apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 04:52:37 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

link_function class renamed as LinkFunction

Browse source
This commit is contained in:
Ricardo 2013-06-05 18:11:08 +01:00
parent a2bc6ec1e6
commit 2da309aab8
3 changed files with 9 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

6
GPy/likelihoods/likelihood_functions.py
View file

@ -21,7 +21,7 @@ class LikelihoodFunction(object):
if link == self._analytical: if link == self._analytical:
self.moments_match = self._moments_match_analytical self.moments_match = self._moments_match_analytical
else: else:
assert isinstance(link,link_functions.link_function) assert isinstance(link,link_functions.LinkFunction)
self.link = link self.link = link
self.moments_match = self._moments_match_numerical self.moments_match = self._moments_match_numerical
@ -79,7 +79,7 @@ class Binomial(LikelihoodFunction):
$$ $$
""" """
def __init__(self,link=None): def __init__(self,link=None):
self._analytical = link_functions.probit self._analytical = link_functions.Probit
if not link: if not link:
link = self._analytical link = self._analytical
super(Binomial, self).__init__(link) super(Binomial, self).__init__(link)
@ -146,7 +146,7 @@ class Poisson(LikelihoodFunction):
def __init__(self,link=None): def __init__(self,link=None):
self._analytical = None self._analytical = None
if not link: if not link:
link = link_functions.log() link = link_functions.Log()
super(Poisson, self).__init__(link) super(Poisson, self).__init__(link)
def _distribution(self,gp,obs): def _distribution(self,gp,obs):

11
GPy/likelihoods/link_functions.py
View file

@ -9,7 +9,7 @@ import pylab as pb
from ..util.plot import gpplot from ..util.plot import gpplot
from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
class link_function(object): class LinkFunction(object):
""" """
Link function class for doing non-Gaussian likelihoods approximation Link function class for doing non-Gaussian likelihoods approximation
@ -19,7 +19,7 @@ class link_function(object):
def __init__(self): def __init__(self):
pass pass
class identity(link_function): class Identity(LinkFunction):
def transf(self,mu): def transf(self,mu):
return mu return mu
@ -29,7 +29,7 @@ class identity(link_function):
def log_inv_transf(self,f): def log_inv_transf(self,f):
return np.log(f) return np.log(f)
class log(link_function): class Log(LinkFunction):
def transf(self,mu): def transf(self,mu):
return np.log(mu) return np.log(mu)
@ -40,7 +40,7 @@ class log(link_function):
def log_inv_transf(self,f): def log_inv_transf(self,f):
return f return f
class log_ex_1(link_function): class Log_ex_1(LinkFunction):
def transf(self,mu): def transf(self,mu):
return np.log(np.exp(mu) - 1) return np.log(np.exp(mu) - 1)
@ -50,11 +50,10 @@ class log_ex_1(link_function):
def log_inv_tranf(self,f): def log_inv_tranf(self,f):
return np.log(np.log(np.exp(f)+1)) return np.log(np.log(np.exp(f)+1))
class probit(link_function): class Probit(LinkFunction):
def inv_transf(self,f): def inv_transf(self,f):
return std_norm_cdf(f) return std_norm_cdf(f)
def log_inv_transf(self,f): def log_inv_transf(self,f):
return np.log(std_norm_cdf(f)) return np.log(std_norm_cdf(f))

2
GPy/models/fitc_classification.py
View file

@ -16,7 +16,7 @@ class FITCClassification(FITC):
:param X: input observations :param X: input observations
:param Y: observed values :param Y: observed values
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function :param likelihood: a GPy likelihood, defaults to Binomial with probit link function
:param kernel: a GPy kernel, defaults to rbf+white :param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales) :param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True :type normalize_X: False|True