def adjust(ap_subsim, ap_subtraj, traj_c, traj_q, opt):
if opt == 'PSS':
if ap_subtraj[1] == len(traj_c) - 1:
_, each_step_f_q = submit(m0, traj_q)
Q = each_step_f_q[0,-1,:]
suffix_h, _ = submit(m0, traj_c[ap_subtraj[0]:])
return np.linalg.norm(Q - suffix_h[-1,0,:]), ap_subtraj
return ap_subsim, ap_subtraj
def step(self, episode, action, index, label='E'):
if action == 0: #non-split
#state transfer
self.h0, _ = submit(m0, self.cand_train_data[episode][index:index + 1], self.h0)
self.presim = np.linalg.norm(self.query_state_data[-1] - self.h0[-1])
# self.sufsim = np.linalg.norm(self.query_state_back_data[0, -1] - self.cand_state_back_data[0, self.length - index - 2])
#print(self.sufsim)
observation = np.array([self.subsim, self.presim]).reshape(1,-1)#
# if self.sufsim < self.subsim:
# self.subsim = self.sufsim
# self.subtraj = [index + 1, self.length - 1]
if self.presim < self.subsim:
self.subsim = self.presim
self.subtraj = [self.split_point, index]
if label == 'T':
last_subsim = self.subsim_real
#if self.subtraj[1] == self.length - 1 and self.subtraj[0] < self.length: #reward may record error
self.subsim_real = min(last_subsim, self.presim)#, np.linalg.norm(self.query_state_data[-1] - self.cand_state_forw_data[-1, self.subtraj[0]])
# else:
# self.subsim_real = self.subsim
#print('0 Rw change')
self.RW = last_subsim - self.subsim_real
return observation, self.RW
#return np.concatenate((self.query_state_data[-1].numpy().reshape(1, -1), self.h0[-1].numpy().reshape(1, -1), self.cand_state_back_data[0, self.length - index - 2].numpy().reshape(1, -1)), 1), self.RW
if action == 1: #split
self.split_point = index
self.h0 = None
self.h0, _ = submit(m0, self.cand_train_data[episode][index:index + 1], self.h0)
#state transfer
self.presim = np.linalg.norm(self.query_state_data[-1] - self.h0[-1])
# self.sufsim = np.linalg.norm(self.query_state_back_data[0, -1] - self.cand_state_back_data[0, self.length - index - 2])
#print(self.sufsim)
observation = np.array([self.subsim, self.presim]).reshape(1,-1)
# if self.sufsim < self.subsim:
# self.subsim = self.sufsim
# self.subtraj = [index + 1, self.length - 1]
if self.presim < self.subsim:
self.subsim = self.presim
self.subtraj = [self.split_point, index]
if label == 'T':
last_subsim = self.subsim_real
#if self.subtraj[1] == self.length - 1 and self.subtraj[0] < self.length: #reward may record error
self.subsim_real = min(last_subsim, self.presim)#, np.linalg.norm(self.query_state_data[-1] - self.cand_state_forw_data[-1, self.subtraj[0]])
#print('1 Rw change')
# else:
# self.subsim_real = self.subsim
self.RW = last_subsim - self.subsim_real
return observation, self.RW
def ExactS(traj_c, traj_q):
_, each_step_f_q = submit(m0, traj_q)
Q = each_step_f_q[0,-1,:]
subsim = 999999
subtraj = [0, len(traj_c)-1]
subset = {}
for i in range(len(traj_c)):
_, each_step_f_c = submit(m0, traj_c[i:])
for j in range(each_step_f_c.size(1)):
#print('sub-range:', [i, i+j])
temp = np.linalg.norm(Q - each_step_f_c[0,j,:])
subset[(i, i+j)] = temp
if temp < subsim:
subsim = temp
subtraj = [i, i+j]
return subsim, subtraj, subset
def SizeS(traj_c, traj_q, par=5):
L = len(traj_q)
L_lo = min(len(traj_c), int((L - par)))
L_up = min(len(traj_c), int((L + par)))
#print('Lower upper', [L_lo, L_up])
_, each_step_f_q = submit(m0, traj_q)
Q = each_step_f_q[0,-1,:]
subsim = 999999
subtraj = [0, len(traj_c)-1]
for i in range(len(traj_c)):
_, each_step_f_c = submit(m0, traj_c[i:i+L_up])
for j in range(each_step_f_c.size(1)):
#print('sub-range:', [i, i+j])
if (j + 1) < L_lo:
continue
temp = np.linalg.norm(Q - each_step_f_c[0,j,:])
if temp < subsim:
