def model(): latent = named.Object("latent") latent.list = named.List() loc = latent.list.add().loc.param_(torch.zeros(1)) latent.dict = named.Dict() foo = latent.dict["foo"].foo.sample_(dist.Normal(loc, torch.ones(1))) latent.object.bar.sample_(dist.Normal(loc, torch.ones(1)), obs=foo)
def model(data, k): latent = named.Object("latent") # Create parameters for a Gaussian mixture model. latent.probs.param_(torch.ones(k) / k, constraint=constraints.simplex) latent.locs.param_(torch.zeros(k)) latent.scales.param_(torch.ones(k), constraint=constraints.positive) # Observe all the data. We pass a local latent in to the local_model. latent.local = named.List() for x in data: local_model(latent.local.add(), latent.probs, latent.locs, latent.scales, obs=x)
def test_eval_str(): state = named.Object("state") state.x = 0 state.ys = named.List() state.ys.add().foo = 1 state.zs = named.Dict() state.zs[42].bar = 2 assert state is eval(str(state)) assert state.x is eval(str(state.x)) assert state.ys is eval(str(state.ys)) assert state.ys[0] is eval(str(state.ys[0])) assert state.ys[0].foo is eval(str(state.ys[0].foo)) assert state.zs is eval(str(state.zs)) assert state.zs[42] is eval(str(state.zs[42])) assert state.zs[42].bar is eval(str(state.zs[42].bar))
def guide(data, k): latent = named.Object("latent") latent.local = named.List() for x in data: # We pass a local latent in to the local_guide. local_guide(latent.local.add(), k)
def guide(data): guide_recurse(data, named.Object("latent"))
def model(data): latent = named.Object("latent") latent.z.sample_(dist.Normal(0.0, 1.0)) model_recurse(data, latent)
def sampler(): nob = named.Object(nob_name) for key, val in yaml_parameters.items(): _parse_entry(key, val, nob, nob_name, device) return nob
def model(): latent = named.Object("latent") loc = latent.loc.param_(torch.zeros(1)) foo = latent.foo.sample_(dist.Normal(loc, torch.ones(1))) latent.bar.sample_(dist.Normal(loc, torch.ones(1)), obs=foo) latent.x.z.sample_(dist.Normal(loc, torch.ones(1)))