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[classification] sparse gp classification and dtc update

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Max Zwiessele 2015-09-11 15:08:30 +01:00
parent 4ea5ebaa68
commit 1d354f5cce
14 changed files with 208 additions and 369 deletions
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3
GPy/likelihoods/bernoulli.py
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@ -60,13 +60,14 @@ class Bernoulli(Likelihood):
if isinstance(self.gp_link, link_functions.Probit):
z = sign*v_i/np.sqrt(tau_i**2 + tau_i)
Z_hat = std_norm_cdf(z)
Z_hat = np.where(Z_hat==0, 1e-15, Z_hat)
phi = std_norm_pdf(z)
mu_hat = v_i/tau_i + sign*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)
elif isinstance(self.gp_link, link_functions.Heaviside):
a = sign*v_i/np.sqrt(tau_i)
Z_hat = std_norm_cdf(a)
Z_hat = np.max(1e-13, std_norm_cdf(z))
N = std_norm_pdf(a)
mu_hat = v_i/tau_i + sign*N/Z_hat/np.sqrt(tau_i)
sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i