def test_seq_pred(self,N_test,d,verb=True):
from TASKS.task_seq_prediction import construct_test_trial, get_dictionary
zero = np.zeros((1,self.S))
corr = 0.0
dic_stim, dic_resp = get_dictionary(d)
for tr in np.arange(N_test):
if verb:
print('TRIAL N.',tr+1,':\t', end="")
#self.reset_memory()
#self.reset_tags()
S, O = construct_test_trial(d,0.5)
o_print = dic_resp[np.argmax(O)]
s_old = zero
self.epsilon = 0
self.beta = 0
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,_ = self.compute_response(Q,'greedy')
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="-")
q_old = q
if i==np.shape(S)[0]-1:
if verb:
print(dic_resp[resp_ind],'\n')
if dic_resp[resp_ind]==o_print:
corr += 1
perc = 100*float(corr/N_test)
return perc
from TASKS.task_seq_prediction import subset_construction, get_dictionary
task = 'seq_prediction'
N_train = 5000
d = 5
## build training dataset
#S_train, O_train = subset_construction(N_train, d)
## or load it
S_train = np.loadtxt('DATA/seq_pred_dataset_d5_t5000_inp.txt')
O_train = np.loadtxt('DATA/seq_pred_dataset_d5_t5000_out.txt')
S_train = np.reshape(S_train, (N_train, -1, d + 2))
dic_stim, dic_resp = get_dictionary(d)
## CONSTRUCTION OF THE AuGMEnT NETWORK
S = d + 2 # dimension of the input = number of possible stimuli
R = 3 # dimension of the regular units
M = 8 # dimension of the memory units
A = 2 # dimension of the activity units = number of possible responses
# value parameters were taken from the
lamb = 0.2 # synaptic tag decay
beta = 0.15 # weight update coefficient
discount = 0.9 # discount rate for future rewards
alpha = 1 - lamb * discount # synaptic permanence
eps = 0.025 # percentage of softmax modality for activity selection
g = 1
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_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
M=4
mem_vec_c=[]
for i in range(M):
mem_vec_c.append('M'+str(i+1)+'-C')
mem_vec_l=[]
for i in range(M):
mem_vec_l.append('M'+str(i+1)+'-L')
mem_vec_h=[]
for i in range(int(M/2)):
mem_vec_h.append('M'+str(i+1)+'-L')
for i in range(int(M/2)):
mem_vec_h.append('M'+str(i+1)+'-C')
from TASKS.task_seq_prediction import get_dictionary
dic_stim3,_ = get_dictionary(3)
dic_stim8,_ = get_dictionary(8)
cues_vec_3 = []
values_vec = list(dic_stim3.values())
for l in values_vec:
cues_vec_3.append(l+'+')
for l in values_vec:
cues_vec_3.append(l+'-')
cues_vec_8 = []
values_vec = list(dic_stim8.values())
for l in values_vec:
cues_vec_8.append(l+'+')
for l in values_vec:
cues_vec_8.append(l+'-')