subsim = temp
subtraj = [i, i+j]
return subsim, subtraj
def reset(self, episode, label='E'):
# prefix_state --> [split_point, index]
# suffix_state --> [index + 1, len - 1]
# return observation
self.query_state_data, _ = submit(m0, self.query_train_data[episode])
#_, self.query_state_back_data = submit(m0, self.query_train_data[episode][::-1])
#_, self.cand_state_back_data = submit(m0, self.cand_train_data[episode][::-1])
self.split_point = 0
self.h0 = None
self.h0, _ = submit(m0, self.cand_train_data[episode][0:1], self.h0)
self.length = len(self.cand_train_data[episode])
#whole = np.linalg.norm(self.query_state_back_data[0, -1] - self.cand_state_back_data[0, -1])
self.presim = np.linalg.norm(self.query_state_data[-1] - self.h0[-1])
#self.sufsim = np.linalg.norm(self.query_state_back_data[0, -1] - self.cand_state_back_data[0, self.length - 2])
observation = np.array([self.presim, self.presim]).reshape(1,-1)
self.subsim = min(self.presim, self.presim)
#print('episode', episode, whole, self.presim, self.sufsim)
# if self.subsim == whole:
# self.subtraj = [0, self.length - 1]
if self.subsim == self.presim:
self.subtraj = [0, 0]
# if self.subsim == self.sufsim:
# self.subtraj = [1, self.length - 1]
if label == 'T':
#t = generate_suffix(self.cand_train_data[episode])
#self.cand_state_forw_data, _ = submit(m0, t)
#whole_real = np.linalg.norm(self.query_state_data[-1] - self.cand_state_forw_data[-1, 0])
#suffix_real = np.linalg.norm(self.query_state_data[-1] - self.cand_state_forw_data[-1, 1])
self.subsim_real = min(self.presim, self.presim)#, suffix_real
return observation, self.length
def reset(self, episode, label='E'):
# prefix_state --> [split_point, index]
# suffix_state --> [index + 1, len - 1]
# return observation
self.query_state_data, _ = submit(m0, self.query_train_data[episode])
self.split_point = 0
self.h0 = None
self.h0, _ = submit(m0, self.cand_train_data[episode][0:1], self.h0)
self.length = len(self.cand_train_data[episode])
self.presim = np.linalg.norm(self.query_state_data[-1] - self.h0[-1])
observation = np.array([self.presim, self.presim]).reshape(1, -1)
self.subsim = min(self.presim, self.presim)
if self.subsim == self.presim:
self.subtraj = [0, 0]
if label == 'T':
self.subsim_real = min(self.presim, self.presim)
self.h0_real = self.h0
return observation, self.length, -1
def output(self, index, episode, label='E'):
# if self.subtraj[1] == self.length - 1 and index == self.length - 1:
# suffix_h, _ = submit(m0, self.cand_train_data[episode][self.subtraj[0]:])
# self.subsim = np.linalg.norm(self.query_state_data[-1] - suffix_h[-1,0,:])
if label == 'T':
print('check', self.subsim, self.subtraj, self.subsim_real)
if label == 'E':
hidden, _ = submit(
m0,
self.cand_train_data[episode][self.subtraj[0]:self.subtraj[1] +
1], None)
self.subsim_real = np.linalg.norm(self.query_state_data[-1] -
hidden[-1])
return [self.subsim_real, self.subtraj]
def heuristic(traj_c, traj_q, opt, delay_K=5):
delay = 0
_, each_step_f_q = submit(m0, traj_q)
Q = each_step_f_q[0,-1,:]
invQ = -1
each_step_b_c = -1
if opt == 'PSS':
_, each_step_b_q = submit(m1, traj_q[::-1])
invQ = each_step_b_q[0,-1,:]
_, each_step_b_c = submit(m1, traj_c[::-1])
subsim = 999999
subtraj = [0, len(traj_c)-1]
split_point = 0
h0 = None
pos_d_coll = []
pos_d_f = False
temp = 'non'
if opt != 'POS-D':
for i in range(len(traj_c)):
#submit prefix
h0, _ = submit(m0, traj_c[i:i+1], h0)
presim = np.linalg.norm(Q - h0[-1,0,:])
sufsim = heuristic_suffix_opt(invQ, Q, traj_c, i+1, opt, each_step_b_c)
#print('-> maintain:', subtraj, subsim)
#print('prefix:', [split_point, i], presim)
