apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-07 11:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

bug fix for mpi SSGPLVM

Browse source
This commit is contained in:
Zhenwen Dai 2014-05-16 10:40:45 +01:00
parent 4ec8f464e2
commit c568bad4fb
2 changed files with 24 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

4
GPy/inference/latent_function_inference/var_dtc_parallel.py
View file

@ -400,6 +400,10 @@ def update_gradients(model, mpi_comm=None):
mpi_comm.Allreduce([np.float64(KL_div), MPI.DOUBLE], [KL_div_all, MPI.DOUBLE]) mpi_comm.Allreduce([np.float64(KL_div), MPI.DOUBLE], [KL_div_all, MPI.DOUBLE])
KL_div = KL_div_all KL_div = KL_div_all
[mpi_comm.Allgatherv([pp.copy(), MPI.DOUBLE], [pa, (model.Y_list*pa.shape[-1], None), MPI.DOUBLE]) for pp,pa in zip(model.get_X_gradients(X),model.get_X_gradients(model.X))] [mpi_comm.Allgatherv([pp.copy(), MPI.DOUBLE], [pa, (model.Y_list*pa.shape[-1], None), MPI.DOUBLE]) for pp,pa in zip(model.get_X_gradients(X),model.get_X_gradients(model.X))]
from ...models import SSGPLVM
if isinstance(model, SSGPLVM):
grad_pi = np.array(model.variational_prior.pi.gradient)
mpi_comm.Allreduce([grad_pi.copy(), MPI.DOUBLE], [model.variational_prior.pi.gradient, MPI.DOUBLE])
model._log_marginal_likelihood -= KL_div model._log_marginal_likelihood -= KL_div
# dL_dthetaL # dL_dthetaL

22
GPy/models/ss_gplvm.py
View file

@ -45,9 +45,9 @@ class SSGPLVM(SparseGP):
gamma = np.empty_like(X, order='F') # The posterior probabilities of the binary variable in the variational approximation gamma = np.empty_like(X, order='F') # The posterior probabilities of the binary variable in the variational approximation
gamma[:] = 0.5 + 0.1 * np.random.randn(X.shape[0], input_dim) gamma[:] = 0.5 + 0.1 * np.random.randn(X.shape[0], input_dim)
gamma[gamma>=1. - 1e-9] = 1e-9 gamma[gamma>1.-1e-9] = 1.-1e-9
gamma[gamma<1e-9] = 1e-9 gamma[gamma<1e-9] = 1e-9
#gamma[:] = 0.5 gamma[:] = 0.5
if group_spike: if group_spike:
gamma[:] = gamma.mean(axis=0) gamma[:] = gamma.mean(axis=0)
@ -142,3 +142,21 @@ class SSGPLVM(SparseGP):
state['Y_local'] = state['Y'][Y_range[0]:Y_range[1]] state['Y_local'] = state['Y'][Y_range[0]:Y_range[1]]
state['X_local'] = state['X'][Y_range[0]:Y_range[1]] state['X_local'] = state['X'][Y_range[0]:Y_range[1]]
return super(SSGPLVM, self).__setstate__(state) return super(SSGPLVM, self).__setstate__(state)
def _grads(self, x):
if self.mpi_comm != None:
self.mpi_comm.Bcast(x, root=0)
obj_grads = super(SSGPLVM, self)._grads(x)
return obj_grads
def _objective(self, x):
if self.mpi_comm != None:
self.mpi_comm.Bcast(x, root=0)
obj = super(SSGPLVM, self)._objective(x)
return obj
def _objective_grads(self, x):
if self.mpi_comm != None:
self.mpi_comm.Bcast(x, root=0)
obj_f, obj_grads = super(SSGPLVM, self)._objective_grads(x)
return obj_f, obj_grads