def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True, dimensions=None):
G = self.predict_wishart_embedding(Xnew, kern, mean, covariance)<EOL>if dimensions is None:<EOL><INDENT>dimensions = self.get_most_significant_input_dimensions()[:<NUM_LIT:2>]<EOL><DEDENT>G = G[:, dimensions][:,:,dimensions]<EOL>from ..util.linalg import jitchol<EOL>mag = np.empty(Xnew.shape[<NUM_LIT:0>])<EOL>for n in range(Xnew.shape[<NUM_LIT:0>]):<EOL><INDENT>try:<EOL><INDENT>mag[n] = np.sqrt(np.exp(<NUM_LIT:2>*np.sum(np.log(np.diag(jitchol(G[n, :, :]))))))<EOL><DEDENT>except:<EOL><INDENT>mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))<EOL><DEDENT><DEDENT>return mag<EOL>
Predict the magnification factor as

sqrt(det(G))

for each point N in Xnew.

:param bool mean: whether to include the mean of the wishart embedding.
:param bool covariance: whether to include the covariance of the wishart embedding.
:param array-like dimensions: which dimensions of the input space to use [defaults to self.get_most_significant_input_dimensions()[:2]]