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

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This commit is contained in:
Max Zwiessele 2013-06-04 18:09:08 +01:00
commit a32f9bf9dd
6 changed files with 22 additions and 18 deletions
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7
GPy/core/model.py
View file

@ -20,7 +20,7 @@ from GPy.core.domains import POSITIVE, REAL
class model(parameterised):
def __init__(self):
parameterised.__init__(self)
self.priors = [None for i in range(self._get_params().size)]
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'tnc'
@ -55,7 +55,7 @@ class model(parameterised):
if self.priors is None:
self.priors = [None for i in range(self._get_params().size)]
which = self.grep_param_names(which)
which = self.grep_param_names(regexp)
# check tied situation
tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]
@ -316,7 +316,10 @@ class model(parameterised):
def __str__(self):
s = parameterised.__str__(self).split('\n')
# add priors to the string
if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors]
else:
strs = ['']*len(self._get_params())
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()

2
GPy/examples/regression.py
View file

@ -275,7 +275,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
# create simple GP model
m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
m.constrain_positive('(variance|lengthscale|precision)')
m.ensure_default_constraints()
m.checkgrad(verbose=1)
m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)

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.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
#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
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])
@ -200,7 +200,7 @@ class EP(likelihood):
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]
#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
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])
@ -295,7 +295,7 @@ class EP(likelihood):
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]
#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
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])

8
GPy/likelihoods/link_functions.py
View file

@ -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))

8
GPy/testing/prior_tests.py
View file

@ -15,12 +15,12 @@ class PriorTests(unittest.TestCase):
X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y)
m.ensure_default_constraints()
lognormal = GPy.priors.log_Gaussian(1, 2)
lognormal = GPy.priors.LogGaussian(1, 2)
m.set_prior('rbf', lognormal)
m.randomize()
self.assertTrue(m.checkgrad())
def test_gamma(self):
def test_Gamma(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
@ -29,8 +29,8 @@ class PriorTests(unittest.TestCase):
X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y)
m.ensure_default_constraints()
gamma = GPy.priors.gamma(1, 1)
m.set_prior('rbf', gamma)
Gamma = GPy.priors.Gamma(1, 1)
m.set_prior('rbf', Gamma)
m.randomize()
self.assertTrue(m.checkgrad())

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