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():
latent = named.Object("latent")
latent.list = named.List()
mu = latent.list.add().mu.param_(Variable(torch.zeros(1)))
latent.dict = named.Dict()
foo = latent.dict["foo"].foo.sample_(dist.normal, mu, ng_ones(1))
latent.object.bar.observe_(dist.normal, foo, mu, ng_ones(1))
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 model(data, k):
latent = named.Object("latent")
# Create parameters for a Gaussian mixture model.
latent.ps.param_(Variable(torch.ones(k) / k, requires_grad=True))
latent.mus.param_(Variable(torch.zeros(k), requires_grad=True))
latent.sigmas.param_(Variable(torch.ones(k), requires_grad=True))
# 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.ps,
latent.mus,
latent.sigmas,
obs=x)
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 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)))
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, ng_zeros(1), ng_ones(1))
model_recurse(data, latent)
def model(data):
latent = named.Object("latent")
latent.z.sample_(dist.Normal(0.0, 1.0))
model_recurse(data, latent)
def model():
latent = named.Object("latent")
mu = latent.mu.param_(Variable(torch.zeros(1)))
foo = latent.foo.sample_(dist.normal, mu, ng_ones(1))
latent.bar.observe_(dist.normal, foo, mu, ng_ones(1))
latent.x.z.sample_(dist.normal, mu, ng_ones(1))