Variables for a Bayesian Optimization

Syntax for Creating Optimization Variables

For each variable in your objective function, create a variable description object usingoptimizableVariable. Each variable has a unique name and a range of values. The minimal syntax for variable creation is

variable = optimizableVariable(Name,Range)

This function creates a real variable that ranges from the lower boundRange(1)to the upper boundRange(2).

You can specify three types of variables in theTypename-value pair:

'real'— Continuous real values between finite bounds. GiveRangeas the two-element vector[lower upper]，代表下限和上限。
'integer'— Integer values between finite bounds, similar to'real'.
'categorical'— Cell array of names of possible values, such as{'red','green','blue'}, that you specify in theRangeargument.

For'real'要么'integer'variables, you can specify thatbayesopt通过设置来搜索日志缩放的空间Transformname-value pair to'log'. For this transformation, ensure that the lower bound in theRangeis strictly positive.

Include variables forbayesopt作为第二个论点的矢量。

results = bayesopt(fun,[xvar,ivar,rvar])

To exclude a variable from an optimization, set优化tofalse, either in the name-value pair ofoptimizableVariable, or by dot notation:

xvar.Optimize = false;

Tip

There are two names associated with anoptimizableVariable:
- The MATLAB^®workspace variable name
- The name of the variable in the optimization
例如，
```
xvar = optimizableVariable('spacevar',[1,100]);
```
xvaris the MATLAB workspace variable, and'spacevar'是变量的优化。
Use these names as follows:
- Usexvar作为您传递的变量矢量中的元素bayesopt. For example,
  results = bayesopt(fun,[xvar,tvar])
- Use'spacevar'as the name of the variable in the optimization. For example, in an objective function,
  function objective = mysvmfun(x,cdata,grp) SVMModel = fitcsvm(cdata,grp,'KernelFunction','rbf',... 'BoxConstraint',x.spacevar,... 'KernelScale',x.tvar); objective = kfoldLoss(crossval(SVMModel));

Variables for Optimization Examples

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Real variable from 0 to 1:

var1 = optimizableVariable('xvar',[0 1])

VAR1 =具有属性的优化变性：名称：'XVAR'范围：[0 1]类型：“真实”变换：'无'优化：1

Integer variable from 1 to 1000 on a log scale:

var2 =优化不变（'ivar',[1 1000],'Type','integer','Transform','log')

var2 = optimizableVariable with properties: Name: 'ivar' Range: [1 1000] Type: 'integer' Transform: 'log' Optimize: 1

Categorical variable of rainbow colors:

var3 = optimizableVariable('rvar',{'r''o''y''g''b''i''v'},'Type','categorical')

var3 = optimizableVariable with properties: Name: 'rvar' Range: {'r' 'o' 'y' 'g' 'b' 'i' 'v'} Type: 'categorical' Transform: 'none' Optimize: 1