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Here's a simplified description of how seeding might work for an MWC generator:
1.-Initialization: The generator's state, which typically consists of two 16-bit integers, is initialized based on the seed value. The seed value might be a single 32-bit integer or two separate 16-bit
2.-Multiplier Constants: MWC generators use specific constants as multipliers to produce a new random value in each iteration. These constants are chosen to ensure good statistical properties of the generated sequence.
3.-Seed Expansion: The seed is used to initialize the internal state, and the generator goes through a series of iterations to mix and expand the seed values across its internal state.
4.-Random Output: Once the generator is seeded, it can produce pseudorandom numbers by iteratively applying the multiply-with-carry operations to its internal state.
Here is a high-level Python-like pseudocode for a simplified MWC1616 seeding process:
class MWC1616Generator:
def __init__(self, seed):
self.state = [0, 0]
self.multiplier = [0xDEAD, 0xBEEF] # Example multipliers
# Seed initialization
self.state[0] = seed & 0xFFFF
self.state[1] = (seed >> 16) & 0xFFFF
# Seed expansion
for _ in range(10): # Arbitrary number of iterations
self.next()
def next(self):
# MWC1616 operation
temp = self.state[0] * self.multiplier[0] + self.state[1]
self.state[1] = (temp >> 16) & 0xFFFF
self.state[0] = temp & 0xFFFF
return self.state[0]
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