def L_MH_sampler(pars, target, initial_state, run_data, likelihood=True):
ds, N = run_data.DataStore, run_data.N
sp = target['State Space']
opt_scale, L = sp['sigma_opt'], sp['Id']
if likelihood:
decision_function, comparison_function = likelihood_acceptance_decision, compose2(
np.log, target['pdf'])
else:
decision_function, comparison_function = acceptance_decision, target[
'pdf']
target_pdf = target['pdf']
current = initial_state
accepted = True
z_samples = get_samples(sp=sp, name='Z')
for n in range(1, N):
save_state(data_store=ds,
step=n,
state=current,
value=target_pdf(current),
accepted_p=accepted)
proposed = L_generate_candidate(center=current,
L=L,
scale=opt_scale,
z_sample=z_samples[n])
accepted = decision_function(current, proposed, target_pdf)
if accepted:
current = proposed
else: # The else clause is redundant but added for readability.
current = current
return run_data
def init_AM_pars(sp):
dim, origin, idty, = sp['dim'], sp['Origin'], sp['Id'],
sigma_0, sigma_opt = 0.1/np.sqrt(dim), sp['sigma_opt']
cov_0, cov_opt = sigma_0**2*idty, sigma_opt**2*idty
return AM_Pars(Origin=origin, Id=idty,
sigma_0=sigma_0, sigma_opt=sigma_opt,
C_0=cov_0, C_opt=cov_opt,
z_samples=get_samples(sp=sp, name='Z'))
def init_CMA_pars(sp):
dim = sp['dim']
return CMA_Parameters(z_samples=get_samples(sp=sp, name='Z'),
s=1,
k=1 + dim / 2,
t_succ=2 / 11,
c_p=1 / 12,
c_c=2 / (dim + 2),
c_cov=2 / (dim**2 + 6),
p_thres=0.44)
def C_GaA_sampler(pars, target, initial_state, run_data):
target_pdf, sp = target['pdf'], target['State Space']
Origin, Id = sp['Origin'], sp['Id']
ds, N = run_data.DataStore, run_data.N
z_samples = get_samples(sp=sp, name='Z')
#Set up and save the initial state
m = x_current = initial_state
sigma = 1
C = Id
save_state(data_store=ds,
step=0,
state=x_current,
value=target_pdf(x_current),
accepted_p=True,
mean=m,
covariance=C,
scale=sigma,
threshold=None)
#Sample and save state
for n in range(1, N):
z_sample = z_samples[n]
x_proposed = C_generate_GaA_candidate(mean=x_current,
C=C,
z_sample=z_sample)
accepted = acceptance_decision(x_current, x_proposed, target_pdf)
if accepted:
x_current = x_proposed
sigma = expand(sigma, pars=pars)
m = GaA_mean_update(mean=m, sample=x_proposed, pars=pars)
C = GaA_C_update(C=C, mean=m, sample=x_proposed, pars=pars)
else:
sigma = contract(sigma, pars=pars)
save_state(data_store=ds,
step=n,
state=x_current,
value=target_pdf(x_current),
accepted_p=accepted,
mean=m,
covariance=C,
scale=sigma,
threshold=None)
return run_data