def __init__(self, Model):
self.input = Model.input
self.output = Model.output
self.input_test = Model.input_test
self.output_test = Model.output_test
self.gradient_ker = diff.gradient_ker(Model)
self.gradient_NL = diff.gradient_NL(Model)
self.hessian_ker = diff.hessian_ker(Model)
self.hessian_NL = diff.hessian_NL(Model)
self.paramKer = Model.paramKer
self.paramNL = Model.paramNL
self.lls = Model.lls
self.Mds = Model.Mds
self.switches = Model.switches
#self.neustd = Model.neustd
self.likelihood = diff.likelihood(Model)
self.sub_membrane_potential = diff.subMembPot(Model, 'training')
self.membrane_potential = diff.MembPot(Model)
def renormalize(self):
std = self.sub_membrane_potential.std()
self.paramKer = (1. / std) * self.paramKer
self.membrane_potential = diff.MembPot(self)
self.update()
def update(self):
self.sub_membrane_potential = diff.subMembPot(self, 'training')
self.membrane_potential = diff.MembPot(self)
self.likelihood = diff.likelihood(self)
self.gradient_ker = diff.gradient_ker(self)
self.hessian_ker = diff.hessian_ker(self)
self.gradient_NL = diff.gradient_NL(self)
self.hessian_NL = diff.hessian_NL(self)
def add_data(self, neuron): #import data from neuron
Nsteps = neuron.total_time / self.dt
self.input = neuron.input
self.output = [neuron.output]
self.paramKer[-1] = -math.log(
len(neuron.output) /
float(neuron.total_time)) #initialize for fit.
#self.neustd = neuron.sub_memb_pot.std()
self.sub_membrane_potential = diff.subMembPot(self, 'training')
self.membrane_potential = diff.MembPot(self)
self.input_test = neuron.input_test
self.output_test = neuron.output_test
def membpot(self):
self.sub_membrane_potential = diff.subMembPot(self, 'training')
self.membrane_potential = diff.MembPot(self)