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bias now looks in shape

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James Hensman 2014-02-21 18:09:05 +00:00
parent 2da256fa93
commit ea5d19bb4e
1 changed files with 31 additions and 51 deletions
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82
GPy/kern/_src/bias.py
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@ -2,80 +2,60 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import Kernpart
from kern import Kern
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
class Bias(Kernpart):
class Bias(Kern):
def __init__(self,input_dim,variance=1.,name=None):
"""
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
super(Bias, self).__init__(input_dim, name)
from ...core.parameterization.transformations import Logexp
self.variance = Param("variance", variance, Logexp())
self.add_parameter(self.variance)
def K(self,X,X2,target):
target += self.variance
def K(self, X, X2=None):
shape = (X.shape[0], X.shape[0] if X2 is None else X2.shape[0])
ret = np.empty(shape, dtype=np.float64)
ret[:] = self.variance
return ret
def Kdiag(self,X,target):
target += self.variance
def Kdiag(self,X):
ret = np.empty((X.shape[0],), dtype=np.float64)
ret[:] = self.variance
return ret
#def dK_dtheta(self,dL_dKdiag,X,X2,target):
#target += dL_dKdiag.sum()
def update_gradients_full(self, dL_dK, X):
def update_gradients_full(self, dL_dK, X, X2=None):
self.variance.gradient = dL_dK.sum()
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target += dL_dKdiag.sum()
def update_gradients_diag(self, dL_dKdiag, X):
self.variance.gradient = dL_dK.sum()
def gradients_X(self, dL_dK,X, X2, target):
pass
return np.zeros(X.shape)
def dKdiag_dX(self,dL_dKdiag,X,target):
pass
def gradients_X_diag(self,dL_dKdiag,X,target):
return np.zeros(X.shape)
#---------------------------------------#
# PSI statistics #
#---------------------------------------#
def psi0(self, Z, mu, S, target):
target += self.variance
def psi0(self, Z, mu, S):
return self.Kdiag(mu)
def psi1(self, Z, mu, S, target):
self._psi1 = self.variance
target += self._psi1
return self.K(mu, S)
def psi2(self, Z, mu, S, target):
target += self.variance**2
ret = np.empty((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
ret[:] = self.variance**2
return ret
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
target += dL_dpsi0.sum()
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
self.variance.gradient = dL_dKmm.sum() + dL_dpsi0.sum() + dL_dpsi1.sum() + 2.*self.variance*dL_dpsi2.sum()
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
target += dL_dpsi1.sum()
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
return np.zeros(Z.shape)
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
target += 2.*self.variance*dL_dpsi2.sum()
def dpsi0_dZ(self, dL_dpsi0, Z, mu, S, target):
pass
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
pass
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
pass
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
pass
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
pass
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
pass
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
return np.zeros(mu.shape), np.zeros(S.shape)