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adapt the new interface of the variational posterior distribution.

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This commit is contained in:
Zhenwen Dai 2014-02-21 17:56:37 +00:00
parent 34d9f90d92
commit 99c6a2095f
7 changed files with 96 additions and 389 deletions
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61
GPy/inference/latent_function_inference/var_dtc.py
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@ -43,9 +43,20 @@ class VarDTC(object):
return Y * prec # TODO chache this, and make it effective
def inference(self, kern, X, X_variance, Z, likelihood, Y):
"""Inference for normal sparseGP"""
uncertain_inputs = False
psi0, psi1, psi2 = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def inference_latent(self, kern, posterior_variational, Z, likelihood, Y):
"""Inference for GPLVM with uncertain inputs"""
uncertain_inputs = True
psi0, psi1, psi2 = _compute_psi_latent(kern, posterior_variational, Z)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def _inference(self, kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs):
#see whether we're using variational uncertain inputs
uncertain_inputs = not (X_variance is None)
_, output_dim = Y.shape
@ -62,10 +73,9 @@ class VarDTC(object):
# do the inference:
het_noise = beta.size < 1
num_inducing = Z.shape[0]
num_data = X.shape[0]
num_data = Y.shape[0]
# kernel computations, using BGPLVM notation
Kmm = kern.K(Z)
psi0, psi1, psi2 = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
Kmm = kern.K(Z)
Lm = jitchol(Kmm)
@ -191,20 +201,31 @@ class VarDTCMissingData(object):
else:
self._subarray_indices = [[slice(None),slice(None)]]
return [Y], [(Y**2).sum()]
def inference(self, kern, X, X_variance, Z, likelihood, Y):
"""Inference for normal sparseGP"""
uncertain_inputs = False
psi0, psi1, psi2 = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def inference_latent(self, kern, posterior_variational, Z, likelihood, Y):
"""Inference for GPLVM with uncertain inputs"""
uncertain_inputs = True
psi0, psi1, psi2 = _compute_psi_latent(kern, posterior_variational, Z)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def _inference(self, kern, psi0_all, psi1_all, psi2_all, Z, likelihood, Y, uncertain_inputs):
Ys, traces = self._Y(Y)
beta_all = 1./likelihood.variance
uncertain_inputs = not (X_variance is None)
het_noise = beta_all.size != 1
import itertools
num_inducing = Z.shape[0]
dL_dpsi0_all = np.zeros(X.shape[0])
dL_dpsi1_all = np.zeros((X.shape[0], num_inducing))
dL_dpsi0_all = np.zeros(Y.shape[0])
dL_dpsi1_all = np.zeros((Y.shape[0], num_inducing))
if uncertain_inputs:
dL_dpsi2_all = np.zeros((X.shape[0], num_inducing, num_inducing))
dL_dpsi2_all = np.zeros((Y.shape[0], num_inducing, num_inducing))
partial_for_likelihood = 0
woodbury_vector = np.zeros((num_inducing, Y.shape[1]))
@ -217,9 +238,6 @@ class VarDTCMissingData(object):
Lm = jitchol(Kmm)
if uncertain_inputs: LmInv = dtrtri(Lm)
# kernel computations, using BGPLVM notation
psi0_all, psi1_all, psi2_all = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
VVT_factor_all = np.empty(Y.shape)
full_VVT_factor = VVT_factor_all.shape[1] == Y.shape[1]
if not full_VVT_factor:
@ -340,15 +358,16 @@ class VarDTCMissingData(object):
return post, log_marginal, grad_dict
def _compute_psi(kern, X, X_variance, Z, uncertain_inputs):
if uncertain_inputs:
psi0 = kern.psi0(Z, X, X_variance)
psi1 = kern.psi1(Z, X, X_variance)
psi2 = kern.psi2(Z, X, X_variance)
else:
psi0 = kern.Kdiag(X)
psi1 = kern.K(X, Z)
psi2 = None
def _compute_psi(kern, X, X_variance, Z):
psi0 = kern.Kdiag(X)
psi1 = kern.K(X, Z)
psi2 = None
return psi0, psi1, psi2
def _compute_psi_latent(kern, posterior_variational, Z):
psi0 = kern.psi0(Z, posterior_variational)
psi1 = kern.psi1(Z, posterior_variational)
psi2 = kern.psi2(Z, posterior_variational)
return psi0, psi1, psi2
def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, Cpsi1Vf, DBi_plus_BiPBi, psi1, het_noise, uncertain_inputs):