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[SSGPLVM] update SSGPLVM with new inferface and merge ssrbf into rbf

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This commit is contained in:
Zhenwen Dai 2014-02-27 16:28:42 +00:00
parent 5cf792504a
commit d8c2f78131
6 changed files with 206 additions and 179 deletions
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61
GPy/kern/_src/rbf.py
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@ -7,6 +7,8 @@ from scipy import weave
from ...util.misc import param_to_array
from stationary import Stationary
from GPy.util.caching import Cache_this
from ...core.parameterization import variational
from rbf_psi_comp import ssrbf_psi_comp
class RBF(Stationary):
"""
@ -36,14 +38,38 @@ class RBF(Stationary):
return self.Kdiag(variational_posterior.mean)
def psi1(self, Z, variational_posterior):
_, _, _, psi1 = self._psi1computations(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)
else:
_, _, _, psi1 = self._psi1computations(Z, variational_posterior)
return psi1
def psi2(self, Z, variational_posterior):
_, _, _, _, _, psi2 = self._psi2computations(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)
else:
_, _, _, _, _, psi2 = self._psi2computations(Z, variational_posterior)
return psi2
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
#contributions from psi0:
self.variance.gradient = np.sum(dL_dpsi0)
#from psi1
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
#from psi2
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
return
l2 = self.lengthscale **2
#contributions from psi0:
@ -77,6 +103,19 @@ class RBF(Stationary):
self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
_, _, _, _, _, _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)
#psi1
grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0)
#psi2
grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1)
return grad
l2 = self.lengthscale **2
#psi1
@ -95,6 +134,24 @@ class RBF(Stationary):
return grad
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
ndata = variational_posterior.mean.shape[0]
_, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
#psi1
grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1)
grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1)
grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1)
#psi2
grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1)
grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1)
grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1)
return grad_mu, grad_S, grad_gamma
l2 = self.lengthscale **2
#psi1
denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)