摘要：數據問題解所有數據平均值平均值回歸方程回歸方程回歸系數估計軌道文件回歸系數預測結果回歸系數預測

300,21182.88,-7044.56,14639.48
600,21707.87,-6930.28,13906.68
900,22207.04,-6828.65,13147.66
1200,22679.16,-6738.66,12363.84
1500,23123.06,-6659.23,11556.71
1800,23537.69,-6589.21,10727.78
2100,23922.07,-6527.40,9878.61
2400,24275.33,-6472.54,9010.81
2700,24596.67,-6423.32,8126.00
3000,24885.42,-6378.40,7225.86
3300,25141.01,-6336.41,6312.08
3600,25362.96,-6295.93,5386.38
3900,25550.92,-6255.54,4450.51

def read_m(path):
    # 　所有數據
    m = []
    # x
    xlist = []
    # y
    ylist = []
    # z
    zlist = []
    # time
    time_list = []

    with open(path, "r") as f:
        for i in f.readlines():
            aa = i.replace("
", "").split(",")
            bb = [eval(a) for a in aa]
            m.append(bb)
            time_list.append(bb[0])
            xlist.append(bb[1])
            ylist.append(bb[2])
            zlist.append(bb[3])
    return {
        "alldata": m,
        "time": time_list,
        "x": xlist,
        "y": ylist,
        "z": zlist,
    }


XXX = None
YYY = None


def xpj():
    """
    X平均值
    :return:
    """
    sum = 0

    for i in range(XXX.__len__()):
        sum += XXX[i]

    return sum / XXX.__len__()


def ypj():
    """
    Y 平均值
    :return:
    """
    sum = 0

    for i in range(YYY.__len__()):
        sum += YYY[i]

    return sum / YYY.__len__()


def sse():
    """
    回歸方程
    :return:
    """
    sum = 0
    xa = xpj()
    ya = ypj()

    for i in range(XXX.__len__()):
        sum += (XXX[i] - xa) * (YYY[i] - ya)

    return sum


def ssx():
    """
    回歸方程
    :return:
    """
    sum = 0
    xa = xpj()
    for i in range(XXX.__len__()):
        sum += (XXX[i] - xa) * (XXX[i] - xa)
    return sum


def getbeta1():
    """
    bate1
    :return:
    """
    bbeta = sse() / ssx()
    return bbeta


def getbeta0():
    """
    beta0
    :return:
    """
    return ypj() - getbeta1() * xpj()


def huiguixishu(x, y):
    """
    回歸系數
    :param x:
    :param y:
    :return:
    """
    global XXX
    global YYY
    XXX = x
    YYY = y

    beta1 = getbeta1()
    beta0 = getbeta0()
    return [beta0, beta1]


def predic(x, beta0, beta1):
    """
    估計
    :param x:
    :param beta0:
    :param beta1:
    :return:
    """
    a = beta0 + beta1 * x
    return a


if __name__ == "__main__":
    d = read_m("軌道文件.txt")
    tm = d["time"]
    x = d["x"]
    y = d["y"]
    z = d["z"]


    print("========回歸系數=========")
    a = huiguixishu(tm, x)
    b = huiguixishu(tm, y)
    c = huiguixishu(tm, z)

    print(a)
    print(b)
    print(c)

    print("========預測=========")
    guji_time = [4200,4500,4800]
    beta0_list = [a[0],b[0],c[0]]
    beta1_list = [a[1],b[1],c[1]]

    for i in range(guji_time.__len__()):
        x = predic(guji_time[i],beta0_list[0],beta1_list[0])
        y = predic(guji_time[i],beta0_list[1],beta1_list[1])
        z = predic(guji_time[i],beta0_list[2],beta1_list[2])

        print(guji_time[i],format(x,"0.3f") ,format(y,"0.3f"),format(z,"0.3f"))

========回歸系數=========
[21146.959615384614, 1.2183738095238088]
[-7019.398461538461, 0.21143040293040288]
[15712.87576923077, -2.8401093406593407]
========預測=========
4200 26264.130 -6131.391 3784.417
4500 26629.642 -6067.962 2932.384
4800 26995.154 -6004.533 2080.351