def __init__(self):
"""Constructor method to load external nGram class, load words, confusion matrix and dictionary."""
self.ng = nGram(True, True, False, False, False)
self.words = sorted(set(self.ng.words))[3246:]
self.loadConfusionMatrix()
self.dict = self.loadDict()
return
def __init__(self):
"""Constructor method to load external nGram class, load words, confusion matrix and dictionary."""
self.ng = nGram(True, True, False, False, False)
self.words = sorted(set(self.ng.words))[3246:]
self.loadConfusionMatrix()
self.dict = self.loadDict()
return
def __init__(self):
self.grams = pd.read_csv('3gram.csv')
self.py = pinyin()
self.ng = nGram()
self.segment = [
'，', '。', '？', '！', '：', '；', '……', '【', '】', '（', '）', '“', '”',
"《", '》', '、'
]
print('init over')
for i, key in enumerate(self.dic.keys()):
f_csv.writerow({
'one': key[0],
'two': key[1],
'three': key[2],
'num': self.dic[key]
})
bar.bar(i, length, "Preprocessed ")
print("\nfinish write: " + self.path)
if __name__ == '__main__':
from ngram import nGram
from visualization import Progress_bar
ng = nGram()
ngg = NGramGenerator(3)
file_dir = 'raw_data'
for files in os.listdir(file_dir)[-10:]:
b = Progress_bar()
with open(os.path.join(file_dir, files), 'r', encoding='utf-8') as f:
data = f.read().split()
l = len(data)
for index, d in enumerate(data):
gs = ng.ngram(d)
ngg.generate(gs)
b.bar(index, l, "Preprocessed " + files)
print("\nfinish index: " + files)
ngg.save()
import ngram
import sys
import pickle
if (len(sys.argv) < 3):
print("Error: Expected arguments: ModuleFilename, N")
sys.exit()
moduleFilename = sys.argv[1]
n = int(sys.argv[2])
model = ngram.nGram(n)
open(moduleFilename, 'w')
model.printInfo()
ngram.saveObject(model, moduleFilename)
import unittest
from ngram import nGram
ng = nGram(n=5, corpus_file=None, cache=False)
class TestNgram(unittest.TestCase):
def test_uni_log(self):
probability = ng.sentence_probability(sentence='hold your horses',
n=1,
form='log')
self.assertAlmostEqual(probability, -24.9337710989)
def test_uni_antilog(self):
probability = ng.sentence_probability(sentence='hold your horses',
n=1,
form='antilog')
self.assertAlmostEqual(probability, 1.48388689281e-11)
def test_bi_log(self):
probability = ng.sentence_probability(sentence='hold your horses',
n=2,
form='log')
self.assertAlmostEqual(probability, -18.655540764)
def test_bi_antilog(self):
probability = ng.sentence_probability(sentence='hold your horses',
n=2,
form='antilog')
self.assertAlmostEqual(probability, 7.90681521418e-09)
def test_tri_log(self):
