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beginnings of neat gradient framework

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James Hensman 2014-01-24 11:17:49 +00:00
parent 1dd5efd869
commit 2c4d7cca76
1 changed files with 7 additions and 0 deletions
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7
GPy/kern/kern.py
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@ -345,6 +345,13 @@ class kern(Parameterized):
[p.K(X[:, i_s], X2[:, i_s], target=target) for p, i_s, part_i_used in zip(self._parameters_, self.input_slices, which_parts) if part_i_used]
return target
def update_gradients_full(self, dL_dK, X):
pass
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
pass
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
pass
def dK_dtheta(self, dL_dK, X, X2=None):
"""
Compute the gradient of the covariance function with respect to the parameters.