bayesopt attempts to minimize an objective function. If, instead, you
want to maximize a function, set the objective function to the negative of the
function you want to maximize. See Maximizing Functions. To include extra
parameters in an objective function, see Parameterizing Functions.
bayesopt passes a table of variables to
the objective function. The variables have the names and types that
you declare; see Variables for a Bayesian Optimization.
The objective function has the following signature:
[objective,coupledconstraints,userdata] = fun(x)
objective — The objective
function value at
x, a real scalar.
coupledconstraints — Value of coupled constraints, if any (optional
output), a vector of real values. A negative value indicates that a
constraint is satisfied, a positive value indicates that it is not
satisfied. For details, see Coupled Constraints.
userdata — Optional data
that your function can return for further uses, such as plotting or
logging (optional output). For an example, see Bayesian Optimization Plot Functions.
This objective function returns the loss in a cross-validated
fit of an SVM model with parameters
The objective also returns a coupled constraint function that is positive
(infeasible) when the number of support vectors exceeds 100 (100 is
feasible, 101 is not).
function [objective,constraint] = mysvmfun(x,cdata,grp) SVMModel = fitcsvm(cdata,grp,'KernelFunction','rbf',... 'BoxConstraint',x.box,... 'KernelScale',x.sigma); objective = kfoldLoss(crossval(SVMModel)); constraint = sum(SVMModel.SupportVectors) - 100.5;
To use the objective function, assuming that
grp exist in the workspace, create an anonymous function that
incorporates the data, as described in Parameterizing Functions.
fun = @(x)mysvmfun(x,cdata,grp); results = bayesopt(fun,vars) % Assumes vars exists
bayesopt deems your objective function to return an error when the
objective function returns anything other than a finite real scalar. For example, if
your objective function returns a complex value,
Inf, or matrix with more than one entry, then
bayesopt deems that your objective function errors. If
bayesopt encounters an error, it continues to optimize, and
automatically updates a Bayesian model of points that lead to errors. This Bayesian
model is the Error model.
incorporates the Error model as a coupled constraint. See Coupled Constraints.
When errors exist, you can plot the Error model by setting the
@plotConstraintModels. Or you can retrospectively
the results of a Bayesian optimization, and include