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added gplbm and sparse gp to new parameterized structure

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Max Zwiessele 2013-11-06 11:40:54 +00:00
parent c8eac84d55
commit 3316d29341
13 changed files with 106 additions and 96 deletions
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34
GPy/core/sparse_gp.py
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@ -51,15 +51,13 @@ class SparseGP(GPBase):
if self.has_uncertain_inputs:
self.X_variance /= np.square(self._Xscale)
self.Z = Param('inducing input', self.Z)
self._const_jitter = None
self.Z = Param('inducing inputs', self.Z)
self.add_parameter(self.Z, gradient=self.dL_dZ, index=0)
self.add_parameter(self.kern, gradient=self.dL_dtheta)
self._compute_kernel_matrices()
self.Z.add_observer(self, lambda Z: self._compute_kernel_matrices())
#self.Z._notify_observers()
self._const_jitter = None
self.add_parameter(self.likelihood, gradient=lambda:self.likelihood._gradients(partial=self.partial_for_likelihood))
#self.Z.add_observer(self, lambda Z: self._compute_kernel_matrices() or self._computations())
def getstate(self):
"""
@ -165,7 +163,7 @@ class SparseGP(GPBase):
# the partial derivative vector for the likelihood
if self.likelihood.num_params == 0:
if self.likelihood.size == 0:
# save computation here.
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:
@ -211,19 +209,19 @@ class SparseGP(GPBase):
#self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
#self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.num_params])
#self.likelihood._set_params(p[self.Z.size + self.kern.num_params:])
#self._compute_kernel_matrices()
self._compute_kernel_matrices()
import ipdb;ipdb.set_trace()
self._computations()
self.Cpsi1V = None
# make sparse_gp compatible with gp_base gradients:
self.dL_dK = self.dL_dKmm
super(SparseGP, self).parameters_changed()
def _get_params(self):
return np.hstack([self.Z.flatten(), self.kern._get_params_transformed(), self.likelihood._get_params()])
def _get_param_names(self):
return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], [])\
+ self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
# def _get_params(self):
# return np.hstack([self.Z.flatten(), self.kern._get_params_transformed(), self.likelihood._get_params()])
#
# def _get_param_names(self):
# return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], [])\
# + self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
#def _get_print_names(self):
# return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
@ -249,8 +247,8 @@ class SparseGP(GPBase):
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self._set_params(self._get_params()) # update the GP
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
# def _log_likelihood_gradients(self):
# return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
def dL_dtheta(self):
"""