apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-30 14:35:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

speed ups for normal cdf

Browse source
This commit is contained in:
James Hensman 2015-04-09 15:42:02 +01:00
parent 337bf67559
commit 1e30ffd730
7 changed files with 38 additions and 96 deletions
Download patch file Download diff file Expand all files Collapse all files

25
GPy/likelihoods/link_functions.py
View file

@ -1,10 +1,9 @@
# Copyright (c) 2012-2014 The GPy authors (see AUTHORS.txt)
# Copyright (c) 2012-2015 The GPy authors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy import stats
from ..util.univariate_Gaussian import std_norm_cdf, std_norm_pdf
import scipy as sp
from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf,inv_std_norm_cdf
_exp_lim_val = np.finfo(np.float64).max
_lim_val = np.log(_exp_lim_val)
@ -64,13 +63,12 @@ class Identity(GPTransformation):
def d3transf_df3(self,f):
return np.zeros_like(f)
class Probit(GPTransformation):
"""
.. math::
g(f) = \\Phi^{-1} (mu)
"""
def transf(self,f):
return std_norm_cdf(f)
@ -79,13 +77,10 @@ class Probit(GPTransformation):
return std_norm_pdf(f)
def d2transf_df2(self,f):
#FIXME
return -f * std_norm_pdf(f)
def d3transf_df3(self,f):
#FIXME
f2 = f**2
return -(1/(np.sqrt(2*np.pi)))*np.exp(-0.5*(f2))*(1-f2)
return (np.square(f)-1.)*std_norm_pdf(f)
class Cloglog(GPTransformation):
@ -98,7 +93,7 @@ class Cloglog(GPTransformation):
or
f = \log (-\log(1-p))
"""
def transf(self,f):
return 1-np.exp(-np.exp(f))
@ -123,16 +118,16 @@ class Log(GPTransformation):
"""
def transf(self,f):
return np.exp(np.clip(f, -_lim_val, _lim_val))
return np.exp(np.clip(f, -np.inf, _lim_val))
def dtransf_df(self,f):
return np.exp(np.clip(f, -_lim_val, _lim_val))
return np.exp(np.clip(f, -np.inf, _lim_val))
def d2transf_df2(self,f):
return np.exp(np.clip(f, -_lim_val, _lim_val))
return np.exp(np.clip(f, -np.inf, _lim_val))
def d3transf_df3(self,f):
return np.exp(np.clip(f, -_lim_val, _lim_val))
return np.exp(np.clip(f, -np.inf, _lim_val))
class Log_ex_1(GPTransformation):
"""
@ -174,7 +169,7 @@ class Heaviside(GPTransformation):
.. math::
g(f) = I_{x \\in A}
g(f) = I_{x \\geq 0}
"""
def transf(self,f):