def applyKalmanFilter(self):
"""
Apply KalmanFilter for each 2nd level grid cell
Note: If Kalman filter is NOT applied, the naive approach is already applied for 2nd time instances.
:return:
"""
# init Kalman filter
kf = KalmanFilterPID(self.param)
for x1 in range(len(self.root.children)):
for y1 in range(len(self.root.children)):
child_l1 = self.root.children[x1][y1]
if child_l1.n_isLeaf or child_l1.children is None:
seq_l1 = child_l1.a_count
if self.sampling:
seq_l1_filtered = kf.kalmanFilter(seq_l1, self.budgetKF, self.param.samplingRate)
else:
seq_l1_filtered = kf.kalmanFilter(seq_l1, self.budgetKF)
child_l1.l_count = seq_l1_filtered
else:
for x2 in range(len(child_l1.children)):
for y2 in range(len(child_l1.children)):
seq_l2 = self.root.children[x1][y1].children[x2][y2].a_count
if self.sampling:
seq_l2_filtered = kf.kalmanFilter(seq_l2, self.budgetKF, self.param.samplingRate)
else:
seq_l2_filtered = kf.kalmanFilter(seq_l2, self.budgetKF)
self.root.children[x1][y1].children[x2][y2].l_count = seq_l2_filtered
def applyKalmanFilter(self):
"""
Apply KalmanFilter for each 2nd level grid cell
Note: If Kalman filter is NOT applied, the naive approach is already applied for 2nd time instances.
:return:
"""
# init Kalman filter
kf = KalmanFilterPID(self.param)
for x1 in range(len(self.AGs[0].root.children)):
for y1 in range(len(self.AGs[0].root.children)):
child_l1 = self.AGs[0].root.children[x1][y1]
if child_l1.n_isLeaf or child_l1.children is None:
seq_l1 = [self.getCountAt(True, i, x1, y1) for i in range(self.instances)]
if self.sampling:
seq_l1_filtered = kf.kalmanFilter(seq_l1, self.budgetKF, self.param.samplingRate)
else:
seq_l1_filtered = kf.kalmanFilter(seq_l1, self.budgetKF)
[self.updateCountAt(seq_l1_filtered[i], i, x1, y1) for i in range(self.instances)]
del seq_l1
del seq_l1_filtered
else:
for x2 in range(len(child_l1.children)):
for y2 in range(len(child_l1.children)):
# child_l2 = child_l1.children[x2][y2]
seq_l2 = [self.getCountAt(True, i, x1, y1, x2, y2) for i in range(self.instances)]
if self.sampling:
seq_l2_filtered = kf.kalmanFilter(seq_l2, self.budgetKF, self.param.samplingRate)
else:
seq_l2_filtered = kf.kalmanFilter(seq_l2, self.budgetKF)
[self.updateCountAt(seq_l2_filtered[i], i, x1, y1, x2, y2) for i in range(self.instances)]
del seq_l2
del seq_l2_filtered
del kf
def applyKalmanFilter(self):
"""
Apply KalmanFilter for each 2nd level grid cell
Note: If Kalman filter is NOT applied, the naive approach is already applied for 2nd time instances.
:return:
"""
# init Kalman filter
kf = KalmanFilterPID(self.param)
for x1 in range(len(self.AGs[0].root.children)):
for y1 in range(len(self.AGs[0].root.children)):
child_l1 = self.AGs[0].root.children[x1][y1]
if child_l1.n_isLeaf or child_l1.children is None:
seq_l1 = [
self.getCountAt(True, i, x1, y1)
for i in range(self.instances)
]
if self.sampling:
seq_l1_filtered = kf.kalmanFilter(
seq_l1, self.budgetKF, self.param.samplingRate)
else:
seq_l1_filtered = kf.kalmanFilter(
seq_l1, self.budgetKF)
[
self.updateCountAt(seq_l1_filtered[i], i, x1, y1)
for i in range(self.instances)
]
del seq_l1
del seq_l1_filtered
else:
for x2 in range(len(child_l1.children)):
for y2 in range(len(child_l1.children)):
# child_l2 = child_l1.children[x2][y2]
seq_l2 = [
self.getCountAt(True, i, x1, y1, x2, y2)
for i in range(self.instances)
]
if self.sampling:
seq_l2_filtered = kf.kalmanFilter(
seq_l2, self.budgetKF,
self.param.samplingRate)
else:
seq_l2_filtered = kf.kalmanFilter(
seq_l2, self.budgetKF)
[
self.updateCountAt(seq_l2_filtered[i], i, x1,
y1, x2, y2)
for i in range(self.instances)
]
del seq_l2
del seq_l2_filtered
del kf
def applyKalmanFilter(self):
"""
Apply KalmanFilter for each 2nd level grid cell
Note: If Kalman filter is NOT applied, the naive approach is already applied for 2nd time instances.
:return:
"""
# init Kalman filter
kf = KalmanFilterPID(self.param)
for x1 in range(len(self.root.children)):
for y1 in range(len(self.root.children)):
child_l1 = self.root.children[x1][y1]
if child_l1.n_isLeaf or child_l1.children is None:
seq_l1 = child_l1.a_count
if self.sampling:
seq_l1_filtered = kf.kalmanFilter(
seq_l1, self.budgetKF, self.param.samplingRate)
else:
seq_l1_filtered = kf.kalmanFilter(
seq_l1, self.budgetKF)
child_l1.l_count = seq_l1_filtered
else:
for x2 in range(len(child_l1.children)):
for y2 in range(len(child_l1.children)):
seq_l2 = self.root.children[x1][y1].children[x2][
y2].a_count
if self.sampling:
seq_l2_filtered = kf.kalmanFilter(
seq_l2, self.budgetKF,
self.param.samplingRate)
else:
seq_l2_filtered = kf.kalmanFilter(
seq_l2, self.budgetKF)
self.root.children[x1][y1].children[x2][
y2].l_count = seq_l2_filtered
error = 0
for i in range(len(publish)):
if orig[i] < 0:
error += distance(publish[i], orig[i])
elif orig[i] == 0:
error += distance(publish[i], 1)
else:
error += distance(max(publish[i], 0), orig[i])
return error / len(publish)
for q in q_list:
for i in range(len(eps_list)):
with open("../log/foursquare/true_count_KF_" + str(eps_list[i]) + ".log") as f:
rel_errs = []
for line in f.readlines():
orig = map(float, line.strip().split("\t"))
p = Params(1000)
p.Eps = eps_list[i]
kf = KalmanFilterPID(p)
kf.setQ(q)
budgetKF = budgetKF = eps_list[i] / 2
# filter = kf.kalmanFilter(seq, budgetKF, p.samplingRate)
publish = kf.kalmanFilter(orig, budgetKF)
rel_err = getRelError(publish, orig)
rel_errs.append(rel_err)
print q, "\t", eps_list[i], "\t", sum(rel_errs) / len(rel_errs)
