def normalize(self, values):
corr_vector=values[0]
norm_pop=values[1]
norm_target=values[2]
corr_vector[nonzero(self.spikecount.count==0)] = -inf
#print self.corr_vector/sqrt(norm_pop)/sqrt(norm_target)
return self.corr_vector/sqrt(norm_pop)/sqrt(norm_target)
def inject_input(self):
# Injects current in consecutive subgroups, where I_offset have the same value
# on successive intervals
I_offset = self.inputs_offset
k = -1
for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)):
I_offset_subgroup_value = I_offset[i]
I_offset_subgroup_length = i - k
sliced_subgroup = self.group.subgroup(I_offset_subgroup_length)
input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps]
sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock))
k = i
def normalize(self, distance_vector):
distance_vector[nonzero(self.spikecount.count==0)] = inf
return -self.distance_vector*self.group.clock.dt
