def get_dataset(CONFIG, X, Y, z_r):
data = gprn.Dataset()
num_data_sources = X.shape[0]
for i in range(num_data_sources):
x = np.array(X[i])
y = np.array(Y[i])
M = x.shape[1]
print(M)
b = 300
b = b if b < x.shape[0] else None
if CONFIG['exp_ignore_i'] is not None:
if i == CONFIG['exp_ignore_i']: continue
data.add_source_dict({
'M': M,
'x': x,
'y': y,
#'z': x,
'batch_size': b
})
data.add_inducing_points(z_r)
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]:
#for i in [1]:
x = X[i]
y = Y[i]
print('dataset: ', i, ' ', x.shape)
M = x.shape[1]
data.add_source_dict({
'active_tasks': [[0], [0]],
'M': M,
'x': x,
'y': y,
'z': z_r,
'batch_size': None
})
#data.add_inducing_points(z_r);
return data
def get_dataset(X, Y, z_r):
data = gprn.Dataset()
rs = lambda x: x.reshape([x.shape[0] * x.shape[1], x.shape[2]])
for i in [1]:
x = X[i][:, 0, :]
y = Y[i]
print('dataset: ', i, ' ', x.shape)
data.add_source_dict({'x': x, 'y': y, 'z': z_r, 'batch_size': None})
#data.add_inducing_points(z_r);
return data