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[pickle] load errors bc of kernel changes, backwards compatibility issues fixed

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Max Zwiessele 2015-11-09 10:09:07 +00:00
parent 6f9c5042f9
commit 850c10beaa
4 changed files with 42 additions and 53 deletions
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18
GPy/kern/src/kern.py
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@ -58,7 +58,11 @@ class Kern(Parameterized):
self.useGPU = self._support_GPU and useGPU
from .psi_comp import PSICOMP_GH
self.psicomp = PSICOMP_GH()
self.psicomp = PSICOMP_GH()
def __setstate__(self, state):
self._all_dims_active = range(0, max(state['active_dims'])+1)
super(Kern, self).__setstate__(state)
@property
def _effective_input_dim(self):
@ -209,15 +213,15 @@ class Kern(Parameterized):
def get_most_significant_input_dimensions(self, which_indices=None):
"""
Determine which dimensions should be plotted
Returns the top three most signification input dimensions
if less then three dimensions, the non existing dimensions are
labeled as None, so for a 1 dimensional input this returns
(0, None, None).
:param which_indices: force the indices to be the given indices.
:type which_indices: int or tuple(int,int) or tuple(int,int,int)
:param which_indices: force the indices to be the given indices.
:type which_indices: int or tuple(int,int) or tuple(int,int,int)
"""
if which_indices is None:
which_indices = np.argsort(self.input_sensitivity())[::-1][:3]
@ -233,7 +237,7 @@ class Kern(Parameterized):
input_1, input_2 = which_indices, None
except ValueError:
# which_indices was a list or array like with only one int
input_1, input_2 = which_indices[0], None
input_1, input_2 = which_indices[0], None
return input_1, input_2, input_3

5
GPy/kern/src/rbf.py
View file

@ -47,12 +47,13 @@ class RBF(Stationary):
return dc
def __setstate__(self, state):
self.use_invLengthscale = False
return super(RBF, self).__setstate__(state)
def spectrum(self, omega):
assert self.input_dim == 1 #TODO: higher dim spectra?
return self.variance*np.sqrt(2*np.pi)*self.lengthscale*np.exp(-self.lengthscale*2*omega**2/2)
def parameters_changed(self):
if self.use_invLengthscale: self.lengthscale[:] = 1./np.sqrt(self.inv_l+1e-200)
super(RBF,self).parameters_changed()
@ -85,7 +86,7 @@ class RBF(Stationary):
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[3:]
def update_gradients_diag(self, dL_dKdiag, X):
super(RBF,self).update_gradients_diag(dL_dKdiag, X)
if self.use_invLengthscale: self.inv_l.gradient =self.lengthscale.gradient*(self.lengthscale**3/-2.)