What does the number in parentheses in `np.random.seed(number)` means?

Question

What is the difference between np.random.seed(0), np.random.seed(42), and np.random.seed(..any number). what is the function of the number in parentheses?

Answer 1

python uses the iterative Mersenne Twister algorithm to generate pseudo-random numbers [1]. The seed is simply where we start iterating.

To be clear, most computers do not have a "true" source of randomness. It is kind of an interesting thing that "randomness" is so valuable to so many applications, and is quite hard to come by (you can buy a specialized device devoted to this purpose). Since it is difficult to make random numbers, but they are nevertheless necessary, many, many, many, many algorithms have been developed to generate numbers that are not random, but nevertheless look as though they are. Algorithms that generate numbers that "look randomish" are called pseudo-random number generators (PRNGs). Since PRNGs are actually deterministic, they can't simply create a number from the aether and have it look randomish. They need an input. It turns out that using some complex operations and modular arithmetic, we can take in an input, and get another number that seems to have little or no relation to the input. Using this intuition, we can simply use the previous output of the PRNG as the next input. We then get a sequence of numbers which, if our PRNG is good, will seem to have no relation to each other.

In order to get our iterative PRNG started, we need an initial input. This initial input is called a "seed". Since the PRNG is deterministic, for a given seed, it will generate an identical sequence of numbers. Usually, there is a default seed that is, itself, sort of randomish. The most common one is the current time. However, the current time isn't a very good random number, so this behavior is known to cause problems sometimes. If you want your program to run in an identical manner each time you run it, you can provide a seed (0 is a popular option, but is entirely arbitrary). Then, you get a sequence of randomish numbers, but if you give your code to someone they can actually entirely recreate the runtime of the program as you witnessed it when you ran it.

Answer 2

That would be the starting key of the generator. Typically if you want to get reproducible results you'll use the same seed over and over again throughout your simulations.

Answer 3

You are setting the seed of the random number generator so you can get reproducible results. Example.

np.random.seed(0)
np.random.randint(0,100,10)

Output:

array([44, 47, 64, 67, 67,  9, 83, 21, 36, 87])

Now, if you ran the same code your computer, you should get the same 10 number output from the random integers from 0 to 100.