download_data(args.input_path)
X, Y, C = load_data(args.input_path, fname)
if len(X.shape) == 1:
X = X[:, None]
n_tasks = Y.shape[0]
n_configs = X.shape[0]
index_task = np.repeat(np.arange(n_tasks), n_configs)
Y_norm, _, _ = normalize_Y(deepcopy(Y.flatten()), index_task)
# train the probabilistic encoder
kern = GPy.kern.Matern52(Q_h, ARD=True)
m_lvm = BayesianGPLVM(Y_norm.reshape(n_tasks, n_configs),
Q_h,
kernel=kern,
num_inducing=n_inducing_lvm)
m_lvm.optimize(max_iters=10000, messages=1)
ls = np.array([
m_lvm.kern.lengthscale[i]
for i in range(m_lvm.kern.lengthscale.shape[0])
])
# generate data to train the multi-task model
task_features_mean = np.array(m_lvm.X.mean / ls)
task_features_std = np.array(np.sqrt(m_lvm.X.variance) / ls)
X_train = []
Y_train = []
C_train = []
