# set up simulator
def simulator(theta):
s_of_theta = model.model_sim(theta, True)
return func.normalize_summary_stats(s_of_theta, m_s_of_prior, s_s_of_prior)
hyper_params = [0.001, 0.001, 0.9, 0.9] # lr_like, lr_post, gamma_post, gamma
if lambda_val > 0:
hyper_params[-1] = lambda_val
if hp_tuning >= 2:
hyper_params = func.sample_hp("snpla", hp_tuning)
print(hyper_params)
flow_lik, flow_post = func.set_up_networks()
optimizer_lik = torch.optim.Adam(flow_lik.parameters(), lr=hyper_params[0])
optimizer_post = torch.optim.Adam(flow_post.parameters(), lr=hyper_params[1], weight_decay=0.0) # used def value before
decay_rate_post = hyper_params[2] # no adaptation of Adam's base rate
nbr_rounds = 5
prob_prior_decay_rate = hyper_params[3]
prob_prior = snpla.calc_prob_prior(nbr_rounds, prob_prior_decay_rate)
print(prob_prior)
nbr_lik = [1000, 1000, 1000, 1000, 1000] # , 2000, 2000]
'/home/samuel/Documents/projects/seq posterior approx w nf/seq posterior approx w nf dev/two_moons'
)
sys.path.append('./')
print(os.getcwd())
id_job = str(dim) + '_' + str(seed) + '_' + str(seed_data)
if hp_tuning > 0:
id_job = id_job + "_" + str(hp_tuning)
import functions as func
print(hp_tuning)
print(func.sample_hp("snl", hp_tuning))
print(torch.rand(1))
print(func.sample_hp("snl", hp_tuning)[0].item())
print(torch.rand(1))
prior = BoxUniform(low=-2 * torch.ones(2), high=2 * torch.ones(2))
x_o, model = func.set_up_model(prior)
def simulator(theta):
return torch.from_numpy(
model.gen_single(theta)).to(dtype=torch.float32).reshape(1, 2)
sys.path.append('./')
print(os.getcwd())
id_job = str(seed) + '_' + str(seed_data)
if hp_tuning > 0:
id_job = id_job + "_" + str(hp_tuning)
# Load all utility functions for all methods
import LotkaVolterra
import functions as func # Set model and generate data
print(hp_tuning)
print(func.sample_hp("snl", hp_tuning))
print(torch.rand(1))
print(func.sample_hp("snl", hp_tuning))
print(torch.rand(1))
# Set model and generate data
x_o, model, theta_true = func.set_up_model()
m_s_of_prior, s_s_of_prior = func.load_summary_stats_mean_and_std()
# set up simulator
def simulator(theta):
s_of_theta = model.model_sim(theta)
return func.normalize_summary_stats(s_of_theta, m_s_of_prior, s_s_of_prior)
