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[gradients xx] getting there

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Max Zwiessele 2016-05-04 09:11:04 +01:00
parent 4efb92c554
commit 5d19039d90
3 changed files with 52 additions and 51 deletions
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GPy/core/gp.py
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@ -449,7 +449,7 @@ class GP(Model):
: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)
G = self.predict_wishart_embedding(Xnew, kern, mean, covariance)
if dimensions is None:
dimensions = self.get_most_significant_input_dimensions()[:2]
G = G[:, dimensions][:,:,dimensions]