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weird Max related stuff is happening

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This commit is contained in:
James Hensman 2014-02-20 14:24:41 +00:00
parent 41b8b7edd8
commit 87ce8fea0b
3 changed files with 9 additions and 22 deletions
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1
GPy/core/sparse_gp.py
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@ -58,6 +58,7 @@ class SparseGP(GP):
if not self.Z.is_fixed:
if self.X_variance is None:
self.Z.gradient = self.kern.gradients_Z_sparse(X=self.X, Z=self.Z, **self.grad_dict)
print self.Z.gradient
else:
self.Z.gradient = self.kern.gradients_Z_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
print self.Z.gradient

4
GPy/kern/_src/add.py
View file

@ -71,7 +71,7 @@ class Add(Kern):
def psi0(self, Z, mu, S):
return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices))],0)
return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
def psi1(self, Z, mu, S):
return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
@ -93,7 +93,7 @@ class Add(Kern):
pass
# rbf X bias
#elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
elif isinstance(p1, Bias) and isinstance(p2, (RBF, Linear))):
elif isinstance(p1, Bias) and isinstance(p2, (RBF, Linear)):
tmp = p2.psi1(Z[:,i2], mu[:,i2], S[:,i2])
psi2 += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
#elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):

26
GPy/kern/_src/kern.py
View file

@ -26,33 +26,15 @@ class Kern(Parameterized):
raise NotImplementedError
def Kdiag(self, Xa ,target):
raise NotImplementedError
def _param_grad_helper(self, dL_dK,X, X2, target):
raise NotImplementedError
def psi0(self,Z,mu,S,target):
raise NotImplementedError
def dpsi0_dtheta(self,dL_dpsi0, Z,mu,S,target):
raise NotImplementedError
def dpsi0_dmuS(self,dL_dpsi0,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def psi1(self,Z,mu,S,target):
raise NotImplementedError
def dpsi1_dtheta(self,Z,mu,S,target):
raise NotImplementedError
def dpsi1_dZ(self,dL_dpsi1,Z,mu,S,target):
raise NotImplementedError
def dpsi1_dmuS(self,dL_dpsi1,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def psi2(self,Z,mu,S,target):
raise NotImplementedError
def dpsi2_dZ(self,dL_dpsi2,Z,mu,S,target):
def gradients_X(self, dL_dK, X, X2):
raise NotImplementedError
def dpsi2_dtheta(self,dL_dpsi2,Z,mu,S,target):
raise NotImplementedError
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def gradients_X(self, dL_dK, X, X2, target):
raise NotImplementedError
def dKdiag_dX(self, dL_dK, X, target):
def gradients_X_diag(self, dL_dK, X):
raise NotImplementedError
def update_gradients_full(self, dL_dK, X):
"""Set the gradients of all parameters when doing full (N) inference."""
@ -63,6 +45,10 @@ class Kern(Parameterized):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
"""Set the gradients of all parameters when doing variational (M) inference with uncertain inputs."""
raise NotImplementedError
def gradients_Z_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
grad = self.gradients_X(dL_dKmm, Z)
grad += self.gradients_X(dL_dKnm.T, Z, X)
return grad
def plot_ARD(self, *args):
"""If an ARD kernel is present, plot a bar representation using matplotlib