apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 12:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

rbf gpu usable

Browse source
This commit is contained in:
Zhenwen Dai 2014-06-20 18:02:35 +01:00
parent e486f3fd99
commit ca1edecce4
5 changed files with 140 additions and 146 deletions
Download patch file Download diff file Expand all files Collapse all files

45
GPy/inference/latent_function_inference/var_dtc_parallel.py
View file

@ -73,13 +73,14 @@ class VarDTC_minibatch(LatentFunctionInference):
else:
return jitchol(tdot(Y))
def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs, het_noise):
def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs):
num_inducing = Z.shape[0]
num_data, output_dim = Y.shape
if self.batchsize == None:
self.batchsize = num_data
het_noise = beta.size > 1
trYYT = self.get_trYYT(Y)
@ -88,46 +89,30 @@ class VarDTC_minibatch(LatentFunctionInference):
psi0_full = 0.
YRY_full = 0.
for n_start in xrange(0,num_data,self.batchsize):
for n_start in xrange(0,num_data,self.batchsize):
n_end = min(self.batchsize+n_start, num_data)
Y_slice = Y[n_start:n_end]
X_slice = X[n_start:n_end]
if het_noise:
b = beta[n_start]
YRY_full += np.inner(Y_slice, Y_slice)*b
else:
b = beta
if uncertain_inputs:
psi0 = kern.psi0(Z, X_slice)
psi1 = kern.psi1(Z, X_slice)
psi2 = kern.psi2(Z, X_slice)
psi2_full += kern.psi2(Z, X_slice)*b
else:
psi0 = kern.Kdiag(X_slice)
psi1 = kern.K(X_slice, Z)
psi2 = None
if het_noise:
beta_slice = beta[n_start:n_end]
psi0_full += (beta_slice*psi0).sum()
psi1Y_full += np.dot(beta_slice*Y_slice.T,psi1) # DxM
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
if uncertain_inputs:
if het_noise:
psi2_full += beta_slice*psi2
else:
psi2_full += psi2
else:
if het_noise:
psi2_full += beta_slice*np.outer(psi1,psi1)
else:
psi2_full += np.dot(psi1.T,psi1)
psi2_full += np.dot(psi1.T,psi1)*b
psi0_full += psi0.sum()*b
psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM
if not het_noise:
psi0_full *= beta
psi1Y_full *= beta
psi2_full *= beta
YRY_full = trYYT*beta
if self.mpi_comm != None:
@ -168,7 +153,7 @@ class VarDTC_minibatch(LatentFunctionInference):
if het_noise:
self.batchsize = 1
psi0_full, psi1Y_full, psi2_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs, het_noise)
psi0_full, psi1Y_full, psi2_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs)
#======================================================================
# Compute Common Components