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[GPU] varDTC_gpu almost done

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Zhenwen Dai 2014-04-04 18:02:53 +01:00
parent 954af5a6c2
commit 7a74c0b80d
2 changed files with 46 additions and 37 deletions
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20
GPy/kern/_src/rbf.py
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@ -74,10 +74,10 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU:
dL_dpsi0_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi0))
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
self.psicomp.update_gradients_expectations(dL_dpsi0_gpu, dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
# dL_dpsi0_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi0))
# dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
# dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
self.psicomp.update_gradients_expectations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else:
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -139,9 +139,9 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU:
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_Z_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
# dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
# dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_Z_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else:
_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -177,9 +177,9 @@ class RBF(Stationary):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
if self.useGPU:
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_qX_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
# dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
# dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
return self.psicomp.gradients_qX_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else:
ndata = variational_posterior.mean.shape[0]