Learning the module random here, in the very beginning there are book-keeping functions, I understand that to set a specific seed is to make sure obtaining same random number.
but, what about the getstate() and setsate()? link
In the documentation, it has no introduction for what this state means, and if I don't know what it means, how could I set it right?
random.getstate()
Return an object capturing the current internal state of the
generator. This object can be passed to setstate() to restore the
state.
random.setstate(state)
state should have been obtained from a previous call to getstate(),
and setstate() restores the internal state of the generator to what
it was at the time getstate() was called.
Thanks,
Why not try it out?
import random
random.seed(42)
print(random.sample(range(20),k=10))
st = random.getstate() # remeber this state
print(random.sample(range(20),k=20)) # print 20
random.setstate(st) # restore state
print(random.sample(range(20),k=10)) #print same first 10
Output:
[12, 0, 4, 3, 11, 10, 19, 1, 5, 18]
[4, 9, 0, 3, 10, 8, 16, 7, 18, 17, 14, 6, 2, 1, 5, 11, 15, 13, 19, 12]
[4, 9, 0, 3, 10, 8, 16, 7, 18, 17]
Obvoiusly, you can go back and reproduce the same values over and over if you get a state and restore it.
You can not use different randoms in between though or you alter the state.
random.setstate(st) # go back again
print(random.sample(range(99),k=2)) # do something different
print(random.sample(range(20),k=18))
Output:
[21, 50] # something different after setting state
[0, 3, 11, 9, 18, 8, 17, 19, 16, 7, 15, 1, 10, 2, 12, 5, 13, 14] # changed values
import random
import timeit
t1 = timeit.timeit(stmt = """random.seed(42)
random.randint(1,10)""",number=10000,setup="import random")
t2 = timeit.timeit(stmt = """
random.randint(1,10)
random.setstate(s)""",number=10000,setup="""import random
s = random.getstate()""")
print(t1,t2)
Output:
# seed() time setstate() time
0.5621587821914207 0.49502014443357545
Python's default generator is a Mersenne Twister with a state space that is 19937 bits, much larger than what you think of as the seed.
You can think of it conceptually as three functions:
- f(seed) -> state0
- g(statei) -> statei+1
- h(statei) -> outcomei
When you start with a seed value using random.seed(), it generates a full state value of 19937 bits one time using function f(). Each time you use the generator, it advances to the next 19937 bit state using g() and returns the output found by collapsing the updated state down a single integer using h().
Normally you don't actually see the internal state which is at the core of the generator. getstate() bypasses the collapsing function h(), and setstate() bypasses the seeding function f(), so that you can reproduce your sequence from any point without having to go all the way back to the beginning and reproduce the entire sequence to that point.
Most people don't need to (and shouldn't) use the get/setstate capability, but it can be useful for pulling some clever mathematical tricks to reduce variability of Monte Carlo estimators.
