class piLearning:
def __init__(self):
self.event_queue = Queue()
def get_mean(self, itemlist):
return sum(itemlist) / len(itemlist)
def get_stdev(self, smeanlist):
grand_mean = self.get_mean(smeanlist)
stdev = 0
for x in smeanlist:
stdev += (x - grand_mean)**2
return stdev
def learn(self):
print("Learning initiated")
# calculate averages
self.moving_average(self, 10)
def moving_average(self, window_size):
if (self.event_queue.size() >= window_size):
samples = self.event_queue.consumeQ(
window_size) # assuming samples is not empty
avg = self.get_mean(samples)
self.movavgs.append(avg)
def rolling_std(self, mu, samples):
self.movstd.append(np.std(
samples,
ddof=1)) # standard deviation for samples (instead of population)
