- 授权协议: MIT
- 开发语言: Python
- 操作系统: 跨平台
- 软件首页: https://github.com/muatik/naive-bayes-classifier
- 软件文档: https://github.com/muatik/naive-bayes-classifier
- 官方下载: https://github.com/muatik/naive-bayes-classifier
软件介绍
这是一个非常简单的 Python 库，实现了朴素贝叶斯分类器。
示例代码：
"""
Suppose you have some texts of news and know their categories.
You want to train a system with this pre-categorized/pre-classified
texts. So, you have better call this data your training set.
"""
from naiveBayesClassifier import tokenizer
from naiveBayesClassifier.trainer import Trainer
from naiveBayesClassifier.classifier import Classifier
newsTrainer = Trainer(tokenizer.Tokenizer(stop_words = [], signs_to_remove = ["?!#%&"]))
# You need to train the system passing each text one by one to the trainer module.
newsSet =[
{'text': 'not to eat too much is not enough to lose weight', 'category': 'health'},
{'text': 'Russia is trying to invade Ukraine', 'category': 'politics'},
{'text': 'do not neglect exercise', 'category': 'health'},
{'text': 'Syria is the main issue, Obama says', 'category': 'politics'},
{'text': 'eat to lose weight', 'category': 'health'},
{'text': 'you should not eat much', 'category': 'health'}
]
for news in newsSet:
newsTrainer.train(news['text'], news['category'])
# When you have sufficient trained data, you are almost done and can start to use
# a classifier.
newsClassifier = Classifier(newsTrainer.data, tokenizer.Tokenizer(stop_words = [], signs_to_remove = ["?!#%&"]))
# Now you have a classifier which can give a try to classifiy text of news whose
# category is unknown, yet.
unknownInstance = "Even if I eat too much, is not it possible to lose some weight"
classification = newsClassifier.classify(unknownInstance)
# the classification variable holds the possible categories sorted by
# their probablity value
print classification
