def MixedEstimator(abg_init, binnedTrain, dp_tol = 1e-2):
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
dp = dp_tol*2.0;
abg = abg_init
while dp > dp_tol:
Tf = binnedTrain.getTf()
xmin = FPMultiPhiSolver.calculate_xmin(Tf, abg)
dx = FPMultiPhiSolver.calculate_dx(abg, xmin)
dt = FPMultiPhiSolver.calculate_dt(dx, abg, xmin, factor = 8.)
S = FPMultiPhiSolver(theta, phis,
dx, dt, Tf, xmin)
Fs = S.solve(abg, visualize=False)
Ss = S.transformSurvivorData(binnedTrain)
Ls = Fs[:,:,-1] - Ss
Nus = S.solveAdjoint(abg, Ls)
dGdp = S.estimateParameterGradient(abg, Fs, Nus)
from numpy.linalg.linalg import norm
dG_normalized = dGdp/ norm(dGdp)
dp = FortetLineEstimator(binnedTrain, abg, dG_normalized, dp_tol)
abg = abg - dp*dG_normalized
print 'dG = ', dG_normalized
print 'dp = ', dp
print 'abg = (%.3g, %.3g, %.3g)'%(abg[0],abg[1],abg[2])
print '-'
return abg
def AdjointEstimator():
N_phi = 20;
print 'N_phi = ', N_phi
phis = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
file_name = 'sinusoidal_spike_train_N=1000_crit_1'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phis)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 100, T_thresh = 10.0)
print 'N_bins = ', len(binnedTrain.bins.keys())
Tf = binnedTrain.getTf()
print 'Tf = ', Tf
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
print 'abg_true = ', abg_true
abg = abg_true
xmin = FPMultiPhiSolver.calculate_xmin(Tf, abg)
dx = FPMultiPhiSolver.calculate_dx(abg, xmin)
dt = FPMultiPhiSolver.calculate_dt(dx, abg, xmin, factor = 8.)
print 'xmin, dx, dt = ', xmin, dx, dt
S = FPMultiPhiSolver(theta, phis,
dx, dt, Tf, xmin)
abg_init = initialize5(binnedTrain)
print 'abg_init = ', abg_init
# start = time.clock()
# abg_est = TNCEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
# start = time.clock()
# abg_est = NMEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
#
# start = time.clock()
# abg_est = COBYLAEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
# start = time.clock()
# abg_est = CGEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
start = time.clock()
abg_est = BFGSEstimator(S, binnedTrain, abg_init)
print 'Est. time = ', time.clock() - start
print 'abg_est = ', abg_est
