def LNKS_fobj(theta, stim, y, options):
'''
LNKS Objective function
-----------------------
This is an objective function for the LNK, LNKS, and LNKS_MP models.
Calls LNKS_fobj_helper function, and numel_gradient function(objectivetools),
if is_grad option is True. The numel_gradient function uses multiprocessing
, a built-in python module, to make the gradient computing process parallel.
Returns objective value(J) and gradient(grad).
If is_grad option is False, only returns objective value.
Inputs
------
theta: model parameters
stim: input data
y: output data (fr)
options (dictionary):
model (string): models are ('LNK', 'LNKS', 'LNKS_MP')
pathway (int): LNK pathway (1 or 2)
is_grad (bool): bool (gradient on(True) or off(False))
Outputs
-------
J: objective value
grad: gradient of objective
'''
basis = LinearFilterBasis_8param()
nzstim = stim - _np.mean(stim) # nzstim: mean subtracted stimulus
options['basis'] = basis
J0 = LNKS_fobj_helper(LNKS, theta, nzstim, y, options) # evaluate function value at original point
if options['is_grad']:
grad = _obj.numel_gradient(LNKS_fobj_helper, LNKS, theta, nzstim, y, J0, options)
return J0, grad
else:
return J0
