如何使用 matplotlib 绘制 3d 高斯分布? [英] How to plot 3d gaussian distribution with matplotlib?
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问题描述
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.
I have obtained the means and sigmas of 3d Gaussian distribution, then I want to plot the 3d distribution with python code, and obtain the distribution figure.
推荐答案
这是基于 mpl_toolkits 的文档和基于 scipy multinormal pdf 的 SO 的答案:
This is based on documentation of mpl_toolkits and an answer on SO based on scipy multinormal pdf:
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()
参考:-
https://matplotlib.org/tutorials/toolkits/mplot3d.html#sphx-glr-tutorials-toolkits-mplot3d-py
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