GPy.inference.optimization package¶

GPy.inference.optimization.stochastics module¶

class SparseGPMissing(model, batchsize=1)[source]

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]

For the sparse gp we need to store the dimension we are in, and the indices corresponding to those

do_stochastics()[source]

Update the internal state to the next batch of the stochastic descent algorithm.

reset()[source]

Reset the state of this stochastics generator.

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

do_stochastics()[source]

Update the internal state to the next batch of the stochastic descent algorithm.

reset()[source]

Reset the state of this stochastics generator.