本文整理汇总了Python中corpus.Corpus.get_feature_name方法的典型用法代码示例。如果您正苦于以下问题：Python Corpus.get_feature_name方法的具体用法？Python Corpus.get_feature_name怎么用？Python Corpus.get_feature_name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corpus.Corpus的用法示例。
在下文中一共展示了Corpus.get_feature_name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: ActivePipeline
# 需要导入模块: from corpus import Corpus [as 别名]
# 或者: from corpus.Corpus import get_feature_name [as 别名]
#.........这里部分代码省略.........
get_labeled_features: A function that receives a class and a list
of features. It must return a list of features associated with the
class. Can return None in case of error.
max_iterations: Optional. An interger. The cicle will execute at
most max_iterations times if the user does not enter stop before.
Returns:
The number of features the user has labeled.
"""
result = 0
while not max_iterations or result < max_iterations:
class_name = get_class(self.get_class_options())
if not class_name:
continue
if class_name == 'stop':
break
if class_name == 'train':
self._train()
self._expectation_maximization()
continue
class_number = self.classes.index(class_name)
feature_numbers = self.get_next_features(class_number)
e_prediction = []
prediction = []
if self.emulate:
e_prediction = [f for f in feature_numbers
if self.feature_corpus[class_number][f] == 1]
feature_numbers = [f for f in feature_numbers
if f not in e_prediction]
print "Adding {0} features from corpus for class {1}".format(
len(e_prediction), class_name
)
if feature_numbers:
feature_names = [self.training_corpus.get_feature_name(pos)
for pos in feature_numbers]
prediction = get_labeled_features(class_name, feature_names)
if prediction == None and not e_prediction:
continue
if prediction == 'stop':
break
if prediction == 'train':
self._train()
self._expectation_maximization()
continue
prediction = [feature_numbers[feature_names.index(f)]
for f in prediction]
self.handle_feature_prediction(class_number,
feature_numbers + e_prediction,
prediction + e_prediction)
result += len(prediction + e_prediction)
return result
def handle_feature_prediction(self, class_number, full_set, prediction):
"""Adds the new information from prediction to user_features.
Args:
class_number: an interger. The position of the class in self.classes
full_set: a list of positions of features that was given to the
user.
prediction: a list of positions of features selected for the class.
The features not present in this class are considered as negative
examples.
"""
for feature in full_set:
if feature in prediction:
self.user_features[class_number][feature] += \