import numpy as np

class EntangledMarkovNet:
def __init__(self, size):
self.size = size
self.states = np.zeros(size)

def update_state(self, new_state):
self.states = np.roll(self.states, -1)
self.states[-1] = new_state

def get_probabilities(self):
return self.states / np.sum(self.states)

# Example Usage
if __name__ == "__main__":
# Create an Entangled Markov Net with a specified size
markov_net = EntangledMarkovNet(1024)

# Example: Update the state with new data
# This can be any data, such as results from a quantum circuit, sensor readings, etc.
new_data = np.random.rand(1024) # Example new data
markov_net.update_state(new_data)

# Get the current state probabilities
probabilities = markov_net.get_probabilities()
print(probabilities)