def model(z1=None, z2=None):
p = pyro.param("p", torch.tensor([0.25, 0.75]))
loc = pyro.param("loc", torch.tensor([-1.0, 1.0]))
z1 = pyro.sample("z1", dist.Categorical(p), obs=z1)
with pyro.plate("data[0]", 3):
pyro.sample("x1", dist.Normal(loc[z1], 1.0), obs=data[0])
with pyro.plate("data[1]", 2):
z2 = pyro.sample("z2", dist.Categorical(p), obs=z2)
pyro.sample("x2", dist.Normal(loc[z2], 1.0), obs=data[1])
def model(z1=None, z2=None):
p = pyro.param("p", torch.tensor([[0.25, 0.75], [0.1, 0.9]]))
loc = pyro.param("loc", torch.tensor([-1.0, 1.0]))
z1 = pyro.sample("z1", dist.Categorical(p[0]), obs=z1)
z2 = pyro.sample("z2", dist.Categorical(p[z1]), obs=z2)
logger.info("z1.shape = {}".format(z1.shape))
logger.info("z2.shape = {}".format(z2.shape))
with pyro.plate("data", 3):
pyro.sample("x1", dist.Normal(loc[z1], 1.0), obs=data[0])
pyro.sample("x2", dist.Normal(loc[z2], 1.0), obs=data[1])
def model2():
data = [torch.tensor([-1.0, -1.0, 0.0]), torch.tensor([-1.0, 1.0])]
p = pyro.param("p", torch.tensor([0.25, 0.75]))
loc = pyro.sample("loc", dist.Normal(0, 1).expand([2]).to_event(1))
# FIXME results in infinite loop in transformeddist_to_funsor.
# scale = pyro.sample("scale", dist.LogNormal(0, 1))
z1 = pyro.sample("z1", dist.Categorical(p))
scale = pyro.sample("scale", dist.Normal(torch.tensor([0.0, 1.0])[z1],
1)).exp()
with pyro.plate("data[0]", 3):
pyro.sample("x1", dist.Normal(loc[z1], scale), obs=data[0])
with pyro.plate("data[1]", 2):
z2 = pyro.sample("z2", dist.Categorical(p))
pyro.sample("x2", dist.Normal(loc[z2], scale), obs=data[1])
def model_0(data, history, vectorized):
x_dim = 3
init = pyro.param("init",
lambda: torch.rand(x_dim),
constraint=constraints.simplex)
trans = pyro.param("trans",
lambda: torch.rand((x_dim, x_dim)),
constraint=constraints.simplex)
locs = pyro.param("locs", lambda: torch.rand(x_dim))
with pyro.plate("sequences", data.shape[0], dim=-3) as sequences:
sequences = sequences[:, None]
x_prev = None
markov_loop = \
pyro.vectorized_markov(name="time", size=data.shape[1], dim=-2, history=history) if vectorized \
else pyro.markov(range(data.shape[1]), history=history)
for i in markov_loop:
x_curr = pyro.sample(
"x_{}".format(i),
dist.Categorical(
init if isinstance(i, int) and i < 1 else trans[x_prev]))
with pyro.plate("tones", data.shape[2], dim=-1):
pyro.sample("y_{}".format(i),
dist.Normal(Vindex(locs)[..., x_curr], 1.),
obs=Vindex(data)[sequences, i])
x_prev = x_curr
def model(z=None):
p = pyro.param("p", torch.tensor([0.75, 0.25]))
iz = pyro.sample("z", dist.Categorical(p), obs=z)
z = torch.tensor([0.0, 1.0])[iz]
logger.info("z.shape = {}".format(z.shape))
with pyro.plate("data", 3):
pyro.sample("x", dist.Normal(z, 1.0), obs=data)
def model_1(data, history, vectorized):
x_dim = 3
init = pyro.param("init",
lambda: torch.rand(x_dim),
constraint=constraints.simplex)
trans = pyro.param("trans",
lambda: torch.rand((x_dim, x_dim)),
constraint=constraints.simplex)
locs = pyro.param("locs", lambda: torch.rand(x_dim))
x_prev = None
markov_loop = (pyro.vectorized_markov(
name="time", size=len(data), dim=-2, history=history) if vectorized
else pyro.markov(range(len(data)), history=history))
for i in markov_loop:
x_curr = pyro.sample(
"x_{}".format(i),
dist.Categorical(
init if isinstance(i, int) and i < 1 else trans[x_prev]),
)
with pyro.plate("tones", data.shape[-1], dim=-1):
pyro.sample(
"y_{}".format(i),
dist.Normal(Vindex(locs)[..., x_curr], 1.0),
obs=data[i],
)
x_prev = x_curr
def model_6(data, history, vectorized):
x_dim = 3
x_init = pyro.param("x_init",
lambda: torch.rand(x_dim),
constraint=constraints.simplex)
x_trans = pyro.param("x_trans",
lambda: torch.rand((len(data) - 1, x_dim, x_dim)),
constraint=constraints.simplex)
locs = pyro.param("locs", lambda: torch.rand(x_dim))
x_prev = None
markov_loop = \
pyro.vectorized_markov(name="time", size=len(data), dim=-2, history=history) if vectorized \
else pyro.markov(range(len(data)), history=history)
for i in markov_loop:
