def training_seq_pred(self,N_trial,d,stop=True,verb=True,policy='eps_greedy',stoc='soft',t_weighted=True):
from TASKS.task_seq_prediction import construct_trial, get_dictionary
zero = np.zeros((1,self.S))
corr = 0
conv_tr = 0
E = np.zeros(N_trial)
dic_stim, dic_resp = get_dictionary(d)
convergence=False
for tr in np.arange(N_trial):
if verb:
print('TRIAL N.',tr+1,':\t', end="")
#self.reset_memory()
#self.reset_tags()
S, O = construct_trial(d,0.5)
o_print = dic_resp[np.argmax(O)]
s_old = zero
for i in np.arange(np.shape(S)[0]):
s_inst = S[i:(i+1),:]
s_trans = self.define_transient(s_inst, s_old)
s_old = s_inst
s_print = dic_stim[np.argmax(s_inst)]
y_r,y_m,Q = self.feedforward(s_inst, s_trans)
resp_ind,P_vec = self.compute_response(Q,policy,stoc,t_weighted)
q = Q[0,resp_ind]
z = np.zeros(np.shape(Q))
z[0,resp_ind] = 1
r_print = self.dic_resp[resp_ind]
if verb:
print(s_print,end="-")
self.update_tags(s_inst,s_trans,y_r,y_m,z,resp_ind)
q_old = q
if i==np.shape(S)[0]-1:
if dic_resp[resp_ind]==o_print:
r = self.rew_pos
corr += 1
else:
r = self.rew_neg
E[tr] = 1
corr = 0
else:
r = 0
RPE = r - q_old # Reward Prediction Error
self.update_weights(RPE)
if verb:
print(dic_resp[resp_ind],'\t(',corr,')')
if corr>=100 and convergence==False:
conv_tr = tr
convergence=True
if stop==True:
break
if conv_tr!=0:
print('SIMULATION CONVERGED AT TRIAL ',conv_tr)
return E, conv_tr
def training_seq_pred_CPT(self,N_trial,d,stop=True,verb=True):
from TASKS.task_seq_prediction_CPT import construct_trial, get_dictionary
zero = np.zeros((1,self.S))
corr = 0
conv_tr = 0
E = np.zeros(N_trial)
dic_stim, dic_resp = get_dictionary(d)
convergence=False
for tr in np.arange(N_trial):
if verb:
print('TRIAL N.',tr+1,':\t', end="")
first = True
#self.reset_memory()
#self.reset_tags()
S, O = construct_trial(d,0.5)
s_old = zero
for i in np.arange(np.shape(S)[0]):
s_inst = S[i:(i+1),:]
s_trans = self.define_transient(s_inst, s_old)
s_old = s_inst
s_print = dic_stim[np.argmax(s_inst)]
#print(self.sTRACE)
o = O[i:(i+1),:]
o_print = dic_resp[np.argmax(o)]
y_r,y_m,Q = self.feedforward(s_inst, s_trans)
resp_ind,P_vec = self.compute_response(Q,policy,stoc,t_weighted,e_weighted)
q = Q[0,resp_ind]
z = np.zeros(np.shape(Q))
z[0,resp_ind] = 1
r_print = dic_resp[resp_ind]
if verb:
if i==0:
print(s_print,end="-")
print(r_print,end="-")
elif i==np.shape(S)[0]-1:
print(r_print,'\t(',corr,')')
else:
print(r_print,end="-")
if first!=True:
RPE = (r + self.discount*q) - q_old # Reward Prediction Error
self.update_weights(RPE)
else:
first = False
self.update_tags(s_inst,s_trans,y_r,y_m,z,resp_ind)
q_old = q
if r_print==o_print:
final = (o_print==dic_resp[self.A-2] or o_print==dic_resp[self.A-1])
r = self.positive_reward_seq_pred(final,self.A-2)
corr += 1
else:
r = self.rew_neg
E[tr] = 1
corr = 0
#print('\t',s_print," - ",r_print,' (',o_print,')','--->',r)
RPE = r - q_old # Reward Prediction Error
self.update_weights(RPE)
if corr>=100 and convergence==False:
conv_tr = tr
convergence=True
if stop==True:
break
if conv_tr!=0:
print('SIMULATION CONVERGED AT TRIAL ',conv_tr)
return E, conv_tr
def training_tXOR(self, N_trial, stop=True, verb=True):
from TASKS.task_tXOR import construct_trial
zero = np.zeros((1, self.S))
corr = 0
conv_tr = 0
E = np.zeros(N_trial)
convergence = False
for tr in np.arange(N_trial):
if verb:
print('TRIAL N.', tr + 1, ':\t', end="")
self.reset_memory()
self.reset_tags()
S, O = construct_trial()
o_print = self.dic_resp[np.argmax(O)]
s_old = zero
for i in np.arange(3):
s_inst = S[i:(i + 1), :]
s_trans = self.define_transient(s_inst, s_old)
s_old = s_inst
if i != 2:
s_print = self.dic_stim[np.argmax(s_inst)]
y_r, y_m, Q = self.feedforward(s_inst, s_trans)
resp_ind, _ = self.compute_response(Q)
q = Q[0, resp_ind]
z = np.zeros(np.shape(Q))
z[0, resp_ind] = 1
r_print = self.dic_resp[resp_ind]
if verb and i != 2:
print(s_print, end="-")
self.update_tags(s_inst, s_trans, y_r, y_m, z, resp_ind)
q_old = q
if i == 2:
if self.dic_resp[resp_ind] == o_print:
r = self.rew_pos
corr += 1
else:
r = self.rew_neg
E[tr] = 1
corr = 0
else:
r = 0
RPE = r - q_old # Reward Prediction Error
self.update_weights(RPE)
if verb:
print(self.dic_resp[resp_ind], '\t(', corr, ')')
if corr >= 100 and convergence == False:
conv_tr = tr
convergence = True
if stop == True:
break
if conv_tr != 0:
print('SIMULATION CONVERGED AT TRIAL ', conv_tr)
return E, conv_tr
