From 977be680151a2d4b0567f20c501ec8e0865eb525 Mon Sep 17 00:00:00 2001 From: Jakob Richter Date: Tue, 8 Aug 2017 14:12:05 +0300 Subject: [PATCH] fix spelling (#398) --- R/MBOResult.R | 2 +- R/OptProblem.R | 6 +++--- R/OptState.R | 14 +++++++------- R/doc_error_handling.R | 4 ++-- R/doc_mbo_parallel.R | 2 +- R/exampleRun.R | 2 +- R/infill_crits.R | 2 +- R/initCrit.R | 2 +- R/makeMBOControl.R | 2 +- R/makeMBOLearner.R | 2 +- R/plotExampleRun.R | 6 +++--- R/plot_MBOResult.R | 2 +- R/renderExampleRunPlot.R | 8 ++++---- R/setMBOControlInfill.R | 2 +- R/setMBOControlMultiObj.R | 10 +++++----- R/setMBOControlMultiPoint.R | 6 +++--- R/zzz.R | 2 +- man-roxygen/arg_plot_MBO.R | 8 ++++---- man/MBOInfillCrit.Rd | 2 +- man/MBOMultiObjResult.Rd | 2 +- man/OptProblem.Rd | 4 ++-- man/OptState.Rd | 2 +- man/error_handling.Rd | 4 ++-- man/exampleRun.Rd | 2 +- man/initCrit.Rd | 2 +- man/makeMBOControl.Rd | 2 +- man/makeMBOLearner.Rd | 2 +- man/mbo_parallel.Rd | 2 +- man/plotExampleRun.Rd | 6 +++--- man/plotMBOResult.Rd | 2 +- man/renderExampleRunPlot.Rd | 8 ++++---- man/setMBOControlInfill.Rd | 2 +- man/setMBOControlMultiObj.Rd | 10 +++++----- man/setMBOControlMultiPoint.Rd | 4 ++-- 34 files changed, 69 insertions(+), 69 deletions(-) diff --git a/R/MBOResult.R b/R/MBOResult.R index 8168ada42..b8f5c618f 100644 --- a/R/MBOResult.R +++ b/R/MBOResult.R @@ -87,7 +87,7 @@ print.MBOResult = function(x, ...) { #' \itemize{ #' \item{pareto.front [\code{matrix}]}{Pareto front of all evaluated points.} #' \item{pareto.set [\code{list} of \code{list}s]}{Pareto set of all evaluated points.} -#' \item{pareto.inds [\code{numeric}]}{Indizes of the Pareto-optimal points in the opt.path} +#' \item{pareto.inds [\code{numeric}]}{Indices of the Pareto-optimal points in the opt.path} #' \item{opt.path [\code{\link[ParamHelpers]{OptPath}}]}{Optimization path. #' Includes all evaluated points and additional information as documented in \link{mbo_OptPath}. #' You can convert it via \code{as.data.frame}.} diff --git a/R/OptProblem.R b/R/OptProblem.R index 645eea195..c5ca42410 100644 --- a/R/OptProblem.R +++ b/R/OptProblem.R @@ -1,7 +1,7 @@ #' @title OptProblem object. #' @description -#' The OptProblem contains all the constants values wich define a OptProblem within our MBO Steps. -#' It is an enviroment and is always pointed at by the OptState. +#' The OptProblem contains all the constants values which define a OptProblem within our MBO Steps. +#' It is an environment and is always pointed at by the OptState. #' @name OptProblem #' @rdname OptProblem NULL @@ -107,4 +107,4 @@ print.OptProblem = function(x, ...) { catf("Objective Function: %s", getName(getOptProblemFun(x))) catf("Surrogate Learner:") print(getOptProblemLearner(x)) -} +} diff --git a/R/OptState.R b/R/OptState.R index 90a9b8eb9..bbf52ddb8 100644 --- a/R/OptState.R +++ b/R/OptState.R @@ -1,18 +1,18 @@ #' @title OptState object. #' @description #' The OptState is the central component of the mbo iterations. -#' This enviroment contains every necessary information needed during optimization in MBO. +#' This environment contains every necessary information needed during optimization in MBO. #' It also links to the \code{\link{OptProblem}} and to the \code{\link{OptResult}}. #' @name OptState #' @rdname OptState NULL # @param loop \code{integer()} \cr -# Tells us in what loop we are at the moment. 0 means we are in the inital phase. +# Tells us in what loop we are at the moment. 0 means we are in the initial phase. # The loop i should change to i+1 as soon as the i-th point is evaluated # @param tasks \code{list()} \cr -# List of \code{RegrTask} which together formulate data neccessary for the surrogate. -# Caching is done to not neccessarly regenerate tasks if the \code{loop} has not changed yes +# List of \code{RegrTask} which together formulate data necessary for the surrogate. +# Caching is done to not necessarily regenerate tasks if the \code{loop} has not changed yes # @param models \code{list()} \cr # List of \code{WrappedModel} which are trained on the \code{tasks} and formulate the surrogate. # Caching is done as above. @@ -22,7 +22,7 @@ NULL # Pointer to the OptResult Object. # @param