apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-07 19:12:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

New functions for EP-matching moments

Browse source
This commit is contained in:
Ricardo 2013-05-13 11:10:27 +01:00
parent c521c243e1
commit 68f493b86c
2 changed files with 39 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

7
GPy/likelihoods/likelihood_functions.py
View file

@ -7,6 +7,7 @@ from scipy import stats
import scipy as sp import scipy as sp
import pylab as pb 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
class likelihood_function: class likelihood_function:
""" """
@ -37,11 +38,11 @@ class probit(likelihood_function):
:param tau_i: precision of the cavity distribution (float) :param tau_i: precision of the cavity distribution (float)
:param v_i: mean/variance of the cavity distribution (float) :param v_i: mean/variance of the cavity distribution (float)
""" """
if data_i == 0: data_i = -1 #NOTE Binary classification algorithm works better with classes {-1,1}, 1D-plotting works better with classes {0,1}. #if data_i == 0: data_i = -1 #NOTE Binary classification algorithm works better with classes {-1,1}, 1D-plotting works better with classes {0,1}.
# TODO: some version of assert # TODO: some version of assert
z = data_i*v_i/np.sqrt(tau_i**2 + tau_i) z = data_i*v_i/np.sqrt(tau_i**2 + tau_i)
Z_hat = stats.norm.cdf(z) Z_hat = std_norm_cdf(z)
phi = stats.norm.pdf(z) phi = std_norm_pdf(z)
mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i)) mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat) sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat

35
GPy/util/univariate_Gaussian.py Normal file
View file

@ -0,0 +1,35 @@
# Copyright (c) 2012, 2013 Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy import weave
def std_norm_pdf(x):
"""Standard Gaussian density function"""
return 1./np.sqrt(2.*np.pi)*np.exp(-.5*x**2)
def std_norm_cdf(x):
"""
Cumulative standard Gaussian distribution
Based on Abramowitz, M. and Stegun, I. (1970)
"""
support_code = "#include <math.h>"
code = """
double sign = 1.0;
if (x < 0.0){
sign = -1.0;
x = -x;
}
x = x/sqrt(2.0);
double t = 1.0/(1.0 + 0.3275911*x);
double erf = 1. - exp(-x*x)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))));
return_val = 0.5*(1.0 + sign*erf);
"""
x = float(x)
return weave.inline(code,arg_names=['x'],support_code=support_code)