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[ep] now calling exact inference instead of copying code

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Max Zwiessele 2015-09-11 15:47:07 +01:00
parent 79bfbfc776
commit 8132084de6
7 changed files with 56 additions and 75 deletions
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38
GPy/inference/latent_function_inference/dtc.py
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@ -28,8 +28,8 @@ class DTC(LatentFunctionInference):
num_data, output_dim = Y.shape
#make sure the noise is not hetero
beta = 1./likelihood.gaussian_variance(Y_metadata)
if beta.size > 1:
gaussian_variance = 1./likelihood.gaussian_variance(Y_metadata)
if gaussian_variance.size > 1:
raise NotImplementedError("no hetero noise with this implementation of DTC")
Kmm = kern.K(Z)
@ -42,7 +42,7 @@ class DTC(LatentFunctionInference):
Kmmi, L, Li, _ = pdinv(Kmm)
# Compute A
LiUTbeta = np.dot(Li, U.T)*np.sqrt(beta)
LiUTbeta = np.dot(Li, U.T)*np.sqrt(gaussian_variance)
A = tdot(LiUTbeta) + np.eye(num_inducing)
# factor A
@ -50,7 +50,7 @@ class DTC(LatentFunctionInference):
# back substutue to get b, P, v
tmp, _ = dtrtrs(L, Uy, lower=1)
b, _ = dtrtrs(LA, tmp*beta, lower=1)
b, _ = dtrtrs(LA, tmp*gaussian_variance, lower=1)
tmp, _ = dtrtrs(LA, b, lower=1, trans=1)
v, _ = dtrtrs(L, tmp, lower=1, trans=1)
tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
@ -59,8 +59,8 @@ class DTC(LatentFunctionInference):
#compute log marginal
log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \
-np.sum(np.log(np.diag(LA)))*output_dim + \
0.5*num_data*output_dim*np.log(beta) + \
-0.5*beta*np.sum(np.square(Y)) + \
0.5*num_data*output_dim*np.log(gaussian_variance) + \
-0.5*gaussian_variance*np.sum(np.square(Y)) + \
0.5*np.sum(np.square(b))
# Compute dL_dKmm
@ -70,11 +70,11 @@ class DTC(LatentFunctionInference):
# Compute dL_dU
vY = np.dot(v.reshape(-1,1),Y.T)
dL_dU = vY - np.dot(vvT_P, U.T)
dL_dU *= beta
dL_dU *= gaussian_variance
#compute dL_dR
Uv = np.dot(U, v)
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./beta + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1))*beta**2
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./gaussian_variance + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1))*gaussian_variance**2
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
@ -97,8 +97,8 @@ class vDTC(object):
num_data, output_dim = Y.shape
#make sure the noise is not hetero
beta = 1./likelihood.gaussian_variance(Y_metadata)
if beta.size > 1:
gaussian_variance = 1./likelihood.gaussian_variance(Y_metadata)
if gaussian_variance.size > 1:
raise NotImplementedError("no hetero noise with this implementation of DTC")
Kmm = kern.K(Z)
@ -111,9 +111,9 @@ class vDTC(object):
Kmmi, L, Li, _ = pdinv(Kmm)
# Compute A
LiUTbeta = np.dot(Li, U.T)*np.sqrt(beta)
LiUTbeta = np.dot(Li, U.T)*np.sqrt(gaussian_variance)
A_ = tdot(LiUTbeta)
trace_term = -0.5*(np.sum(Knn)*beta - np.trace(A_))
trace_term = -0.5*(np.sum(Knn)*gaussian_variance - np.trace(A_))
A = A_ + np.eye(num_inducing)
# factor A
@ -121,7 +121,7 @@ class vDTC(object):
# back substutue to get b, P, v
tmp, _ = dtrtrs(L, Uy, lower=1)
b, _ = dtrtrs(LA, tmp*beta, lower=1)
b, _ = dtrtrs(LA, tmp*gaussian_variance, lower=1)
tmp, _ = dtrtrs(LA, b, lower=1, trans=1)
v, _ = dtrtrs(L, tmp, lower=1, trans=1)
tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
@ -131,8 +131,8 @@ class vDTC(object):
#compute log marginal
log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \
-np.sum(np.log(np.diag(LA)))*output_dim + \
0.5*num_data*output_dim*np.log(beta) + \
-0.5*beta*np.sum(np.square(Y)) + \
0.5*num_data*output_dim*np.log(gaussian_variance) + \
-0.5*gaussian_variance*np.sum(np.square(Y)) + \
0.5*np.sum(np.square(b)) + \
trace_term
@ -145,15 +145,15 @@ class vDTC(object):
vY = np.dot(v.reshape(-1,1),Y.T)
#dL_dU = vY - np.dot(vvT_P, U.T)
dL_dU = vY - np.dot(vvT_P - Kmmi, U.T)
dL_dU *= beta
dL_dU *= gaussian_variance
#compute dL_dR
Uv = np.dot(U, v)
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./beta + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1) )*beta**2
dL_dR -=beta*trace_term/num_data
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./gaussian_variance + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1) )*gaussian_variance**2
dL_dR -=gaussian_variance*trace_term/num_data
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn) + -0.5*beta, 'dL_dKnm':dL_dU.T, 'dL_dthetaL':dL_dthetaL}
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn) + -0.5*gaussian_variance, 'dL_dKnm':dL_dU.T, 'dL_dthetaL':dL_dthetaL}
#construct a posterior object
post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)