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unit_tests corrected

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Ricardo 2013-06-04 17:39:38 +01:00
parent bae714f72c
commit 7396e5ad21
2 changed files with 8 additions and 6 deletions
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8
GPy/likelihoods/link_functions.py
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@ -19,8 +19,6 @@ class link_function(object):
def __init__(self): def __init__(self):
pass pass
class identity(link_function): class identity(link_function):
def transf(self,mu): def transf(self,mu):
return mu return mu
@ -53,6 +51,10 @@ class log_ex_1(link_function):
return np.log(np.log(np.exp(f)+1)) return np.log(np.log(np.exp(f)+1))
class probit(link_function): 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))

6
GPy/testing/unit_tests.py
View file

@ -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] 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] Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
kernel = GPy.kern.rbf(1) kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.probit() distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution) likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.core.GP(X, likelihood, kernel) m = GPy.core.GP(X, likelihood, kernel)
m.ensure_default_constraints() m.ensure_default_constraints()
@ -183,7 +183,7 @@ class GradientTests(unittest.TestCase):
Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None] Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
Z = np.linspace(0,15,4)[:,None] Z = np.linspace(0,15,4)[:,None]
kernel = GPy.kern.rbf(1) kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.probit() distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution) likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.core.sparse_GP(X, likelihood, kernel,Z) m = GPy.core.sparse_GP(X, likelihood, kernel,Z)
m.ensure_default_constraints() 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] 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) k = GPy.kern.rbf(1) + GPy.kern.white(1)
Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None] 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 = GPy.models.generalized_FITC(X,likelihood,k,inducing=4)
m.constrain_positive('(var|len)') m.constrain_positive('(var|len)')
m.approximate_likelihood() m.approximate_likelihood()