GPy/GPy/likelihoods/link_functions.py
2013-06-24 18:15:16 +01:00

100 lines
2 KiB
Python

# Copyright (c) 2012, 2013 Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy import stats
import scipy as sp
import pylab as pb
from ..util.plot import gpplot
from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf,inv_std_norm_cdf
class LinkFunction(object):
"""
Link function class for doing non-Gaussian likelihoods approximation
:param Y: observed output (Nx1 numpy.darray)
..Note:: Y values allowed depend on the likelihood_function used
"""
def __init__(self):
pass
class Identity(LinkFunction):
"""
$$
g(f) = f
$$
"""
def transf(self,mu):
return mu
def inv_transf(self,f):
return f
def dinv_transf_df(self,f):
return 1.
def d2inv_transf_df2(self,f):
return 0
class Probit(LinkFunction):
"""
$$
g(f) = \\Phi^{-1} (mu)
$$
"""
def transf(self,mu):
return inv_std_norm_cdf(mu)
def inv_transf(self,f):
return std_norm_cdf(f)
def dinv_transf_df(self,f):
return std_norm_pdf(f)
def d2inv_transf_df2(self,f):
return -f * std_norm_pdf(f)
class Log(LinkFunction):
"""
$$
g(f) = \log(\mu)
$$
"""
def transf(self,mu):
return np.log(mu)
def inv_transf(self,f):
return np.exp(f)
def dinv_transf_df(self,f):
return np.exp(f)
def d2inv_transf_df2(self,f):
return np.exp(f)
class Log_ex_1(LinkFunction):
"""
$$
g(f) = \log(\exp(\mu) - 1)
$$
"""
def transf(self,mu):
"""
function: output space -> latent space
"""
return np.log(np.exp(mu) - 1)
def inv_transf(self,f):
"""
function: latent space -> output space
"""
return np.log(np.exp(f)+1)
def dinv_transf_df(self,f):
return np.exp(f)/(1.+np.exp(f))
def d2inv_transf_df2(self,f):
aux = np.exp(f)/(1.+np.exp(f))
return aux*(1.-aux)