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Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

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This commit is contained in:
Neil Lawrence 2013-10-09 11:14:47 +01:00
commit b2cea56c11
14 changed files with 38 additions and 30 deletions
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12
GPy/kern/kern.py
View file

@ -31,7 +31,7 @@ class kern(Parameterized):
"""
self.parts = parts
self.Nparts = len(parts)
self.num_parts = len(parts)
self.num_params = sum([p.num_params for p in self.parts])
self.input_dim = input_dim
@ -61,7 +61,7 @@ class kern(Parameterized):
here just all the indices, rest can get recomputed
"""
return Parameterized.getstate(self) + [self.parts,
self.Nparts,
self.num_parts,
self.num_params,
self.input_dim,
self.input_slices,
@ -73,7 +73,7 @@ class kern(Parameterized):
self.input_slices = state.pop()
self.input_dim = state.pop()
self.num_params = state.pop()
self.Nparts = state.pop()
self.num_parts = state.pop()
self.parts = state.pop()
Parameterized.setstate(self, state)
@ -308,7 +308,7 @@ class kern(Parameterized):
def K(self, X, X2=None, which_parts='all'):
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
assert X.shape[1] == self.input_dim
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
@ -359,7 +359,7 @@ class kern(Parameterized):
def Kdiag(self, X, which_parts='all'):
"""Compute the diagonal of the covariance function for inputs X."""
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
assert X.shape[1] == self.input_dim
target = np.zeros(X.shape[0])
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self.parts, self.input_slices, which_parts) if part_on]
@ -497,7 +497,7 @@ class kern(Parameterized):
def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
if which_parts == 'all':
which_parts = [True] * self.Nparts
which_parts = [True] * self.num_parts
if self.input_dim == 1:
if x is None:
x = np.zeros((1, 1))

2
GPy/kern/parts/periodic_Matern32.py
View file

@ -113,7 +113,7 @@ class PeriodicMatern32(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

2
GPy/kern/parts/periodic_Matern52.py
View file

@ -115,7 +115,7 @@ class PeriodicMatern52(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

2
GPy/kern/parts/periodic_exponential.py
View file

@ -111,7 +111,7 @@ class PeriodicExponential(Kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)