python多元线性回归拟合_如果我有两个自变量和一个因变量，如何绘制多元线性回归的最佳拟合线...

下面是一个示例曲面拟合，它可以生成三维散射图、三维曲面图和等高线图。在import numpy, scipy, scipy.optimize

import matplotlib

from mpl_toolkits.mplot3d import Axes3D

from matplotlib import cm # to colormap 3D surfaces from blue to red

import matplotlib.pyplot as plt

graphWidth = 800 # units are pixels

graphHeight = 600 # units are pixels

# 3D contour plot lines

numberOfContourLines = 16

def SurfacePlot(func, data, fittedParameters):

f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)

matplotlib.pyplot.grid(True)

axes = Axes3D(f)

x_data = data[0]

y_data = data[1]

z_data = data[2]

xModel = numpy.linspace(min(x_data), max(x_data), 20)

yModel = numpy.linspace(min(y_data), max(y_data), 20)

X, Y = numpy.meshgrid(xModel, yModel)

Z = func(numpy.ar