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merged params here

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Max Zwiessele 2014-03-10 16:01:32 +00:00
parent dab35dcbb0
commit 7e9078b0f9
1 changed files with 3 additions and 4 deletions
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7
GPy/kern/_src/add.py
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@ -24,6 +24,9 @@ class Add(Kern):
super(Add, self).__init__(input_dim, 'add') super(Add, self).__init__(input_dim, 'add')
self.add_parameters(*subkerns) self.add_parameters(*subkerns)
@property
def parts(self):
return self._parameters_
def K(self, X, X2=None): def K(self, X, X2=None):
""" """
@ -107,8 +110,6 @@ class Add(Kern):
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from static import White, Bias from static import White, Bias
mu, S = variational_posterior.mean, variational_posterior.variance
for p1, is1 in zip(self._parameters_, self.input_slices): for p1, is1 in zip(self._parameters_, self.input_slices):
#compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2! #compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2!
@ -129,7 +130,6 @@ class Add(Kern):
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior): def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from static import White, Bias from static import White, Bias
target = np.zeros(Z.shape) target = np.zeros(Z.shape)
for p1, is1 in zip(self._parameters_, self.input_slices): for p1, is1 in zip(self._parameters_, self.input_slices):
@ -151,7 +151,6 @@ class Add(Kern):
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from static import White, Bias from static import White, Bias
target_mu = np.zeros(variational_posterior.shape) target_mu = np.zeros(variational_posterior.shape)
target_S = np.zeros(variational_posterior.shape) target_S = np.zeros(variational_posterior.shape)
for p1, is1 in zip(self._parameters_, self.input_slices): for p1, is1 in zip(self._parameters_, self.input_slices):