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ssmrd kernel issue

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This commit is contained in:
Zhenwen Dai 2014-10-21 10:37:35 +01:00
parent 3b727e5118
commit 7326d19848
1 changed files with 5 additions and 5 deletions
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10
GPy/models/ss_mrd.py
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@ -13,7 +13,7 @@ from numpy.linalg.linalg import LinAlgError
class SSMRD(Model): class SSMRD(Model):
def __init__(self, Ylist, input_dim, X=None, X_variance=None, Gammas=None, initx = 'PCA_concat', initz = 'permute', 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_methods=None, likelihoods=None, num_inducing=10, Zs=None, kernels=None, inference_methods=None, likelihoods=None,
pi=0.5, name='ss_mrd', Ynames=None, mpi_comm=None): pi=0.5, name='ss_mrd', Ynames=None, mpi_comm=None):
super(SSMRD, self).__init__(name) super(SSMRD, self).__init__(name)
self.mpi_comm = mpi_comm self.mpi_comm = mpi_comm
@ -21,8 +21,8 @@ class SSMRD(Model):
# initialize X for individual models # initialize X for individual models
X, X_variance, Gammas, fracs = self._init_X(Ylist, input_dim, X, X_variance, Gammas, initx) X, X_variance, Gammas, fracs = self._init_X(Ylist, input_dim, X, X_variance, Gammas, initx)
if kernel is None: if kernels is None:
kernel = RBF(input_dim, lengthscale=1./fracs, ARD=True) kernels = [RBF(input_dim, lengthscale=1./fracs, ARD=True) for i in xrange(len(Ylist))]
if Zs is None: if Zs is None:
Zs = [None]* len(Ylist) Zs = [None]* len(Ylist)
if likelihoods is None: if likelihoods is None:
@ -31,8 +31,8 @@ class SSMRD(Model):
inference_methods = [None]* len(Ylist) 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.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.copy(), X_variance=X_variance, Gamma=Gammas[i], num_inducing=num_inducing,Z=Zs[i], learnPi=False, group_spike=True, self.models = [SSGPLVM(y, input_dim, X=X.copy(), X_variance=X_variance.copy(), Gamma=Gammas[i], num_inducing=num_inducing,Z=Zs[i], learnPi=False, group_spike=True,
kernel=kernel.copy(),inference_method=inference_methods[i],likelihood=likelihoods[i], variational_prior=self.var_priors[i], kernel=kernels[i],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)] name='model_'+str(i), mpi_comm=mpi_comm) for i,y in enumerate(Ylist)]
self.link_parameters(*(self.models)) self.link_parameters(*(self.models))