from graphviz import Digraph
from markov import Chain
from markov.plot import transitions_to_graph
def double_dice():
return int(random() * 6) + int(random() * 6)
chain = Chain(11, double_dice())
# flip coin many times and build Markov chain for this process
# let 0 be heads and 1 tails
for i in range(1000000):
chain.learn(double_dice())
g = Digraph(format='svg',
engine='dot',
graph_attr={
'overlap': 'false',
'pad': '0.1',
'nodesep': '0.35',
'ranksep': '0.35'
},
edge_attr={
'fontname': 'Helvetica',
'fontsize': '8.0'
},
node_attr={'fontname': 'Helvetica'})
from random import betavariate
from graphviz import Digraph
from markov import Chain
from markov.plot import transitions_to_graph
def rigged_dice():
return int((betavariate(0.5, 0.7) * 6))
chain = Chain(6, rigged_dice())
# roll dice many times and build Markov chain for this process
for i in range(100000):
chain.learn(rigged_dice())
def state_mapping(state):
if state == 0: return u'⚀'
if state == 1: return u'⚁'
if state == 2: return u'⚂'
if state == 3: return u'⚃'
if state == 4: return u'⚄'
if state == 5: return u'⚅'
g = Digraph(format='svg', engine='dot',
graph_attr={'pad': '0.1', 'nodesep': '0.15', 'ranksep': '0.5'},
edge_attr={'fontsize': '6.0', 'fontname':'Helvetica'})