if isinstance(i, int) and i < 1:
x_probs = x_init
elif isinstance(i, int):
x_probs = x_trans[i - 1, x_prev]
else:
x_probs = Vindex(x_trans)[(i - 1)[:, None], x_prev]
x_curr = pyro.sample("x_{}".format(i), dist.Categorical(x_probs))
with pyro.plate("tones", data.shape[-1], dim=-1):
pyro.sample("y_{}".format(i),
dist.Normal(Vindex(locs)[..., x_curr], 1.),
obs=data[i])
x_prev = x_curr
def model():
locs = pyro.param("locs", torch.randn(3), constraint=constraints.real)
scales = pyro.param("scales",
torch.randn(3).exp(),
constraint=constraints.positive)
p = torch.tensor([0.5, 0.3, 0.2])
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], scales[x]), obs=data)
def model_zzxx():
# loc,scale
# / \
# +-------/-+ +--------\------+
# z1 --|--> x1 | | z2 ---> x2 |
# | 3 | | 2 |
# +---------+ +---------------+
data = [torch.tensor([-1.0, -1.0, 0.0]), torch.tensor([-1.0, 1.0])]
p = pyro.param("p", torch.tensor([0.25, 0.75]))
loc = pyro.sample("loc", dist.Normal(0, 1).expand([2]).to_event(1))
# FIXME results in infinite loop in transformeddist_to_funsor.
# scale = pyro.sample("scale", dist.LogNormal(0, 1))
scale = pyro.sample("scale", dist.Normal(0, 1)).exp()
z1 = pyro.sample("z1", dist.Categorical(p))
with pyro.plate("data[0]", 3):
pyro.sample("x1", dist.Normal(loc[z1], scale), obs=data[0])
with pyro.plate("data[1]", 2):
z2 = pyro.sample("z2", dist.Categorical(p))
pyro.sample("x2", dist.Normal(loc[z2], scale), obs=data[1])
def hmm(data, hidden_dim=10):
transition = 0.3 / hidden_dim + 0.7 * torch.eye(hidden_dim)
means = torch.arange(float(hidden_dim))
states = [0]
for t in pyro.markov(range(len(data))):
states.append(
pyro.sample("states_{}".format(t),
dist.Categorical(transition[states[-1]])))
data[t] = pyro.sample("obs_{}".format(t),
dist.Normal(means[states[-1]], 1.0),
obs=data[t])
return states, data
def model():
loc = pyro.param("loc", torch.tensor(2.0))
scale = pyro.param("scale", torch.tensor(1.0))
x = pyro.sample("x", dist.Normal(loc, scale))
return x
def guide():
loc = pyro.param("loc", torch.tensor(0.))
y = pyro.sample("y", dist.Normal(loc, 1.))
pyro.sample("x", dist.Normal(y, 1.))
def model():
x = pyro.sample("x", dist.Normal(0., 1.))
pyro.sample("y", dist.Normal(x, 1.))
def model():
locs = pyro.param("locs", torch.tensor([-1., 0., 1.]))
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(torch.ones(3) / 3))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
def model(z=None):
p = pyro.param("p", torch.tensor([0.75, 0.25]))
z = pyro.sample("z", dist.Categorical(p), obs=z)
logger.info("z.shape = {}".format(z.shape))
with pyro.plate("data", 3), handlers.mask(mask=mask):
pyro.sample("x", dist.Normal(z.type_as(data), 1.0), obs=data)
def model(data):
loc = pyro.sample("loc", dist.Normal(0., 1.))
with pyro.plate("data", len(data), dim=-1):
pyro.sample("obs", dist.Normal(loc, 1.), obs=data)
def model(data):
loc = pyro.param("loc", torch.tensor(0.0))
pyro.sample("x", dist.Normal(loc, 1.), obs=data)
def model(data=None):
loc = pyro.param("loc", torch.tensor(2.0))
scale = pyro.param("scale", torch.tensor(1.0))
with pyro.plate("data", 1000, dim=-1):
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
def model():
return pyro.sample("x", dist.Normal(0, 1))
def model():
locs = pyro.param("locs", torch.tensor([0.2, 0.3, 0.5]))
p = torch.tensor([0.2, 0.3, 0.5])
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
def guide():
loc = pyro.param("loc", torch.tensor(0.))
scale = pyro.param("scale", torch.tensor(1.))
with pyro.plate("plate_outer", data.size(-1), dim=-1):
pyro.sample("x", dist.Normal(loc, scale))
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = pyro.param("guide_scale", torch.tensor(1.),
constraint=constraints.positive)
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))
def model():
loc = torch.tensor(3.0)
with pyro.plate("plate_outer", data.size(-1), dim=-1):
x = pyro.sample("x", dist.Normal(loc, 1.))
with pyro.plate("plate_inner", data.size(-2), dim=-2):
pyro.sample("y", dist.Normal(x, 1.), obs=data)
