def optimize(p0,
data1,
data2,
cache1,
cache2,
model_func,
sel_dist,
scal_fac1,
scal_fac2,
theta1,
theta2,
lower_bound=None,
upper_bound=None,
verbose=0,
flush_delay=0.5,
epsilon=1e-3,
gtol=1e-5,
multinom=False,
maxiter=None,
full_output=False,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_file=None):
if output_file:
output_stream = file(output_file, 'w')
else:
output_stream = sys.stdout
args = (data1, data2, cache1, cache2, model_func, sel_dist, scal_fac1,
scal_fac2, theta1, theta2, lower_bound, upper_bound, verbose,
multinom, flush_delay, func_args, func_kwargs, fixed_params,
ll_scale, output_stream)
p0 = Inference._project_params_down(p0, fixed_params)
outputs = scipy.optimize.fmin_bfgs(_object_func,
p0,
epsilon=epsilon,
args=args,
gtol=gtol,
full_output=True,
disp=False,
maxiter=maxiter)
xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = outputs
xopt = Inference._project_params_up(xopt, fixed_params)
if output_file:
output_stream.close()
if not full_output:
return [-fopt, xopt]
else:
return xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag
def optimize_cons(p0,
data,
model_func,
sel_dist,
theta,
lower_bound=None,
upper_bound=None,
verbose=0,
flush_delay=0.5,
epsilon=1e-4,
constraint=None,
gtol=1e-6,
multinom=False,
maxiter=None,
full_output=False,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_file=None):
"""
Constrained optimization needs a constraint function and bounds.
"""
if output_file:
output_stream = file(output_file, 'w')
else:
output_stream = sys.stdout
if not (lower_bound is None):
lower_bound_a = lower_bound + [0]
if not (upper_bound is None):
upper_bound_a = upper_bound + [numpy.inf]
args = (data, model_func, sel_dist, theta, lower_bound, upper_bound,
verbose, multinom, flush_delay, func_args, func_kwargs,
fixed_params, ll_scale, output_stream)
p0 = Inference._project_params_down(p0, fixed_params)
####make sure to define consfunc and bnds ####
if (not lower_bound is None) and (not upper_bound is None):
bnds = tuple((x, y) for x, y in zip(lower_bound, upper_bound))
outputs = scipy.optimize.fmin_slsqp(_object_func,
p0,
bounds=bnds,
args=args,
f_eqcons=constraint,
epsilon=epsilon,
iter=maxiter,
full_output=True,
disp=False)
xopt, fopt, func_calls, grad_calls, warnflag = outputs
xopt = Inference._project_params_up(xopt, fixed_params)
if output_file:
output_stream.close()
if not full_output:
return [-fopt, xopt]
else:
return xopt, fopt, func_calls, grad_calls, warnflag
