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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Max Zwiessele 2014-05-15 14:06:04 +01:00
commit dfdb1c24e6
12 changed files with 247 additions and 126 deletions
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28
GPy/inference/latent_function_inference/var_dtc_parallel.py
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@ -71,12 +71,13 @@ class VarDTC_minibatch(LatentFunctionInference):
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.variance, 1e-6)
het_noise = beta.size > 1
if het_noise:
self.batchsize = 1
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = beta*self.get_YYTfactor(Y)
YYT_factor = Y
trYYT = self.get_trYYT(Y)
psi2_full = np.zeros((num_inducing,num_inducing))
psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM
psi0_full = 0
@ -105,19 +106,18 @@ class VarDTC_minibatch(LatentFunctionInference):
YRY_full += (beta_slice*np.square(Y_slice).sum(axis=-1)).sum()
else:
psi0_full += psi0.sum()
psi1Y_full += np.dot(Y_slice.T,psi1) # DxM
psi1Y_full += np.dot(Y_slice.T,psi1) # DxM
if uncertain_inputs:
if het_noise:
psi2_full += np.einsum('n,nmo->mo',beta_slice,psi2)
psi2_full += beta_slice*psi2
else:
psi2_full += psi2.sum(axis=0)
psi2_full += psi2
else:
if het_noise:
psi2_full += np.einsum('n,nm,no->mo',beta_slice,psi1,psi1)
psi2_full += beta_slice*np.outer(psi1,psi1)
else:
psi2_full += tdot(psi1.T)
psi2_full += np.outer(psi1,psi1)
if not het_noise:
psi0_full *= beta
@ -224,7 +224,7 @@ class VarDTC_minibatch(LatentFunctionInference):
psi2 = None
if het_noise:
beta = beta[n_start:n_end]
beta = beta[n_start] # assuming batchsize==1
betaY = beta*Y_slice
betapsi1 = np.einsum('n,nm->nm',beta,psi1)
@ -245,7 +245,7 @@ class VarDTC_minibatch(LatentFunctionInference):
dL_dpsi1 = np.dot(betaY,v.T)
if uncertain_inputs:
dL_dpsi2 = np.einsum('n,mo->nmo',beta * np.ones((n_end-n_start,)),dL_dpsi2R)
dL_dpsi2 = beta* dL_dpsi2R
else:
dL_dpsi1 += np.dot(betapsi1,dL_dpsi2R)*2.
dL_dpsi2 = None
@ -263,11 +263,11 @@ class VarDTC_minibatch(LatentFunctionInference):
dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1)
else:
if uncertain_inputs:
psiR = np.einsum('mo,nmo->',dL_dpsi2R,psi2)
psiR = np.einsum('mo,mo->',dL_dpsi2R,psi2)
else:
psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R)
dL_dthetaL = ((np.square(betaY)).sum() + np.square(beta)*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum()
dL_dthetaL = ((np.square(betaY)).sum() + beta*beta*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - beta*beta*psiR- (betaY*np.dot(betapsi1,v)).sum()
if uncertain_inputs:
grad_dict = {'dL_dpsi0':dL_dpsi0,
@ -297,7 +297,7 @@ def update_gradients(model):
kern_grad = model.kern.gradient.copy()
#gradients w.r.t. Z
model.Z.gradient[:,model.kern.active_dims] = model.kern.gradients_X(dL_dKmm, model.Z)
model.Z.gradient = model.kern.gradients_X(dL_dKmm, model.Z)
isEnd = False
while not isEnd:
@ -310,8 +310,8 @@ def update_gradients(model):
kern_grad += model.kern.gradient
#gradients w.r.t. Z
model.Z.gradient[:,model.kern.active_dims] += model.kern.gradients_Z_expectations(
grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=X_slice)
model.Z.gradient += model.kern.gradients_Z_expectations(
dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=X_slice)
#gradients w.r.t. posterior parameters of X
X_grad = model.kern.gradients_qX_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])