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changed X_uncertainty for X_variance (in the code) for consistency with actual naming (in the printing)

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James Hensman 2013-04-10 09:34:15 +01:00
parent fd0b172e81
commit 45403105f9
4 changed files with 31 additions and 31 deletions
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30
GPy/models/sparse_GP.py
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@ -22,8 +22,8 @@ class sparse_GP(GP):
:type likelihood: GPy.likelihood.(Gaussian | EP)
:param kernel : the kernel/covariance function. See link kernels
:type kernel: a GPy kernel
:param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
:type X_uncertainty: np.ndarray (N x Q) | None
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x Q) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:param Zslices: slices for the inducing inputs (see slicing TODO: link)
@ -33,7 +33,7 @@ class sparse_GP(GP):
:type normalize_(X|Y): bool
"""
def __init__(self, X, likelihood, kernel, Z, X_uncertainty=None, Xslices=None,Zslices=None, normalize_X=False):
def __init__(self, X, likelihood, kernel, Z, X_variance=None, Xslices=None,Zslices=None, normalize_X=False):
self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
self.Z = Z
@ -42,12 +42,12 @@ class sparse_GP(GP):
self.M = Z.shape[0]
self.likelihood = likelihood
if X_uncertainty is None:
if X_variance is None:
self.has_uncertain_inputs=False
else:
assert X_uncertainty.shape==X.shape
assert X_variance.shape==X.shape
self.has_uncertain_inputs=True
self.X_uncertainty = X_uncertainty
self.X_variance = X_variance
if not self.likelihood.is_heteroscedastic:
self.likelihood.trYYT = np.trace(np.dot(self.likelihood.Y, self.likelihood.Y.T)) # TODO: something more elegant here?
@ -56,16 +56,16 @@ class sparse_GP(GP):
#normalize X uncertainty also
if self.has_uncertain_inputs:
self.X_uncertainty /= np.square(self._Xstd)
self.X_variance /= np.square(self._Xstd)
def _compute_kernel_matrices(self):
# kernel computations, using BGPLVM notation
self.Kmm = self.kern.K(self.Z)
if self.has_uncertain_inputs:
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty)
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_variance)
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_variance).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices)
self.psi1 = self.kern.K(self.Z,self.X)
@ -210,9 +210,9 @@ class sparse_GP(GP):
"""
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
if self.has_uncertain_inputs:
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_uncertainty)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_uncertainty)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_uncertainty)
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_variance)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_variance)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_variance)
else:
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
@ -225,8 +225,8 @@ class sparse_GP(GP):
"""
dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm,self.Z)#factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
if self.has_uncertain_inputs:
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1,self.Z,self.X, self.X_uncertainty)
dL_dZ += 2.*self.kern.dpsi2_dZ(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty) # 'stripes'
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1,self.Z,self.X, self.X_variance)
dL_dZ += 2.*self.kern.dpsi2_dZ(self.dL_dpsi2,self.Z,self.X, self.X_variance) # 'stripes'
else:
dL_dZ += self.kern.dK_dX(self.dL_dpsi1,self.Z,self.X)
return dL_dZ