def test_sequential_sum_product_bias_2(num_steps, num_sensors, dim):
time = Variable("time", bint(num_steps))
bias = Variable("bias", reals(num_sensors, dim))
bias_dist = random_gaussian(
OrderedDict([
("bias", reals(num_sensors, dim)),
]))
trans = random_gaussian(
OrderedDict([
("time", bint(num_steps)),
("x_prev", reals(dim)),
("x_curr", reals(dim)),
]))
obs = random_gaussian(
OrderedDict([
("time", bint(num_steps)),
("x_curr", reals(dim)),
("bias", reals(dim)),
]))
# Each time step only a single sensor observes x,
# and each sensor has a different bias.
sensor_id = Tensor(torch.arange(num_steps) % 2,
OrderedDict(time=bint(num_steps)),
dtype=2)
with interpretation(eager_or_die):
factor = trans + obs(bias=bias[sensor_id]) + bias_dist
assert set(factor.inputs) == {"time", "bias", "x_prev", "x_curr"}
result = sequential_sum_product(ops.logaddexp, ops.add, factor, time,
{"x_prev": "x_curr"})
assert set(result.inputs) == {"bias", "x_prev", "x_curr"}
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
ndims = max(len(self.batch_shape), value.dim() - self.event_dim)
time = Variable("time", Bint[self.event_shape[0]])
value = tensor_to_funsor(value, ("time", ),
event_output=self.event_dim - 1,
dtype=self.dtype)
# Compare with pyro.distributions.hmm.DiscreteHMM.log_prob().
obs = self._obs(value=value)
result = self._trans + obs
result = sequential_sum_product(ops.logaddexp, ops.add, result, time,
{"state": "state(time=1)"})
result = self._init + result.reduce(ops.logaddexp, "state(time=1)")
result = result.reduce(ops.logaddexp, "state")
result = funsor_to_tensor(result, ndims=ndims)
return result
def test_mixed_sequential_sum_product(duration, num_segments):
sum_op, prod_op = ops.logaddexp, ops.add
time_var = Variable("time", bint(duration))
step = {"Px": "x"}
trans_inputs = ((time_var.name, bint(duration)),) + \
tuple((k, bint(2)) for k in step.keys()) + \
tuple((v, bint(2)) for v in step.values())
trans = random_tensor(OrderedDict(trans_inputs))
expected = sequential_sum_product(sum_op, prod_op, trans, time_var, step)
actual = mixed_sequential_sum_product(sum_op,
prod_op,
trans,
time_var,
step,
num_segments=num_segments)
assert_close(actual, expected)
def test_sequential_sum_product_bias_1(num_steps, dim):
time = Variable("time", bint(num_steps))
bias_dist = random_gaussian(OrderedDict([
("bias", reals(dim)),
]))
trans = random_gaussian(
OrderedDict([
("time", bint(num_steps)),
("x_prev", reals(dim)),
("x_curr", reals(dim)),
]))
obs = random_gaussian(
OrderedDict([
("time", bint(num_steps)),
("x_curr", reals(dim)),
("bias", reals(dim)),
]))
factor = trans + obs + bias_dist
assert set(factor.inputs) == {"time", "bias", "x_prev", "x_curr"}
result = sequential_sum_product(ops.logaddexp, ops.add, factor, time,
{"x_prev": "x_curr"})
assert set(result.inputs) == {"bias", "x_prev", "x_curr"}