Go linear regression

main.go

package main;

import(
	"fmt"
)

type LinearFunction struct {
	a float64
	b float64
}

func (f *LinearFunction) a_derivative(N, alpha, xi, yi float64) {
	f.a -= (1.0/N)*alpha*(-2*xi*(yi-(f.a*xi+f.b)));
}

func (f *LinearFunction) b_derivative(N, alpha, xi, yi float64) {
	f.b -= (1.0/N)*alpha*(-2*(yi-(f.a*xi+f.b)));
}

func (f *LinearFunction) descent(x, y []float64, alpha float64) {
	var N int = len(x)

	for i := 0; i < N; i++ {
		f.a_derivative(float64(N), alpha, x[i], y[i]);
		f.b_derivative(float64(N), alpha, x[i], y[i]);
	}
}

func (f *LinearFunction) train(x, y []float64, epochs int, alpha float64) {
	for e := 0; e < epochs; e++ {
		f.descent(x, y, alpha)
	}
}

func (f *LinearFunction) predict(v float64) float64 {
	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));
}