基于高斯分布的异常检测代码实现

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于 2018-07-24 11:24:43 发布 · 3.1k 阅读

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CC 4.0 BY-SA版权

本文详细介绍了如何使用高斯分布进行异常检测。通过理解数据的均值和方差，构建高斯模型，识别偏离正常模式的观测值，从而实现异常检测。代码实现部分包含关键步骤的解释，帮助读者掌握这一技术。

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import matplotlib.pyplot as plt
import numpy as np
import csv
from numpy import genfromtxt
from scipy.stats import multivariate_normal
from sklearn.metrics import f1_score

#画图设置
plt.style.use('ggplot')

plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Ubuntu'
plt.rcParams['font.monospace'] = 'Ubuntu Mono'
plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 11
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.titlesize'] = 12
plt.rcParams['xtick.labelsize'] = 9
plt.rcParams['ytick.labelsize'] = 9
plt.rcParams['legend.fontsize'] = 11
plt.rcParams['figure.titlesize'] = 13



#读取文件
reader = csv.reader(open("train_server_data.csv", "r"), delimiter=",")
reader1 = csv.reader(open("crossval_server_data.csv", "r"), delimiter=",")
reader2 = csv.reader(open("test_server_data.csv", "r"), delimiter=",")
#转为list格式
tr = list(reader)
cv = list(reader1)
ts = list(reader2)
#得到训练，交叉，测试数据
train_data = np.arra