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some gplvm related fixes

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This commit is contained in:
James Hensman 2014-01-24 16:37:20 +00:00
parent a71bbc0d21
commit 563fbd257b
6 changed files with 19 additions and 26 deletions
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12
GPy/kern/kern.py
View file

@ -347,11 +347,11 @@ class kern(Parameterized):
def update_gradients_full(self, dL_dK, X):
[p.update_gradients_full(dL_dK, X) for p in self._parameters_]
pass
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
pass
raise NotImplementedError
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
pass
raise NotImplementedError
def dK_dtheta(self, dL_dK, X, X2=None):
"""
@ -375,7 +375,7 @@ class kern(Parameterized):
return self._transform_gradients(target)
def dK_dX(self, dL_dK, X, X2=None):
def gradients_X(self, dL_dK, X, X2=None):
"""Compute the gradient of the objective function with respect to X.
:param dL_dK: An array of gradients of the objective function with respect to the covariance function.
@ -387,9 +387,9 @@ class kern(Parameterized):
target = np.zeros_like(X)
if X2 is None:
[p.dK_dX(dL_dK, X[:, i_s], None, target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.gradients_X(dL_dK, X[:, i_s], None, target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
else:
[p.dK_dX(dL_dK, X[:, i_s], X2[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.gradients_X(dL_dK, X[:, i_s], X2[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
return target
def Kdiag(self, X, which_parts='all'):

20
GPy/kern/parts/bias.py
View file

@ -16,17 +16,6 @@ class Bias(Kernpart):
super(Bias, self).__init__(input_dim, name)
self.variance = Param("variance", variance)
self.add_parameter(self.variance)
#self._set_params(np.array([variance]).flatten())
# def _get_params(self):
# return self.variance
#
# def _set_params(self,x):
# assert x.shape==(1,)
# self.variance = x
#
# def _get_param_names(self):
# return ['variance']
def K(self,X,X2,target):
target += self.variance
@ -34,18 +23,21 @@ class Bias(Kernpart):
def Kdiag(self,X,target):
target += self.variance
def dK_dtheta(self,dL_dKdiag,X,X2,target):
target += dL_dKdiag.sum()
#def dK_dtheta(self,dL_dKdiag,X,X2,target):
#target += dL_dKdiag.sum()
def update_gradients_full(self, dL_dK, X):
self.variance.gradient = dL_dK.sum()
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target += dL_dKdiag.sum()
def dK_dX(self, dL_dK,X, X2, target):
def gradients_X(self, dL_dK,X, X2, target):
pass
def dKdiag_dX(self,dL_dKdiag,X,target):
pass
#---------------------------------------#
# PSI statistics #
#---------------------------------------#

6
GPy/kern/parts/rbf.py
View file

@ -161,7 +161,7 @@ class RBF(Kernpart):
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)
def _gradients_X(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
#if self._X is None or X.base is not self._X.base or X2 is not None:
self._K_computations(X, X2)
if X2 is None:
@ -260,7 +260,7 @@ class RBF(Kernpart):
}
"""
num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
X = param_to_array(X)
X, dvardLdK = param_to_array(X, dvardLdK)
weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
code = """
@ -277,7 +277,7 @@ class RBF(Kernpart):
}
"""
num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
X, X2 = param_to_array(X, X2)
X, X2, dvardLdK = param_to_array(X, X2, dvardLdK)
weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
return target