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Merge branch 'devel' into saul_merge

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Alan Saul 2015-03-27 14:17:34 +00:00
commit 3c7a1b9a91
23 changed files with 372 additions and 389 deletions
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2
GPy/likelihoods/bernoulli.py
View file

@ -77,7 +77,7 @@ class Bernoulli(Likelihood):
return Z_hat, mu_hat, sigma2_hat
def variational_expectations(self, Y, m, v, gh_points=None):
def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
if isinstance(self.gp_link, link_functions.Probit):
if gh_points is None:

9
GPy/likelihoods/gaussian.py
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@ -310,18 +310,17 @@ class Gaussian(Likelihood):
Ysim = np.array([np.random.normal(self.gp_link.transf(gpj), scale=np.sqrt(self.variance), size=1) for gpj in gp])
return Ysim.reshape(orig_shape)
def log_predictive_density(self, y_test, mu_star, var_star):
def log_predictive_density(self, y_test, mu_star, var_star, Y_metadata=None):
"""
assumes independence
"""
v = var_star + self.variance
return -0.5*np.log(2*np.pi) -0.5*np.log(v) - 0.5*np.square(y_test - mu_star)/v
def variational_expectations(self, Y, m, v, gh_points=None):
def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
lik_var = float(self.variance)
F = -0.5*np.log(2*np.pi) -0.5*np.log(lik_var) - 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/lik_var
dF_dmu = (Y - m)/lik_var
dF_dv = np.ones_like(v)*(-0.5/lik_var)
dF_dlik_var = np.sum(-0.5/lik_var + 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/(lik_var**2))
dF_dtheta = [dF_dlik_var]
return F, dF_dmu, dF_dv, dF_dtheta
dF_dtheta = -0.5/lik_var + 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/(lik_var**2)
return F, dF_dmu, dF_dv, dF_dtheta.reshape(1, Y.shape[0], Y.shape[1])

7
GPy/likelihoods/likelihood.py
View file

@ -178,7 +178,12 @@ class Likelihood(Parameterized):
if np.any(np.isnan(dF_dm)) or np.any(np.isinf(dF_dm)):
stop
dF_dtheta = None # Not yet implemented
if self.size:
dF_dtheta = self.dlogpdf_dtheta(X, Y[:,None]) # Ntheta x (orig size) x N_{quad_points}
dF_dtheta = np.dot(dF_dtheta, gh_w)
dF_dtheta = dF_dtheta.reshape(self.size, shape[0], shape[1])
else:
dF_dtheta = None # Not yet implemented
return F.reshape(*shape), dF_dm.reshape(*shape), dF_dv.reshape(*shape), dF_dtheta
def predictive_mean(self, mu, variance, Y_metadata=None):

7
GPy/likelihoods/student_t.py
View file

@ -35,8 +35,8 @@ class StudentT(Likelihood):
self.log_concave = False
def parameters_changed(self):
self.variance = (self.v / float(self.v - 2)) * self.sigma2
#def parameters_changed(self):
#self.variance = (self.v / float(self.v - 2)) * self.sigma2
def update_gradients(self, grads):
"""
@ -180,7 +180,8 @@ class StudentT(Likelihood):
:rtype: float
"""
e = y - inv_link_f
dlogpdf_dvar = self.v*(e**2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e**2))
e2 = np.square(e)
dlogpdf_dvar = self.v*(e2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e2))
return dlogpdf_dvar
def dlogpdf_dlink_dvar(self, inv_link_f, y, Y_metadata=None):