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[GPU] psicommputation

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This commit is contained in:
Zhenwen Dai 2014-03-27 17:12:17 +00:00
parent bc59cb8b22
commit 4465c5be8d
5 changed files with 153 additions and 19 deletions
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24
GPy/kern/_src/rbf.py
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@ -8,7 +8,8 @@ from ...util.misc import param_to_array
from stationary import Stationary
from GPy.util.caching import Cache_this
from ...core.parameterization import variational
from psi_comp import ssrbf_psi_gpucomp as ssrbf_psi_comp
from psi_comp import ssrbf_psi_comp
from psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF
class RBF(Stationary):
"""
@ -19,10 +20,15 @@ class RBF(Stationary):
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
"""
_support_GPU = True
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf'):
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
self.weave_options = {}
self.group_spike_prob = False
if self.useGPU:
self.psicomp = PSICOMP_SSRBF()
def K_of_r(self, r):
return self.variance * np.exp(-0.5 * r**2)
@ -35,18 +41,28 @@ class RBF(Stationary):
#---------------------------------------#
def psi0(self, Z, variational_posterior):
return self.Kdiag(variational_posterior.mean)
if self.useGPU:
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[0]
else:
return self.Kdiag(variational_posterior.mean)
def psi1(self, Z, variational_posterior):
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
if self.useGPU:
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[1]
else:
psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
else:
_, _, _, psi1 = self._psi1computations(Z, variational_posterior)
return psi1
def psi2(self, Z, variational_posterior):
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
if self.useGPU:
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[2]
else:
psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
else:
_, _, _, _, psi2 = self._psi2computations(Z, variational_posterior)
return psi2