state \code{character(1)} \cr # Tells us in what state we are in text. So far we know: -# init - right after iniitilization of mbo +# init - right after initialization of mbo # iter - within an iteration # term.iter - maximal number of iterations reached # term.time - maximal running time exceeded @@ -31,13 +31,13 @@ NULL # term.fevals - maximal number of function evaluations reached # term.custom - terminated due to custom termination condition # @param opt.path \code{OptPath} \cr -# Here we keep the opt.path. It delivers the data for the tasks and other usefull information. +# Here we keep the opt.path. It delivers the data for the tasks and other useful information. # @param time.last.saved \code{POSIXct} \cr # The \code{Sys.time()} when the last save on disk was done. # @param loop.starttime \code{POSIXct} \cr # The \code{Sys.time()} when the mbo iteration was started. # @param time.used \code{integer(1)} \cr -# The time in seconds we are alrady used for optimization since the verry start. +# The time in seconds we are already used for optimization since the very start. # This counts all iterations together and is necessary for continuation with a given time budget. # IMPORTANT NOTE: diff --git a/R/doc_error_handling.R b/R/doc_error_handling.R index 31d7df698..6832b2767 100644 --- a/R/doc_error_handling.R +++ b/R/doc_error_handling.R @@ -37,7 +37,7 @@ #' #' #' \strong{Mechanism II - The mlr's on.learner.error} -#' This procdues a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones) +#' This produces a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones) #' are proposed now for the current iteration. And we pray that we can fit the model again in the #' next iteration. # @@ -46,7 +46,7 @@ #' #' \strong{Mechanism III - Filtering of proposed point which are too close} #' -#' Issue 6 is solved by filtering points that are to close to other porposed points or points already +#' Issue 6 is solved by filtering points that are to close to other proposed points or points already #' proposed in preceding iterations. Filtering in this context means replacing the proposed points by #' a randomly generated new point. The heuristics mechanism is (de)activated via the logical #' \code{filter.proposed.points.tol} parameter of the \code{\link{setMBOControlInfill}} function, which defaults to diff --git a/R/doc_mbo_parallel.R b/R/doc_mbo_parallel.R index db3376d83..9a25ef801 100644 --- a/R/doc_mbo_parallel.R +++ b/R/doc_mbo_parallel.R @@ -1,7 +1,7 @@ #' @title Parallelization in mlrMBO #' #' @description -#' In mlrMBO you can parallize the tuning on two different levels to speed up computation: +#' In mlrMBO you can parallelize the tuning on two different levels to speed up computation: #' \itemize{ #' \item{\code{mlrMBO.feval}}{Multiple evaluations of the target function.} #' \item{\code{mlrMBO.propose.points}}{Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)} diff --git a/R/exampleRun.R b/R/exampleRun.R index 6ca942492..d5e90124f 100644 --- a/R/exampleRun.R +++ b/R/exampleRun.R @@ -12,7 +12,7 @@ #' Please note the following things: #' - The true objective function (and later everything which is predicted from our surrogate model) #' is evaluated on a regular spaced grid. These evaluations are stored in the result object. -#' You can control the resultion of this grid via \code{points.per.dim}. +#' You can control the resolution of this grid via \code{points.per.dim}. #' Parallelization of these evaluations is possible with the R package parallelMap on the level \code{mlrMBO.feval}. #' - In every iteration the fitted, approximating surrogate model is stored in the result object #' (via \code{store.model.at} in \code{control}) so we can later visualize it quickly. diff --git a/R/infill_crits.R b/R/infill_crits.R index 0416f3072..eca1dab1d 100644 --- a/R/infill_crits.R +++ b/R/infill_crits.R @@ -180,7 +180,7 @@ makeMBOInfillCritAEI = function(aei.use.nugget = FALSE, se.threshold = 1e-6) { } return(res) }, - name = "Augmeted expected improvement", + name = "Augmented expected improvement", id = "aei", components = c("se", "mean", "tau"), params = list(aei.use.nugget = aei.use.nugget), diff --git