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
