def insertAnomaly()

in aiops/AnomalyDetection/utils/utils.py [0:0]

• 31 lines of code
• 10 McCabe index (conditional complexity)

def insertAnomaly(datas,anomalyRatio,slowNumRatio,slowTimeRange,labels,exeTime,edges):
    #labels=np.zeros((datas.shape[0],datas.shape[1]))
    exeTime=exeTime[:-1]
    edges=edges[1:]
    #exeTime=np.array([5,15,25,35,55,90,130,170,210,255,305,355,405])
    #edges=[10,20,30,40,70,110,150,190,230,280,330,380,430]
    for i,data in enumerate(datas):
        length,interval=data.shape
        anomalyNum=math.ceil(length*anomalyRatio)
        positions=[random.randint(0,length-1) for i in range(anomalyNum)]
        for position in positions:
            slowNum=np.random.random(interval-1)
            slowNum[-1]=0
            slowNum/=slowNum.sum()
            slowNum*=slowNumRatio
            slowTime=np.random.randint(slowTimeRange[0],slowTimeRange[1],size=interval-1)
            presTime=exeTime+slowTime
            slowDur = random.randint(2, 5)
            for k in range(min(slowDur,length-position)):
                flag = True
                for j in range(interval - 1):
                    transfer=min(datas[i,position+k,j],slowNum[j])
                    datas[i,position + k, j] = max(datas[i,position + k, j] - slowNum[j], 0)
                    target = 0
                    if transfer>0:
                        flag=False
                    for edge in edges:
                        if presTime[j] < edge:
                            break
                        target += 1
                    datas[i,position + k, target] += transfer
                if not flag:
                    labels[i,position+k] = 1.
    return datas,labels