Python 通过子函数播种
Python seed through the subfunction
我有一个函数和一个子函数，它们中的每一个都生成了一些随机数组。为了使结果可重现，我使用了种子。但是我看到了奇怪的情况。
当我在子函数中有一个种子时，主函数中的随机数也受到子函数中种子的影响。而且，从主功能到子功能没有这种影响。
例如，考虑以下代码。
import random
from random import randint
import numpy as np
def randgene():
a=np.random.randint(0,5,size=5)
print "a in function", a
np.random.seed(seed=15)
b=np.random.randint(0,5,size=5)
print "b in function", b
d=np.random.choice(50, size=5, replace = False)
print "d in function", d
# np.random.seed(seed=None)
def main():
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
np.random.seed(seed=10)
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
print "b-with seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
if __name__ == '__main__':
main()
对于这段代码，我得到了这样的结果：
d-without seed [14 29 9 42 18]
a-without seed [3 0 0 3 4]
b-without seed [3 2 0 2 1]
a in function [2 3 1 2 3]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-without seed [ 8 21 32 39 11]
a-without seed [3 0 3 3 0]
b-without seed [1 2 2 1 4]
a in function [4 4 0 2 2]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-without seed [ 8 21 32 39 11]
a-without seed [3 0 3 3 0]
b-without seed [1 2 2 1 4]
d-with seed [37 23 44 42 47]
a-with seed [2 0 0 4 4]
b-with seed [0 0 2 4 2]
a in function [0 0 2 3 0]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-with seed [ 8 21 32 39 11]
a-with seed [3 0 3 3 0]
a in function [1 2 2 1 4]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-with seed [ 8 21 32 39 11]
a-with seed [3 0 3 3 0]
其中，每当我调用子函数时，您都会在主函数中看到 d-with seed [ 8 21 32 39 11], a-with seed [3 0 3 3 0]
重复。
但是，如果我在子函数中注释 np.random.seed(seed=15) 行，我会得到这个结果：
d-without seed [17 20 23 36 28]
a-without seed [3 1 1 2 0]
b-without seed [3 2 1 1 3]
a in function [1 2 2 0 4]
b in function [4 4 0 4 2]
d in function [ 9 46 19 7 47]
d-without seed [39 42 10 17 4]
a-without seed [2 3 0 2 4]
b-without seed [1 4 1 3 2]
a in function [1 1 3 3 2]
b in function [1 3 4 4 3]
d in function [ 0 2 45 5 19]
d-without seed [24 20 47 3 29]
a-without seed [3 0 3 3 3]
b-without seed [1 0 0 2 3]
d-with seed [37 23 44 42 47]
a-with seed [2 0 0 4 4]
b-with seed [0 0 2 4 2]
a in function [0 0 2 3 0]
b in function [4 4 0 1 1]
d in function [ 6 11 35 4 7]
d-with seed [19 47 43 38 15]
a-with seed [0 4 2 1 2]
a in function [1 2 1 3 2]
b in function [3 4 4 0 2]
d in function [38 31 17 43 2]
d-with seed [ 7 15 39 2 49]
a-with seed [3 4 1 4 0]
显然没有重复。因此，主函数中的种子对子函数没有影响。
如果我同时保留 np.random.seed(seed=15) 和 np.random.seed(seed=None) 未注释，我将得到类似的结果。
有人可以向我解释发生了什么吗？
提前致谢，
阿夫欣
随机数生成器种子用于设置其全局状态。该状态用于该点之后生成的所有数字（并且生成的每个数字都会导致状态发生变化）。您可以使用全局变量将您的示例与本示例进行比较：
def print_and_increment(): # this is like generating a random number
global x
print("x was {}. Incrementing...".format(x))
x += 1
print("x is now", x)
def func():
global x
print("func before seed")
print_and_increment()
x = 10 # this is equivalent to calling seed()
print("func after seed")
print_and_increment()
x = 0 # seed
print("top")
print_and_increment()
func()
print("middle")
print_and_increment()
func()
print("bottom")
print_and_increment()
如果你想让你的函数有自己的种子，它总是使用，但不改变外部使用的随机数生成器的状态，你可以使用 numpy.random.get_state 和 numpy.random.set_state .
