def objective_u( u=init_u ):
targets = { 'f':quadratic_Poisson, 'barrier':eig_barrier }
targets = kb.reparameterize(targets,UVs(NRGC))
# list_targets= kb.reparameterize({'eigsM':eigsM, 'invM':invM,
# 'logdetIM':logdetIM, 'log_detIM':log_detIM },UVs(NRGC),
# reducer=lambda r,x: r + [x], zero=[])
# targets.update( list_targets )
targets = kb.reparameterize(targets,linear_reparameterization)
return kolia_theano.Objective( init_params={'u': u }, differentiate=['f'],
mode='FAST_RUN' , **targets )
def LL_V1( v1 ):
targets = { 'f':quadratic_Poisson, 'LL':LNLEP , 'barrier':eig_barrier }
targets = kb.reparameterize(targets,UVs(N_cells[2]))
return kolia_theano.Objective( init_params={'V1': v1 }, differentiate=['f'],
mode='FAST_COMPILE' , **targets )
def objective_V1u( v1 = V1 ):
targets = { 'f':quadratic_Poisson, 'barrier':eig_barrier , 'LL':LNLEP }
targets = kb.reparameterize(targets,UVs(NRGC))
targets = kb.reparameterize(targets,linear_reparameterization)
return kolia_theano.Objective( init_params={'V1': v1 }, differentiate=['f'],
mode='FAST_RUN' , **targets )
def objective_V1( v1 = np.zeros( (NRGC,Ncones) ) ):
targets = { 'f':quadratic_Poisson, 'barrier':eig_barrier , 'LL':LNLEP }
targets = kb.reparameterize(targets,UVs(NRGC))
return kolia_theano.Objective( init_params={'V1': v1 }, differentiate=['f'],
mode='FAST_RUN' , **targets )
def objective_V1( v1 = R['model']['V'] ):
targets = { 'f':quadratic_Poisson, 'barrier':eig_barrier }
targets = kb.reparameterize(targets,UVs(NRGC))
return kolia_theano.Objective( init_params={'V1': v1 }, differentiate=['f'],
mode='FAST_COMPILE' , **targets )
