def forward_LQLEP( stimulus, all_spikes, model, indices, vardict=u2c_parameterization()):
print
print 'Preparing forward LQLEP model.'
sys.stdout.flush()
vardict = Poisson_LL( **RGC_LE( **subunit_LQ( **vardict)))
variables = ['stimulus','all_spikes','u','V2','sv1','T','c','uc']
knowns = model
knowns.update({'T':retina.place_cells( cones, cones, shapes )})
unknowns = {'stimulus':stimulus, 'all_spikes':all_spikes}
outputs = {'LL': vardict['loglikelihood'], 'rates': vardict['rgc_out']}
# 'theta': vardict['theta']}
NRGC = len( all_spikes )
return make_global_objective(unknowns,knowns,vardict,variables,outputs,indices,NRGC)
def optimize_LQLEP( rgc_type, filename=None, maxiter=maxiter, indices=None, description='',
unknowns=['sv1','V2','u','uc','c','ud','d'],
vardict = LQLEP_wBarrier( **LQLEP( **thetaM( **u2c_parameterization())))):
# unknowns = {'sv1':sv1, 'V2':init_V2 , 'u':old['u'], 'uc':old['u'], 'c':init_V2}
defaults = extract( { 'sv1':init_sv1( rgc_type ), 'V2':init_V2 , 'u':init_u,
'uc':numpy.zeros_like(init_u), 'c':init_V2,
'ud':0.001*numpy.ones_like(init_u), 'd':0.0001+init_V2},
list( set(unknowns).intersection( set(vardict.keys()) ) ) + ['sv1'])
if filename is not None:
print 'Re-optimizing',filename
unknowns = kb.load(filename)
for name in unknowns.keys():
if not defaults.has_key(name): del unknowns[name]
default(unknowns,defaults)
else:
unknowns = defaults
# if rgc_type[:3] == 'off':
# unknowns['u'] = -0.01*numpy.abs(unknowns['u'])
if vardict.has_key('barrier_positiveV1'):
unknowns['sv1'] = numpy.abs(unknowns['sv1'])
else:
unknowns['sv1'] = unknowns['sv1']*0.01
train_LQLEP = global_objective( unknowns, {}, vardict, run=linear_stats(rgc_type,( 5,0)), indices=indices)
test_LQLEP = global_objective( unknowns, {}, vardict, run=linear_stats(rgc_type,(-5,0)), indices=indices)
test_LQLEP.description = 'Test_LQLEP'
train_LQLEP.with_callback(partial(callback,other=
{'Test_LNP':test_LNP(rgc_type)['LL'], 'nonlinearity':test_LQLEP.nonlinearity},
objectives=[test_LQLEP]))
train_LQLEP.description = description+rgc_type
unknowns['V2'] = unknowns['V2']*0.001
trained = optimize_objective( train_LQLEP, unknowns, gtol=1e-10 , maxiter=maxiter)
print 'RGC type:', rgc_type
test_global_objective( train_LQLEP, trained )
train_LQLEP.callback( trained, force=True )
train_LQLEP.callback( train_LQLEP.unflat( trained ), force=True )
return trained
