apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Python2->Python3

Browse source
This commit is contained in:
Aki Vehtari 2016-06-09 14:50:53 +03:00
parent 52fb928dff
commit c3963928f1
10 changed files with 38 additions and 38 deletions
Download patch file Download diff file Expand all files Collapse all files

4
GPy/models/ss_gplvm.py
View file

@ -291,12 +291,12 @@ class SSGPLVM(SparseGP_MPI):
Xs[b>self.X.gamma.values] = 0
invcov = (Xs[:,:,:,None]*Xs[:,:,None,:]).sum(1)/noise_var+np.eye(Q)
cov = np.array([pdinv(invcov[s_idx])[0] for s_idx in xrange(invcov.shape[0])])
cov = np.array([pdinv(invcov[s_idx])[0] for s_idx in range(invcov.shape[0])])
Ws = np.empty((nSamples, Q, D))
tmp = (np.transpose(Xs, (0,2,1)).reshape(nSamples*Q,N).dot(self.Y)).reshape(nSamples,Q,D)
mean = (cov[:,:,:,None]*tmp[:,None,:,:]).sum(2)/noise_var
zeros = np.zeros((Q,))
for s_idx in xrange(Xs.shape[0]):
for s_idx in range(Xs.shape[0]):
Ws[s_idx] = (np.random.multivariate_normal(mean=zeros,cov=cov[s_idx],size=(D,))).T+mean[s_idx]
if raw_samples:

10
GPy/models/ss_mrd.py
View file

@ -25,7 +25,7 @@ class SSMRD(Model):
self.X = NormalPosterior(means=X, variances=X_variance)
if kernels is None:
kernels = [RBF(input_dim, lengthscale=1./fracs, ARD=True) for i in xrange(len(Ylist))]
kernels = [RBF(input_dim, lengthscale=1./fracs, ARD=True) for i in range(len(Ylist))]
if Zs is None:
Zs = [None]* len(Ylist)
if likelihoods is None:
@ -34,9 +34,9 @@ class SSMRD(Model):
inference_methods = [None]* len(Ylist)
if IBP:
self.var_priors = [IBPPrior_SSMRD(len(Ylist),input_dim,alpha=alpha) for i in xrange(len(Ylist))]
self.var_priors = [IBPPrior_SSMRD(len(Ylist),input_dim,alpha=alpha) for i in range(len(Ylist))]
else:
self.var_priors = [SpikeAndSlabPrior_SSMRD(nModels=len(Ylist),pi=pi,learnPi=False, group_spike=group_spike) for i in xrange(len(Ylist))]
self.var_priors = [SpikeAndSlabPrior_SSMRD(nModels=len(Ylist),pi=pi,learnPi=False, group_spike=group_spike) for i in range(len(Ylist))]
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=group_spike,
kernel=kernels[i],inference_method=inference_methods[i],likelihood=likelihoods[i], variational_prior=self.var_priors[i], IBP=IBP, tau=None if taus is None else taus[i],
name='model_'+str(i), mpi_comm=mpi_comm, sharedX=True) for i,y in enumerate(Ylist)]
@ -73,7 +73,7 @@ class SSMRD(Model):
# Divide latent dimensions
idx = np.empty((input_dim,),dtype=np.int)
residue = (input_dim)%(len(Ylist))
for i in xrange(len(Ylist)):
for i in range(len(Ylist)):
if i < residue:
size = input_dim/len(Ylist)+1
idx[i*size:(i+1)*size] = i
@ -86,7 +86,7 @@ class SSMRD(Model):
X = np.empty((Ylist[0].shape[0],input_dim))
fracs = np.empty((input_dim,))
from ..util.initialization import initialize_latent
for i in xrange(len(Ylist)):
for i in range(len(Ylist)):
Y = Ylist[i]
dim = (idx==i).sum()
if dim>0:

2
GPy/models/state_space_main.py
View file

@ -13,7 +13,7 @@ import scipy as sp
import scipy.linalg as linalg
try:
import state_space_setup
from . import state_space_setup
setup_available = True
except ImportError as e:
setup_available = False