def freeze_A_to_solution_and_fit():
# used to debug infs
# from tests.test_vi import test_elbo_components, test_q_E_logstick
SCALE = 1.
N = 500
X = generate_gg_blocks_dataset(N, 0.05)
model = InfiniteIBP(1.5, 6, 0.1, 0.05, 36)
model.phi.data[:4] = SCALE * gg_blocks()
model.init_z(N)
model.train()
visualize_A_save(model.phi.detach().numpy(), 0)
visualize_nu_save(model.nu.detach().numpy(), 0)
optimizer = torch.optim.Adam(model.parameters(), 0.1)
for i in range(20):
model.cavi(X)
print("[Epoch {:<3}] ELBO = {:.3f}".format(i + 1,
model.elbo(X).item()))
print("CHANGE OF REGIME")
visualize_A_save(model.phi.detach().numpy(), 20)
visualize_nu_save(model.nu.detach().numpy(), 20)
import ipdb
ipdb.set_trace()
def check_that_naive_doesnt_work():
N = 500
X = generate_gg_blocks_dataset(N, 0.05)
model = InfiniteIBP(1.5, 6, 0.1, 0.05, 36)
model.init_z(N)
model.eval()
for i in range(100):
if i % 5 == 0:
visualize_A_save(model.phi.detach().numpy(), i)
if (i + 1) % 10 == 0:
model._nu.data = torch.randn(model._nu.shape)
model.cavi(X)
print("[Epoch {:<3}] ELBO = {:.3f}".format(i + 1,
model.elbo(X).item()))