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- from sklearn.naive_bayes import GaussianNB
- from sklearn.model_selection import cross_val_score, train_test_split
- from sklearn.feature_extraction.text import CountVectorizer
- import pydotplus
- from datasets import Datasets
- # 特征提取，使用词集将操作命令向量化
- def get_feature(cmd, fdist):
- feature = []
- for block in cmd:
- v = [0] * len(fdist)
- for i in range(0, len(fdist)):
- if fdist[i] in block:
- v[i] += 1
- feature.append(v)
- return feature
- def main():
- data, y, fdist = Datasets.load_Schonlau('User3')
- # 特征提取
- x = get_feature(data, fdist)
- x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
- # 朴素贝叶斯
- gnb = GaussianNB()
- gnb.fit(x_train, y_train)
- print(gnb.score(x_test, y_test)) # 1.0
- # 交叉验证
- scores = cross_val_score(gnb, x, y, cv=10, scoring="accuracy")
- print(scores.mean()) # 0.9933333333333334
- if __name__ == "__main__":
- main()
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