如下所示：

from matplotlib import pyplot as pltimport numpy as npfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure()ax = Axes3D(fig)#列出实验数据point=[[2,3,48],[4,5,50],[5,7,51],[8,9,55],[9,12,56]]plt.xlabel("X1")plt.ylabel("X2")#表示矩阵中的值ISum = 0.0X1Sum = 0.0X2Sum = 0.0X1_2Sum = 0.0X1X2Sum = 0.0X2_2Sum = 0.0YSum = 0.0X1YSum = 0.0X2YSum = 0.0#在图中显示各点的位置for i in range(0,len(point)): x1i=point[i][0] x2i=point[i][1] yi=point[i][2] ax.scatter(x1i, x2i, yi, color="red") show_point = "["+ str(x1i) +","+ str(x2i)+","+str(yi) + "]" ax.text(x1i,x2i,yi,show_point) ISum = ISum+1 X1Sum = X1Sum+x1i X2Sum = X2Sum+x2i X1_2Sum = X1_2Sum+x1i**2 X1X2Sum = X1X2Sum+x1i*x2i X2_2Sum = X2_2Sum+x2i**2 YSum = YSum+yi X1YSum = X1YSum+x1i*yi X2YSum = X2YSum+x2i*yi# 进行矩阵运算# _mat1 设为 mat1 的逆矩阵m1=[[ISum,X1Sum,X2Sum],[X1Sum,X1_2Sum,X1X2Sum],[X2Sum,X1X2Sum,X2_2Sum]]mat1 = np.matrix(m1)m2=[[YSum],[X1YSum],[X2YSum]]mat2 = np.matrix(m2)_mat1 =mat1.getI()mat3 = _mat1*mat2# 用list来提取矩阵数据m3=mat3.tolist()a0 = m3[0][0]a1 = m3[1][0]a2 = m3[2][0]# 绘制回归线x1 = np.linspace(0,9)x2 = np.linspace(0,12)y = a0+a1*x1+a2*x2ax.plot(x1,x2,y)show_line = "y="+str(a0)+"+"+str(a1)+"x1"+"+"+str(a2)+"x2"plt.title(show_line)plt.show()

以上这篇python实现三维拟合的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。