#### Markov Chain

I
```from __future__ import annotations

from collections import Counter
from random import random

class MarkovChainGraphUndirectedUnweighted:
"""
Undirected Unweighted Graph for running Markov Chain Algorithm
"""

def __init__(self):
self.connections = {}

def add_node(self, node: str) -> None:
self.connections[node] = {}

self, node1: str, node2: str, probability: float
) -> None:
if node1 not in self.connections:
if node2 not in self.connections:
self.connections[node1][node2] = probability

def get_nodes(self) -> list[str]:
return list(self.connections)

def transition(self, node: str) -> str:
current_probability = 0
random_value = random()

for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""

def get_transitions(
start: str, transitions: list[tuple[str, str, float]], steps: int
) -> dict[str, int]:
"""
Running Markov Chain algorithm and calculating the number of times each node is
visited

>>> transitions = [
... ('a', 'a', 0.9),
... ('a', 'b', 0.075),
... ('a', 'c', 0.025),
... ('b', 'a', 0.15),
... ('b', 'b', 0.8),
... ('b', 'c', 0.05),
... ('c', 'a', 0.25),
... ('c', 'b', 0.25),
... ('c', 'c', 0.5)
... ]

>>> result = get_transitions('a', transitions, 5000)

>>> result['a'] > result['b'] > result['c']
True
"""

graph = MarkovChainGraphUndirectedUnweighted()

for node1, node2, probability in transitions:

visited = Counter(graph.get_nodes())
node = start

for _ in range(steps):
node = graph.transition(node)
visited[node] += 1

return visited

if __name__ == "__main__":
import doctest

doctest.testmod()
```