From c568bad4fbef7a0e91079af0c14bb808770a8be8 Mon Sep 17 00:00:00 2001
From: Zhenwen Dai <z.dai@shef.ac.uk>
Date: Fri, 16 May 2014 10:40:45 +0100
Subject: [PATCH] bug fix for mpi SSGPLVM

---
 .../var_dtc_parallel.py                       |  4 ++++
 GPy/models/ss_gplvm.py                        | 22 +++++++++++++++++--
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py
index 4123e5b2..44babf25 100644
--- a/GPy/inference/latent_function_inference/var_dtc_parallel.py
+++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py
@@ -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])
         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))]
+        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
 
     # dL_dthetaL
diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index 76e2f0ef..27d5158f 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -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[:] = 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[:] = 0.5
+        gamma[:] = 0.5
         
         if group_spike:
             gamma[:] = gamma.mean(axis=0)
@@ -142,3 +142,21 @@ class SSGPLVM(SparseGP):
         state['Y_local'] = state['Y'][Y_range[0]:Y_range[1]]
         state['X_local'] = state['X'][Y_range[0]:Y_range[1]]
         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