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[GPU] GPU kernel

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This commit is contained in:
Zhenwen Dai 2014-03-26 10:47:33 +00:00
parent b5b17b9715
commit 53627ee282
7 changed files with 90 additions and 60 deletions
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12
GPy/models/ss_gplvm.py
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@ -25,7 +25,7 @@ class SSGPLVM(SparseGP):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike-and-Slab GPLVM', **kwargs):
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike-and-Slab GPLVM', group_spike=False, **kwargs):
if X == None: # The mean of variational approximation (mu)
from ..util.initialization import initialize_latent
@ -38,6 +38,9 @@ class SSGPLVM(SparseGP):
gamma = np.empty_like(X) # The posterior probabilities of the binary variable in the variational approximation
gamma[:] = 0.5 + 0.01 * np.random.randn(X.shape[0], input_dim)
if group_spike:
gamma[:] = gamma.mean(axis=0)
if Z is None:
Z = np.random.permutation(X.copy())[:num_inducing]
assert Z.shape[1] == X.shape[1]
@ -47,11 +50,16 @@ class SSGPLVM(SparseGP):
if kernel is None:
kernel = kern.SSRBF(input_dim)
pi = np.empty((input_dim))
pi[:] = 0.5
self.variational_prior = SpikeAndSlabPrior(pi=pi) # the prior probability of the latent binary variable b
X = SpikeAndSlabPosterior(X, X_variance, gamma)
if group_spike:
kernel.group_spike_prob = True
self.variational_prior.group_spike_prob = True
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs)
self.add_parameter(self.X, index=0)