apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-12 13:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

big merge

Browse source
This commit is contained in:
Nicolo Fusi 2013-04-10 12:23:23 +01:00
commit 60dfc34e9b
23 changed files with 867 additions and 102 deletions
Download patch file Download diff file Expand all files Collapse all files

33
GPy/core/model.py
View file

@ -23,13 +23,13 @@ class model(parameterised):
self._set_params(self._get_params())
self.preferred_optimizer = 'tnc'
def _get_params(self):
raise NotImplementedError, "this needs to be implemented to utilise the model class"
raise NotImplementedError, "this needs to be implemented to use the model class"
def _set_params(self,x):
raise NotImplementedError, "this needs to be implemented to utilise the model class"
raise NotImplementedError, "this needs to be implemented to use the model class"
def log_likelihood(self):
raise NotImplementedError, "this needs to be implemented to utilise the model class"
raise NotImplementedError, "this needs to be implemented to use the model class"
def _log_likelihood_gradients(self):
raise NotImplementedError, "this needs to be implemented to utilise the model class"
raise NotImplementedError, "this needs to be implemented to use the model class"
def set_prior(self,which,what):
"""
@ -427,6 +427,28 @@ class model(parameterised):
grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4])
print grad_string
def input_sensitivity(self):
"""
return an array describing the sesitivity of the model to each input
NB. Right now, we're basing this on the lengthscales (or variances) of the kernel.
TODO: proper sensitivity analysis
"""
if not hasattr(self,'kern'):
raise ValueError, "this model has no kernel"
k = [p for p in self.kern.parts if p.name in ['rbf','linear']]
if (not len(k)==1) or (not k[0].ARD):
raise ValueError, "cannot determine sensitivity for this kernel"
k = k[0]
if k.name=='rbf':
return k.lengthscale
elif k.name=='linear':
return 1./k.variances
def pseudo_EM(self,epsilon=.1,**kwargs):
"""
TODO: Should this not bein the GP class?
@ -471,3 +493,6 @@ class model(parameterised):
if ll_change < epsilon:
stop = True
iteration += 1
if stop:
print "%s iterations." %iteration