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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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James Hensman 2013-06-04 17:54:49 +01:00
commit 5a2722bec6
3 changed files with 11 additions and 10 deletions
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6
GPy/likelihoods/EP.py
View file

@ -110,7 +110,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self.data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
@ -200,7 +200,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self.data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
@ -295,7 +295,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i] self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i] self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments #Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self.data[i],self.tau_[i],self.v_[i]) self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])

8
GPy/likelihoods/link_functions.py
View file

@ -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))

7
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()
@ -183,20 +183,19 @@ 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()
m.update_likelihood_approximation() m.update_likelihood_approximation()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@unittest.skip("FITC will be broken for a while")
def test_generalized_FITC(self): def test_generalized_FITC(self):
N = 20 N = 20
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()