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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-05 17:06:40 -0400 |
|---|---|---|
| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-05 17:07:51 -0400 |
| commit | 93f34bdd35732578cfe2a8d14562df57669b47ea (patch) | |
| tree | 32f3d0b960c7e4ede2f21bd739456409f6a1c3f5 /R | |
| parent | add cran comments to git (diff) | |
| download | rgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.tar.gz rgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.zip | |
fix spelling
Diffstat (limited to 'R')
| -rw-r--r-- | R/gensvm.grid.R | 4 | ||||
| -rw-r--r-- | R/gensvm.maxabs.scale.R | 2 |
2 files changed, 3 insertions, 3 deletions
diff --git a/R/gensvm.grid.R b/R/gensvm.grid.R index 8fa187e..599b588 100644 --- a/R/gensvm.grid.R +++ b/R/gensvm.grid.R @@ -8,7 +8,7 @@ #' #' @param x training data matrix. We denote the size of this matrix by #' n_samples x n_features. -#' @param y training vector of class labes of length n_samples. The number of +#' @param y training vector of class labels of length n_samples. The number of #' unique labels in this vector is denoted by n_classes. #' @param param.grid String (\code{'tiny'}, \code{'small'}, or \code{'full'}) #' or data frame with parameter configurations to evaluate. Typically this is @@ -48,7 +48,7 @@ #' \item{cv.idx}{Array with cross validation indices used to split the data} #' #' @section Using a Parameter Grid: -#' To evaluate certain paramater configurations, a data frame can be supplied +#' To evaluate certain parameter configurations, a data frame can be supplied #' to the \code{param.grid} argument of the function. Such a data frame can #' easily be generated using the R function \code{expand.grid}, or could be #' created through other ways to test specific parameter configurations. diff --git a/R/gensvm.maxabs.scale.R b/R/gensvm.maxabs.scale.R index 47a6340..0c0f6f3 100644 --- a/R/gensvm.maxabs.scale.R +++ b/R/gensvm.maxabs.scale.R @@ -1,6 +1,6 @@ #' @title Scale each column of a matrix by its maximum absolute value #' -#' @description Scaling a dataset can creatly decrease the computation time of +#' @description Scaling a dataset can greatly decrease the computation time of #' GenSVM. This function scales the data by dividing each column of a matrix by #' the maximum absolute value of that column. This preserves sparsity in the #' data while mapping each column to the interval [-1, 1]. |
