Generating text using Markov Chains. This example uses a text example ('textexample.txt') which is a single text file with lots and lots of example sentences; we use this to build the Markov Chain library; once the chain is built, we can create our out 'new' text based on the probablistic factor (e.g., what word usually follows ...).
import numpy as np
# Trump's speeches here: https://github.com/ryanmcdermott/trump-speeches
trump = open('textexample.txt', encoding='utf8').read()
corpus = trump.split()
def make_pairs(corpus):
for i in range(len(corpus)-1):
yield (corpus[i], corpus[i+1])
pairs = make_pairs(corpus)
word_dict = {}
for word_1, word_2 in pairs:
if word_1 in word_dict.keys():
word_dict[word_1].append(word_2)
else:
word_dict[word_1] = [word_2]
first_word = np.random.choice(corpus)
while first_word.islower():
first_word = np.random.choice(corpus)
chain = [first_word]
n_words = 50
for i in range(n_words):
chain.append(np.random.choice(word_dict[chain[-1]]))
' '.join(chain)
 | Resources |  |
• Gutenberg Library Free Online Book Collection [LINK]
• Pixel Markov Chains [LINK]
 |
Other Data Mining and Machine Learning Texts |
 |
|
