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Changes to allow heteroscedastic inference

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This commit is contained in:
Ricardo 2014-03-17 18:57:34 +00:00
parent d4735e54c8
commit fe82678d80
1 changed files with 20 additions and 17 deletions
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37
GPy/inference/latent_function_inference/var_dtc.py
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@ -2,7 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from posterior import Posterior
from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify
from ...util.linalg import mdot, jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify
from ...util import diag
from ...core.parameterization.variational import VariationalPosterior
import numpy as np
@ -48,7 +48,7 @@ class VarDTC(object):
def get_VVTfactor(self, Y, prec):
return Y * prec # TODO chache this, and make it effective
def inference(self, kern, X, Z, likelihood, Y):
def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
if isinstance(X, VariationalPosterior):
uncertain_inputs = True
psi0 = kern.psi0(Z, X)
@ -65,7 +65,9 @@ class VarDTC(object):
_, output_dim = Y.shape
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.variance, 1e-6)
#beta = 1./np.fmax(likelihood.variance, 1e-6)
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
@ -74,7 +76,7 @@ class VarDTC(object):
trYYT = self.get_trYYT(Y)
# do the inference:
het_noise = beta.size < 1
het_noise = beta.size > 1
num_inducing = Z.shape[0]
num_data = Y.shape[0]
# kernel computations, using BGPLVM notation
@ -134,16 +136,16 @@ class VarDTC(object):
# log marginal likelihood
log_marginal = _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise,
psi0, A, LB, trYYT, data_fit)
psi0, A, LB, trYYT, data_fit, Y)
#put the gradients in the right places
dL_dR = _compute_dL_dR(likelihood,
het_noise, uncertain_inputs, LB,
_LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A,
psi0, psi1, beta,
data_fit, num_data, output_dim, trYYT)
data_fit, num_data, output_dim, trYYT, Y)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR,Y_metadata)
if uncertain_inputs:
grad_dict = {'dL_dKmm': dL_dKmm,
@ -385,7 +387,7 @@ def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, C
return dL_dpsi0, dL_dpsi1, dL_dpsi2
def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A, psi0, psi1, beta, data_fit, num_data, output_dim, trYYT):
def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A, psi0, psi1, beta, data_fit, num_data, output_dim, trYYT, Y):
# the partial derivative vector for the likelihood
if likelihood.size == 0:
# save computation here.
@ -394,19 +396,20 @@ def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf,
if uncertain_inputs:
raise NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented"
else:
from ...util.linalg import chol_inv
LBi = chol_inv(LB)
#from ...util.linalg import chol_inv
#LBi = chol_inv(LB)
LBi, _ = dtrtrs(LB,np.eye(LB.shape[0]))
Lmi_psi1, nil = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, Lmi_psi1, lower=1, trans=0)
dL_dR = -0.5 * beta + 0.5 * likelihood.V**2
dL_dR = -0.5 * beta + 0.5 * (beta*Y)**2
dL_dR += 0.5 * output_dim * (psi0 - np.sum(Lmi_psi1**2,0))[:,None] * beta**2
dL_dR += 0.5*np.sum(mdot(LBi.T,LBi,Lmi_psi1)*Lmi_psi1,0)[:,None]*beta**2
dL_dR += -np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T * likelihood.Y * beta**2
dL_dR += -np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T * Y * beta**2
dL_dR += 0.5*np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T**2 * beta**2
else:
# likelihood is not heteroscedatic
dL_dR = -0.5 * num_data * output_dim * beta + 0.5 * trYYT * beta ** 2
@ -414,11 +417,11 @@ def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf,
dL_dR += beta * (0.5 * np.sum(A * DBi_plus_BiPBi) - data_fit)
return dL_dR
def _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise, psi0, A, LB, trYYT, data_fit):
#compute log marginal likelihood
def _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise, psi0, A, LB, trYYT, data_fit,Y):
#compute log marginal likelihood
if het_noise:
lik_1 = -0.5 * num_data * output_dim * np.log(2. * np.pi) + 0.5 * np.sum(np.log(beta)) - 0.5 * np.sum(likelihood.V * likelihood.Y)
lik_2 = -0.5 * output_dim * (np.sum(beta * psi0) - np.trace(A))
lik_1 = -0.5 * num_data * output_dim * np.log(2. * np.pi) + 0.5 * np.sum(np.log(beta)) - 0.5 * np.sum(beta * Y**2)
lik_2 = -0.5 * output_dim * (np.sum(beta.flatten() * psi0) - np.trace(A))
else:
lik_1 = -0.5 * num_data * output_dim * (np.log(2. * np.pi) - np.log(beta)) - 0.5 * beta * trYYT
lik_2 = -0.5 * output_dim * (np.sum(beta * psi0) - np.trace(A))