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renaming: posterior_variationa -> variational_posterior

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James Hensman 2014-02-24 19:31:13 +00:00
parent 17f9764a55
commit da4686dd3c
9 changed files with 58 additions and 63 deletions
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34
GPy/kern/_src/linear.py
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@ -106,52 +106,52 @@ class Linear(Kern):
# variational #
#---------------------------------------#
def psi0(self, Z, posterior_variational):
return np.sum(self.variances * self._mu2S(posterior_variational), 1)
def psi0(self, Z, variational_posterior):
return np.sum(self.variances * self._mu2S(variational_posterior), 1)
def psi1(self, Z, posterior_variational):
return self.K(posterior_variational.mean, Z) #the variance, it does nothing
def psi1(self, Z, variational_posterior):
return self.K(variational_posterior.mean, Z) #the variance, it does nothing
def psi2(self, Z, posterior_variational):
def psi2(self, Z, variational_posterior):
ZA = Z * self.variances
ZAinner = self._ZAinner(posterior_variational, Z)
ZAinner = self._ZAinner(variational_posterior, Z)
return np.dot(ZAinner, ZA.T)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
mu, S = posterior_variational.mean, posterior_variational.variance
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
mu, S = variational_posterior.mean, variational_posterior.variance
# psi0:
tmp = dL_dpsi0[:, None] * self._mu2S(posterior_variational)
tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior)
if self.ARD: grad = tmp.sum(0)
else: grad = np.atleast_1d(tmp.sum())
#psi1
self.update_gradients_full(dL_dpsi1, mu, Z)
grad += self.variances.gradient
#psi2
tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(posterior_variational, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
if self.ARD: grad += tmp.sum(0).sum(0).sum(0)
else: grad += tmp.sum()
#from Kmm
self.update_gradients_full(dL_dKmm, Z, None)
self.variances.gradient += grad
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
# Kmm
grad = self.gradients_X(dL_dKmm, Z, None)
#psi1
grad += self.gradients_X(dL_dpsi1.T, Z, posterior_variational.mean)
grad += self.gradients_X(dL_dpsi1.T, Z, variational_posterior.mean)
#psi2
self._weave_dpsi2_dZ(dL_dpsi2, Z, posterior_variational, grad)
self._weave_dpsi2_dZ(dL_dpsi2, Z, variational_posterior, grad)
return grad
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
grad_mu, grad_S = np.zeros(posterior_variational.mean.shape), np.zeros(posterior_variational.mean.shape)
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape)
# psi0
grad_mu += dL_dpsi0[:, None] * (2.0 * posterior_variational.mean * self.variances)
grad_mu += dL_dpsi0[:, None] * (2.0 * variational_posterior.mean * self.variances)
grad_S += dL_dpsi0[:, None] * self.variances
# psi1
grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
# psi2
self._weave_dpsi2_dmuS(dL_dpsi2, Z, posterior_variational, grad_mu, grad_S)
self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S)
return grad_mu, grad_S