def next(self):
self.currfile += 1
if self.currfile > self.n_files:
raise StopIteration
else:
return (self.filelist[self.currfile - 1],
read_timeseries(
os.path.join(self.fullpath,
self.filelist[self.currfile - 1])))
def detect_SMA(path, window, threshold):
series = read_timeseries(path)
values = np.array(map(lambda x: x[1], series))
s_ma = moving_avg(values, window)
anomalies = []
times = list()
values = list()
for i in range(window + 1, len(series) - window):
dist = abs(float(s_ma[i] - series[i][1]))
#if dist >= values.ptp()*threshold:
anomalies.append((i, series[i][0], dist))
times.append(series[i][0])
values.append(dist)
filtered_anomalies = naive.get_anomalies_from_series(times, values, 3)
#filtered_anomalies = naive.get_anomalies_from_series(map(lambda x:x[1:],anomalies),3)
return filtered_anomalies
def detect_SMA(path, window, threshold):
series = read_timeseries(path)
values = np.array(map(lambda x:x[1],series))
s_ma = moving_avg(values,window)
anomalies = []
times = list()
values= list()
for i in range(window+1,len(series)-window):
dist = abs(float(s_ma[i]-series[i][1]))
#if dist >= values.ptp()*threshold:
anomalies.append((i, series[i][0], dist))
times.append(series[i][0])
values.append(dist)
filtered_anomalies = naive.get_anomalies_from_series(times, values, 3)
#filtered_anomalies = naive.get_anomalies_from_series(map(lambda x:x[1:],anomalies),3)
return filtered_anomalies
def next(self):
self.currfile+= 1
if self.currfile > self.n_files:
raise StopIteration
else:
return (self.filelist[self.currfile-1], read_timeseries(os.path.join(self.fullpath, self.filelist[self.currfile-1])))
