apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-06 10:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

addedHeraviside functionality to EP

Browse source
This commit is contained in:
James Hensman 2013-09-16 16:20:26 +01:00
parent aafce736f8
commit f2fa9bd74d
3 changed files with 26 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/likelihoods/noise_model_constructors.py
View file

@ -19,7 +19,7 @@ def binomial(gp_link=None):
analytical_mean = True analytical_mean = True
analytical_variance = False analytical_variance = False
elif isinstance(gp_link,noise_models.gp_transformations.Step): elif isinstance(gp_link,noise_models.gp_transformations.Heaviside):
analytical_mean = True analytical_mean = True
analytical_variance = True analytical_variance = True

27
GPy/likelihoods/noise_models/binomial_noise.py
View file

@ -49,15 +49,32 @@ class Binomial(NoiseDistribution):
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)
elif isinstance(self.gp_link,gp_transformations.Step): elif isinstance(self.gp_link,gp_transformations.Heaviside):
Z_hat = None a = data_i*v_i/np.sqrt(tau_i)
mu_hat = None Z_hat = std_norm_cdf(a)
sigma2_hat = None N = std_norm_pdf(a)
mu_hat = v_i/tau_i + data_i*N/Z_hat/np.sqrt(tau_i)
sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i
if np.any(np.isnan([Z_hat, mu_hat, sigma2_hat])):
stop
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def _predictive_mean_analytical(self,mu,sigma): def _predictive_mean_analytical(self,mu,sigma):
return stats.norm.cdf(mu/np.sqrt(1+sigma**2)) if isinstance(self.gp_link,gp_transformations.Probit):
return stats.norm.cdf(mu/np.sqrt(1+sigma**2))
elif isinstance(self.gp_link,gp_transformations.Heaviside):
return stats.norm.cdf(mu/sigma)
else:
raise NotImplementedError
def _predictive_variance_analytical(self,mu,sigma, pred_mean):
if isinstance(self.gp_link,gp_transformations.Heaviside):
return 0.
else:
raise NotImplementedError
def _mass(self,gp,obs): def _mass(self,gp,obs):
#NOTE obs must be in {0,1} #NOTE obs must be in {0,1}

6
GPy/likelihoods/noise_models/gp_transformations.py
View file

@ -108,7 +108,7 @@ class Reciprocal(GPTransformation):
def d2transf_df2(self,f): def d2transf_df2(self,f):
return 2./f**3 return 2./f**3
class Step(GPTransformation): class Heaviside(GPTransformation):
""" """
$$ $$
g(f) = I_{x \in A} g(f) = I_{x \in A}
@ -119,7 +119,7 @@ class Step(GPTransformation):
return np.where(f>0, 1, -1) return np.where(f>0, 1, -1)
def dtransf_df(self,f): def dtransf_df(self,f):
pass raise NotImplementedError, "this function is not differentiable!"
def d2transf_df2(self,f): def d2transf_df2(self,f):
pass raise NotImplementedError, "this function is not differentiable!"