Python Example of Markov Chain with 1 Agent
In this simple example, a Markov Chain is implemented considering 1 agent. The term "agent" in this example refers to the number of people in the zone.
This code enables the stochastic simulation of occupancy in a specific zone based on the probabilities set in the transition matrix. The core of the code lies in the np.random.choice() function, which is used to generate a pseudo-random number conditioned by the probabilities.
There are two versions of the same code: simple_procedural.py and extended.py. The latter is simply a parameterized version of the former that loops through the length of the transition matrix to pick the corresponding value.