Created
April 1, 2018 08:44
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markov chain n-gram language model generator
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
import sys | |
import argparse | |
import random | |
from pathlib import Path | |
from itertools import chain | |
# fname = Path('~/Downloads/vpylm/out_2018-04-01T040258.txt') | |
def Markov_table(wakati, n=2, stop='\n'): | |
""" | |
wakati: list of str(words) | |
""" | |
if n < 2: | |
raise (ValueError('n must be >= 2 integer')) | |
Markov = {} | |
w = [stop] * (n-1) # dict key: forward (n-1) word | |
w[:(n-1)] = wakati[:n-1] # initial arrange | |
wakati = wakati[n-1:] | |
for word in wakati: | |
# shift word subsequence | |
# count words arrangement | |
if tuple(w) not in Markov.keys(): | |
Markov[tuple(w)] = [] | |
Markov[tuple(w)] += [word] | |
w = w[1:] + [word] | |
return(Markov) | |
def gen_phrase(Markov, stop='\n', Max=10000, seed=None): | |
if seed is not None: | |
random.seed(seed) | |
# choice initial word(s) | |
w = random.choice([x for x in Markov.keys() if x[0] == stop]) | |
n = len(w) | |
phrase = list(w)[1:] | |
plen = len(' '.join(phrase)) | |
# w = [''] * len(next(iter(Markov))) | |
# w = [x for x in Markov.keys() if x[0] == stopword] | |
while plen <= Max: | |
nw = random.choice(Markov[w]) | |
if plen + len(nw) + 1 <= Max: | |
phrase += [nw] | |
if nw == stop: | |
break | |
plen += len(nw) + 1 | |
w = tuple(phrase[-n:]) | |
return(' '.join(phrase)) | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser() | |
parser.add_argument('-f', | |
type=str, | |
help='入力テキスト') | |
parser.add_argument('-n', | |
type=int, | |
default=10, | |
help='生成する文章の数.') | |
parser.add_argument('--n-gram', | |
type=int, | |
default=2, | |
help='n-gram の n.') | |
parser.add_argument('-s', '--stop-word', | |
type=str, | |
default='\n', | |
help='ストップワード. デフォルト: \\n') | |
parser.add_argument('-M', '--max', | |
type=int, | |
default=10000, | |
help='文章の最大文字数. デフォルト:10000') | |
parser.add_argument('-S', '--seed', | |
type=int, | |
help='乱数の種') | |
args = parser.parse_args() | |
with Path(args.f).expanduser().open('r') as f: | |
scentence = f.readlines() | |
scentence = [x.split(' ') for x in scentence] | |
scentence = list(chain.from_iterable(scentence)) | |
random.seed(args.seed) | |
seeds = [random.randint(-sys.maxsize - 1, | |
sys.maxsize) for x in range(args.n)] | |
for s in seeds: | |
print(gen_phrase(Markov_table(scentence, n=args.n_gram, | |
stop=args.stop_word), | |
stop=args.stop_word, Max=args.max, seed=s)) |
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