rename master->main

.github/workflows/tic.yml

@@ -5,7 +5,7 @@ on:
   workflow_dispatch:
   push:
     branches:
-      - master
+      - main
   pull_request:
   # for now, CRON jobs only run on the default branch of the repo (i.e. usually on master)
   schedule:

README.md

@@ -3,21 +3,21 @@
 Package website: [release](https://bbotk.mlr-org.com/)
 
 <!-- badges: start -->
-[![tic](https://github.com/mlr-org/bbotk/workflows/tic/badge.svg?branch=master)](https://github.com/mlr-org/bbotk/actions)
+[![tic](https://github.com/mlr-org/bbotk/workflows/tic/badge.svg?branch=main)](https://github.com/mlr-org/bbotk/actions)
 [![CRAN Status Badge](https://www.r-pkg.org/badges/version-ago/bbotk)](https://cran.r-project.org/package=bbotk)
 [![CodeFactor](https://www.codefactor.io/repository/github/mlr-org/bbotk/badge)](https://www.codefactor.io/repository/github/mlr-org/bbotk)
 <!-- badges: end -->
 
-This package provides a common framework for optimization including 
+This package provides a common framework for optimization including
 
 * `Optimizer`: Objects of this class allow you to optimize an object of the class `OptimInstance`.
-* `OptimInstance`: Defines the optimization problem, consisting of an `Objective`, the `search_space` and a `Terminator`. 
+* `OptimInstance`: Defines the optimization problem, consisting of an `Objective`, the `search_space` and a `Terminator`.
    All evaluations on the `OptimInstance` will be automatically stored in its own `Archive`.
-* `Objective`: Objects of this class contain the objective function. 
+* `Objective`: Objects of this class contain the objective function.
    The class ensures that the objective function is called in the right way and defines, whether the function should be minimized or maximized.
-* `Terminator`: Objects of this class control the termination of the optimization independent of the optimizer.  
+* `Terminator`: Objects of this class control the termination of the optimization independent of the optimizer.
 
-Various optimization methods are already implemented e.g. grid search, random search and generalized simulated annealing. 
+Various optimization methods are already implemented e.g. grid search, random search and generalized simulated annealing.
 
 ## Installation
 
@@ -46,7 +46,7 @@ fun = function(xs) {
 
 # Set domain
 domain = ParamSet$new(list(
-  ParamDbl$new("x1", -10, 10), 
+  ParamDbl$new("x1", -10, 10),
   ParamDbl$new("x2", -5, 5)
 ))
 
@@ -59,7 +59,7 @@ codomain = ParamSet$new(list(
 obfun = ObjectiveRFun$new(
   fun = fun,
   domain = domain,
-  codomain = codomain, 
+  codomain = codomain,
   properties = "deterministic"
 )
 
@@ -68,7 +68,7 @@ terminator = trm("evals", n_evals = 20)
 
 # Create optimization instance
 instance = OptimInstanceSingleCrit$new(
-  objective = obfun, 
+  objective = obfun,
   terminator = terminator
 )