def test_ParameterInDistribution(self):
shape = 10, 100
a = 1e-2 * torch.ones((shape[0], 1))
dt = 1e-2
dist = Normal(loc=0., scale=Parameter(Exponential(10.)))
init = Normal(a, 1.)
sde = AffineEulerMaruyama((f_sde, g_sde), (a, 0.15),
init,
dist,
dt=dt,
num_steps=10)
sde.sample_params(shape)
# ===== Initialize ===== #
x = sde.i_sample(shape)
# ===== Propagate ===== #
num = 1000
samps = [x]
for t in range(num):
samps.append(sde.propagate(samps[-1]))
samps = torch.stack(samps)
self.assertEqual(samps.size(), torch.Size([num + 1, *shape]))
# ===== Sample path ===== #
path = sde.sample_path(num + 1, shape)
self.assertEqual(samps.shape, path.shape)
def test_SDE(self):
shape = 1000, 100
a = 1e-2 * torch.ones((shape[0], 1))
dt = 0.1
norm = Normal(0., math.sqrt(dt))
init = Normal(a, 1.)
sde = AffineEulerMaruyama((f_sde, g_sde), (a, 0.15),
init,
norm,
dt=dt,
num_steps=10)
# ===== Initialize ===== #
x = sde.i_sample(shape)
# ===== Propagate ===== #
num = 100
samps = [x]
for t in range(num):
samps.append(sde.propagate(samps[-1]))
samps = torch.stack(samps)
self.assertEqual(samps.size(), torch.Size([num + 1, *shape]))
# ===== Sample path ===== #
path = sde.sample_path(num + 1, shape)
self.assertEqual(samps.shape, path.shape)