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fixing EP and merging it with GP_regression

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Ricardo Andrade 2013-01-25 18:14:28 +00:00
parent b6ffb57263
commit 6a2e0a1fe5
7 changed files with 403 additions and 93 deletions
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31
GPy/inference/likelihoods.py
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@ -19,7 +19,7 @@ class likelihood:
self.Y = Y
self.N = self.Y.shape[0]
def plot1Da(self,X_new,Mean_new,Var_new,X_u,Mean_u,Var_u):
def plot1Da(self,X,mean,var,Z=None,mean_Z=None,var_Z=None):
"""
Plot the predictive distribution of the GP model for 1-dimensional inputs
@ -30,10 +30,18 @@ class likelihood:
:param Mean_u: mean values at X_u
:param Var_new: variance values at X_u
"""
assert X_new.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X_new,Mean_new,Var_new)
pb.errorbar(X_u.flatten(),Mean_u.flatten(),2*np.sqrt(Var_u.flatten()),fmt='r+')
pb.plot(X_u,Mean_u,'ro')
assert X.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X,mean,var.flatten())
pb.errorbar(Z.flatten(),mean_Z.flatten(),2*np.sqrt(var_Z.flatten()),fmt='r+')
pb.plot(Z,mean_Z,'ro')
def plot1Db(self,X_obs,X,phi,Z=None):
assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X,phi,np.zeros(X.shape[0]))
pb.plot(X_obs,(self.Y+1)/2,'kx',mew=1.5)
pb.ylim(-0.2,1.2)
if Z is not None:
pb.plot(Z,Z*0+.5,'r|',mew=1.5,markersize=12)
def plot2D(self,X,X_new,F_new,U=None):
"""
@ -88,16 +96,11 @@ class probit(likelihood):
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
return Z_hat, mu_hat, sigma2_hat
def plot1Db(self,X,X_new,F_new,U=None):
assert X.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X_new,F_new,np.zeros(X_new.shape[0]))
pb.plot(X,(self.Y+1)/2,'kx',mew=1.5)
pb.ylim(-0.2,1.2)
if U is not None:
pb.plot(U,U*0+.5,'r|',mew=1.5,markersize=12)
def predictive_mean(self,mu,variance):
return stats.norm.cdf(mu/np.sqrt(1+variance))
def predictive_mean(self,mu,var):
mu = mu.flatten()
var = var.flatten()
return stats.norm.cdf(mu/np.sqrt(1+var))
def _log_likelihood_gradients():
raise NotImplementedError