syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.mean module
- class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.mean.MeanFunction(**kwargs)[source]
Bases:
Block
Mean function, parameterizing a surrogate model together with a kernel function.
Note: KernelFunction also inherits from this interface.
- class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.mean.ScalarMeanFunction(initial_mean_value=0.0, **kwargs)[source]
Bases:
MeanFunction
Mean function defined as a scalar (fitted while optimizing the marginal likelihood).
- Parameters:
initial_mean_value – A scalar to initialize the value of the mean
- forward(X)[source]
Actual computation of the scalar mean function We compute mean_value * vector_of_ones, whose dimensions are given by the the first column of X
- Parameters:
X – input data of size (n,d) for which we want to compute the mean (here, only useful to extract the right dimension)
- class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.mean.ZeroMeanFunction(**kwargs)[source]
Bases:
MeanFunction
- forward(X)[source]
Overrides to implement forward computation using
NDArray
. Only accepts positional arguments. Parameters ———- *args : list of NDArrayInput tensors.