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adapt the new interface of the variational posterior distribution.

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This commit is contained in:
Zhenwen Dai 2014-02-21 17:56:37 +00:00
parent 34d9f90d92
commit 99c6a2095f
7 changed files with 96 additions and 389 deletions
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23
GPy/kern/_src/rbf.py
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@ -79,16 +79,21 @@ class RBF(Kern):
ret[:] = self.variance
return ret
def psi0(self, Z, mu, S):
def psi0(self, Z, posterior_variational):
mu = posterior_variational.mean
ret = np.empty(mu.shape[0], dtype=np.float64)
ret[:] = self.variance
return ret
def psi1(self, Z, mu, S):
def psi1(self, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
return self._psi1
def psi2(self, Z, mu, S):
def psi2(self, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
return self._psi2
@ -121,7 +126,9 @@ class RBF(Kern):
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
#contributions from psi0:
@ -155,7 +162,9 @@ class RBF(Kern):
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
#psi1
@ -173,7 +182,9 @@ class RBF(Kern):
return grad
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
#psi1
tmp = self._psi1[:, :, None] / self.lengthscale2 / self._psi1_denom