def test_BatchedParameter(self):
norm = DistributionWrapper(Normal, loc=0.0, scale=1.0)
shape = 1000, 100
a = torch.ones((shape[0], 1))
init = DistributionWrapper(Normal, loc=a, scale=1.0)
linear = AffineProcess((f, g), (a, 1.0), init, norm)
# ===== Initialize ===== #
x = linear.i_sample(shape)
self.assert_timeseries_sampling(100, linear, x, shape)
def test_LinearNoBatch(self):
norm = Normal(0., 1.)
linear = AffineProcess((f, g), (1., 1.), norm, norm)
# ===== Initialize ===== #
x = linear.i_sample()
# ===== Propagate ===== #
num = 100
samps = [x]
for t in range(num):
samps.append(linear.propagate(samps[-1]))
samps = torch.stack(samps)
self.assertEqual(samps.size(), torch.Size([num + 1]))
# ===== Sample path ===== #
path = linear.sample_path(num + 1)
self.assertEqual(samps.shape, path.shape)
def test_BatchedParameter(self):
norm = Normal(0., 1.)
shape = 1000, 100
a = torch.ones((shape[0], 1))
init = Normal(a, 1.)
linear = AffineProcess((f, g), (a, 1.), init, norm)
# ===== Initialize ===== #
x = linear.i_sample(shape)
# ===== Propagate ===== #
num = 100
samps = [x]
for t in range(num):
samps.append(linear.propagate(samps[-1]))
samps = torch.stack(samps)
self.assertEqual(samps.size(), torch.Size([num + 1, *shape]))
# ===== Sample path ===== #
path = linear.sample_path(num + 1, shape)
self.assertEqual(samps.shape, path.shape)