GPy.inference.optimization package¶
Submodules¶
GPy.inference.optimization.stochastics module¶

class
SparseGPMissing
(model, batchsize=1)[source]¶ Bases:
GPy.inference.optimization.stochastics.StochasticStorage
Here we want to loop over all dimensions everytime. Thus, we can just make sure the loop goes over self.d every time. We will try to get batches which look the same together which speeds up calculations significantly.

class
SparseGPStochastics
(model, batchsize=1, missing_data=True)[source]¶ Bases:
GPy.inference.optimization.stochastics.StochasticStorage
For the sparse gp we need to store the dimension we are in, and the indices corresponding to those

class
StochasticStorage
(model)[source]¶ Bases:
object
This is a container for holding the stochastic parameters, such as subset indices or step length and so on.
self.d has to be a list of lists: [dimension indices, nan indices for those dimensions] so that the minibatches can be used as efficiently as possible.
Initialize this stochastic container using the given model