Parse numpy.random.get_ State() and numpy.random.set_ state()

• analysis numpy.random.get_state() and numpy.random.set_state()
• get_state()： It can be understood as setting state , Record the operation that the array is disrupted
• set_state()： receive get_state() The value returned , And do the same
• General combination random.shuffle() Function USES
• Scramble the two arrays of instance and label at the same time , But after the disruption , Instances and labels are still one-to-one correspondence

# -*- coding: utf-8 -*-
"""
Created on Wed Jul  4 15:30:47 2018

@author: muli
"""

import numpy as np

#random.getstate()
# Returns the current internal state of the object capture generator . This object can be passed to setstate（） To recover 
#random.setstate(state)
# The state should have been obtained from previous calls getstate（）,
# as well as setstate（） Restore the generator getstate（） Internal state when called .
a = np.arange(10)
print("a:")
print(a)
b=['A','B','C','D','E','F','G','H','I','J']
print("b:")
print(b)
print("----------------------------")
state=np.random.get_state()
np.random.shuffle(a)
print(a)
np.random.set_state(state)
np.random.shuffle(b)
print(b)

The result is ：

a:
[0 1 2 3 4 5 6 7 8 9]
b:
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
----------------------------
[8 3 4 1 6 2 9 5 7 0]
['I', 'D', 'E', 'B', 'G', 'C', 'J', 'F', 'H', 'A']