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extend inference X for all gp models

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This commit is contained in:
Zhenwen Dai 2014-11-03 16:04:15 +00:00
parent 3eb2fa3a8f
commit 1840b7e6b8
5 changed files with 78 additions and 37 deletions
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69
GPy/inference/latent_function_inference/inferenceX.py
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@ -51,8 +51,18 @@ class InferenceX(Model):
self.kern.GPU(True)
from copy import deepcopy
self.posterior = deepcopy(model.posterior)
self.variational_prior = model.variational_prior.copy()
self.Z = model.Z.copy()
if hasattr(model, 'variational_prior'):
self.uncertain_input = True
self.variational_prior = model.variational_prior.copy()
else:
self.uncertain_input = False
if hasattr(model, 'inducing_inputs'):
self.sparse_gp = True
self.Z = model.Z.copy()
else:
self.sparse_gp = False
self.uncertain_input = False
self.Z = model.X.copy()
self.Y = Y
self.X = self._init_X(model, Y, init=init)
self.compute_dL()
@ -72,6 +82,8 @@ class InferenceX(Model):
dist = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
elif init=='NCC':
dist = Y_new.dot(Y.T)
elif init=='rand':
dist = np.random.rand(Y_new.shape[0],Y.shape[0])
idx = dist.argmin(axis=1)
from ...models import SSGPLVM
@ -81,7 +93,11 @@ class InferenceX(Model):
if model.group_spike:
X.gamma.fix()
else:
X = variational.NormalPosterior(param_to_array(model.X.mean[idx]), param_to_array(model.X.variance[idx]))
if self.uncertain_input and self.sparse_gp:
X = variational.NormalPosterior(param_to_array(model.X.mean[idx]), param_to_array(model.X.variance[idx]))
else:
from ...core import Param
X = Param('latent mean',param_to_array(model.X[idx]).copy())
return X
@ -99,29 +115,42 @@ class InferenceX(Model):
else:
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_dpsi0 = -beta/2.* np.ones(self.Y.shape[0]) #self.dL_dpsi0[:] = 0
self.dL_dpsi0 = -beta/2.* np.ones(self.Y.shape[0])
def parameters_changed(self):
psi0 = self.kern.psi0(self.Z, self.X)
psi1 = self.kern.psi1(self.Z, self.X)
psi2 = self.kern.psi2(self.Z, self.X)
if self.uncertain_input:
psi0 = self.kern.psi0(self.Z, self.X)
psi1 = self.kern.psi1(self.Z, self.X)
psi2 = self.kern.psi2(self.Z, self.X)
else:
psi0 = self.kern.Kdiag(self.X)
psi1 = self.kern.K(self.X, self.Z)
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()
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)
from ...core.parameterization.variational import SpikeAndSlabPrior
if isinstance(self.variational_prior, SpikeAndSlabPrior):
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
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)
else:
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X)
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood += -KL_div
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(psi1,self.dL_dpsi2)
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):
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
else:
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X)
# 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