November 2019
Intermediate to advanced
346 pages
9h 36m
English
We begin the recipe by importing the Markovify library, a library for Markov chain computations, and reading in text, which will inform our Markov model (step 1). In step 2, we create a Markov chain model using the text. The following is a relevant snippet from the text object's initialization code:
class Text(object): reject_pat = re.compile(r"(^')|('$)|\s'|'\s|[\"(\(\)\[\])]") def __init__(self, input_text, state_size=2, chain=None, parsed_sentences=None, retain_original=True, well_formed=True, reject_reg=''): """ input_text: A string. state_size: An integer, indicating the number of words in the model's state. chain: A trained markovify.Chain instance for this text, if pre-processed. parsed_sentences: A list of lists, where ...