mt19937predictor module

mt19937predictor.LOWER_MASK = 2147483647

mt19937predictor.M = 397

mt19937predictor.MATRIX_A = 2567483615

class mt19937predictor.MT19937Predictor

Bases: random.Random

Usage:

>>> import random
>>> from mt19937predictor import MT19937Predictor
>>> predictor = MT19937Predictor()
>>> for _ in range(624):
...     x = random.getrandbits(32)
...     predictor.setrandbits(x, 32)
>>> random.getrandbits(32) == predictor.getrandbits(32)
True
>>> random.random() == predictor.random()
True
>>> a = list(range(100))
>>> b = list(range(100))
>>> random.shuffle(a)
>>> predictor.shuffle(b)
>>> a == b
True

gauss(*args)

Raises:NotImplementedError

genrand_int32()

getrandbits(bits)

The interface for random.Random.getrandbits() in Python’s Standard Library

getstate(*args)

Raises:NotImplementedError

random()

The interface for random.Random.random() in Python’s Standard Library

seed(*args)

Raises:NotImplementedError

setrand_int32(y)

Feceive the target PRNG’s outputs and reconstruct the inner state. when 624 consecutive DOWRDs is given, the inner state is uniquely determined.

setrandbits(y, bits)

The interface for random.Random.getrandbits() in Python’s Standard Library

setstate(*args)

Raises:NotImplementedError

mt19937predictor.N = 624

624 values (of 32bit) is just enough to reconstruct the internal state

mt19937predictor.UPPER_MASK = 2147483648

mt19937predictor.generate(mt, kk)

mt19937predictor.genrand_int32(mt, mti)

mt19937predictor.tempering(y)

mt19937predictor.untempering(y)