import numpy as np
from scipy import optimize

# 高精度の浮動小数点数を使用する
p = np.float64(input())
points = []
for _ in range(3):
    x, y = map(np.float64, input().split())
    points.append((x, y))

def Lp(x, y):
    x = np.abs(x)
    y = np.abs(y)
    ma = max(x, y)
    if ma < 1e-9:
        return np.float64(1e-9)  # 非常に小さな値を返す
    x /= ma
    y /= ma
    x = x ** p
    y = y ** p
    return (x + y) ** (1.0 / p) * ma

def f(point):
    x, y = point
    distances = [Lp(x - px, y - py) for px, py in points]
    d1, d2, d3 = distances
    return (d2 - d1) ** 2 + (d3 - d1) ** 2 + (d3 - d2) ** 2

options={"maxiter":10000}
x_min = optimize.minimize(f, x0=[0, 0], method='Nelder-Mead', tol=1e-8, options=options)

print(*x_min.x)