apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Refactored gradients wrt parameters slightly, need to future proof against _get_param_names() disappearing

Browse source
This commit is contained in:
Alan Saul 2013-10-17 18:33:08 +01:00
parent 1848653fce
commit 10f3f7d14a
2 changed files with 32 additions and 15 deletions
Download patch file Download diff file Expand all files Collapse all files

5
GPy/likelihoods/laplace.py
View file

@ -123,7 +123,9 @@ class Laplace(likelihood):
dL_dfhat, I_KW_i = self._shared_gradients_components()
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = self.noise_model._laplace_gradients(self.f_hat, self.data, extra_data=self.extra_data)
num_params = len(dlik_dthetaL)
#len(dlik_dthetaL)
num_params = len(self._get_param_names())
print num_params
# make space for one derivative for each likelihood parameter
dL_dthetaL = np.zeros(num_params)
for thetaL_i in range(num_params):
@ -138,6 +140,7 @@ class Laplace(likelihood):
dL_dthetaL_imp = np.dot(dL_dfhat, dfhat_dthetaL)
dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
print dL_dthetaL
return dL_dthetaL
def _compute_GP_variables(self):

42
GPy/likelihoods/noise_models/noise_distributions.py
View file

@ -270,6 +270,7 @@ class NoiseDistribution(object):
def _predictive_mean_numerical(self,mu,sigma):
"""
Laplace approximation to the predictive mean: E(Y_star|Y) = E( E(Y_star|f_star, Y) )
if self.
:param mu: cavity distribution mean
:param sigma: cavity distribution standard deviation
@ -541,32 +542,45 @@ class NoiseDistribution(object):
"""
TODO: Doc strings
"""
link_f = self.gp_link.transf(f)
return self.dlogpdf_link_dtheta(link_f, y, extra_data=extra_data)
if len(self._get_param_names()) > 0:
link_f = self.gp_link.transf(f)
return self.dlogpdf_link_dtheta(link_f, y, extra_data=extra_data)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([1, 0])
def dlogpdf_df_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
if len(self._get_param_names()) > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
def d2logpdf_df2_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
d2link_df2 = self.gp_link.d2transf_df2(f)
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(link_f, y, extra_data=extra_data)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
#FIXME: Why isn't this chain_1?
#return chain_1(d2logpdf_dlink2_dtheta, d2link_df2)
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
if len(self._get_param_names()) > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
d2link_df2 = self.gp_link.d2transf_df2(f)
d2logpdf_dlink2_dtheta = self.d2logpdf_dlink2_dtheta(link_f, y, extra_data=extra_data)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
#FIXME: Why isn't this chain_1?
#return chain_1(d2logpdf_dlink2_dtheta, d2link_df2)
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
def _laplace_gradients(self, f, y, extra_data=None):
#Bit nasty we recompute thesesome of these but it keeps it modular
#link_f = self.gp_link.transf(f)
#dlink_df = self.gp_link.dtransf_df(f)
#d2link_df2 = self.gp_link.d2transf_df2(f)