python - 如何使用 matplotlib 绘制 3d 高斯分布？

Question

我已经获得了3d高斯分布的均值和西格玛，然后我想用python代码绘制3d分布，并获得分布图。

Answer 1

这是基于 mpl_toolkits 的文档和基于 scipy multinormal pdf 的 SO 答案:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

import numpy as np
from scipy.stats import multivariate_normal

x, y = np.mgrid[-1.0:1.0:30j, -1.0:1.0:30j]

# Need an (N, 2) array of (x, y) pairs.
xy = np.column_stack([x.flat, y.flat])

mu = np.array([0.0, 0.0])

sigma = np.array([.5, .5])
covariance = np.diag(sigma**2)

z = multivariate_normal.pdf(xy, mean=mu, cov=covariance)

# Reshape back to a (30, 30) grid.
z = z.reshape(x.shape)





fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')



ax.plot_surface(x,y,z)
#ax.plot_wireframe(x,y,z)

plt.show()

引用:-

1. Generating 3D Gaussian distribution in Python

2. https://matplotlib.org/tutorials/toolkits/mplot3d.html#sphx-glr-tutorials-toolkits-mplot3d-py