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mostly docstring noodling

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James Hensman 2013-12-11 15:42:25 -08:00
parent 9011d8fe2f
commit f1a08fbfdb
2 changed files with 19 additions and 5 deletions
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5
GPy/likelihoods/bernoulli.py
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@ -18,6 +18,9 @@ class Bernoulli(Likelihood):
.. Note::
Y is expected to take values in {-1, 1}
Probit likelihood usually used
.. See also::
likelihood.py, for the parent class
"""
def __init__(self, gp_link=None, analytical_mean=False, analytical_variance=False):
super(Bernoulli, self).__init__(gp_link, analytical_mean, analytical_variance)
@ -38,7 +41,7 @@ class Bernoulli(Likelihood):
Y_prep[Y.flatten() == 0] = -1
return Y_prep
def _moments_match_analytical(self, data_i, tau_i, v_i):
def moments_match_ep(self, data_i, tau_i, v_i):
"""
Moments match of the marginal approximation in EP algorithm

19
GPy/likelihoods/likelihood.py
View file

@ -15,15 +15,26 @@ from ..core.parameterized import Parameterized
class Likelihood(Parameterized):
"""
Likelihood base class
Likelihood base class, used to defing p(y|f).
All instances use _inverse_ link functions, which can be swapped out. It is
expected that inherriting classes define a default inverse link function
To use this class, inherrit and define missing functionality.
To enable use with EP, ...
Inherriting classes *must* implement:
pdf_link : a bound method which turns the output of the link function into the pdf
logpdf_link : the logarithm of the above
To enable use with Laplace approximation, ...
To enable use with EP, inherriting classes *must* define:
TODO: a suitable derivative function for any parameters of the class
It is also desirable to define:
moments_match_ep : a function to compute the EP moments If this isn't defined, the moments will be computed using 1D quadrature.
For exact Gaussian inference, define ...
To enable use with Laplace approximation, inherriting classes *must* define:
Some derivative functions *AS TODO*
For exact Gaussian inference, define *JH TODO*
"""
def __init__(self, gp_link, name):