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If I use the Python function random.seed(my_seed) in one class in my module, will this seed remain for all the other classes instantiated in this module?
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random. seed(a, version) in python is used to initialize the pseudo-random number generator (PRNG). PRNG is algorithm that generates sequence of numbers approximating the properties of random numbers. These random numbers can be reproduced using the seed value.
The numpy random seed is a numerical value that generates a new set or repeats pseudo-random numbers. The value in the numpy random seed saves the state of randomness. If we call the seed function using value 1 multiple times, the computer displays the same random numbers.
Definition and Usage The seed() method is used to initialize the random number generator. The random number generator needs a number to start with (a seed value), to be able to generate a random number. By default the random number generator uses the current system time.
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. For a seed to be used in a pseudorandom number generator, it does not need to be random.
Yes, the seed is set for the (hidden) global Random() instance in the module. From the documentation:
The functions supplied by this module are actually bound methods of a hidden instance of the
random.Randomclass. You can instantiate your own instances ofRandomto get generators that don’t share state.
Use separate Random() instances if you need to keep the seeds separate; you can pass in a new seed when you instantiate it:
>>> from random import Random >>> myRandom = Random(anewseed) >>> randomvalue = myRandom.randint(0, 10) The class supports the same interface as the module.
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