What is a random sentence generator?
What is Random Sentence Generator?
How does the Random Sentence Generator work?
You can use random.choice within a list comprehension then concatenate the selected list with join:
>>> l=[nouns,verbs,adj,adv]
>>> ' '.join([random.choice(i) for i in l])
'girl runs dirty crazily.'
>>> ' '.join([random.choice(i) for i in l])
'monkey hits clueless occasionally.'
The easiest way for your understanding:
import random
nouns = ("puppy", "car", "rabbit", "girl", "monkey")
verbs = ("runs", "hits", "jumps", "drives", "barfs")
adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.")
adj = ("adorable", "clueless", "dirty", "odd", "stupid")
num = random.randrange(0,5)
print nouns[num] + ' ' + verbs[num] + ' ' + adv[num] + ' ' + adj[num]
To generate random text, U need to use Markov Chains
code to do that: from here
import random
class Markov(object):
def __init__(self, open_file):
self.cache = {}
self.open_file = open_file
self.words = self.file_to_words()
self.word_size = len(self.words)
self.database()
def file_to_words(self):
self.open_file.seek(0)
data = self.open_file.read()
words = data.split()
return words
def triples(self):
""" Generates triples from the given data string. So if our string were
"What a lovely day", we'd generate (What, a, lovely) and then
(a, lovely, day).
"""
if len(self.words) < 3:
return
for i in range(len(self.words) - 2):
yield (self.words[i], self.words[i+1], self.words[i+2])
def database(self):
for w1, w2, w3 in self.triples():
key = (w1, w2)
if key in self.cache:
self.cache[key].append(w3)
else:
self.cache[key] = [w3]
def generate_markov_text(self, size=25):
seed = random.randint(0, self.word_size-3)
seed_word, next_word = self.words[seed], self.words[seed+1]
w1, w2 = seed_word, next_word
gen_words = []
for i in xrange(size):
gen_words.append(w1)
w1, w2 = w2, random.choice(self.cache[(w1, w2)])
gen_words.append(w2)
return ' '.join(gen_words)
Explaination: Generating pseudo random text with Markov chains using Python
You should be "training" the Markov model with multiple sequences, so that you accurately sample the starting state probabilities as well (called "pi" in Markov-speak). If you use a single sequence then you will always start in the same state.
In the case of Orwell's 1984 you would want to use sentence tokenization first (NLTK is very good at it), then word tokenization (yielding a list of lists of tokens, not just a single list of tokens) and then feed each sentence separately to the Markov model. This will allow it to properly model sequence starts, instead of being stuck on a single way to start every sequence.