本文整理汇总了Python中classifier.Classifier.signal_end_of_training方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.signal_end_of_training方法的具体用法？Python Classifier.signal_end_of_training怎么用？Python Classifier.signal_end_of_training使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类classifier.Classifier的用法示例。
在下文中一共展示了Classifier.signal_end_of_training方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Classifier
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import signal_end_of_training [as 别名]
c = Classifier()
# Train
train_file = TRAINING_FILE
validate_file = VALIDATE_FILE
training_files = [train_file]
for file_name in training_files:
with codecs.open(file_name, 'r', 'utf-8') as f:
for line in f:
class_name, text = line.split('\t', 1)
text = text.strip()
tokens = []
for token in token_iterator(text, TOKEN_PATTERN):
tokens.append(token)
c.train(class_name, tokens)
c.signal_end_of_training()
# Unsupervised learning
# Note that optimally this would be done in the test file
# but we do it here so the expensive process can be done once and then
# saved to be used in testing.
batch = [] # Unknown data.
with codecs.open(TEST_FILE, 'r', 'utf-8') as f:
for line in f:
class_name, text = line.split('\t', 1)
text = text.strip()
tokens = []
for token in token_iterator(text, TOKEN_PATTERN):
tokens.append(token)
batch.append(tokens)
print "Number of unsupervised learning:", len(batch)