尝试这样的事情：
def randgene():
prev_state = np.random.get_state()
a=np.random.randint(0,5,size=5)
print "a in function", a
np.random.seed(seed=15)
b=np.random.randint(0,5,size=5)
print "b in function", b
d=np.random.choice(50, size=5, replace = False)
print "d in function", d
# np.random.seed(seed=None)
np.random.set_state(prev_state)
现在您的函数应该不会影响外部代码对随机数生成器的使用。
对于局部随机状态，只需使用 numpy.random.RandomState 而不是 numpy.random.seed 设置的全局状态。
一个例子：
import numpy as np
seed1 = 1
seed2 = 42
rs1 = np.random.RandomState(seed1)
rs2 = np.random.RandomState(seed2)
rs3 = np.random.RandomState(seed1) # same seed value used for rs1
a = rs1.randint(0, 5, size=5)
b = rs1.randint(0, 5, size=5)
c = rs2.randint(0, 5, size=5)
e = rs3.randint(0, 5, size=5)
f = rs3.randint(0, 5, size=5)
eq = np.testing.assert_array_equal
try:
eq(a, b, err_msg='OK, a should be != b')
except AssertionError:
pass
try:
eq(a, c, err_msg='OK, a should be != c')
except AssertionError:
pass
eq(a, e, err_msg='ERROR') # a and e are equal due to same seed
eq(b, f, err_msg='ERROR') # b and f are equal due to same seed
print('OK')
我有一个函数和一个子函数，它们中的每一个都生成了一些随机数组。为了使结果可重现，我使用了种子。但是我看到了奇怪的情况。
当我在子函数中有一个种子时，主函数中的随机数也受到子函数中种子的影响。而且，从主功能到子功能没有这种影响。 例如，考虑以下代码。
import random
from random import randint
import numpy as np
def randgene():
a=np.random.randint(0,5,size=5)
print "a in function", a
np.random.seed(seed=15)
b=np.random.randint(0,5,size=5)
print "b in function", b
d=np.random.choice(50, size=5, replace = False)
print "d in function", d
# np.random.seed(seed=None)
def main():
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-without seed", np.random.choice(50, size=5, replace = False)
print "a-without seed", np.random.randint(0,5,size=5)
print "b-without seed", np.random.randint(0,5,size=5)
np.random.seed(seed=10)
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
print "b-with seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
f = randgene()
print "d-with seed", np.random.choice(50, size=5, replace = False)
print "a-with seed", np.random.randint(0,5,size=5)
if __name__ == '__main__':
main()
对于这段代码，我得到了这样的结果：
d-without seed [14 29 9 42 18]
a-without seed [3 0 0 3 4]
b-without seed [3 2 0 2 1]
a in function [2 3 1 2 3]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-without seed [ 8 21 32 39 11]
a-without seed [3 0 3 3 0]
b-without seed [1 2 2 1 4]
a in function [4 4 0 2 2]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-without seed [ 8 21 32 39 11]
a-without seed [3 0 3 3 0]
b-without seed [1 2 2 1 4]
d-with seed [37 23 44 42 47]
a-with seed [2 0 0 4 4]
b-with seed [0 0 2 4 2]
a in function [0 0 2 3 0]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-with seed [ 8 21 32 39 11]
a-with seed [3 0 3 3 0]
a in function [1 2 2 1 4]
b in function [0 4 0 4 3]
d in function [41 16 22 24 14]
