Try to rebuild the pseudo-random algorithm Mersenne Twister, which is used in python's random library.
Also with a basic Random class and some simple methods for easily testing.
Main part of the algorithm.
Convert the pseudocode in Mersenne Twister to python code.
Coefficients follow the standard of MT19937-32.
A class named Random.
Firstly, build a Random object. if no input, seed will default to 0.
>>> name = Random(seed)
.random():
return uniform ditribution in [0,1)
>>> name.random()
0.1786995275775844
.randint(begin_number, end_number):
return random int in [a,b)
>>> name.randin(1,10)
9
.shuffle(sequence):
shuffle the input sequence
>>> name.shuffle([1,2,3,4,5])
[2, 1, 5, 3, 4]
.choice(sequence, replace=True, size=1):
choice an element randomly in the sequence.
replace: choose with replacement or not.
size: the number of element to be chosen, if size != 1, will return a list contains those element.
>>> name.choice([1,2,3,4,5])
1
>>> name.choice([1,2,3,4,5],size=3)
[2, 3, 2]
>>> name.choice([1,2,3,4,5],replace=False,size=3)
[2, 5, 1]
.bern(p):
generate a Bernoulli Random Variable
p: the probability of True
>>> name.bern(0.5)
True
>>> name.bern(0.5)
False
.binomial(n, p):
generate a Binomial Random Variable
n: total times
p: probability of success
>>> name.binomial(10, 0.5)
6
>>> name.binomial(10, 0.5)
3
.geometric(p):
generate a Geometric Random Variable
p: probability of success
>>> name.geometric(0.5)
1
>>> name.geometric(0.5)
2The file Testing for Randomness.ipynb contains several basic randomness testing result for this algorithm.