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sparse gp missing data

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Max Zwiessele 2014-02-26 08:23:08 +00:00
parent 51dca0fcbc
commit b928044f40
4 changed files with 22 additions and 17 deletions
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25
GPy/core/sparse_gp.py
View file

@ -46,28 +46,33 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
self.q = NormalPosterior(X, X_variance)
GP.__init__(self, self.q.mean, Y, kernel, likelihood, inference_method=inference_method, name=name)
if not (X_variance is None):
self.q = NormalPosterior(X, X_variance)
GP.__init__(self, self.q.mean, Y, kernel, likelihood, inference_method=inference_method, name=name)
else:
self.X = X
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name)
self.add_parameter(self.Z, index=0)
self.parameters_changed()
def has_uncertain_inputs(self):
return self.q.has_uncertain_inputs()
return hasattr(self, 'q')
def parameters_changed(self):
if self.has_uncertain_inputs():
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference_latent(self.kern, self.q, self.Z, self.likelihood, self.Y)
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
if self.has_uncertain_inputs():
# gradients
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
self.kern.update_gradients_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, None, self.Z, self.likelihood, self.Y)
# gradients
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
self.kern.update_gradients_sparse(X=self.X, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_sparse(X=self.X, Z=self.Z, **self.grad_dict)
def _raw_predict(self, Xnew, X_variance_new=None, full_cov=False):
"""
Make a prediction for the latent function values

6
GPy/examples/dimensionality_reduction.py
View file

@ -164,7 +164,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
_np.random.seed(0)
data = GPy.util.datasets.oil()
kernel = GPy.kern.RBF(Q, 1., _np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
Y = data['X'][:N]
m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
@ -270,7 +270,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
from GPy import kern
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
@ -293,7 +293,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
from GPy.models import BayesianGPLVM
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 5, 9
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)

6
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -45,7 +45,7 @@ class VarDTC(object):
def inference(self, kern, X, X_variance, Z, likelihood, Y):
"""Inference for normal sparseGP"""
uncertain_inputs = False
psi0, psi1, psi2 = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
psi0, psi1, psi2 = _compute_psi(kern, X, Z)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def inference_latent(self, kern, posterior_variational, Z, likelihood, Y):
@ -205,7 +205,7 @@ class VarDTCMissingData(object):
def inference(self, kern, X, X_variance, Z, likelihood, Y):
"""Inference for normal sparseGP"""
uncertain_inputs = False
psi0, psi1, psi2 = _compute_psi(kern, X, X_variance, Z, uncertain_inputs)
psi0, psi1, psi2 = _compute_psi(kern, X, Z)
return self._inference(kern, psi0, psi1, psi2, Z, likelihood, Y, uncertain_inputs)
def inference_latent(self, kern, posterior_variational, Z, likelihood, Y):
@ -358,7 +358,7 @@ class VarDTCMissingData(object):
return post, log_marginal, grad_dict
def _compute_psi(kern, X, X_variance, Z):
def _compute_psi(kern, X, Z):
psi0 = kern.Kdiag(X)
psi1 = kern.K(X, Z)
psi2 = None

2
GPy/models/bayesian_gplvm.py
View file

@ -41,7 +41,7 @@ class BayesianGPLVM(SparseGP):
if likelihood is None:
likelihood = Gaussian()
self.q = NormalPosterior(X, X_variance)
#self.q = NormalPosterior(X, X_variance)
self.variational_prior = NormalPrior()
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, X_variance, name, **kwargs)