def __init__(self, nu=1.5, dt=1.0, obs_dim=2, num_gps=1,
length_scale_init=None, kernel_scale_init=None,
obs_noise_scale_init=None):
self.nu = nu
self.dt = dt
assert obs_dim > 1, "If obs_dim==1 you should use IndependentMaternGP"
self.obs_dim = obs_dim
self.num_gps = num_gps
if obs_noise_scale_init is None:
obs_noise_scale_init = 0.2 * torch.ones(obs_dim)
assert obs_noise_scale_init.shape == (obs_dim,)
self.dt = dt
self.obs_dim = obs_dim
self.num_gps = num_gps
super().__init__()
self.kernel = MaternKernel(nu=nu, num_gps=num_gps,
length_scale_init=length_scale_init,
kernel_scale_init=kernel_scale_init)
self.full_state_dim = num_gps * self.kernel.state_dim
self.obs_noise_scale = PyroParam(obs_noise_scale_init,
constraint=constraints.positive)
self.A = nn.Parameter(0.3 * torch.randn(self.num_gps, self.obs_dim))
def __init__(
self,
nu=1.5,
dt=1.0,
obs_dim=1,
length_scale_init=None,
kernel_scale_init=None,
obs_noise_scale_init=None,
):
self.nu = nu
self.dt = dt
self.obs_dim = obs_dim
if obs_noise_scale_init is None:
obs_noise_scale_init = 0.2 * torch.ones(obs_dim)
assert obs_noise_scale_init.shape == (obs_dim, )
super().__init__()
self.kernel = MaternKernel(
nu=nu,
num_gps=obs_dim,
length_scale_init=length_scale_init,
kernel_scale_init=kernel_scale_init,
)
self.obs_noise_scale = PyroParam(obs_noise_scale_init,
constraint=constraints.positive)
obs_matrix = [1.0] + [0.0] * (self.kernel.state_dim - 1)
self.register_buffer("obs_matrix",
torch.tensor(obs_matrix).unsqueeze(-1))
def __init__(
self,
nu=1.5,
dt=1.0,
obs_dim=1,
linearly_coupled=False,
length_scale_init=None,
obs_noise_scale_init=None,
):
if nu != 1.5:
raise NotImplementedError("The only supported value of nu is 1.5")
self.dt = dt
self.obs_dim = obs_dim
if obs_noise_scale_init is None:
obs_noise_scale_init = 0.2 * torch.ones(obs_dim)
assert obs_noise_scale_init.shape == (obs_dim, )
super().__init__()
self.kernel = MaternKernel(nu=nu,
num_gps=obs_dim,
length_scale_init=length_scale_init)
self.full_state_dim = self.kernel.state_dim * obs_dim
# we demote self.kernel.kernel_scale from being a nn.Parameter
# since the relevant scales are now encoded in the wiener noise matrix
del self.kernel.kernel_scale
self.kernel.register_buffer("kernel_scale", torch.ones(obs_dim))
self.obs_noise_scale = PyroParam(obs_noise_scale_init,
constraint=constraints.positive)
self.wiener_noise_tril = PyroParam(
torch.eye(obs_dim) + 0.03 * torch.randn(obs_dim, obs_dim).tril(-1),
constraint=constraints.lower_cholesky,
)
if linearly_coupled:
self.obs_matrix = nn.Parameter(
0.3 * torch.randn(self.obs_dim, self.obs_dim))
else:
obs_matrix = torch.zeros(self.full_state_dim, obs_dim)
for i in range(obs_dim):
obs_matrix[self.kernel.state_dim * i, i] = 1.0
self.register_buffer("obs_matrix", obs_matrix)
