# create link inputs (link inputs used repeatedly but can have link input per group)
# permute to bring diagonal to first position
link_inputs = [[] for _ in range(output_dim)]
for i in range(output_dim):
idx = list(range(d_link.shape[0]))
link_inputs[i] = d_link[[idx.pop(i)] + idx, :]
link_inputs = link_inputs + [1.0 for i in range(output_dim)
] # for full W row blocks, independent nodes
# create 'between' kernel list
klink_rows = [
kernels.CompositeKernel('mul', [
kernels.RadialBasis(
2, std_dev=2.0, lengthscale=1.0, white=0.01, input_scaling=IS_ARD),
kernels.CompactSlice(
2, active_dims=[0, 1], lengthscale=2.0, input_scaling=IS_ARD)
]) for i in range(output_dim)
]
klink_f = [1.0 for i in range(node_dim)]
kernlink = klink_rows + klink_f
# create 'within' kernel
# kern
lag_active_dims_s = [[] for _ in range(output_dim)]
for i in range(output_dim):
lag_active_dims_s[i] = list(range(lag_dim * i, lag_dim * (i + 1)))
k_rows = [
kernels.CompositeKernel('mul', [
kernels.RadialBasisSlice(lag_dim,
startsite = 0 # ignore for mtg
# extract dataset
d = get_inputs()
Ytr, Yte, Xtr, Xte = d['Ytr'], d['Yte'], d['Xtr'], d['Xte']
Ytr, Yte = Ytr.reshape(Ytr.shape[0],1), Yte.reshape(Yte.shape[0],1)
data = datasets.DataSet(Xtr.astype(np.float32), Ytr.astype(np.float32), shuffle=False)
test = datasets.DataSet(Xte.astype(np.float32), Yte.astype(np.float32), shuffle=False)
print("dataset created")
klags = kernels.RadialBasisSlice(lag_dim, active_dims=[0,1], lengthscale=0.01,
std_dev = 0.0, input_scaling = IS_ARD, white = 0.05)
kspat = kernels.CompositeKernel('mul',[kernels.RadialBasisSlice(2, active_dims=[2,3],
std_dev=2.0, lengthscale=0.5, white=0.05, input_scaling = IS_ARD),
kernels.CompactSlice(2, active_dims=[2,3], lengthscale = 2.0,
white = 0.05, input_scaling = IS_ARD)] )
ktime = kernels.PeriodicSliceFixed(1, active_dims=[Xtr.shape[1]-1], lengthscale=0.5,
std_dev=1.5, period=144, white = 0.05)
k_spattemp = kernels.CompositeKernel('mul', kernel_inputs=[kspat, ktime])
kern = [kernels.CompositeKernel('mul', kernel_inputs=[klags, k_spattemp])]
likelihood = likelihoods.Gaussian(0.1)
print("likelihood and kernels set")
Z = init_z(data.X, NUM_INDUCING)
m = mmgp.IndependentMMGP(output_dim, likelihood, kern, Z, num_components=NUM_COMPONENTS,
diag_post=DIAG_POST,
sparse_post=SPARSE_POST,
num_samples=NUM_SAMPLES,
predict_samples=PRED_SAMPLES)
print("inducing points and model set")