markov.py

#!/usr/bin/env python3
from multiprocessing import Pool
from random import random

samples = 10000000
samples_mul = 8
states_repr = ['sleep', 'code', 'tg']
probs = [
  [0.8, 0.1, 0.1],
  [0.2, 0, 0.8],
  [0.5, 0.5, 0]
]
states_count = len(states_repr)

def walk(state):
  row = probs[state]
  r = random()
  for i, v in enumerate(row):
    if r <= v:
      return i
    r -= v

def mcmc(_):
  state = 0
  counter = [0] * states_count
  for _ in range(samples):
    state = walk(state)
    counter[state] += 1
  return counter
  
if __name__ == '__main__':
  counter = [0] * states_count
  with Pool() as p:
    for c in p.map(mcmc, range(samples_mul)):
      for i in range(states_count):
        counter[i] += c[i]
  t_samples = samples_mul * samples
  for i, v in enumerate(counter):
    print(f'{states_repr[i]}: {v} ({v / t_samples})')