From e9d33ddc7e82ce82dd70c9c19adc522a95b4101c Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Fri, 17 Oct 2014 13:09:59 +0100 Subject: [PATCH] attempt for mpi support for ss_mrd --- GPy/models/ss_mrd.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/GPy/models/ss_mrd.py b/GPy/models/ss_mrd.py index e292a6cd..497215ef 100644 --- a/GPy/models/ss_mrd.py +++ b/GPy/models/ss_mrd.py @@ -12,8 +12,8 @@ from ..kern import RBF class SSMRD(Model): def __init__(self, Ylist, input_dim, X=None, X_variance=None, Gammas=None, initx = 'PCA_concat', initz = 'permute', - num_inducing=10, Zs=None, kernel=None, inference_method=None, likelihoods=None, - pi=0.5, name='ss_mrd', Ynames=None): + num_inducing=10, Zs=None, kernel=None, inference_methods=None, likelihoods=None, + pi=0.5, name='ss_mrd', Ynames=None, mpi_comm=None): super(SSMRD, self).__init__(name) # initialize X for individual models @@ -25,11 +25,13 @@ class SSMRD(Model): Zs = [None]* len(Ylist) if likelihoods is None: likelihoods = [None]* len(Ylist) + if inference_methods is None: + inference_methods = [None]* len(Ylist) self.var_priors = [VarPrior_SSMRD(nModels=len(Ylist),pi=pi,learnPi=False, group_spike=True) for i in xrange(len(Ylist))] self.models = [SSGPLVM(y, input_dim, X=X, X_variance=X_variance, Gamma=Gammas[i], num_inducing=num_inducing,Z=Zs[i], learnPi=False, group_spike=True, - kernel=kernel.copy(),inference_method=inference_method,likelihood=likelihoods[i], variational_prior=self.var_priors[i], - name='model_'+str(i)) for i,y in enumerate(Ylist)] + kernel=kernel.copy(),inference_method=inference_methods[i],likelihood=likelihoods[i], variational_prior=self.var_priors[i], + name='model_'+str(i), mpi_comm=mpi_comm) for i,y in enumerate(Ylist)] self.link_parameters(*(self.models)) self.models[0].X.mean.tie_vector(*[m.X.mean for m in self.models[1:]])