d-with seed [ 8 21 32 39 11]
a-with seed [3 0 3 3 0]
其中，每当我调用子函数时，您都会在主函数中看到 d-with seed [ 8 21 32 39 11], a-with seed [3 0 3 3 0]
重复。
但是，如果我在子函数中注释 np.random.seed(seed=15) 行，我会得到这个结果：
d-without seed [17 20 23 36 28]
a-without seed [3 1 1 2 0]
b-without seed [3 2 1 1 3]
a in function [1 2 2 0 4]
b in function [4 4 0 4 2]
d in function [ 9 46 19 7 47]
d-without seed [39 42 10 17 4]
a-without seed [2 3 0 2 4]
b-without seed [1 4 1 3 2]
a in function [1 1 3 3 2]
b in function [1 3 4 4 3]
d in function [ 0 2 45 5 19]
d-without seed [24 20 47 3 29]
a-without seed [3 0 3 3 3]
b-without seed [1 0 0 2 3]
d-with seed [37 23 44 42 47]
a-with seed [2 0 0 4 4]
b-with seed [0 0 2 4 2]
a in function [0 0 2 3 0]
b in function [4 4 0 1 1]
d in function [ 6 11 35 4 7]
d-with seed [19 47 43 38 15]
a-with seed [0 4 2 1 2]
a in function [1 2 1 3 2]
b in function [3 4 4 0 2]
d in function [38 31 17 43 2]
d-with seed [ 7 15 39 2 49]
a-with seed [3 4 1 4 0]
显然没有重复。因此，主函数中的种子对子函数没有影响。
如果我同时保留 np.random.seed(seed=15) 和 np.random.seed(seed=None) 未注释，我将得到类似的结果。
有人可以向我解释发生了什么吗？
提前致谢， 阿夫欣
随机数生成器种子用于设置其全局状态。该状态用于该点之后生成的所有数字（并且生成的每个数字都会导致状态发生变化）。您可以使用全局变量将您的示例与本示例进行比较：
def print_and_increment(): # this is like generating a random number
global x
print("x was {}. Incrementing...".format(x))
x += 1
print("x is now", x)
def func():
global x
print("func before seed")
print_and_increment()
x = 10 # this is equivalent to calling seed()
print("func after seed")
print_and_increment()
x = 0 # seed
print("top")
print_and_increment()
func()
print("middle")
print_and_increment()
func()
print("bottom")
print_and_increment()
如果你想让你的函数有自己的种子，它总是使用，但不改变外部使用的随机数生成器的状态，你可以使用 numpy.random.get_state 和 numpy.random.set_state .
尝试这样的事情：
def randgene():
prev_state = np.random.get_state()
a=np.random.randint(0,5,size=5)
print "a in function", a
np.random.seed(seed=15)
b=np.random.randint(0,5,size=5)
print "b in function", b
d=np.random.choice(50, size=5, replace = False)
print "d in function", d
# np.random.seed(seed=None)
np.random.set_state(prev_state)
现在您的函数应该不会影响外部代码对随机数生成器的使用。
对于局部随机状态，只需使用 numpy.random.RandomState 而不是 numpy.random.seed 设置的全局状态。
一个例子：
import numpy as np
seed1 = 1
seed2 = 42
rs1 = np.random.RandomState(seed1)
rs2 = np.random.RandomState(seed2)
rs3 = np.random.RandomState(seed1) # same seed value used for rs1
a = rs1.randint(0, 5, size=5)
b = rs1.randint(0, 5, size=5)
c = rs2.randint(0, 5, size=5)
e = rs3.randint(0, 5, size=5)
f = rs3.randint(0, 5, size=5)
eq = np.testing.assert_array_equal
try:
eq(a, b, err_msg='OK, a should be != b')
except AssertionError:
pass
try:
eq(a, c, err_msg='OK, a should be != c')
except AssertionError:
pass
eq(a, e, err_msg='ERROR') # a and e are equal due to same seed
eq(b, f, err_msg='ERROR') # b and f are equal due to same seed
print('OK')