B.epsilon = 1e-8
wbml.out.report_time = True
wd = WorkingDirectory("_experiments", "simulators", log=f"log{suffix}.txt")
# Load data.
loc, temp, sims = load()
sims = {k: v for k, v in list(sims.items())}
x_data = np.array([(day - temp.index[0]).days
for day in temp.index[:args.n]])
y_data = np.concatenate([sim.to_numpy()[:args.n] for sim in sims.values()],
axis=1)
wbml.out.out("Data loaded")
# Normalise training data.
normaliser = Normaliser(y_data)
y_data = normaliser.normalise(y_data)
# Determine initialisation of spatial length scales.
scales_init = 0.5 * np.array(loc.max() - loc.min())
# Convert to PyTorch.
loc = torch.tensor(np.array(loc), dtype=torch.float64)
x_data = torch.tensor(x_data, dtype=torch.float64)
y_data = torch.tensor(y_data, dtype=torch.float64)
# Determine number of latent processes.
m_r = args.mr
m_s = args.ms
m = m_r * m_s
from wbml.data.exchange import load
from wbml.experiment import WorkingDirectory
if __name__ == "__main__":
wbml.out.report_time = True
wd = WorkingDirectory("_experiments", "exchange_ilmm")
B.epsilon = 1e-8
_, train, test = load()
x = np.array(train.index)
y = np.array(train)
# Normalise data.
normaliser = Normaliser(y)
y_norm = normaliser.normalise(y)
p = B.shape(y)[1]
m = 3
vs = Vars(torch.float64)
def construct_model(vs):
kernels = [
vs.pos(1, name=f"{i}/var") *
Matern12().stretch(vs.pos(0.1, name=f"{i}/scale"))
for i in range(m)
]
noise = vs.pos(1e-2, name="noise")
latent_noises = vs.pos(1e-2 * B.ones(m), name="latent_noises")
h = Dense(vs.get(shape=(p, m), name="h"))
min_periods=1,
win_type="hamming")
temp = temp.mean().iloc[::31, :]
# Create train and test splits
x = np.array([(day - temp.index[0]).days for day in temp.index])
y = np.array(temp)
# Divide into training and test set.
x_train = x[:250]
y_train = y[:250]
x_test = x[250:350]
y_test = y[250:350]
# Perform normalisation.
normaliser = Normaliser(y_train)
y_train_norm = normaliser.normalise(y_train)
# Determine initialisation of spatial length scales.
scales_init = np.maximum(0.2 * np.array(loc.max() - loc.min()), 1)
# Convert to PyTorch.
loc = torch.tensor(np.array(loc))
p = B.shape(y)[1]
m = args.m
vs = Vars(torch.float64)
def construct_model(vs):
kernels = [
vs.pos(0.5, name=f"{i}/k_var") *