def rbf_lin_sum_kern2():
return kernels.Sum([
kernels.Linear(Data.D_in, variance=rng.rand()),
kernels.RBF(Data.D_in, variance=rng.rand(), lengthscales=rng.rand() + 1.),
kernels.Linear(Data.D_in, variance=rng.rand()),
kernels.RBF(Data.D_in, variance=rng.rand(), lengthscales=rng.rand() + 1.),
])
"rbf_act_dim_0":
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0,
active_dims=[0]),
"rbf_act_dim_1":
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0,
active_dims=[1]),
"lin_act_dim_0":
kernels.Linear(variance=rng.rand(), active_dims=[0]),
"lin_act_dim_1":
kernels.Linear(variance=rng.rand(), active_dims=[1]),
"rbf_lin_sum":
kernels.Sum([
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0),
kernels.Linear(variance=rng.rand()),
]),
"rbf_lin_sum2":
kernels.Sum([
kernels.Linear(variance=rng.rand()),
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0),
kernels.Linear(variance=rng.rand()),
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0),
]),
"rbf_lin_prod":
kernels.Product([
kernels.SquaredExponential(variance=rng.rand(),
lengthscales=rng.rand() + 1.0,
class Data:
rng = np.random.RandomState(1)
num_data = 5
num_ind = 4
D_in = 2
D_out = 2
Xmu = rng.randn(num_data, D_in)
L = gen_L(rng, num_data, D_in, D_in)
Xvar = np.array([l @ l.T for l in L])
Z = rng.randn(num_ind, D_in)
# distributions don't need to be compiled (No Parameter objects)
# but the members should be Tensors created in the same graph
graph = tf.Graph()
with test_util.session_context(graph) as sess:
gauss = Gaussian(tf.constant(Xmu), tf.constant(Xvar))
dirac = Gaussian(tf.constant(Xmu),
tf.constant(np.zeros((num_data, D_in, D_in))))
gauss_diag = DiagonalGaussian(tf.constant(Xmu),
tf.constant(rng.rand(num_data, D_in)))
dirac_diag = DiagonalGaussian(tf.constant(Xmu),
tf.constant(np.zeros((num_data, D_in))))
dirac_markov_gauss = MarkovGaussian(
tf.constant(Xmu), tf.constant(np.zeros((2, num_data, D_in, D_in))))
# create the covariance for the pairwise markov-gaussian
dummy_gen = lambda rng, n, *shape: np.array(
[rng.randn(*shape) for _ in range(n)])
L_mg = dummy_gen(rng, num_data, D_in, 2 * D_in) # N+1 x D x 2D
LL = np.concatenate((L_mg[:-1], L_mg[1:]), 1) # N x 2D x 2D
Xcov = LL @ np.transpose(LL, (0, 2, 1))
Xc = np.concatenate((Xcov[:, :D_in, :D_in], Xcov[-1:, D_in:, D_in:]),
0) # N+1 x D x D
Xcross = np.concatenate(
(Xcov[:, :D_in, D_in:], np.zeros(
(1, D_in, D_in))), 0) # N+1 x D x D
Xcc = np.stack([Xc, Xcross]) # 2 x N+1 x D x D
markov_gauss = MarkovGaussian(Xmu, Xcc)
with gpflow.decors.defer_build():
# features
ip = features.InducingPoints(Z)
# kernels
rbf_prod_seperate_dims = kernels.Product([
kernels.RBF(1,
variance=rng.rand(),
lengthscales=rng.rand(),
active_dims=[0]),
kernels.RBF(1,
variance=rng.rand(),
lengthscales=rng.rand(),
active_dims=[1])
])
rbf_lin_sum = kernels.Sum([
kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()),
kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()),
kernels.Linear(D_in, variance=rng.rand())
])
rbf = kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand())
lin_kern = kernels.Linear(D_in, variance=rng.rand())
# mean functions
lin = mean_functions.Linear(rng.rand(D_in, D_out), rng.rand(D_out))
iden = mean_functions.Identity(
D_in) # Note: Identity can only be used if Din == Dout
zero = mean_functions.Zero(output_dim=D_out)
const = mean_functions.Constant(rng.rand(D_out))
