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[magnification] prediction now accepts dimensions

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Max Zwiessele 2016-04-04 10:24:44 +01:00
parent 14c881834e
commit 1753b89334
1 changed files with 9 additions and 2 deletions
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11
GPy/core/gp.py
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@ -437,15 +437,22 @@ class GP(Model):
warnings.warn("Wrong naming, use predict_wishart_embedding instead. Will be removed in future versions!", DeprecationWarning)
return self.predict_wishart_embedding(Xnew, kern, mean, covariance)
def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True):
def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True, dimensions=None):
"""
Predict the magnification factor as
sqrt(det(G))
for each point N in Xnew
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]]
"""
G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
if dimensions is None:
dimensions = self.get_most_significant_input_dimensions()[:2]
G = G[:, dimensions][:,:,dimensions]
from ..util.linalg import jitchol
mag = np.empty(Xnew.shape[0])
for n in range(Xnew.shape[0]):