Create a gist now

Instantly share code, notes, and snippets.

What would you like to do?
shmarkov.py, a set of functions for simple and concise markov chain text generation in Python. should work in python 2 and python 3
# shmarkov.py, simple and concise markov chain text generation
# Copyright (C) 2018 Allison Parrish
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the “Software”), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import print_function
import random
def add_to_model(model, n, seq):
seq = list(seq[:]) + [None]
for i in range(len(seq)-n):
gram = tuple(seq[i:i+n])
next_item = seq[i+n]
if gram not in model:
model[gram] = []
model[gram].append(next_item)
def markov_model(n, seq):
model = {}
add_to_model(model, n, seq)
return model
def gen_from_model(n, model, start=None, max_gen=100):
if start is None:
start = random.choice(list(model.keys()))
output = list(start)
for i in range(max_gen):
start = tuple(output[-n:])
next_item = random.choice(model[start])
if next_item is None:
break
else:
output.append(next_item)
return output
def markov_model_from_sequences(n, sequences):
model = {}
for item in sequences:
add_to_model(model, n, item)
return model
def markov_generate_from_sequences(n, sequences, count, max_gen=100):
starts = [item[:n] for item in sequences if len(item) >= n]
model = markov_model_from_sequences(n, sequences)
return [gen_from_model(n, model, random.choice(starts), max_gen)
for i in range(count)]
def markov_generate_from_lines_in_file(n, filehandle, count, level='char', max_gen=100):
if level == 'char':
glue = ''
sequences = [item.strip() for item in filehandle.readlines()]
elif level == 'word':
glue = ' '
sequences = [item.strip().split() for item in filehandle.readlines()]
generated = markov_generate_from_sequences(n, sequences, count, max_gen)
return [glue.join(item) for item in generated]
if __name__ == '__main__':
import sys
try:
n = int(sys.argv[1])
except (ValueError, IndexError):
n = 3
for item in markov_generate_from_lines_in_file(n, sys.stdin, 20):
print(item)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment