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[infer_newX] updated for missing data

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Max Zwiessele 2014-11-14 11:09:51 +00:00
parent e7aac70c0a
commit c635de54b9
2 changed files with 23 additions and 23 deletions
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4
GPy/core/gp.py
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@ -453,11 +453,7 @@ class GP(Model):
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the posterior estimation of X and the model that optimize X
<<<<<<< HEAD
:rtype: (GPy.core.parameterization.variational.VariationalPosterior or numpy.ndarray, GPy.core.Model)
=======
:rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` or numpy.ndarray, :class:`~GPy.core.model.Model`)
>>>>>>> 22d30d9d39c70f806fe5bcb815cce9c8eb0f8dca
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)

42
GPy/inference/latent_function_inference/inferenceX.py
View file

@ -7,37 +7,38 @@ from ...core.parameterization import variational
def infer_newX(model, Y_new, optimize=True, init='L2'):
"""
Infer the distribution of X for the new observed data *Y_new*.
:param model: the GPy model used in inference
:type model: GPy.core.Model
:param Y_new: the new observed data for inference
:type Y_new: numpy.ndarray
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the estimated posterior distribution of X and the model that optimize X
:return: a tuple containing the estimated posterior distribution of X and the model that optimize X
:rtype: (GPy.core.parameterization.variational.VariationalPosterior, GPy.core.Model)
"""
infr_m = InferenceX(model, Y_new, init=init)
if optimize:
infr_m.optimize()
return infr_m.X, infr_m
class InferenceX(Model):
"""
The class for inference of new X with given new Y. (do_test_latent)
:param model: the GPy model used in inference
:type model: GPy.core.Model
:param Y: the new observed data for inference
:type Y: numpy.ndarray
"""
def __init__(self, model, Y, name='inferenceX', init='L2'):
if np.isnan(Y).any():
if np.isnan(Y).any() or getattr(model, 'missing_data', False):
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.valid_dim = np.logical_not(np.isnan(Y[0]))
self.ninan = getattr(model, 'ninan', None)
else:
self.missing_data = False
super(InferenceX, self).__init__(name)
@ -66,12 +67,12 @@ class InferenceX(Model):
self.Y = Y
self.X = self._init_X(model, Y, init=init)
self.compute_dL()
self.link_parameter(self.X)
def _init_X(self, model, Y_new, init='L2'):
# Initialize the new X by finding the nearest point in Y space.
Y = model.Y
if self.missing_data:
Y = Y[:,self.valid_dim]
@ -85,7 +86,7 @@ class InferenceX(Model):
elif init=='rand':
dist = np.random.rand(Y_new.shape[0],Y.shape[0])
idx = dist.argmin(axis=1)
from ...models import SSGPLVM
from ...util.misc import param_to_array
if isinstance(model, SSGPLVM):
@ -98,9 +99,9 @@ class InferenceX(Model):
else:
from ...core import Param
X = Param('latent mean',param_to_array(model.X[idx]).copy())
return X
def compute_dL(self):
# Common computation
beta = 1./np.fmax(self.likelihood.variance, 1e-6)
@ -108,15 +109,18 @@ class InferenceX(Model):
wv = self.posterior.woodbury_vector
if self.missing_data:
wv = wv[:,self.valid_dim]
output_dim = self.valid_dim.sum()
self.dL_dpsi2 = beta*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))/2.
if self.ninan is not None:
self.dL_dpsi2 = beta/2.*(self.posterior.woodbury_inv[:, :, self.valid_dim] - np.einsum('md,od->mo',wv, wv)[:, :, None])
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_dpsi0 = - beta/2.* np.ones(self.Y.shape[0])
else:
self.dL_dpsi2 = beta*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))/2.
self.dL_dpsi2 = beta/2.*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))
self.dL_dpsi1 = beta*np.dot(self.Y, wv.T)
self.dL_dpsi0 = -beta/2.* np.ones(self.Y.shape[0])
def parameters_changed(self):
if self.uncertain_input:
psi0 = self.kern.psi0(self.Z, self.X)
@ -128,7 +132,7 @@ class InferenceX(Model):
psi2 = np.dot(psi1.T,psi1)
self._log_marginal_likelihood = (self.dL_dpsi2*psi2).sum()+(self.dL_dpsi1*psi1).sum()+(self.dL_dpsi0*psi0).sum()
if self.uncertain_input:
X_grad = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.dL_dpsi0, dL_dpsi1=self.dL_dpsi1, dL_dpsi2=self.dL_dpsi2)
self.X.set_gradients(X_grad)
@ -137,7 +141,7 @@ class InferenceX(Model):
X_grad = self.kern.gradients_X_diag(self.dL_dpsi0, self.X)
X_grad += self.kern.gradients_X(dL_dpsi1, self.X, self.Z)
self.X.gradient = X_grad
if self.uncertain_input:
from ...core.parameterization.variational import SpikeAndSlabPrior
if isinstance(self.variational_prior, SpikeAndSlabPrior):
@ -151,7 +155,7 @@ class InferenceX(Model):
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood += -KL_div
def log_likelihood(self):
return self._log_marginal_likelihood