prediction_np = (vb.bmcmodel.prediction(delta_x.reshape(-1,1),cov="none")*vb.evals_std + vb.evals_mean).\
numpy().astype(float).flatten()
prediction_list.append(prediction_np)
samples_dict = {0: (vb.samples.numpy(), vb.evaluations.numpy())}
#%% Main loop
for i in range(100):
tictoc.tic()
_ = vb.update(maxiter_nc=300,
lr_nc=0.01,
n_samples_nc=500,
n_samples_sn=300,
n_iter_sn=300)
if ((i + 1) % 10) == 0:
try:
vb.update_bmcmodel(acquisition=acquisition, vreg=1e-3)
except:
continue
samples_dict[i + 1] = (vb.samples.numpy(), vb.evaluations.numpy())
# vb.update_full()
vb.cutweights(1e-3)
#%% Save trackings
elapsed = tictoc.toc(printing=False)
dmeans.append(np.linalg.norm(vb.currentq_mean.cpu() - true_mean, 2))
dcovs.append(np.linalg.norm(vb.currentq_cov.cpu() - true_cov, 2))
elbo_list.append(vb.evidence_lower_bound(nsamples=10000).cpu().numpy())
step_list.append(i + 1)
time_list.append(elapsed)
vbp = vb.current_logq(delta_x.reshape(
-1, 1)).cpu().flatten().numpy().astype(float)
vbp_list.append(vbp)
noise=1e-4,
kernel_function="PMat",
matern_coef=1.5,
numstab=-50.0,
degree_hermite=50)
vb.optimize_bmc_model(maxiter=500, verbose=1, lr=0.05)
#%%
elbo_list = [vb.evidence_lower_bound(nsamples=10000).cpu().numpy()]
kl_list = [vb.kullback_proposal_bmc(10000).item()]
step_list = [0]
time_list = [0.0]
#%%
print("Active sampling...")
for i in range(10 * dim):
vb.update_bmcmodel(acquisition="mmlt", mode="optimizing", vreg=1e-2)
vb.update_full()
folder_name = "testheat1c"
try:
os.mkdir(folder_name)
except FileExistsError:
pass
vb.save_distribution("%s/mvn%i" % (folder_name, 0))
#%%
print(torch.sigmoid(vb.currentq_mean))
print(elbo_list[-1])
print(kl_list[-1])
#%%
for i in range(100):
tictoc.tic()
_ = vb.update(maxiter_nc=300,
#Set up parameters
dim=2 #Dimension of problem
samples = torch.randn(20,dim) #Initial samples
mu0 = torch.zeros(dim) #Initial mean
cov0 = 20.0*torch.ones(dim) #Initial covariance
acquisition = "prospective" #Acquisition function
#Initialize algorithm
vb = VariationalBoosting(dim,logjoint,samples,mu0,cov0)
vb.optimize_bmc_model() #Optimize GP model
vb.update_full() #Fit first component
#Training loop
for i in range(100):
_ = vb.update() #Choose new boosting component
vb.update_bmcmodel(acquisition=acquisition) #Choose new evaluation
vb.cutweights(1e-3) #Weights prunning
if ((i+1)%20) == 0:
vb.update_full(cutoff=1e-3) #Joint parameter updating
vb.save_distribution("finaldistrib") #Save distribution
#%%
import math
distrib = torch.load("finaldistrib")
nplot = 21
x,y = torch.linspace(-6,6,nplot),torch.linspace(-6,6,nplot)
X,Y = torch.meshgrid(x,y)
Z1 = logjoint(torch.stack([X,Y],dim=-1).reshape(-1,2)).reshape(*X.shape)-\
2*math.log(math.pi)
Z2 = distrib.logprob(torch.stack([X,Y],dim=-1).reshape(-1,2)).reshape(*X.shape)
#%%