導語

很多情況下，為了能夠觀察到資料之間的內部的關系，可以使用繪圖來更好的顯示規律，

比如在下面的幾張動圖中，使用matplotlib中的三維顯示命令，使得我們可以對于logistic回歸網路的性能與相關引數有了更好的理解，

下面的動圖顯示了在訓練網路時，不同的學習速率對于演算法收斂之間的影響，

下面給出了繪制這些動態曲線的相關的python指令：

?01 3D plot

1.基本語法

在安裝matplotlib之后，自動安裝有 mpl_toolkits.mplot3d，

#Importing Libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d

#3D Plotting
fig = plt.figure()
ax = plt.axes(projection="3d")

#Labeling
ax.set_xlabel('X Axes')
ax.set_ylabel('Y Axes')
ax.set_zlabel('Z Axes')

plt.show()

2.Python Cmd

使用pythoncmd 插入相應的陳述句，

3.舉例

(1) Ex1

#!/usr/local/bin/python
# -*- coding: gbk -*-
#******************************
# TEST2.PY                     -- by Dr. ZhuoQing 2020-11-16
#
# Note:
#******************************

from headm import *
from mpl_toolkits.mplot3d import axes3d

ax = plt.axes(projection='3d')
x = [1,2,3,4,5,6,7,8,9]
y = [2,3,4,6,7,8,9,5,1]
z = [5,6,2,4,8,6,5,6,1]

ax.plot3D(x,y,z)
ax.set_xlabel('X Axes')
ax.set_ylabel('Y Axes')
ax.set_zlabel('Z Axes')

plt.show()

#------------------------------------------------------------
#        END OF FILE : TEST2.PY
#******************************

▲ 3D plot的演示

(2) Ex2

from mpl_toolkits.mplot3d import axes3d

ax = plt.axes(projection='3d')

angle = linspace(0, 2*pi*5, 400)
x = cos(angle)
y = sin(angle)
z = linspace(0, 5, 400)

ax.plot3D(x,y,z)
ax.set_xlabel('X Axes')
ax.set_ylabel('Y Axes')
ax.set_zlabel('Z Axes')

plt.show()

▲ 3D繪制的例子

(3) Ex3

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

mpl.rcParams['legend.fontsize'] = 10

fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()

plt.show()

?02 繪制Scatter

利用和上面的相同的繪制命令，將原來的plot3D修改成為 scatter即可，

from mpl_toolkits.mplot3d import axes3d

ax = plt.axes(projection='3d')

angle = linspace(0, 2*pi*5, 40)
x = cos(angle)
y = sin(angle)
z = linspace(0, 5, 40)

ax.scatter(x,y,z, color='b')
ax.set_xlabel('X Axes')
ax.set_ylabel('Y Axes')
ax.set_zlabel('Z Axes')

plt.show()

▲ Scatter 的例子

?03 繪制3D Surface

(1) Ex1

▲ 3D surface例子

#!/usr/local/bin/python
# -*- coding: gbk -*-
#******************************
# TEST2.PY                     -- by Dr. ZhuoQing 2020-11-16
#
# Note:
#******************************

from headm import *
from mpl_toolkits.mplot3d import axes3d

ax = plt.axes(projection='3d')

x = arange(-5, 5, 0.1)
y = arange(-5, 5, 0.1)
x,y = meshgrid(x, y)
R = sqrt(x**2+y**2)
z = sin(R)

ax.plot_surface(x, y, z)
ax.set_xlabel('X Axes')
ax.set_ylabel('Y Axes')
ax.set_zlabel('Z Axes')

plt.show()

#------------------------------------------------------------
#        END OF FILE : TEST2.PY
#******************************

▲ 3D 繪制Surface

▲ 繪制3D球表面

(2) 舉例

'''
***********
3D surface (color map)
***********

Demonstrates plotting a 3D surface colored with the coolwarm color map.
The surface is made opaque by using antialiased=False.

Also demonstrates using the LinearLocator and custom formatting for the
z axis tick labels.
'''

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')

# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False)

# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)

plt.show()

▲ 彩色表面繪制

end

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