A small program to demonstrate markov chains in python.

markov_chain_python.py

#this program is to create a markov chain in python

import numpy as np

class MarkovChain:

    def __init__(self, transition_matrix,states):
        self.transition_matrix = np.array(transition_matrix)
        self.states = states

    def next_state(self,current_state:str) -> str:
        current_state_matrix = np.array([0]*len(self.states))
        current_state_matrix[self.states.index(current_state)] = 1
        next_state = current_state_matrix.dot(self.transition_matrix)
        random_state_probablity = np.random.choice(len(self.states),p=next_state)
        return self.states[random_state_probablity]
    
    def nStates(self,starting_state:str,nstates:int):
        current_state = np.array([0]*len(self.states))
        current_state[self.states.index(starting_state)] = 1
        n_states = []
        state_transition_record = []
        for i in range(nstates):
            state_transition_record.append(current_state)
            next_state = current_state.dot(self.transition_matrix)
            random_state_probablity = np.random.choice(len(self.states),p=next_state)
            n_states.append(self.states[random_state_probablity])
            current_state = next_state
        return n_states,state_transition_record 

if __name__ == "__main__":
    mc = MarkovChain([[0.7,0.3],[0.2,0.8]],["sunny","rainy"])
    print(mc.next_state("rainy"))
    print(mc.nStates("rainy",10))