a/R/initCrit.R b/R/initCrit.R index b6402b747..3a4dc1f27 100644 --- a/R/initCrit.R +++ b/R/initCrit.R @@ -6,7 +6,7 @@ #' To actually set these default values, this function is called, which returns a fully #' initialized [\code{\link{MBOInfillCrit}}]. #' This function is mainly for internal use. If a custom infill criterion is created, it may be -#' required to create a seperate method \code{initCrit.InfillCritID} where \code{ID} is the +#' required to create a separate method \code{initCrit.InfillCritID} where \code{ID} is the #' \code{id} of the custom \link{MBOInfillCrit}. #' #' @param crit [\code{\link{MBOInfillCrit}}]\cr diff --git a/R/makeMBOControl.R b/R/makeMBOControl.R index 3545b84eb..e2f677f5f 100644 --- a/R/makeMBOControl.R +++ b/R/makeMBOControl.R @@ -37,7 +37,7 @@ #' Default is \code{NULL} which means to stop if the objective function did not produce the desired #' result. #' @param trafo.y.fun [\code{MBOTrafoFunction}]\cr -#' Sometimes it is favourable to transform the target function values before modelling. Provide a +#' Sometimes it is favorable to transform the target function values before modeling. Provide a #' MBO transformation function to do so. #' @param suppress.eval.errors [\code{logical(1)}]\cr #' Should reporting of error messages during target function evaluations be suppressed? diff --git a/R/makeMBOLearner.R b/R/makeMBOLearner.R index 813268578..81c4752f7 100644 --- a/R/makeMBOLearner.R +++ b/R/makeMBOLearner.R @@ -42,7 +42,7 @@ #' special class label \code{"__miss__"}} #' } #' Both of these techniques make sense for tree-based methods and are usually hard to beat, see -#' Ding et al. (2010). +#' Ding et.al. (2010). #' #' @references #' Ding, Yufeng, and Jeffrey S. Simonoff. An investigation of missing data methods for diff --git a/R/plotExampleRun.R b/R/plotExampleRun.R index 220ce40f7..a37db473c 100644 --- a/R/plotExampleRun.R +++ b/R/plotExampleRun.R @@ -45,7 +45,7 @@ #' and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuristic might #' change the \code{ylim} setting between plot iterations. #' @param point.size [\code{numeric(1)}]\cr -#' Point size for ploted points. Default ist 3. +#' Point size for plotted points. Default is 3. #' @param line.size [\code{numeric(1)}]\cr #' Line width of the graphs of plotted functions. #' @param trafo [\code{list}]\cr @@ -59,8 +59,8 @@ #' If a single function is provided, this function is used for all plots. #' @param colors [\code{character(3)}] #' Specify colors for point in the plots. Must be a vector of length 3, -#' each element a color for the type design, prop and seq respectivly. -#' Default is red for the initial design, blue for allready proposed points +#' each element a color for the type design, prop and seq respectively. +#' Default is red for the initial design, blue for already proposed points #' and green for the actual iteration. #' @param gg.objects [\code{list)}] #' List of \code{gg} objects that should be added to all ggplots. diff --git a/R/plot_MBOResult.R b/R/plot_MBOResult.R index 62c4efc66..584566e93 100644 --- a/R/plot_MBOResult.R +++ b/R/plot_MBOResult.R @@ -1,7 +1,7 @@ #' @title MBO Result Plotting #' #' @description -#' Plots any MBO result objects. Plots for X-Space, Y-Space and any coloumn in +#' Plots any MBO result objects. Plots for X-Space, Y-Space and any column in #' the optimization path are available. This function uses #' \code{\link[ParamHelpers]{plotOptPath}} from package \code{ParamHelpers}. #' diff --git a/R/renderExampleRunPlot.R b/R/renderExampleRunPlot.R index da9483947..d1fc21652 100644 --- a/R/renderExampleRunPlot.R +++ b/R/renderExampleRunPlot.R @@ -41,7 +41,7 @@ #' and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuristic might #' change the \code{ylim} setting between plot iterations. #' @param point.size [\code{numeric(1)}]\cr -#' Point size for ploted points. Default ist 3. +#' Point size for plotted points. Default is 3. #' @param line.size [\code{numeric(1)}]\cr #' Line width of the graphs of plotted functions. #' @param trafo [\code{list}]\cr @@ -55,12 +55,12 @@ #' If a single function is provided, this function is used for all plots. #' @param colors [\code{character(3)}] #' Specify colors for point in the plots. Must be a vector of length 3, -#' each element a color for the type design, prop and seq respectivly. -#' Default is red for the initial design, blue for allready proposed points +#' each element a color for the type design, prop and seq respectively. +#' Default is red for the initial design, blue for already proposed points #' and green for the actual iteration. #' @param ... [any]\cr #' Currently not used. -#' @return [\code{list}]. List containing seperate ggplot object. The number of plots depends on +#' @return [\code{list}]. List containing separate ggplot object. The number of plots depends on #' the type of MBO problem. See the description for details. #' @export renderExampleRunPlot = function(object, iter, densregion = TRUE, diff --git a/R/setMBOControlInfill.R b/R/setMBOControlInfill.R index 761468590..055910b28 100644 --- a/R/setMBOControlInfill.R +++ b/R/setMBOControlInfill.R @@ -39,7 +39,7 @@ #' to optimize infill criterion. If all CMA-ES runs fail, a random point is generated #' instead and a warning informs about it. #' \dQuote{ea}: Use an (mu+1) EA to optimize infill criterion. -#' \dQuote{nsga2}: NSGA2 for multi obj. optimizationen. Needed for mspot. +#' \dQuote{nsga2}: NSGA2 for multi obj. optimizations. Needed for mspot. #' Default is \dQuote{focussearch}. #' Alternatively, you may pass a function name as string. #' @param opt.restarts [\code{integer(1)}]\cr diff --git a/R/setMBOControlMultiObj.R b/R/setMBOControlMultiObj.R index d51730a5e..0cb2ae156 100644 --- a/R/setMBOControlMultiObj.R +++ b/R/setMBOControlMultiObj.R @@ -9,7 +9,7 @@ #' Which multi-objective method should be used? #' \dQuote{parego}: The ParEGO algorithm. #' \dQuote{dib}: Direct indicator-based method. Subsumes SMS-EGO and epsilon-EGO. -#' \dQuote{mspot}: Directly optimizes multcrit problem where we substitute the true +#' \dQuote{mspot}: Directly optimizes multicrit problem where we substitute the true #' objectives with model-based infill crits via an EMOA. #' All methods can also propose multiple points in parallel. #' Default is \dQuote{dib}. @@ -40,8 +40,8 @@ #' Default is 1, if only 1 point is proposed each iteration, otherwise 5. #' @param parego.use.margin.points [\code{logical}]\cr #' For each target function: Should the weight vector (0, ..., 0, 1, 0, ..., 0), -#' i.e. the weight vector with only 0 and a single 1 at the i.th position for -#' the i.th target function, be drawn with probability 1? Number of TRUE entries +#' i.e. the weight vector with only 0 and a single 1 at the i-th position for +#' the i-th target function, be drawn with probability 1? Number of TRUE entries #' must be less or equal to \code{propose.points} #' Default is not to do this. #' @param parego.normalize [\code{character}] \cr @@ -53,7 +53,7 @@ #' @param mspot.select.crit [\code{\link{MBOInfillCrit}}]\cr #' Which infill.crit to use in the candidate selection. After the NSGA2 #' proposed a set of candidates, \dQuote{propose.points} are selected via -#' the hypervoume contribution of this infill.crit. +#' the hypervolume contribution of this infill.crit. #' Possible values are \dQuote{crit.mr} and \dQuote{crit.cb} (or any other InfillCrit generated with \code{\link{makeMBOInfillCritCB}}), default is \dQuote{crit.mr}. #' @return [\code{\link{MBOControl}}]. #' @@ -89,7 +89,7 @@ #' Jeong, S.; Obayashi, S.: Efficient global optimization (EGO) for Multi-Objective Problem and Data Mining. #' In: Proc. IEEE Congress on #' Evolutionary Computation (CEC 2005), Edinburgh, UK, Corne, D.; -#' et al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145 +#' et.al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145 #' #' @family MBOControl #' @export diff --git a/R/setMBOControlMultiPoint.R b/R/setMBOControlMultiPoint.R index f66feba5e..2118cf332 100644 --- a/R/setMBOControlMultiPoint.R +++ b/R/setMBOControlMultiPoint.R @@ -16,7 +16,7 @@ #' The selection criterion is \code{moimbo.selection}. #' If this method is selected the infill criterion in \code{setMBOInfillCrit} is ignored. #' \dQuote{cl}: Proposes points by constant liar strategy. -#' Only meaningfull if \code{infill.crit == "cb"} +#' Only meaningful if \code{infill.crit == "cb"} #' In the first step the kriging model is fitted based on the real data and the best point is calculated #' according to the regular EI-criterion. #' Then, the function value of the best point is simply guessed by the worst seen function evaluation. @@ -31,9 +31,9 @@ #' Default is \dQuote{ei.dist}. #' @param moimbo.dist [\code{character(1)}]\cr #' Distance function used in multi-objective EA. -#' Possible values are: \dQuote{nearest.neigbor}, \dQuote{nearest.better}. +#' Possible values are: \dQuote{nearest.neighbor}, \dQuote{nearest.better}. #' Default is \dQuote{nearest.better}. -#FIXME: a link to the definition of nearest.better and nearest.neigbor? +#FIXME: a link to the definition of nearest.better and nearest.neighbor? #' @param moimbo.selection [\code{character(1)}]\cr #' Method used for selecting 1 element for removal from the population #' in each iteration of the multi-objective EA. diff --git a/R/zzz.R b/R/zzz.R index 1b303b13b..c363837db 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -27,7 +27,7 @@ NULL #' \item{crit.cb}{Confidence bound with lambda automatically chosen, see \code{\link{infillcrits}}} #' \item{crit.cb1}{Confidence bound with lambda=1} #' \item{crit.cb2}{Confidence bound with lambda=2} -#' \item{crit.aei}{Augmeted expected improvement} +#' \item{crit.aei}{Augmented expected improvement} #' \item{crit.eqi}{Expected quantile improvement} #' \item{crit.dib1}{Direct indicator-based with lambda=1} #' } diff --git a/man-roxygen/arg_plot_MBO.R b/man-roxygen/arg_plot_MBO.R index f65526b4d..276522c94 100644 --- a/man-roxygen/arg_plot_MBO.R +++ b/man-roxygen/arg_plot_MBO.R @@ -1,14 +1,14 @@ #' @param crit.plot [\code{logical(1)}]\cr #' Should the infill criterion versus the iteration be plotted? For multi objective -#' and method = MSPOT multiple infill criterion values per iterion do exist, +#' and method = MSPOT multiple infill criterion values per iteration do exist, #' so a multi-dimensional plot is used. Default is TRUE #' @param hv.plot [\code{logical(1)}]\cr #' Should the hypervolume development be plotted? Default is FALSE for single -#' objective und TRUE for multi objective. +#' objective and TRUE for multi objective. #' @param extra.measures [\code{character}]\cr #' Additional measures to be plotted versus the iteration. Must be the name -#' of a coloumn available in the optimization path. Notice that at most 4 plost -#' can be renderd at the same time. Since X and Y Space plots will allways be +#' of a column available in the optimization path. Notice that at most 4 plots +#' can be rendered at the same time. Since X and Y Space plots will always be #' rendered, at most 2 addition plots are possible. You must deactivate crit.plot #' or hv.plot, if you want to add additional plots via extra.measure. #' @param ref.point [\code{numeric}]\cr diff --git a/man/MBOInfillCrit.Rd b/man/MBOInfillCrit.Rd index b26cb6c60..6e3d13bbc 100644 --- a/man/MBOInfillCrit.Rd +++ b/man/MBOInfillCrit.Rd @@ -80,7 +80,7 @@ the package allows for the creation of custom infill criteria. \item{crit.cb}{Confidence bound with lambda automatically chosen, see \code{\link{infillcrits}}} \item{crit.cb1}{Confidence bound with lambda=1} \item{crit.cb2}{Confidence bound with lambda=2} -\item{crit.aei}{Augmeted expected improvement} +\item{crit.aei}{Augmented expected improvement} \item{crit.eqi}{Expected quantile improvement} \item{crit.dib1}{Direct indicator-based with lambda=1} } diff --git a/man/MBOMultiObjResult.Rd b/man/MBOMultiObjResult.Rd index f4d35c141..0163f9426 100644 --- a/man/MBOMultiObjResult.Rd +++ b/man/MBOMultiObjResult.Rd @@ -7,7 +7,7 @@ \itemize{ \item{pareto.front [\code{matrix}]}{Pareto front of all evaluated points.} \item{pareto.set [\code{list} of \code{list}s]}{Pareto set of all evaluated points.} - \item{pareto.inds [\code{numeric}]}{Indizes of the Pareto-optimal points in the opt.path} + \item{pareto.inds [\code{numeric}]}{Indices of the Pareto-optimal points in the opt.path} \item{opt.path [\code{\link[ParamHelpers]{OptPath}}]}{Optimization path. Includes all evaluated points and additional information as documented in \link{mbo_OptPath}. You can convert it via \code{as.data.frame}.} diff --git a/man/OptProblem.Rd b/man/OptProblem.Rd index bc81c5fcc..174fbc157 100644 --- a/man/OptProblem.Rd +++ b/man/OptProblem.Rd @@ -4,6 +4,6 @@ \alias{OptProblem} \title{OptProblem object.} \description{ -The OptProblem contains all the constants values wich define a OptProblem within our MBO Steps. -It is an enviroment and is always pointed at by the OptState. +The OptProblem contains all the constants values which define a OptProblem within our MBO Steps. +It is an environment and is always pointed at by the OptState. } diff --git a/man/OptState.Rd b/man/OptState.Rd index e5a52a4ac..d7889dfca 100644 --- a/man/OptState.Rd +++ b/man/OptState.Rd @@ -5,6 +5,6 @@ \title{OptState object.} \description{ The OptState is the central component of the mbo iterations. -This enviroment contains every necessary information needed during optimization in MBO. +This environment contains every necessary information needed during optimization in MBO. It also links to the \code{\link{OptProblem}} and to the \code{\link{OptResult}}. } diff --git a/man/error_handling.Rd b/man/error_handling.Rd index 142aa5f03..fc776e650 100644 --- a/man/error_handling.Rd +++ b/man/error_handling.Rd @@ -40,7 +40,7 @@ Hence, mbo allows imputation of bad values via the control option \code{impute.y \strong{Mechanism II - The mlr's on.learner.error} -This procdues a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones) +This produces a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones) are proposed now for the current iteration. And we pray that we can fit the model again in the next iteration. \strong{Logging:} The entry \dQuote{model.error} is set in the opt.path. @@ -48,7 +48,7 @@ next iteration. \strong{Mechanism III - Filtering of proposed point which are too close} -Issue 6 is solved by filtering points that are to close to other porposed points or points already +Issue 6 is solved by filtering points that are to close to other proposed points or points already proposed in preceding iterations. Filtering in this context means replacing the proposed points by a randomly generated new point. The heuristics mechanism is (de)activated via the logical \code{filter.proposed.points.tol} parameter of the \code{\link{setMBOControlInfill}} function, which defaults to diff --git a/man/exampleRun.Rd b/man/exampleRun.Rd index c742e25e2..4c25b6dbc 100644 --- a/man/exampleRun.Rd +++ b/man/exampleRun.Rd @@ -61,7 +61,7 @@ Rendering the plots without displaying them is possible via the function Please note the following things: - The true objective function (and later everything which is predicted from our surrogate model) is evaluated on a regular spaced grid. These evaluations are stored in the result object. - You can control the resultion of this grid via \code{points.per.dim}. + You can control the resolution of this grid via \code{points.per.dim}. Parallelization of these evaluations is possible with the R package parallelMap on the level \code{mlrMBO.feval}. - In every iteration the fitted, approximating surrogate model is stored in the result object (via \code{store.model.at} in \code{control}) so we can later visualize it quickly. diff --git a/man/initCrit.Rd b/man/initCrit.Rd index b5ea951d8..36009f728 100644 --- a/man/initCrit.Rd +++ b/man/initCrit.Rd @@ -30,6 +30,6 @@ Some infill criteria have parameters that are dependent on values in the paramet To actually set these default values, this function is called, which returns a fully initialized [\code{\link{MBOInfillCrit}}]. This function is mainly for internal use. If a custom infill criterion is created, it may be - required to create a seperate method \code{initCrit.InfillCritID} where \code{ID} is the + required to create a separate method \code{initCrit.InfillCritID} where \code{ID} is the \code{id} of the custom \link{MBOInfillCrit}. } diff --git a/man/makeMBOControl.Rd b/man/makeMBOControl.Rd index e9a21411d..e3b533944 100644 --- a/man/makeMBOControl.Rd +++ b/man/makeMBOControl.Rd @@ -55,7 +55,7 @@ Default is \code{NULL} which means to stop if the objective function did not pro result.