'/home/samuel/Documents/projects/seq posterior approx w nf/seq posterior approx w nf dev/lotka_volterra'
)
sys.path.append('./')
print(os.getcwd())
id_job = str(seed) + '_' + str(seed_data)
# Load all utility functions for all methods
import LotkaVolterra
import functions as func
# 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)
s_x_o = LotkaVolterra.calc_summary_stats(
x_o.reshape(1, x_o.shape[0], x_o.shape[1]))
s_x_o = func.normalize_summary_stats(s_x_o, m_s_of_prior, s_s_of_prior)
# %%
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)
# check simulator and prior
simulator, prior = prepare_for_sbi(simulator, model.prior)
# function that builds the network
def build_custom_like_net(batch_theta, batch_x):
flow_lik, flow_post = func.set_up_networks(seed)
id_job = id_job + "_" + str(lambda_val)
import LotkaVolterra
import functions as func # Set model and generate data
if lunarc == 1:
sys.path.append('/home/samwiq/snpla/seq-posterior-approx-w-nf-dev/algorithms')
import snpla as snpla
else:
sys.path.append(
'/home/samuel/Documents/projects/seq posterior approx w nf/seq posterior approx w nf dev/algorithms')
import snpla as snpla
# Set model and generate data
x_o, model, theta_true = func.set_up_model(method="snpla")
m_s_of_prior, s_s_of_prior = func.load_summary_stats_mean_and_std()
s_x_o = LotkaVolterra.calc_summary_stats(x_o.reshape(1, x_o.shape[0], x_o.shape[1]))
s_x_o = func.normalize_summary_stats(s_x_o, m_s_of_prior, s_s_of_prior)
# 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
