From 7396e5ad214822917289c62f0a3a06ef13d5f95f Mon Sep 17 00:00:00 2001 From: Ricardo Date: Tue, 4 Jun 2013 17:39:38 +0100 Subject: [PATCH] unit_tests corrected --- GPy/likelihoods/link_functions.py | 8 +++++--- GPy/testing/unit_tests.py | 6 +++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/GPy/likelihoods/link_functions.py b/GPy/likelihoods/link_functions.py index 28beac71..f3b48d21 100644 --- a/GPy/likelihoods/link_functions.py +++ b/GPy/likelihoods/link_functions.py @@ -19,8 +19,6 @@ class link_function(object): def __init__(self): pass - - class identity(link_function): def transf(self,mu): return mu @@ -53,6 +51,10 @@ class log_ex_1(link_function): return np.log(np.log(np.exp(f)+1)) class probit(link_function): - pass + def inv_transf(self,f): + return std_norm_cdf(f) + + def log_inv_transf(self,f): + return np.log(std_norm_cdf(f)) diff --git a/GPy/testing/unit_tests.py b/GPy/testing/unit_tests.py index 4bdbdca8..9f8eb000 100644 --- a/GPy/testing/unit_tests.py +++ b/GPy/testing/unit_tests.py @@ -169,7 +169,7 @@ class GradientTests(unittest.TestCase): X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None] Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None] kernel = GPy.kern.rbf(1) - distribution = GPy.likelihoods.likelihood_functions.probit() + distribution = GPy.likelihoods.likelihood_functions.binomial() likelihood = GPy.likelihoods.EP(Y, distribution) m = GPy.core.GP(X, likelihood, kernel) m.ensure_default_constraints() @@ -183,7 +183,7 @@ class GradientTests(unittest.TestCase): Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None] Z = np.linspace(0,15,4)[:,None] kernel = GPy.kern.rbf(1) - distribution = GPy.likelihoods.likelihood_functions.probit() + distribution = GPy.likelihoods.likelihood_functions.binomial() likelihood = GPy.likelihoods.EP(Y, distribution) m = GPy.core.sparse_GP(X, likelihood, kernel,Z) m.ensure_default_constraints() @@ -196,7 +196,7 @@ class GradientTests(unittest.TestCase): X = np.hstack([np.random.rand(N/2)+1,np.random.rand(N/2)-1])[:,None] k = GPy.kern.rbf(1) + GPy.kern.white(1) Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None] - likelihood = GPy.inference.likelihoods.probit(Y) + likelihood = GPy.inference.likelihoods.binomial(Y) m = GPy.models.generalized_FITC(X,likelihood,k,inducing=4) m.constrain_positive('(var|len)') m.approximate_likelihood()