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lots of fixes, including prediction being mean and variance only

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This commit is contained in:
James Hensman 2014-03-13 14:42:03 +00:00
parent 365b8ae1e1
commit cc96f5b3d5
13 changed files with 118 additions and 128 deletions
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38
GPy/inference/latent_function_inference/var_dtc.py
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@ -137,25 +137,25 @@ class VarDTC(object):
psi0, A, LB, trYYT, data_fit)
#put the gradients in the right places
partial_for_likelihood = _compute_partial_for_likelihood(likelihood,
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)
#likelihood.update_gradients(partial_for_likelihood)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
if uncertain_inputs:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dpsi0':dL_dpsi0,
'dL_dpsi1':dL_dpsi1,
'dL_dpsi2':dL_dpsi2,
'partial_for_likelihood':partial_for_likelihood}
'dL_dthetaL':dL_dthetaL}
else:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dKdiag':dL_dpsi0,
'dL_dKnm':dL_dpsi1,
'partial_for_likelihood':partial_for_likelihood}
'dL_dthetaL':dL_dthetaL}
#get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
@ -232,7 +232,7 @@ class VarDTCMissingData(object):
if uncertain_inputs:
dL_dpsi2_all = np.zeros((Y.shape[0], num_inducing, num_inducing))
partial_for_likelihood = 0
dL_dR = 0
woodbury_vector = np.zeros((num_inducing, Y.shape[1]))
woodbury_inv_all = np.zeros((num_inducing, num_inducing, Y.shape[1]))
dL_dKmm = 0
@ -308,7 +308,7 @@ class VarDTCMissingData(object):
psi0, A, LB, trYYT, data_fit)
#put the gradients in the right places
partial_for_likelihood += _compute_partial_for_likelihood(likelihood,
dL_dR += _compute_dL_dR(likelihood,
het_noise, uncertain_inputs, LB,
_LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A,
psi0, psi1, beta,
@ -334,12 +334,12 @@ class VarDTCMissingData(object):
'dL_dpsi0':dL_dpsi0_all,
'dL_dpsi1':dL_dpsi1_all,
'dL_dpsi2':dL_dpsi2_all,
'partial_for_likelihood':partial_for_likelihood}
'dL_dR':dL_dR}
else:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dKdiag':dL_dpsi0_all,
'dL_dKnm':dL_dpsi1_all,
'partial_for_likelihood':partial_for_likelihood}
'dL_dR':dL_dR}
#get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
@ -385,11 +385,11 @@ def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, C
return dL_dpsi0, dL_dpsi1, dL_dpsi2
def _compute_partial_for_likelihood(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):
# the partial derivative vector for the likelihood
if likelihood.size == 0:
# save computation here.
partial_for_likelihood = None
dL_dR = None
elif het_noise:
if uncertain_inputs:
raise NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented"
@ -399,20 +399,20 @@ def _compute_partial_for_likelihood(likelihood, het_noise, uncertain_inputs, LB,
Lmi_psi1, nil = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, Lmi_psi1, lower=1, trans=0)
partial_for_likelihood = -0.5 * beta + 0.5 * likelihood.V**2
partial_for_likelihood += 0.5 * output_dim * (psi0 - np.sum(Lmi_psi1**2,0))[:,None] * beta**2
dL_dR = -0.5 * beta + 0.5 * likelihood.V**2
dL_dR += 0.5 * output_dim * (psi0 - np.sum(Lmi_psi1**2,0))[:,None] * beta**2
partial_for_likelihood += 0.5*np.sum(mdot(LBi.T,LBi,Lmi_psi1)*Lmi_psi1,0)[:,None]*beta**2
dL_dR += 0.5*np.sum(mdot(LBi.T,LBi,Lmi_psi1)*Lmi_psi1,0)[:,None]*beta**2
partial_for_likelihood += -np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T * likelihood.Y * beta**2
partial_for_likelihood += 0.5*np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T**2 * beta**2
dL_dR += -np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T * likelihood.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
partial_for_likelihood = -0.5 * num_data * output_dim * beta + 0.5 * trYYT * beta ** 2
partial_for_likelihood += 0.5 * output_dim * (psi0.sum() * beta ** 2 - np.trace(A) * beta)
partial_for_likelihood += beta * (0.5 * np.sum(A * DBi_plus_BiPBi) - data_fit)
return partial_for_likelihood
dL_dR = -0.5 * num_data * output_dim * beta + 0.5 * trYYT * beta ** 2
dL_dR += 0.5 * output_dim * (psi0.sum() * beta ** 2 - np.trace(A) * beta)
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