def predict_wishart_embedding(self, Xnew, kern=None, mean=True, covariance=True):
if kern is None:<EOL><INDENT>kern = self.kern<EOL><DEDENT>mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)<EOL>mumuT = np.einsum('<STR_LIT>', mu_jac, mu_jac)<EOL>Sigma = np.zeros(mumuT.shape)<EOL>if var_jac.ndim == <NUM_LIT:4>: <EOL><INDENT>Sigma = var_jac.sum(-<NUM_LIT:1>)<EOL><DEDENT>else:<EOL><INDENT>Sigma = self.output_dim*var_jac<EOL><DEDENT>G = <NUM_LIT:0.><EOL>if mean:<EOL><INDENT>G += mumuT<EOL><DEDENT>if covariance:<EOL><INDENT>G += Sigma<EOL><DEDENT>return G<EOL>
Predict the wishart embedding G of the GP. This is the density of the
input of the GP defined by the probabilistic function mapping f.
G = J_mean.T*J_mean + output_dim*J_cov.

:param array-like Xnew: The points at which to evaluate the magnification.
:param :py:class:`~GPy.kern.Kern` kern: The kernel to use for the magnification.

Supplying only a part of the learning kernel gives insights into the density
of the specific kernel part of the input function. E.g. one can see how dense the
linear part of a kernel is compared to the non-linear part etc.