markov chain python code

import numpy as np class MarkovChain(object):    def __init__(self, transition_prob):        """        Initialize the MarkovChain instance.         Parameters        ----------        transition_prob: dict            A dict object representing the transition             probabilities in Markov Chain.             Should be of the form:                 {'state1': {'state1': 0.1, 'state2': 0.4},                  'state2': {...}}        """        self.transition_prob = transition_prob        self.states = list(transition_prob.keys())     def next_state(self, current_state):        """        Returns the state of the random variable at the next time         instance.         Parameters        ----------        current_state: str            The current state of the system.        """        return np.random.choice(            self.states,             p=[self.transition_prob[current_state][next_state]                for next_state in self.states]        )     def generate_states(self, current_state, no=10):        """        Generates the next states of the system.         Parameters        ----------        current_state: str            The state of the current random variable.         no: int            The number of future states to generate.        """        future_states = []        for i in range(no):            next_state = self.next_state(current_state)            future_states.append(next_state)            current_state = next_state        return future_states