CODEWITHSUNDEEP
pythonrandom
Guido Salducci
Guido Salducci

Reputation: 53

How does Python seed the Mersenne twister

How does Python seed its Mersenne twister pseudorandom number generator used in the built-in random library if no explicit seed value is provided? Is it based on the clock somehow? If so, is the seed found when the random module is imported or when it is first called?

Python's documentation does not seem to have the answer.

Upvotes: 5

Views: 2619

Answers (4)

taxilian
taxilian

Reputation: 14324

The other answers are correct, but to summarize something from comments above which might be missed by someone else looking for the answer I tracked down today:

The typical reference implementations of Mersenne Twister take a seed and then internally (usually in the constructor) call this.init_genrand(seed)

If you do that and use a simple number you will get different results than what Python uses -- and probably wonder why like I did.

In order to get the same results in another language (node.js in my case) that you would in python you need an implementation which supports the init_by_array method and then initialize it with init_by_array([seed]).

This example is if you're just using a simple 32 bit int val -- if your seed is something else then python passes it in a different way (e.g. larger than 32 bit numbers are split up and sent in 32 bits per array element, etc) but that should at least help someone get going in the right direction.

The node.js implementation I ended up using was https://gist.github.com/banksean/300494 and it worked beautifully. I could not find one in npm which had the support I needed -- might have to add one.

Upvotes: 0

tom
tom

Reputation: 22939

The seed is based on the clock or (if available) an operating system source. The random module creates (and hence seeds) a shared Random instance when it is imported, not when first used.

References

Python docs for random.seed:

random.seed(a=None, version=2)

Initialize the random number generator.

If a is omitted or None, the current system time is used. If randomness sources are provided by the operating system, they are used instead of the system time (see the os.urandom() function for details on availability).

Source of random.py (heavily snipped):

from os import urandom as _urandom

class Random(_random.Random):

    def __init__(self, x=None):
        self.seed(x)

    def seed(self, a=None, version=2):
        if a is None:
            try:
                a = int.from_bytes(_urandom(32), 'big')
            except NotImplementedError:
                import time
                a = int(time.time() * 256) # use fractional seconds

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.

_inst = Random()

The last line is at the top level, so it is executed when the module is loaded.

Upvotes: 2

bks
bks

Reputation: 1360

In modern versions of python (c.f. http://svn.python.org/projects/python/branches/release32-maint/Lib/random.py) Random.seed tries to use 32 bytes read from /dev/urandom. If that doesn't work, it uses the current time: (a is an optional value which can be used to explicitly seed the PRNG.)

    if a is None:
        try:
            a = int.from_bytes(_urandom(32), 'big')
        except NotImplementedError:
            import time
            a = int(time.time() * 256) # use fractional seconds

Upvotes: 3

isaach1000
isaach1000

Reputation: 1839

From this answer, I found the source of random.py. In the Random class, the seed is set when the object is constructed. The module instantiates a Random object and uses it for all of the module methods. So if the random number is produced with random.random() or another module method, then the seed was set at the time of the import. If the random number is produced by another instance of Random, then the seed was set at the time of the construction of that instance.

From the source:

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.

Upvotes: 2

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