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Minor merges.

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Neil Lawrence 2014-02-24 21:20:00 +00:00
commit 8c5fe76bb1
51 changed files with 1342 additions and 2752 deletions
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37
GPy/kern/_src/kern.py
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@ -26,11 +26,11 @@ class Kern(Parameterized):
raise NotImplementedError
def Kdiag(self, Xa):
raise NotImplementedError
def psi0(self,Z,posterior_variational):
def psi0(self,Z,variational_posterior):
raise NotImplementedError
def psi1(self,Z,posterior_variational):
def psi1(self,Z,variational_posterior):
raise NotImplementedError
def psi2(self,Z,posterior_variational):
def psi2(self,Z,variational_posterior):
raise NotImplementedError
def gradients_X(self, dL_dK, X, X2):
raise NotImplementedError
@ -49,28 +49,32 @@ class Kern(Parameterized):
self._collect_gradient(target)
self._set_gradient(target)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
"""Set the gradients of all parameters when doing variational (M) inference with uncertain inputs."""
raise NotImplementedError
def gradients_Z_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
grad = self.gradients_X(dL_dKmm, Z)
grad += self.gradients_X(dL_dKnm.T, Z, X)
return grad
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
raise NotImplementedError
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
raise NotImplementedError
def plot_ARD(self, *args):
"""If an ARD kernel is present, plot a bar representation using matplotlib
See GPy.plotting.matplot_dep.plot_ARD
"""
def plot_ARD(self, *args, **kw):
if "matplotlib" in sys.modules:
from ...plotting.matplot_dep import kernel_plots
self.plot_ARD.__doc__ += kernel_plots.plot_ARD.__doc__
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ...plotting.matplot_dep import kernel_plots
return kernel_plots.plot_ARD(self,*args)
return kernel_plots.plot_ARD(self,*args,**kw)
def input_sensitivity(self):
"""
Returns the sensitivity for each dimension of this kernel.
"""
return np.zeros(self.input_dim)
def __add__(self, other):
""" Overloading of the '+' operator. for more control, see self.add """
return self.add(other)
@ -101,7 +105,7 @@ class Kern(Parameterized):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)
def __pow__(self, other, tensor=False):
def __pow__(self, other):
"""
Shortcut for tensor `prod`.
"""
@ -127,11 +131,12 @@ from GPy.core.model import Model
class Kern_check_model(Model):
"""This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgrad() to be called independently on a kernel."""
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
from GPy.kern import RBF
Model.__init__(self, 'kernel_test_model')
num_samples = 20
num_samples2 = 10
if kernel==None:
kernel = GPy.kern.rbf(1)
kernel = RBF(1)
if X==None:
X = np.random.randn(num_samples, kernel.input_dim)
if dL_dK==None: