1. Preface
Enable random seeds in Python to ensure that your random number generation process is repeatable. By setting a random seed, you can ensure that the sequence of random numbers generated every time you run the code is the same. This is very useful in debugging, testing, or scientific calculations that require repeatable results.
PythonrandomModules andnumpyThe random number generators of the library all support setting random seeds. The following describes how to set up random seeds in these two modules.
2. Use the random module
In Python standard libraryrandomThe module is used to generate pseudo-random numbers. You can pass()Function to set random seeds.
import random # Set random seeds(42) # Generate random numbersprint(()) print((1, 10))
In this example,(42)The random seed is set to 42. Every time this code is run,()and(1, 10)The same output will be generated.
3. Random number generator using numpy library
numpyIt is a powerful scientific computing library with its own random number generation module. Again, you can()to set random seeds.
import numpy as np # Set random seeds(42) # Generate random numbersprint(()) # Sample output: 0.6394267985610321print((1, 10)) # Sample output：4
andrandomThe module is similar. After setting a random seed,()and(1, 10)The same output is generated each time.
Things to note
- Once the random seed is set until you call again
seed()The function sets a new seed or resets explicitly (if reset is provided), and the random number generator will remain predictable. - In parallel or distributed computing environments, it is important to ensure that each process or thread uses different random seeds to avoid generating the same sequence of random numbers.
By setting random seeds, you can improve the repeatability of your code, and especially importantly you can get the reproducibility of the problem phenomenon required in solving problems, which is very important for areas such as scientific research, data analysis and machine learning.
4. How to get random seeds?
The above describes how to artificially set random seeds. However, in another scenario, it is hoped that the program can start with a randomly selected random seed every time, to ensure that each run is not based on the exact same random data sequence. What should I do?
If you want to use a different random seed every time the simulation runs, and don't want to manually set the random seed, you can have the program generate a random seed automatically. However, note that if you simply call the random number generator without setting the seed, in some cases (especially when the program restarts or runs repeatedly over a short period of time), the generated random number sequence may exhibit some degree of predictability or repetition due to the internal state of the computer or the scheduling of the operating system.
To avoid this and make sure you get a really different sequence of random numbers every time you run, you can take one of several strategies:
4.1 Use timestamps as seeds
Use the timestamp of the current time (usually the number of seconds or milliseconds since a fixed point in time) as a random seed. Since the timestamps will be different every time you run, this ensures that you get a different sequence of random numbers every time you run.
import random import time # Use the timestamp of the current time as seedseed = int(()) (seed) # Now you can generate random numbersprint(())
However, one potential problem with this approach is that if the time between runs is very close (e.g., within the same second), then they may get the same seed.
4.2 Using the system random number generator
Some operating systems provide an interface to access system-level random number generators. In Python, you can use()Function to get a certain number of random bytes and then convert them into integers as seeds.
import random import os # Get a random seed from the system random number generatorseed = int.from_bytes((4), 'big') # Get 4 bytes of random data and convert it into integers in big-endian format(seed) # Now you can generate random numbersprint(())
This approach is usually safer because it relies on the underlying random number generation mechanisms provided by the operating system, which are often designed to be more robust and unpredictable.
4.3 Not setting seeds (but not usually recommended)
If you just call it simplyrandomModule orIf a module's function does not set a seed, then Python will use its internal default seed generation mechanism. However, this mechanism may be affected by various factors at the start of the program and may therefore exhibit predictability in some cases. Therefore, it is generally not recommended to rely on this default behavior to obtain unpredictable sequences of random numbers.
4.4 Summary
To sum up, to ensure that each simulation run can get a truly different sequence of random numbers, it is recommended to use one of the first or second methods to generate random seeds. If you have very high security requirements (for example, in cryptography applications), then you may need to research more professional random number generation techniques and libraries.
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