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

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Conflicts:
	GPy/inference/latent_function_inference/inferenceX.py
This commit is contained in:
Max Zwiessele 2014-11-14 11:28:19 +00:00
commit 118ed2733e
2 changed files with 23 additions and 27 deletions
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6
GPy/core/parameterization/variational.py
View file

@ -169,7 +169,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
def gamma_probabilities(self): def gamma_probabilities(self):
prob = np.zeros_like(param_to_array(self.gamma)) prob = np.zeros_like(param_to_array(self.gamma))
prob[self.gamma>-710] = 1./(1.+np.exp(-self.gamma[self.gamma>-710])) prob[self.gamma>-710] = 1./(1.+np.exp(-self.gamma[self.gamma>-710]))
prob1 = np.zeros_like(param_to_array(self.gamma)) prob1 = -np.zeros_like(param_to_array(self.gamma))
prob1[self.gamma<710] = 1./(1.+np.exp(self.gamma[self.gamma<710])) prob1[self.gamma<710] = 1./(1.+np.exp(self.gamma[self.gamma<710]))
return prob, prob1 return prob, prob1
@ -177,8 +177,8 @@ class SpikeAndSlabPosterior(VariationalPosterior):
def gamma_log_prob(self): def gamma_log_prob(self):
loggamma = param_to_array(self.gamma).copy() loggamma = param_to_array(self.gamma).copy()
loggamma[loggamma>-40] = -np.log1p(np.exp(-loggamma[loggamma>-40])) loggamma[loggamma>-40] = -np.log1p(np.exp(-loggamma[loggamma>-40]))
loggamma1 = param_to_array(self.gamma).copy() loggamma1 = -param_to_array(self.gamma).copy()
loggamma1[loggamma1<40] = -np.log1p(np.exp(loggamma1[loggamma1<40])) loggamma1[loggamma1>-40] = -np.log1p(np.exp(-loggamma1[loggamma1>-40]))
return loggamma,loggamma1 return loggamma,loggamma1
def set_gradients(self, grad): def set_gradients(self, grad):

14
GPy/inference/latent_function_inference/inferenceX.py
View file

@ -34,11 +34,10 @@ class InferenceX(Model):
:type Y: numpy.ndarray :type Y: numpy.ndarray
""" """
def __init__(self, model, Y, name='inferenceX', init='L2'): def __init__(self, model, Y, name='inferenceX', init='L2'):
if np.isnan(Y).any() or getattr(model, 'missing_data', False): if np.isnan(Y).any():
assert Y.shape[0]==1, "The current implementation of inference X only support one data point at a time with missing data!" assert Y.shape[0]==1, "The current implementation of inference X only support one data point at a time with missing data!"
self.missing_data = True self.missing_data = True
self.valid_dim = np.logical_not(np.isnan(Y[0])) self.valid_dim = np.logical_not(np.isnan(Y[0]))
self.ninan = getattr(model, 'ninan', None)
else: else:
self.missing_data = False self.missing_data = False
super(InferenceX, self).__init__(name) super(InferenceX, self).__init__(name)
@ -109,17 +108,14 @@ class InferenceX(Model):
wv = self.posterior.woodbury_vector wv = self.posterior.woodbury_vector
if self.missing_data: if self.missing_data:
wv = wv[:,self.valid_dim] wv = wv[:,self.valid_dim]
if self.ninan is not None: output_dim = self.valid_dim.sum()
self.dL_dpsi2 = beta/2.*(self.posterior.woodbury_inv[:, :, self.valid_dim] - np.einsum('md,od->mo',wv, wv)[:, :, None]) self.dL_dpsi2 = beta*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))/2.
self.dL_dpsi2 = self.dL_dpsi2.sum(-1)
else:
self.dL_dpsi2 = beta/2.*(self.valid_dim.sum() * self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))
self.dL_dpsi1 = beta*np.dot(self.Y[:,self.valid_dim], wv.T) self.dL_dpsi1 = beta*np.dot(self.Y[:,self.valid_dim], wv.T)
self.dL_dpsi0 = - beta/2.* np.ones(self.Y.shape[0]) self.dL_dpsi0 = - beta/2.* np.ones(self.Y.shape[0])
else: else:
self.dL_dpsi2 = beta/2.*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv)) self.dL_dpsi2 = beta*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))/2.
self.dL_dpsi1 = beta*np.dot(self.Y, wv.T) self.dL_dpsi1 = beta*np.dot(self.Y, wv.T)
self.dL_dpsi0 = -beta/2.* np.ones(self.Y.shape[0]) self.dL_dpsi0 = -beta/2.*output_dim* np.ones(self.Y.shape[0])
def parameters_changed(self): def parameters_changed(self):
if self.uncertain_input: if self.uncertain_input: