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linear without caching, derivatives done

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Max Zwiessele 2014-02-21 09:14:31 +00:00
parent 1d722c4f28
commit 0c92fca31a
7 changed files with 71 additions and 54 deletions
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26
GPy/core/sparse_gp.py
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@ -44,26 +44,26 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
if not (X_variance is None):
assert X_variance.shape == X.shape
self.X_variance = X_variance
if self.has_uncertain_inputs():
assert X_variance.shape == X.shape
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name)
self.add_parameter(self.Z, index=0)
self.parameters_changed()
def update_gradients_Z(self):
#The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
if not self.Z.is_fixed:
if self.X_variance is None:
self.Z.gradient = self.kern.gradients_Z_sparse(X=self.X, Z=self.Z, **self.grad_dict)
else:
self.Z.gradient = self.kern.gradients_Z_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
def has_uncertain_inputs(self):
return not (self.X_variance is None)
def parameters_changed(self):
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.update_gradients_Z()
if self.has_uncertain_inputs():
self.kern.update_gradients_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
else:
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):
"""
@ -97,12 +97,10 @@ class SparseGP(GP):
"""
return GP._getstate(self) + [self.Z,
self.num_inducing,
self.has_uncertain_inputs,
self.X_variance]
def _setstate(self, state):
self.X_variance = state.pop()
self.has_uncertain_inputs = state.pop()
self.num_inducing = state.pop()
self.Z = state.pop()
GP._setstate(self, state)