X = orth(X)
return np.dot(X,np.dot(uu,X))
data = {}
init_params = {}
#init_params = 0.1*( ones(len(UU)) + 2.*R.randn(len(UU)) )
#init_params = UU
iii = 1
data = [{ 'STA':STA[i] , 'STC':STC[i] } for i in range(iii)]
init_params = [{'theta':STA[i] * 0.05 , 'M':STC[iii] * 0.05} for i in range(iii)]
term = kolia_theano.term(init_params=init_params[0],differentiate=['f'],
f=quadratic_Poisson, barrier=barrier, ldet=ldet, eigs=eigs)
terms = [deepcopy(term) for i in range(iii)]
def syM(x):
xx = x['M']
x['M'] = (xx+np.transpose(xx))/2.
return x
def symm(X):
X = [ syM(x) for x in objective.inflate(X)]
return objective.flatten(X)
def callback_one(ip,d):
pp = objective.inflate(ip)
print
quad = lambda x : 0.2*(x+1.)**2
(N_spikes,STA,STC), U, V1, bbar, Cb , STAB = LNLNP(T=10000,sigma=sigma,NL=quad,N=27)
Nsub, N = U.shape
NRGC, Nsub = V1.shape
iii = NRGC
data = [{ 'STA':STA[i] , \
'STC':STC[i] } for i in range(iii)]
init_params = [{'theta':data[i]['STA'] * 0.1 , \
'M':data[i]['STC'] * 0.1} for i in range(iii)]
term = kolia_theano.term(init_params=init_params[0],differentiate=['f'],
f=quadratic_Poisson, barrier=eig_barrier)
class Objective: pass
objective = Objective()
def f(params,args):
return np.sum([term.f(params[i*N*(N+1):(i+1)*N*(N+1)],args[i]) for i in range(iii)],0)
def barrier(params,args):
return np.sum([term.barrier(params[i*N*(N+1):(i+1)*N*(N+1)],args[i]) for i in range(iii)],0)
def df(params,args):
return np.concatenate([term.df(params[i*N*(N+1):(i+1)*N*(N+1)],args[i]) for i in range(iii)])
objective.f = f
objective.df = df
