def single_objective_u():
arg = ['u','STA','STC','V2','v1','N_spike','T']
result = ['LQLEP_wPrior','dLQLEP_wPrior','barrier']
vardict = LQLEP_wBarrier( **LQLEP( **thetaM( **linear_reparameterization())))
vardict['dLQLEP_wPrior'] = th.grad(cost = vardict['LQLEP_wPrior'],
wrt = vardict['u'],
consider_constant = extract( vardict, arg[1:]))
print 'Simplifying single objective_u...'
sys.stdout.flush()
t0 = time.time()
inputs, outputs = kolia_theano.simplify( extract(vardict,arg), extract(vardict,result) )
t1 = time.time()
print 'done simplifying single objective_u in ', t1-t0, ' sec.'
sys.stdout.flush()
return inputs, outputs
def _test_LNP( rgc_type='off parasol' ):
vardict = LNP( **thetaM( **linear_reparameterization()))
init_LNP = LNP_model( init_sv1(rgc_type) )
indices = extract( linear_stats( rgc_type, (5,0) ), ['sparse_index', 'subunit_index'] )
indices['N_subunits'] = len(cones)
unknown = extract(init_LNP,['sv1'])
train_LNP = global_objective( unknown, extract(init_LNP,['u','V2']),
vardict, run=linear_stats( rgc_type, (5,0) ),
indices=indices)
train_LNP.with_callback(callback)
train_LNP.description = 'LNP'
sv1 = optimize.optimizer( train_LNP )( init_params=unknown, maxiter=5000, gtol=1e-7 )
model = LNP_model( train_LNP.unflat( sv1 )['sv1'] )
model['LL'] = global_objective( unknown, extract(init_LNP,['u','V2']),
vardict, run=linear_stats( rgc_type, (-5,0)),
indices=indices).LL(sv1)
save(model,'LNP_'+rgc_type)
return model
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