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fix the pickle problem for models with psi statistics

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This commit is contained in:
Zhenwen Dai 2014-08-18 18:03:08 +01:00
parent abcea70291
commit f5f90b3a2c
2 changed files with 10 additions and 7 deletions
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4
GPy/kern/_src/psi_comp/__init__.py
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@ -9,7 +9,7 @@ import ssrbf_psi_comp
import sslinear_psi_comp import sslinear_psi_comp
import linear_psi_comp import linear_psi_comp
class PSICOMP_RBF(object): class PSICOMP_RBF(Pickleable):
@Cache_this(limit=2, ignore_args=(0,)) @Cache_this(limit=2, ignore_args=(0,))
def psicomputations(self, variance, lengthscale, Z, variational_posterior): def psicomputations(self, variance, lengthscale, Z, variational_posterior):
@ -29,7 +29,7 @@ class PSICOMP_RBF(object):
else: else:
raise ValueError, "unknown distriubtion received for psi-statistics" raise ValueError, "unknown distriubtion received for psi-statistics"
class PSICOMP_Linear(object): class PSICOMP_Linear(Pickleable):
@Cache_this(limit=2, ignore_args=(0,)) @Cache_this(limit=2, ignore_args=(0,))
def psicomputations(self, variance, Z, variational_posterior): def psicomputations(self, variance, Z, variational_posterior):

13
GPy/models/ss_gplvm.py
View file

@ -23,7 +23,7 @@ class SSGPLVM(SparseGP):
:type init: 'PCA'|'random' :type init: 'PCA'|'random'
""" """
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10, def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True, **kwargs): Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True, **kwargs):
self.mpi_comm = mpi_comm self.mpi_comm = mpi_comm
@ -41,10 +41,13 @@ class SSGPLVM(SparseGP):
if X_variance is None: # The variance of the variational approximation (S) if X_variance is None: # The variance of the variational approximation (S)
X_variance = np.random.uniform(0,.1,X.shape) X_variance = np.random.uniform(0,.1,X.shape)
gamma = np.empty_like(X) # The posterior probabilities of the binary variable in the variational approximation if Gamma is None:
gamma[:] = 0.5 + 0.1 * np.random.randn(X.shape[0], input_dim) gamma = np.empty_like(X) # The posterior probabilities of the binary variable in the variational approximation
gamma[gamma>1.-1e-9] = 1.-1e-9 gamma[:] = 0.5 + 0.1 * np.random.randn(X.shape[0], input_dim)
gamma[gamma<1e-9] = 1e-9 gamma[gamma>1.-1e-9] = 1.-1e-9
gamma[gamma<1e-9] = 1e-9
else:
gamma = Gamma.copy()
if Z is None: if Z is None:
Z = np.random.permutation(X.copy())[:num_inducing] Z = np.random.permutation(X.copy())[:num_inducing]