Python 绘制随机数和推进位生成器会产生不同的结果

为什么要假设advance等同于生成一个给定类型的样本>；

In [715]: rng=np.random.default_rng()

In [716]: timeit rng.bit_generator.advance(1000)
2.66 µs ± 5.72 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Uniform和Normal使用不同的时间，表示它们的生成方式不同:

In [717]: timeit rng.random(1000)
10.2 µs ± 9.81 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

In [718]: timeit rng.standard_normal(1000)
19.3 µs ± 41.7 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

advance个doctor 说:

Advancing a RNG updates the underlying RNG state as-if a given
number of calls to the underlying RNG have been made. In general
there is not a one-to-one relationship between the number output
random values from a particular distribution and the number of
draws from the core RNG.  

uniform是更简单的分布，花费的时间更少，并且更有可能与advance有一对一的关系:

In [727]: rng = np.random.default_rng(2024)
     ...: s = rng.bit_generator.state
    
In [728]: rng.bit_generator.state = s
     ...: for i in range(5):
     ...:     rng.bit_generator.state = rng.bit_generator.advance(10).state
     ...:     print('{i:3d} - {state:40d} {inc:40d}'.format(i=i, **rng.bit_generator.state['state']))
     ...:     
  0 -  317319928805135160732659717497650841180  263843294879837360010514471918415607657
  1 -  152261761237047187080279356346465986730  263843294879837360010514471918415607657
  2 -  262244773524709030991986015855808125320  263843294879837360010514471918415607657
  3 -  174718512225906865463277455530823048310  263843294879837360010514471918415607657
  4 -  213819718814773627130094102456555149556  263843294879837360010514471918415607657

In [730]: rng.bit_generator.state = s
     ...: for i in range(5):
     ...:     rng.standard_normal(10)
     ...:     print('{i:3d} - {state:40d} {inc:40d}'.format(i=i, **rng.bit_generator.state['state']))
     ...:     
  0 -  317319928805135160732659717497650841180  263843294879837360010514471918415607657
  1 -  152261761237047187080279356346465986730  263843294879837360010514471918415607657
  2 -   12941317612297752247903241541248335614  263843294879837360010514471918415607657
  3 -  167375039976652124381293896870728401724  263843294879837360010514471918415607657
  4 -   78535979019656695810700673044059278986  263843294879837360010514471918415607657

In [731]: rng.bit_generator.state = s
     ...: for i in range(5):
     ...:     rng.random(10)
     ...:     print('{i:3d} - {state:40d} {inc:40d}'.format(i=i, **rng.bit_generator.state['state']))
     ...:     
  0 -  317319928805135160732659717497650841180  263843294879837360010514471918415607657
  1 -  152261761237047187080279356346465986730  263843294879837360010514471918415607657
  2 -  262244773524709030991986015855808125320  263843294879837360010514471918415607657
  3 -  174718512225906865463277455530823048310  263843294879837360010514471918415607657
  4 -  213819718814773627130094102456555149556  263843294879837360010514471918415607657

ramdom与advance匹配，standard_normal不匹配.