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New functions for EP-matching moments

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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
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7
GPy/likelihoods/likelihood_functions.py
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@ -7,6 +7,7 @@ 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
class likelihood_function:
"""
@ -37,11 +38,11 @@ class probit(likelihood_function):
:param tau_i: precision 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
z = data_i*v_i/np.sqrt(tau_i**2 + tau_i)
Z_hat = stats.norm.cdf(z)
phi = stats.norm.pdf(z)
Z_hat = std_norm_cdf(z)
phi = std_norm_pdf(z)
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)
return Z_hat, mu_hat, sigma2_hat