Fixes issue #8 and updates roxygen

jkrijthe · Aug 27, 2018 · f992648 · f992648
1 parent 3bacb50
commit f992648
Show file tree

Hide file tree

Showing 53 changed files with 116 additions and 69 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -79,4 +79,4 @@ Collate:
     'svmd.R'
     'svmlin.R'
     'testdata-data.R'
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.0
diff --git a/R/LearningCurve.R b/R/LearningCurve.R
@@ -21,9 +21,9 @@ print.LearningCurve <- function(x,...) {
 #' 
 #' \code{measures} is a named list of performance measures. These are functions that accept seven arguments: a trained classifier, a numeric design matrix of the labeled objects, a factor of labels, a numeric design  matrix of unlabeled objects and a factor of labels for the unlabeled objects, a numeric design matrix of the test objects and a factor of labels of the test objects. See \code{\link{measure_accuracy}} for an example.
 #' 
-#' This function allows for two different types of learning curves to be generated. If \code{type="unlabeled"}, the number of labeled objects remains fixed at the value of \code{n_l}, where \code{sizes} controls the number of unlabeled objects. \code{n_test} controls the number of objects used for the test set, while all remaining objects are used if \code{with_replacement=FALSE} if which case objects are drawn without replacement from the input dataset. We make sure each class is represented by at least \code{n_min} labeled objects of each class. For \code{n_l}, additional options include: "enough" which takes the max of the number of features and 20, max(ncol(X)+5,20), "d" which takes the number of features or "2d" which takes 2 times the number of features.
+#' This function allows for two different types of learning curves to be generated. If \code{type="unlabeled"}, the number of labeled objects remains fixed at the value of \code{n_l}, where \code{sizes} controls the number of unlabeled objects. \code{n_test} controls the number of objects used for the test set, while all remaining objects are used if \code{with_replacement=FALSE} in which case objects are drawn without replacement from the input dataset. We make sure each class is represented by at least \code{n_min} labeled objects of each class. For \code{n_l}, additional options include: "enough" which takes the max of the number of features and 20, max(ncol(X)+5,20), "d" which takes the number of features or "2d" which takes 2 times the number of features.
 #' 
-#'  If \code{type="unlabeled"} the total number of objects remains fixed, while the fraction of labeled objects is changed. \code{frac} sets the fractions of labeled objects that should be considered, while \code{test_fraction} determines the amount of objects left out to serve as the test set.
+#'  If \code{type="fraction"} the total number of objects remains fixed, while the fraction of labeled objects is changed. \code{frac} sets the fractions of labeled objects that should be considered, while \code{test_fraction} determines the fraction of the total number of objects left out to serve as the test set.
 #'  
 #' @family RSSL utilities
 #' 

diff --git a/man/CrossValidationSSL.Rd b/man/CrossValidationSSL.Rd
diff --git a/man/EMLeastSquaresClassifier.Rd b/man/EMLeastSquaresClassifier.Rd
diff --git a/man/EMLinearDiscriminantClassifier.Rd b/man/EMLinearDiscriminantClassifier.Rd
diff --git a/man/GRFClassifier.Rd b/man/GRFClassifier.Rd
diff --git a/man/Generate2ClassGaussian.Rd b/man/Generate2ClassGaussian.Rd
diff --git a/man/GenerateFourClusters.Rd b/man/GenerateFourClusters.Rd
diff --git a/man/GenerateSlicedCookie.Rd b/man/GenerateSlicedCookie.Rd
diff --git a/man/ICLeastSquaresClassifier.Rd b/man/ICLeastSquaresClassifier.Rd
diff --git a/man/ICLinearDiscriminantClassifier.Rd b/man/ICLinearDiscriminantClassifier.Rd
diff --git a/man/KernelICLeastSquaresClassifier.Rd b/man/KernelICLeastSquaresClassifier.Rd
diff --git a/man/KernelLeastSquaresClassifier.Rd b/man/KernelLeastSquaresClassifier.Rd
diff --git a/man/LaplacianKernelLeastSquaresClassifier.Rd b/man/LaplacianKernelLeastSquaresClassifier.Rd
diff --git a/man/LaplacianSVM.Rd b/man/LaplacianSVM.Rd
diff --git a/man/LearningCurveSSL.Rd b/man/LearningCurveSSL.Rd
diff --git a/man/LeastSquaresClassifier.Rd b/man/LeastSquaresClassifier.Rd
diff --git a/man/LinearDiscriminantClassifier.Rd b/man/LinearDiscriminantClassifier.Rd
diff --git a/man/LinearSVM.Rd b/man/LinearSVM.Rd
diff --git a/man/LinearTSVM.Rd b/man/LinearTSVM.Rd
diff --git a/man/LogisticLossClassifier.Rd b/man/LogisticLossClassifier.Rd
diff --git a/man/LogisticRegression.Rd b/man/LogisticRegression.Rd
diff --git a/man/LogisticRegressionFast.Rd b/man/LogisticRegressionFast.Rd
diff --git a/man/MCLinearDiscriminantClassifier.Rd b/man/MCLinearDiscriminantClassifier.Rd
diff --git a/man/MCNearestMeanClassifier.Rd b/man/MCNearestMeanClassifier.Rd
diff --git a/man/MCPLDA.Rd b/man/MCPLDA.Rd
diff --git a/man/MajorityClassClassifier.Rd b/man/MajorityClassClassifier.Rd
diff --git a/man/NearestMeanClassifier.Rd b/man/NearestMeanClassifier.Rd
diff --git a/man/QuadraticDiscriminantClassifier.Rd b/man/QuadraticDiscriminantClassifier.Rd
diff --git a/man/S4VM.Rd b/man/S4VM.Rd
diff --git a/man/SSLDataFrameToMatrices.Rd b/man/SSLDataFrameToMatrices.Rd
diff --git a/man/SVM.Rd b/man/SVM.Rd
diff --git a/man/SelfLearning.Rd b/man/SelfLearning.Rd
diff --git a/man/TSVM.Rd b/man/TSVM.Rd
diff --git a/man/USMLeastSquaresClassifier.Rd b/man/USMLeastSquaresClassifier.Rd
diff --git a/man/WellSVM.Rd b/man/WellSVM.Rd
diff --git a/man/add_missinglabels_mar.Rd b/man/add_missinglabels_mar.Rd
diff --git a/man/df_to_matrices.Rd b/man/df_to_matrices.Rd
diff --git a/man/evaluation-measures.Rd b/man/evaluation-measures.Rd