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fix: predictive_gradients for new posterior class

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mzwiessele 2017-01-10 14:38:09 +00:00
parent 14046705cf
commit 6fc0cc630c
1 changed files with 11 additions and 3 deletions
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14
GPy/core/gp.py
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@ -339,9 +339,17 @@ class GP(Model):
# gradients wrt the diagonal part k_{xx}
dv_dX = kern.gradients_X(np.eye(Xnew.shape[0]), Xnew)
#grads wrt 'Schur' part K_{xf}K_{ff}^{-1}K_{fx}
alpha = -2.*np.dot(kern.K(Xnew, self._predictive_variable), self.posterior.woodbury_inv)
dv_dX += kern.gradients_X(alpha, Xnew, self._predictive_variable)
return mean_jac, dv_dX
if self.posterior.woodbury_inv.ndim == 3:
tmp = np.empty(dv_dX.shape + (self.posterior.woodbury_inv.shape[2],))
tmp[:] = dv_dX[:,:,None]
for i in range(self.posterior.woodbury_inv.shape[2]):
alpha = -2.*np.dot(kern.K(Xnew, self._predictive_variable), self.posterior.woodbury_inv[:, :, i])
tmp[:, :, i] += kern.gradients_X(alpha, Xnew, self._predictive_variable)
else:
tmp = dv_dX
alpha = -2.*np.dot(kern.K(Xnew, self._predictive_variable), self.posterior.woodbury_inv)
tmp += kern.gradients_X(alpha, Xnew, self._predictive_variable)
return mean_jac, tmp
def predict_jacobian(self, Xnew, kern=None, full_cov=False):
"""