def __init__(self,
X,
Y,
kernel=None,
warping_function=None,
warping_terms=3):
if kernel is None:
kernel = kern.RBF(X.shape[1])
if warping_function == None:
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (
np.random.randn(self.warping_function.n_terms * 3 + 1, ) * 1)
else:
self.warping_function = warping_function
self.scale_data = False
if self.scale_data:
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False
likelihood = likelihoods.Gaussian()
GP.__init__(self,
X,
self.transform_data(),
likelihood=likelihood,
kernel=kernel)
self.link_parameter(self.warping_function)
def __init__(self,
X,
Y,
kernel=None,
warping_function=None,
warping_terms=3,
normalize_X=False,
normalize_Y=False):
if kernel is None:
kernel = kern.rbf(X.shape[1])
if warping_function == None:
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (
np.random.randn(self.warping_function.n_terms * 3 + 1, ) * 1)
self.scale_data = False
if self.scale_data:
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False
likelihood = likelihoods.Gaussian(self.transform_data(),
normalize=normalize_Y)
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
