视频监控异常检测 python_Python实现异常检测AnomalyDetection

importnumpy as npimportpandas as pdimportmatplotlib.pyplot as pltimportscipy.io as spiodefdisplay_2d_data(X,marker):#plt.figure(figsize=(10,8))

plt.plot(X[:,0],X[:,1],marker)returnpltdef estimateGaussian(X): #求均值与方差

m,n =X.shape

mu= np.zeros((n,1))

sigma2= np.zeros((n,1))

mu= np.mean(X,axis =0)

sigma2= np.var(X,axis = 0) * (m-1) /m#在概率论中计算sigma2时，除以的是(1-m)，机器学习中，除以(1-m)和除以m的差别不大

returnmu,sigma2defmultivariateGaussian(X,mu,Sigma2):

k=len(mu)if (Sigma2.shape[0]>1):

Sigma2=np.diag(Sigma2)'''多元高斯分布函数'''X= X-mu

argu= (2*np.pi)**(-k/2)*np.linalg.det(Sigma2)**(-0.5)

p= argu*np.exp(-0.5*np.sum(np.dot(X,np.linalg.inv(Sigma2))*X,axis=1))#axis表示每行

returnpdefvisualizeFit(X,mu,sigma2):

x= np.arange(0, 36, 0.5) #0-36，步长0.5

y = np.arange(0, 36, 0.5)

X1,X2= np.meshgrid(x,y) #要画等高线，所以meshgird

Z = multivariateGaussian(np.hstack((X1.reshape(-1,1),X2.reshape(-1,1))), mu, sigma2) #计算对应的高斯分布函数

Z = Z.reshape(X1.shape) #调整形状

plt.figure(figsize=(10,8))

plt.plot(X[:,0],X[:,1],'bx')if np.sum(np.isinf(Z).astype(float)) == 0: #如果计算的为无穷，就不用画了

#plt.contourf(X1,X2,Z,10.**np.arange(-20, 0, 3),linewidth=.5)

CS = plt.contour(X1,X2,Z,10.**np.arange(-20, 0, 3),color='black',linewidth=.5)#画等高线，Z的值在10.**np.arange(-20, 0, 3)

#plt.clabel(CS)

plt.show()#选择最优的epsilon，即：使F1Score最大

defselectThreshold(yval,pval):'''初始化所需变量'''bestEpsilon=0.

bestF1=0.

F1=0.

step= (np.max(pval)-np.min(pval))/1000

'''计算'''

for epsilon innp.arange(np.min(pval),np.max(pval),step):

cvPrecision= pval

tp= np.sum((cvPrecision == 1) & (yval == 1).ravel()).astype(float) #sum求和是int型的，需要转为float

fp = np.sum((cvPrecision == 1) & (yval ==0).ravel()).astype(float)

fn= np.sum((cvPrecision == 0) & (yval == 1).ravel()).astype(float)

precision= tp/(tp+fp) #精准度

recision = tp/(tp+fn) #召回率

F1 = (2*precision*recision)/(precision+recision) #F1Score计算公式

if F1 > bestF1: #修改最优的F1 Score

bestF1 =F1

bestEpsilon=epsilonreturnbestEpsilon,bestF1defAnomalyDetection2():

data= spio.loadmat("data2.mat")

X= data['X']

Xval= data['Xval']

yval= data['yval']#print(pd.DataFrame(X))

mu,sigma2 =estimateGaussian(X)#print(mu,sigma2)

p =multivariateGaussian(X,mu,sigma2)#print(pd.DataFrame(p))

pval =multivariateGaussian(Xval,mu,sigma2)

epsilon,F1=selectThreshold(yval,pval)print("the best epsilon is",epsilon)print("the best F1 is",F1)print("Outliers found",np.sum(p

AnomalyDetection2()defAnomalyDetection():

data= spio.loadmat("data.mat")#print(data)

X = data['X']

Xval= data['Xval']

yval= data['yval'] #y = 1 为异常

#plt.plot(X[:,0],X[:,1],'x')

plt = display_2d_data(X,'x')

plt.title("Origin data")

plt.show()

mu,sigma2=estimateGaussian(X)#print(mu,sigma2)

p =multivariateGaussian(X,mu,sigma2)#print(p)

visualizeFit(X,mu,sigma2) #显示图像

#选择异常点

pval =multivariateGaussian(Xval,mu,sigma2)

epsilon,F1=selectThreshold(yval,pval)print(u'在CV上得到的最好的epsilon是：%e'%epsilon)print(u'对应的F1Score值为：%f'%F1)

outliers= np.where(p

#plt.figure(figsize=(10,8))

plt.plot(X[outliers,0],X[outliers,1],'o',markeredgecolor='r',markerfacecolor='w',markersize=10.)

plt= display_2d_data(X, 'bx')

plt.show()if __name__ == "__main__":

AnomalyDetection()