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
527 Dec 4, 2022
43 Dec 24, 2022
12 Dec 27, 2022
3 Jan 19, 2022
85 Dec 9, 2022
1 Nov 11, 2021
4 Jul 18, 2021
1k Dec 16, 2022
60 Nov 16, 2022
0 Sep 5, 2021
6k Jan 6, 2023
106 Nov 6, 2022
![[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'](https://github.com/eric8607242/SGNAS/raw/master/resource/SGNAS_framework.png)
35 Dec 1, 2022
40 Dec 1, 2022
2 Jul 23, 2022
1 Jan 3, 2022
182 Dec 29, 2022
67 Dec 29, 2022
3 Sep 02, 2022
5 Jul 01, 2022
332 Dec 31, 2022
1 Nov 12, 2021
18 Jan 27, 2022
49 Sep 10, 2022
2 Jan 23, 2022
11 Jul 17, 2022
4 Aug 01, 2022
91 Dec 15, 2022
3 Sep 17, 2022
42 Oct 26, 2022
3.3k Dec 30, 2022
8 Nov 19, 2022
1 Jan 05, 2022
167 Nov 20, 2022
49 Sep 22, 2022
24 Jul 15, 2022