} \item{trafo.y.fun}{[\code{MBOTrafoFunction}]\cr -Sometimes it is favourable to transform the target function values before modelling. Provide a +Sometimes it is favorable to transform the target function values before modeling. Provide a MBO transformation function to do so.} \item{suppress.eval.errors}{[\code{logical(1)}]\cr diff --git a/man/makeMBOLearner.Rd b/man/makeMBOLearner.Rd index 2baf991aa..f173523d6 100644 --- a/man/makeMBOLearner.Rd +++ b/man/makeMBOLearner.Rd @@ -66,7 +66,7 @@ We simply handle those with an imputation method, added to the random forest: special class label \code{"__miss__"}} } Both of these techniques make sense for tree-based methods and are usually hard to beat, see -Ding et al. (2010). +Ding et.al. (2010). } \references{ Ding, Yufeng, and Jeffrey S. Simonoff. An investigation of missing data methods for diff --git a/man/mbo_parallel.Rd b/man/mbo_parallel.Rd index 5222c1ba9..037e46876 100644 --- a/man/mbo_parallel.Rd +++ b/man/mbo_parallel.Rd @@ -4,7 +4,7 @@ \alias{mbo_parallel} \title{Parallelization in mlrMBO} \description{ -In mlrMBO you can parallize the tuning on two different levels to speed up computation: +In mlrMBO you can parallelize the tuning on two different levels to speed up computation: \itemize{ \item{\code{mlrMBO.feval}}{Multiple evaluations of the target function.} \item{\code{mlrMBO.propose.points}}{Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)} diff --git a/man/plotExampleRun.Rd b/man/plotExampleRun.Rd index e1900c427..62d99084a 100644 --- a/man/plotExampleRun.Rd +++ b/man/plotExampleRun.Rd @@ -49,7 +49,7 @@ and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuris change the \code{ylim} setting between plot iterations.} \item{point.size}{[\code{numeric(1)}]\cr -Point size for ploted points. Default ist 3.} +Point size for plotted points. Default is 3.} \item{line.size}{[\code{numeric(1)}]\cr Line width of the graphs of plotted functions.} @@ -66,8 +66,8 @@ If a single function is provided, this function is used for all plots.} \item{colors}{[\code{character(3)}] Specify colors for point in the plots. Must be a vector of length 3, -each element a color for the type design, prop and seq respectivly. -Default is red for the initial design, blue for allready proposed points +each element a color for the type design, prop and seq respectively. +Default is red for the initial design, blue for already proposed points and green for the actual iteration.} \item{gg.objects}{[\code{list)}] diff --git a/man/plotMBOResult.Rd b/man/plotMBOResult.Rd index 1977fcb64..54d88ea4b 100644 --- a/man/plotMBOResult.Rd +++ b/man/plotMBOResult.Rd @@ -27,7 +27,7 @@ Default is \code{interactive()}.} function in package \code{ParamHelpers}.} } \description{ -Plots any MBO result objects. Plots for X-Space, Y-Space and any coloumn in +Plots any MBO result objects. Plots for X-Space, Y-Space and any column in the optimization path are available. This function uses \code{\link[ParamHelpers]{plotOptPath}} from package \code{ParamHelpers}. } diff --git a/man/renderExampleRunPlot.Rd b/man/renderExampleRunPlot.Rd index 2ce1c014d..49905745d 100644 --- a/man/renderExampleRunPlot.Rd +++ b/man/renderExampleRunPlot.Rd @@ -44,7 +44,7 @@ and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuris change the \code{ylim} setting between plot iterations.} \item{point.size}{[\code{numeric(1)}]\cr -Point size for ploted points. Default ist 3.} +Point size for plotted points. Default is 3.} \item{line.size}{[\code{numeric(1)}]\cr Line width of the graphs of plotted functions.} @@ -61,15 +61,15 @@ If a single function is provided, this function is used for all plots.} \item{colors}{[\code{character(3)}] Specify colors for point in the plots. Must be a vector of length 3, -each element a color for the type design, prop and seq respectivly. -Default is red for the initial design, blue for allready proposed points +each element a color for the type design, prop and seq respectively. +Default is red for the initial design, blue for already proposed points and green for the actual iteration.} \item{...}{[any]\cr Currently not used.} } \value{ -[\code{list}]. List containing seperate ggplot object. The number of plots depends on +[\code{list}]. List containing separate ggplot object. The number of plots depends on the type of MBO problem. See the description for details. } \description{ diff --git a/man/setMBOControlInfill.Rd b/man/setMBOControlInfill.Rd index 4b0e1caa4..b6ccc585a 100644 --- a/man/setMBOControlInfill.Rd +++ b/man/setMBOControlInfill.Rd @@ -54,7 +54,7 @@ focused on an especial promising region according to infill criterion. to optimize infill criterion. If all CMA-ES runs fail, a random point is generated instead and a warning informs about it. \dQuote{ea}: Use an (mu+1) EA to optimize infill criterion. -\dQuote{nsga2}: NSGA2 for multi obj. optimizationen. Needed for mspot. +\dQuote{nsga2}: NSGA2 for multi obj. optimizations. Needed for mspot. Default is \dQuote{focussearch}. Alternatively, you may pass a function name as string.} diff --git a/man/setMBOControlMultiObj.Rd b/man/setMBOControlMultiObj.Rd index 1cc97895c..4dfba869b 100644 --- a/man/setMBOControlMultiObj.Rd +++ b/man/setMBOControlMultiObj.Rd @@ -18,7 +18,7 @@ Control object for mbo.} Which multi-objective method should be used? \dQuote{parego}: The ParEGO algorithm. \dQuote{dib}: Direct indicator-based method. Subsumes SMS-EGO and epsilon-EGO. -\dQuote{mspot}: Directly optimizes multcrit problem where we substitute the true +\dQuote{mspot}: Directly optimizes multicrit problem where we substitute the true objectives with model-based infill crits via an EMOA. All methods can also propose multiple points in parallel. Default is \dQuote{dib}.} @@ -51,8 +51,8 @@ suggested in parego paper.} \item{parego.use.margin.points}{[\code{logical}]\cr For each target function: Should the weight vector (0, ..., 0, 1, 0, ..., 0), -i.e. the weight vector with only 0 and a single 1 at the i.th position for -the i.th target function, be drawn with probability 1? Number of TRUE entries +i.e. the weight vector with only 0 and a single 1 at the i-th position for +the i-th target function, be drawn with probability 1? Number of TRUE entries must be less or equal to \code{propose.points} Default is not to do this.} @@ -72,7 +72,7 @@ Default is \dQuote{sms}.} \item{mspot.select.crit}{[\code{\link{MBOInfillCrit}}]\cr Which infill.crit to use in the candidate selection. After the NSGA2 proposed a set of candidates, \dQuote{propose.points} are selected via -the hypervoume contribution of this infill.crit. +the hypervolume contribution of this infill.crit. Possible values are \dQuote{crit.mr} and \dQuote{crit.cb} (or any other InfillCrit generated with \code{\link{makeMBOInfillCritCB}}), default is \dQuote{crit.mr}.} } \value{ @@ -113,7 +113,7 @@ doi:10.1007/978-3-642-37140-0 56} Jeong, S.; Obayashi, S.: Efficient global optimization (EGO) for Multi-Objective Problem and Data Mining. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, Corne, D.; -et al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145 +et.al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145 } \seealso{ Other MBOControl: \code{\link{makeMBOControl}}, diff --git a/man/setMBOControlMultiPoint.Rd b/man/setMBOControlMultiPoint.Rd index 1cf6b461b..d939eb10f 100644 --- a/man/setMBOControlMultiPoint.Rd +++ b/man/setMBOControlMultiPoint.Rd @@ -27,7 +27,7 @@ The population size is set to \code{propose.points}. The selection criterion is \code{moimbo.selection}. If this method is selected the infill criterion in \code{setMBOInfillCrit} is ignored. \dQuote{cl}: Proposes points by constant liar strategy. -Only meaningfull if \code{infill.crit == "cb"} +Only meaningful if \code{infill.crit == "cb"} In the first step the kriging model is fitted based on the real data and the best point is calculated according to the regular EI-criterion. Then, the function value of the best point is simply guessed by the worst seen function evaluation. @@ -45,7 +45,7 @@ Default is \dQuote{ei.dist}.} \item{moimbo.dist}{[\code{character(1)}]\cr Distance function used in multi-objective EA. -Possible values are: \dQuote{nearest.neigbor}, \dQuote{nearest.better}. +Possible values are: \dQuote{nearest.neighbor}, \dQuote{nearest.better}. Default is \dQuote{nearest.better}.} \item{moimbo.selection}{[\code{character(1)}]\cr