数据
300,21182.88,-7044.56,14639.48600,21707.87,-6930.28,13906.68900,22207.04,-6828.65,13147.661200,22679.16,-6738.66,12363.841500,23123.06,-6659.23,11556.711800,23537.69,-6589.21,10727.782100,23922.07,-6527.40,9878.612400,24275.33,-6472.54,9010.812700,24596.67,-6423.32,8126.003000,24885.42,-6378.40,7225.863300,25141.01,-6336.41,6312.083600,25362.96,-6295.93,5386.383900,25550.92,-6255.54,4450.51
问题
解
def read_m(path): # 所有数据 m = [] # x xlist = [] # y ylist = [] # z zlist = [] # time time_list = [] with open(path, 'r') as f: for i in f.readlines(): aa = i.replace('\n', '').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 = NoneYYY = Nonedef 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(): """ 回归方程 :return: """ sum = 0 xa = xpj() ya = ypj() for i in range(XXX.__len__()): sum += (XXX[i] - xa) * (YYY[i] - ya) return sumdef ssx(): """ 回归方程 :return: """ sum = 0 xa = xpj() for i in range(XXX.__len__()): sum += (XXX[i] - xa) * (XXX[i] - xa) return sumdef getbeta1(): """ bate1 :return: """ bbeta = sse() / ssx() return bbetadef getbeta0(): """ beta0 :return: """ return ypj() - getbeta1() * xpj()def huiguixishu(x, y): """ 回归系数 :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): """ 估计 :param x: :param beta0: :param beta1: :return: """ a = beta0 + beta1 * x return aif __name__ == '__main__': d = read_m("轨道文件.txt") tm = d["time"] x = d["x"] y = d["y"] z = d["z"] print("========回归系数=========") a = huiguixishu(tm, x) b = huiguixishu(tm, y) c = huiguixishu(tm, z) print(a) print(b) print(c) print("========预测=========") 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')) 结果
========回归系数=========[21146.959615384614, 1.2183738095238088][-7019.398461538461, 0.21143040293040288][15712.87576923077, -2.8401093406593407]========预测=========4200 26264.130 -6131.391 3784.4174500 26629.642 -6067.962 2932.3844800 26995.154 -6004.533 2080.351