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EP plots samples now for the phi transformation.

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Ricardo Andrade 2013-01-30 12:14:32 +00:00
parent d1a0883c12
commit 29eb61d65e
5 changed files with 23 additions and 8 deletions
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24
GPy/inference/likelihoods.py
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@ -83,12 +83,20 @@ class probit(likelihood):
var = var.flatten()
return stats.norm.cdf(mu/np.sqrt(1+var))
def predictive_var(self,mu,var):
p=self.predictive_mean(mu,var)
return p*(1-p)
def _log_likelihood_gradients():
raise NotImplementedError
def plot(self,X,phi,X_obs,Z=None):
def plot(self,X,mu,var,phi,X_obs,Z=None,samples=0):
assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X,phi,np.zeros(X.shape[0]))
phi_var = self.predictive_var(mu,var)
gpplot(X,phi,phi_var)
if samples:
phi_samples = np.vstack([np.random.binomial(1,phi.flatten()) for s in range(samples)])
pb.plot(X,phi_samples.T,'x', alpha = 0.4, c='#3465a4' )
pb.plot(X_obs,(self.Y+1)/2,'kx',mew=1.5)
if Z is not None:
pb.plot(Z,Z*0+.5,'r|',mew=1.5,markersize=12)
@ -164,16 +172,22 @@ class poisson(likelihood):
sigma2_hat = m2 - mu_hat**2 # Second central moment
return float(Z_hat), float(mu_hat), float(sigma2_hat)
def predictive_mean(self,mu,variance):
def predictive_mean(self,mu,var):
return np.exp(mu*self.scale + self.location)
def predictive_var(self,mu,var):
return predictive_mean(mu,var)
def _log_likelihood_gradients():
raise NotImplementedError
def plot(self,X,phi,X_obs,Z=None):
def plot(self,X,mu,var,phi,X_obs,Z=None,samples=0):
assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
gpplot(X,phi,np.zeros(X.shape[0]))
gpplot(X,phi,phi.flatten())
pb.plot(X_obs,self.Y,'kx',mew=1.5)
if samples:
phi_samples = np.vstack([np.random.poisson(phi.flatten(),phi.size) for s in range(samples)])
pb.plot(X,phi_samples.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
if Z is not None:
pb.plot(Z,Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)