From 0f60fba125e91f41041ebb38b084b55626969fd6 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Wed, 27 Nov 2013 12:32:42 +0000 Subject: [PATCH] Fixed student_t approximation demo and changed convergence critera to difference of f --- GPy/examples/laplace_approximations.py | 2 +- GPy/likelihoods/laplace.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/GPy/examples/laplace_approximations.py b/GPy/examples/laplace_approximations.py index 64185885..ce47554d 100644 --- a/GPy/examples/laplace_approximations.py +++ b/GPy/examples/laplace_approximations.py @@ -106,7 +106,7 @@ def student_t_approx(): corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution) m = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood) m.ensure_default_constraints() - m.constrain_positive('t_noise') + m.constrain_bounded('t_noise', 1e-6, 10.) m.constrain_fixed('white', 1e-4) m.randomize() for a in range(1): diff --git a/GPy/likelihoods/laplace.py b/GPy/likelihoods/laplace.py index 6941de48..3aa78ffc 100644 --- a/GPy/likelihoods/laplace.py +++ b/GPy/likelihoods/laplace.py @@ -65,11 +65,10 @@ class Laplace(likelihood): self.old_Ki_f = None - def predictive_values(self, mu, var, full_cov): + def predictive_values(self,mu,var,full_cov,**noise_args): if full_cov: - raise NotImplementedError("Cannot make correlated predictions\ - with an Laplace likelihood") - return self.noise_model.predictive_values(mu, var) + raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood" + return self.noise_model.predictive_values(mu,var,**noise_args) def log_predictive_density(self, y_test, mu_star, var_star): """ @@ -209,6 +208,7 @@ class Laplace(likelihood): - 0.5*self.f_Ki_f + 0.5*self.y_Wi_Ki_i_y ) + #print "Term, {}, {}, {}, {}, {}".format(self.lik, - 0.5*self.ln_B_det, + 0.5*self.ln_det_Wi_K, - 0.5*self.f_Ki_f, + 0.5*self.y_Wi_Ki_i_y) #Convert to float as its (1, 1) and Z must be a scalar self.Z = np.float64(Z_tilde) @@ -380,8 +380,8 @@ class Laplace(likelihood): #difference = abs(new_obj - old_obj) #old_obj = new_obj.copy() - #difference = np.abs(np.sum(f - f_old)) - difference = np.abs(np.sum(Ki_f - old_Ki_f)) + difference = np.abs(np.sum(f - f_old)) + #difference = np.abs(np.sum(Ki_f - old_Ki_f)) old_Ki_f = Ki_f.copy() i += 1