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[GPU] GPU version of varDTC is ready

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This commit is contained in:
Zhenwen Dai 2014-04-07 10:25:16 +01:00
commit bbcba2553c
59 changed files with 2012 additions and 1186 deletions
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13
GPy/kern/_src/rbf.py
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@ -74,9 +74,6 @@ 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, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else:
@ -99,7 +96,7 @@ class RBF(Stationary):
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
else:
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum()
elif isinstance(variational_posterior, variational.NormalPosterior):
l2 = self.lengthscale**2
if l2.size != self.input_dim:
@ -107,8 +104,6 @@ class RBF(Stationary):
#contributions from psi0:
self.variance.gradient = np.sum(dL_dpsi0)
if self._debug:
num_grad = self.lengthscale.gradient.copy()
self.lengthscale.gradient = 0.
#from psi1
@ -128,8 +123,6 @@ class RBF(Stationary):
else:
self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2)
if self._debug:
import ipdb;ipdb.set_trace()
self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance
else:
@ -139,8 +132,6 @@ 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, 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)
@ -177,8 +168,6 @@ 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, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
else:
ndata = variational_posterior.mean.shape[0]