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_highest_parent_ now follows the tree, dK_dX > gradient_X, added update_grads_variational to linear, bgplvm for new framework

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Max Zwiessele 2014-02-10 15:12:49 +00:00
parent 87dab55fe1
commit e0c68d5eb3
41 changed files with 269 additions and 291 deletions
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6
GPy/kern/parts/rbf_inv.py
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@ -142,14 +142,14 @@ class RBFInv(RBF):
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK) * (-self.lengthscale2)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if X2 is None:
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass