This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Variables for a Bayesian Optimization

Syntax for Creating Optimization Variables

For each variable in your objective function, create a variable description object using optimizableVariable. 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 bound Range(1) to the upper bound Range(2).

You can specify three types of variables in the Type name-value pair:

  • 'real' — Continuous real values between finite bounds. Give Range as the two-element vector [lower upper], which represent the lower and upper bounds.

  • '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 the Range argument.

For 'real' or 'integer' variables, you can specify that bayesopt searches in a log-scaled space by setting the Transform name-value pair to 'log'. For this transformation, ensure that the lower bound in the Range is strictly positive.

Include variables for bayesopt as a vector in the second argument.

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

To exclude a variable from an optimization, set Optimize to false, either in the name-value pair of optimizableVariable, or by dot notation:

xvar.Optimize = false;


  • There are two names associated with an optimizableVariable:

    • The MATLAB® workspace variable name

    • The name of the variable in the optimization

    For example,

    xvar = optimizableVariable('spacevar',[1,100]);

    xvar is the MATLAB workspace variable, and 'spacevar' is the variable in the optimization.

    Use these names as follows:

    • Use xvar as an element in the vector of variables you pass to 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',...
      objective = kfoldLoss(crossval(SVMModel));

Variables for Optimization Examples

Real variable from 0 to 1:

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

         Name: 'xvar'
        Range: [0 1]
         Type: 'real'
    Transform: 'none'
     Optimize: 1

Integer variable from 1 to 1000 on a log scale:

var2 = optimizableVariable('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

Related Topics