def unnormalise_y(y_):
return B.add(B.multiply(y_, stds), means)
from wbml.out import Counter
from .model import GPAR, per_output
__all__ = ["GPARRegressor", "log_transform", "squishing_transform"]
log = logging.getLogger(__name__)
_dispatch = Dispatcher()
#: Log transform for the data.
log_transform = (B.log, B.exp)
#: Squishing transform for the data.
squishing_transform = (
lambda x: B.sign(x) * B.log(B.add(1, B.abs(x))),
lambda x: B.sign(x) * B.subtract(B.exp(B.abs(x)), 1),
)
def _vector_from_init(init, length):
# If only a single value is given, create ones.
if np.size(init) == 1:
return init * np.ones(length)
# Multiple values are given. Check that enough values are available.
init_squeezed = np.squeeze(init)
if np.ndim(init_squeezed) != 1:
raise ValueError("Incorrect shape {} of hyperparameters."
"".format(np.shape(init)))
if np.size(init_squeezed) < length: # Squeezed doesn't change size.