为何numpy RandomState执行结果存在差异？两段代码输出不同的原因解析

Let’s break down exactly what’s happening here—this all boils down to how random number generator (RNG) state works in NumPy, and whether you’re reusing the same generator instance or resetting it every time.

First, let’s recap your code and outputs for clarity: #

第一段代码

import numpy as np
rng = np.random.RandomState(42)
rng.randint(10, size=1)
np.random.RandomState(rng.randint(10, size=1)).randint(10, size=3)

Output: array([8, 9, 3])

第二段代码

import numpy as np
np.random.RandomState(42).randint(10, size=1)
np.random.RandomState(np.random.RandomState(42).randint(10, size=1)).randint(10, size=3)

Output: array([9, 3, 4])

The Core Difference: Reusing vs. Resetting the RNG #

The key here is whether you’re modifying a single RNG’s internal state or starting fresh with a new generator every time:

1. In the first snippet:

   • You create one RNG instance rng seeded with 42.
   • When you call rng.randint(10, size=1), you don’t just get a random number—you also advance rng’s internal state. It’s no longer in the initial "seeded with 42" state.
   • The second call to rng.randint(10, size=1) uses this updated state to generate a different number than the first call.
   • You then use this second, unique number as the seed for a new RNG, which produces the [8,9,3] array.

2. In the second snippet:

   • Every time you write np.random.RandomState(42), you’re creating a brand new RNG instance, starting completely fresh with the seed 42.
   • The first np.random.RandomState(42).randint(...) generates a number (let’s say 6), but since you don’t save the instance, it’s discarded immediately.
   • The second np.random.RandomState(42).randint(...) creates another fresh RNG with seed 42, so it generates the exact same number (6) as the first call—no state changes here, because it’s a new generator every time.
   • Using this repeat number as the seed for the final RNG gives you the [9,3,4] array, which differs from the first snippet because the seed value used here is different.

Let’s Verify With Explicit Values #

If we print the intermediate steps, it becomes obvious:

• First snippet:

  import numpy as np
  rng = np.random.RandomState(42)
  first_num = rng.randint(10, size=1)  # Output: array([6])
  second_num = rng.randint(10, size=1) # Output: array([3]) (state advanced!)
  np.random.RandomState(second_num).randint(10, size=3) # Seed 3 → [8,9,3]
  

• Second snippet:

  import numpy as np
  first_num = np.random.RandomState(42).randint(10, size=1) # Output: array([6])
  second_num = np.random.RandomState(42).randint(10, size=1) # Also array([6]) (fresh generator!)
  np.random.RandomState(second_num).randint(10, size=3) # Seed 6 → [9,3,4]
  

The Big Takeaway #

NumPy’s RandomState keeps track of an internal state that updates every time you generate a random number. Reusing the same instance means subsequent builds on that state; creating a new instance with the same seed every time will always produce identical sequences from scratch.

内容的提问来源于stack exchange，提问作者Le Noff