在Python的3D图中绘制一个1维高斯分布的平面。

Question

在Python的3D图中绘制一个1维高斯分布的平面。

pythonmatplotlibplotgaussianmplot3d

4

我有如下代码和生成的图表。我的目标是在右边的第二个图上绘制一个1D高斯分布，如所示的红色平面。

这样做的目的是显示重叠部分（表示条件概率）是高斯分布。我不关心分布的精确方差是否正确，只是想将其直观地展示出来。

有没有在Python中实现这个简单的方法？

谢谢，P

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import bivariate_normal
from mpl_toolkits.mplot3d import Axes3D

#Make a 3D plot
fig = plt.figure(figsize=plt.figaspect(0.5))

################ First Plot ##############
#Parameters to set
mu_x = 0
sigma_x = np.sqrt(5)

mu_y = 0
sigma_y = np.sqrt(5)

#Create grid and multivariate normal
x = np.linspace(-10,10,500)
y = np.linspace(-10,10,500)
X, Y = np.meshgrid(x,y)
Z = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x,mu_y)

# Create plane
x_p = 2
y_p = np.linspace(-10,10,500)
z_p = np.linspace(0,0.02,500)
Y_p, Z_p = np.meshgrid(y_p, z_p)


# ax = fig.gca(projection='3d')
ax = fig.add_subplot(1,2,1, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis',linewidth=0)
ax.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.5)
plt.tight_layout()

################ Second Plot ##############
x_p = 2
y_p = np.linspace(-10,10,500)
z_p = np.linspace(0,0.02,500)
Y_p, Z_p = np.meshgrid(y_p, z_p)


# ax2 = fig.gca(projection='3d')
ax2 = fig.add_subplot(1,2,2,projection='3d')
ax2.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.3)
plt.show()

- prax1telis

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Yacola · Accepted Answer

您可以尝试使用np.where获取到离平面x_p = 2最接近且误差小于tol的X坐标，然后使用得到的索引idx_x_p作为掩码来选择相应的Y和Z值。这将带您进入以下代码：


import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import bivariate_normal
from mpl_toolkits.mplot3d import Axes3D

#Parameters to set for Gaussian distribution
mu_x = 0
sigma_x = np.sqrt(5)
mu_y = 0
sigma_y = np.sqrt(5)

#Create grid and multivariate normal
x = np.linspace(-10,10,500)
y = np.linspace(-10,10,500)
X, Y = np.meshgrid(x,y)
Z = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x,mu_y)

# Create plane
x_p = 2
y_p = np.linspace(-10,10,500)
z_p = np.linspace(0,0.02,500)
Y_p, Z_p = np.meshgrid(y_p, z_p)

# Finding closest idx values of X mesh to x_p
tol = 1e-4
idx_x_p = (np.where(x < x_p+tol) and np.where(x > x_p-tol))[0][0]
# Select the corresponding values of X, Y, Z (carefully switch X and Y)
x_c, y_c, z_c = Y[idx_x_p], X[idx_x_p], Z[idx_x_p]

# Plot
fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis',linewidth=0,zorder=0)
ax.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.5,zorder=5)
ax.plot(x_c,y_c,z_c,zorder=10)

plt.tight_layout()

这些图展示了不同 x_p 值的高斯型重叠。假设 x_p in np.linspace(-10,10,20)：