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Fixed likelihood tests for new parameters structure

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This commit is contained in:
Alan Saul 2014-02-07 15:16:52 +00:00
parent c28f11f291
commit 186feb45a1
6 changed files with 207 additions and 168 deletions
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12
GPy/inference/latent_function_inference/laplace.py
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@ -33,7 +33,6 @@ class LaplaceInference(object):
self._mode_finding_max_iter = 40
self.bad_fhat = True
def inference(self, kern, X, likelihood, Y, Y_metadata=None):
"""
Returns a Posterior class containing essential quantities of the posterior
@ -50,6 +49,7 @@ class LaplaceInference(object):
Ki_f_init = np.zeros_like(Y)
else:
Ki_f_init = self._previous_Ki_fhat
f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
#Compute hessian and other variables at mode
@ -57,6 +57,7 @@ class LaplaceInference(object):
#likelihood.gradient = self.likelihood_gradients()
kern.update_gradients_full(dL_dK, X)
likelihood.update_gradients(np.ones(10))
self._previous_Ki_fhat = Ki_fhat.copy()
return Posterior(woodbury_vector=woodbury_vector, woodbury_inv = K_Wi_i, K=K), log_marginal, {'dL_dK':dL_dK}
@ -157,9 +158,12 @@ class LaplaceInference(object):
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
d3lik_d3fhat = likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata)
dL_dfhat = 0.5*(np.diag(Ki_W_i)[:, None]*d3lik_d3fhat) #why isn't this -0.5? s2 in R&W p126 line 9.
dW_df = likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata) # d3lik_d3fhat
woodbury_vector = likelihood.dlogpdf_df(f_hat, Y, extra_data=Y_metadata)
dL_dfhat = 0.5*(np.diag(Ki_W_i)[:, None]*dW_df) #why isn't this -0.5? s2 in R&W p126 line 9.
#implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(np.eye(Y.shape[0]) - np.dot(K, K_Wi_i))
BiK, _ = dpotrs(L, K, lower=1)
#dL_dfhat = 0.5*np.diag(BiK)[:, None]*dW_df
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(np.eye(Y.shape[0]) - np.dot(K, K_Wi_i))
dL_dK = explicit_part + implicit_part
@ -219,7 +223,7 @@ class LaplaceInference(object):
LiW12, _ = dtrtrs(L, np.diagflat(W_12), lower=1, trans=0)
K_Wi_i = np.dot(LiW12.T, LiW12) # R = W12BiW12, in R&W p 126, eq 5.25
#here's a better way to compute the required matrix.
#here's a better way to compute the required matrix.
# you could do the model finding witha backsub, instead of a dot...
#L2 = L/W_12
#K_Wi_i_2 , _= dpotri(L2)