#print('suffix:', [i+1, len(traj_c)-1], sufsim)S
if presim < subsim or sufsim < subsim:
temp = i + 1
subsim = min(presim, sufsim)
if presim < sufsim:
subtraj = [split_point, (temp-1)]
else:
subtraj = [temp, len(traj_c)-1]
split_point = temp
h0 = None
else:
i = -1
while True:
i = i + 1
if temp != 'non':
i = temp
temp = 'non'
if i == len(traj_c) - 1:
break
#submit prefix
h0, _ = submit(m0, traj_c[i:i+1], h0)
presim = np.linalg.norm(Q - h0[-1,0,:])
if pos_d_f == False and presim < subsim: #open delay
pos_d_f = True
if pos_d_f == True and delay < delay_K:
delay = delay + 1
pos_d_coll.append((presim, i))
continue
if pos_d_f == True and delay == delay_K:
sort = sorted(pos_d_coll, key=lambda d: d[0])
temp = sort[0][1] + 1
subsim = sort[0][0]
subtraj = [split_point, (temp-1)]
split_point = temp
h0 = None
delay = 0
pos_d_f = False
pos_d_coll = []
if subsim == 999999: #for extreme cases
if pos_d_coll == []:
h0, _ = submit(m0, traj_c[i:i+1], h0)
presim = np.linalg.norm(Q - h0[-1,0,:])
pos_d_coll.append((presim, i))
sort = sorted(pos_d_coll, key=lambda d: d[0])
temp = sort[0][1] + 1
subsim = sort[0][0]
subtraj = [split_point, (temp-1)]
return subsim, subtraj
def step(self, episode, action, index, label='E'):
if action == 0: #non-split
if index == self.length - 1:
done = True
else:
done = False
#state transfer
self.h0, _ = submit(m0,
self.cand_train_data[episode][index:index + 1],
self.h0)
self.presim = np.linalg.norm(self.query_state_data[-1] -
self.h0[-1])
observation = np.array([self.subsim, self.presim]).reshape(1, -1)
if self.presim < self.subsim:
self.subsim = self.presim
self.subtraj = [self.split_point, index]
if label == 'T':
self.h0_real, _ = submit(
m0, self.cand_train_data[episode][index:index + 1],
self.h0_real)
self.presim_real = np.linalg.norm(self.query_state_data[-1] -
self.h0_real[-1])
last_subsim = self.subsim_real
self.subsim_real = min(last_subsim, self.presim_real)
self.RW = last_subsim - self.subsim_real
return observation, self.RW, done, -1
if action == 1: #split
if index == self.length - 1:
done = True
else:
done = False
self.split_point = index
self.h0 = None
self.h0, _ = submit(m0,
self.cand_train_data[episode][index:index + 1],
self.h0)
#state transfer
self.presim = np.linalg.norm(self.query_state_data[-1] -
self.h0[-1])
observation = np.array([self.subsim, self.presim]).reshape(1, -1)
if self.presim < self.subsim:
self.subsim = self.presim
self.subtraj = [self.split_point, index]
if label == 'T':
self.h0_real = None
self.h0_real, _ = submit(
m0, self.cand_train_data[episode][index:index + 1],
self.h0_real)
self.presim_real = np.linalg.norm(self.query_state_data[-1] -
self.h0_real[-1])
last_subsim = self.subsim_real
self.subsim_real = min(last_subsim, self.presim_real)
self.RW = last_subsim - self.subsim_real
return observation, self.RW, done, -1
if action > 1: #skipping
#state transfer
INX = min(index + action - 1, self.length - 1)
if INX == self.length - 1:
done = True
else:
done = False
self.h0, _ = submit(m0, self.cand_train_data[episode][INX:INX + 1],
self.h0)
self.presim = np.linalg.norm(self.query_state_data[-1] -
self.h0[-1])
observation = np.array([self.subsim, self.presim]).reshape(1, -1)
if self.presim < self.subsim:
self.subsim = self.presim
self.subtraj = [self.split_point, INX]
if label == 'T':
self.h0_real, _ = submit(
m0, self.cand_train_data[episode][index:INX + 1],
self.h0_real)
self.presim_real = np.linalg.norm(self.query_state_data[-1] -
self.h0_real[-1])
last_subsim = self.subsim_real
self.subsim_real = min(last_subsim, self.presim_real)
self.RW = last_subsim - self.subsim_real
return observation, self.RW, done, INX