def run(self,sorn):
super(Experiment_hesselmann,self).run(sorn)
c = self.params.c
sorn.simulation(c.steps_plastic)
sorn.update = False
## Generate Training and test data
# Andreea also did this
sorn.x = np.zeros(sorn.c.N_e)
sorn.y = np.zeros(sorn.c.N_i)
sorn.source = self.trainsource
# Generate Training Data
sorn.simulation(c.steps_noplastic_train)
sorn.x = np.zeros(sorn.c.N_e)
sorn.y = np.zeros(sorn.c.N_i)
# Generate new source with mixed letters
# Save old mappings
old_W_eu = sorn.W_eu.W
source = self.source.source
A_neurons = where(sorn.W_eu.W[:,source.lookup['A']]==1)[0]
B_neurons = where(sorn.W_eu.W[:,source.lookup['B']]==1)[0]
X_neurons = where(sorn.W_eu.W[:,source.lookup['X']]==1)[0]
# First generate source with len(frac_A) words of equal prob
N_words = len(c.frac_A)
letters = "CDEFGHIJKL"
Xes = (sorn.c.source.N_x*'X')
word_list = ['A%s_'%Xes,'B%s_'%Xes]
frac_A_letters = ['B']
for i in range(N_words-2):
word_list.append('%s%s_'%(letters[i],Xes))
frac_A_letters.append(letters[i])
frac_A_letters.append('A')
probs = np.ones((N_words,N_words))*(1.0/N_words)
source_new = CountingSource(word_list,probs,c.N_u_e,True,
permute_ambiguous=c.source.permute_ambiguous)
# Then take weight matrices of these guys and set them according
# to frac_A
new_W_eu = source_new.generate_connection_e(c.N_e)
new_W_eu.W *= 0
new_W_eu.W[A_neurons,source_new.lookup['A']] = 1
new_W_eu.W[B_neurons,source_new.lookup['B']] = 1
new_W_eu.W[X_neurons,source_new.lookup['X']] = 1
# from 1 to len-1 because 0 and 1 already included in fracs
for i in range(1,len(frac_A_letters)-1):
new_neurons = hstack(
(A_neurons[:int(len(A_neurons)*c.frac_A[i])],
B_neurons[int(len(A_neurons)*c.frac_A[i]):]))
new_W_eu.W[new_neurons,
source_new.lookup[frac_A_letters[i]]] = 1
# Assign the source and new Matrix to SORN
sorn.W_eu = new_W_eu
sorn.W_iu = source_new.generate_connection_i(c.N_i)
trialsource_new = TrialSource(source_new, c.wait_min_test,
c.wait_var_test, zeros(source_new.N_a),
'reset')
sorn.source = trialsource_new
sorn.simulation(c.steps_noplastic_test)
return {'source_plastic':self.source,
'source_train':self.trainsource,
'source_test':trialsource_new}