Description
marc is a small, but flexible, library that implements Markov chains in pure python.
marc alternatives and similar packages
Based on the "Random Generator" category.
Alternatively, view marc alternatives based on common mentions on social networks and blogs.
Scout Monitoring - Free Django app performance insights with Scout Monitoring
Do you think we are missing an alternative of marc or a related project?
README
About
marc (markov chain) is a small, but flexible Markov chain generator.
Usage
marc is easy to use. To build a MarkovChain pass the object a sequence of items:
from marc import MarkovChain
sequence = [
'Rock', 'Rock', 'Rock', 'Paper', 'Rock', 'Scissors',
'Paper', 'Paper', 'Scissors', 'Rock', 'Scissors',
'Scissors', 'Paper', 'Scissors', 'Rock', 'Rock', 'Rock',
'Paper', 'Scissors', 'Scissors', 'Scissors', 'Rock'
]
chain = MarkovChain(sequence)
The learned transition matrix can be accessed through the matrix attribute:
print(chain.matrix)
# [[0.5, 0.25, 0.25], [0.2, 0.2, 0.6], [0.375, 0.25, 0.375]]
Though, the output is perhaps better viewed as a pandas DataFrame:
import pandas as pd
df = pd.DataFrame(
chain.matrix,
index=chain.encoder.index_,
columns=chain.encoder.index_
)
print(df)
# Rock Paper Scissors
# Rock 0.500 0.25 0.250
# Paper 0.200 0.20 0.600
# Scissors 0.375 0.25 0.375
Use the next method to generate the next state (seeded or unseeded):
chain.next('Rock')
# 'Rock'
chain.next()
# Paper
The next method can also generate multiple states with the n argument:
chain.next('Paper', n=5)
# ['Scissors', 'Paper', 'Rock', 'Paper', 'Scissors']
MarkovChain objects are iterable. This means that they can be passed directly to the next function:
next(chain)
# 'Scissors'
next(chain)
# Rock
Example
A fully worked example of marc in action (block text provided by quote):
import random
import re
from quote import quote
from marc import MarkovChain
quotes = quote('shakespeare', 250)
print(quotes[0])
# {'author': 'William Shakespeare',
# 'book': 'As You Like It',
# 'quote': 'The fool doth think he is wise, but the wise man knows himself to be a fool.'}
text = '\n'.join([q['quote'] for q in quotes])
text = text.lower()
tokens = re.findall(r"[\w']+|[.,!?;]", text)
tokens[:5]
# ['the', 'fool', 'doth', 'think', 'he']
chain = MarkovChain(tokens)
def generate_sentences(chain, n=2, length=(10, 20)):
for _ in range(n):
l = random.randint(length[0], length[1])
nonsense = ' '.join(chain.next(n=l))
print(nonsense)
generate_sentences(chain)
# and unless by some are fascinated by the hour upon the wind faithful
# those that hath had a very much as flaws go
Install
pip install -U marc