parser.set(th, 'betas', .01 * K_chol.dot(np.random.randn(Vspec, NUM_BASES)).T)
parser.set(th, 'omegas', .01 * npr.randn(Nspec, NUM_BASES))
parser.set(th, 'mus', .01 * npr.randn(Nspec))
### if file exists, at least pull basis out
#if os.path.exists("basis_fit_K-%d_V-2728.pkl"%NUM_BASES):
# print "basis fit file exists! - checking basis"
# th_disk, lam0_disk, lam0_delta_disk, parser_disk = \
# qfb.load_basis_fit("basis_fit_K-%d_V-2728.pkl"%NUM_BASES)
# if np.all(lam0_disk == lam0) and :
# print "basis size is consistent - initializing..."
# parser.set(th, 'betas', parser_disk.get('betas'))
## sanity check gradient
check_grad(fun = lambda th: loss_fun(th) + prior_loss(th), # X, Lam),
jac = lambda th: loss_grad(th) + prior_loss_grad(th), #, X, Lam),
th = th)
obj_vals = []
min_val = np.inf
min_x = None
def callback(x, i, g):
global min_val
global min_x
global obj_vals
if i % 10 == 0:
loss_val = loss_fun(x) + prior_loss(x)
if loss_val < min_val:
min_x = x
min_val = loss_val
print " %d, loss = %2.4g, grad = %2.4g " % \
.01 * K_chol.dot(np.random.randn(Vspec, NUM_BASES)).T)
parser.set(th, 'omegas', .01 * npr.randn(Nspec, NUM_BASES))
parser.set(th, 'mus', .01 * npr.randn(Nspec))
### if file exists, at least pull basis out
#if os.path.exists("basis_fit_K-%d_V-2728.pkl"%NUM_BASES):
# print "basis fit file exists! - checking basis"
# th_disk, lam0_disk, lam0_delta_disk, parser_disk = \
# qfb.load_basis_fit("basis_fit_K-%d_V-2728.pkl"%NUM_BASES)
# if np.all(lam0_disk == lam0) and :
# print "basis size is consistent - initializing..."
# parser.set(th, 'betas', parser_disk.get('betas'))
## sanity check gradient
check_grad(
fun=lambda th: loss_fun(th) + prior_loss(th), # X, Lam),
jac=lambda th: loss_grad(th) + prior_loss_grad(th), #, X, Lam),
th=th)
obj_vals = []
min_val = np.inf
min_x = None
def callback(x, i, g):
global min_val
global min_x
global obj_vals
if i % 1 == 0:
loss_val = loss_fun(x) + prior_loss(x)
if loss_val < min_val:
min_x = x
min_val = loss_val
([srcs[0].theta, srcs[0].sigma, srcs[0].phi,
srcs[0].rho], srcs[0].u, [srcs[0].fluxes[b] for b in BANDS]))
Z_s = [img.nelec for img in imgs]
ll = galaxy_source_like(th,
Z_s,
imgs,
check_overlap=True,
unconstrained=True)
ll_grad = galaxy_source_like_grad(th, Z_s, imgs)
print ll
print ll_grad
# check to make sure the gradient is correct
check_grad(fun=lambda (th): galaxy_source_like(th, Z_s, imgs),
jac=lambda (th): galaxy_source_like_grad(th, Z_s, imgs),
th=th,
compwise=True)
# do quick gradient ascent
from scipy.optimize import minimize
res = minimize(fun=lambda (th): -galaxy_source_like(th, Z_s, imgs),
jac=lambda (th): -galaxy_source_like_grad(th, Z_s, imgs),
x0=res.x,
method='L-BFGS-B')
print galaxy_source_like(res.x, Z_s, imgs)
check_grad(fun=lambda (th): galaxy_source_like(th, Z_s, imgs),
jac=lambda (th): galaxy_source_like_grad(th, Z_s, imgs),
th=res.x,
compwise=True)