def get_dataset(CONFIG, X, Y, z_r):
data = gprn.Dataset()
num_data_sources = X.shape[0]
r = [0, 1, 2]
#for i in range(num_data_sources):
for i in r:
x = X[i]
y = Y[i]
print('dataset: ', i, ' ', x.shape)
M = x.shape[1]
b = y.shape[0]
data.add_source_dict({
'M': M,
'x': x,
'y': y,
#'z': x,
'batch_size': b,
'active_tasks': [[0], [0], [0]]
})
data.add_inducing_points(z_r)
return data
def get_data(self, N):
np.random.seed(0)
self.x = np.expand_dims(np.random.random(N), -1).astype(np.float32)
self.y = np.expand_dims(np.random.random(N), -1).astype(np.float32)
data = gprn.Dataset()
data.add_source_dict({'x': self.x, 'y': self.y, 'batch_size': None})
return data
def get_dataset(X, Y, z_r):
data = gprn.Dataset()
num_data_sources = X.shape[0]
#for i in range(num_data_sources):
for i in [0, 1, 2]:
x = X[i]
y = Y[i]
print('dataset: ', i, ' ', x.shape)
M = x.shape[1]
data.add_source_dict({
'M': M,
'x': x,
'y': y,
#'z': x,
'batch_size': y.shape[0]
})
data.add_inducing_points(z_r)
return data