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 __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 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 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 update(self): self.membrane_potential = diff.MembPot(self) Np = int(np.size(self.paramNL)*0.5) self.NL = np.dot(self.paramNL[:Np],self.basisNL) 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 __init__(self,Model): self.input = Model.input self.output = Model.output 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.likelihood = diff.likelihood(Model) Np = int(np.size(Model.paramNL)*0.5) #number of basis functions per NL.
