def get_least_squares_value(value, value_function, m):
B = np.zeros((m, m))
f = np.zeros((m, ))
for i in range(m):
for j in range(m):
sum = 0
for k in range(n):
sum += pow(get_x(k), i + j)
B[i, j] = sum
for i in range(m):
sum = 0
for k in range(n):
sum += value_function(get_x(k)) * pow(get_x(k), i)
f[i] = sum
solution = np.linalg.solve(B, f)
p = Polynomial(m - 1, np.flip(solution, 0))
return p.get_result_horner(value)
