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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2016-05-09 20:55:24 +0200 |
|---|---|---|
| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2016-05-09 20:55:24 +0200 |
| commit | 7c8a5e4b2a7cff7573b1a308daf19d2dbd558a9c (patch) | |
| tree | 2c4774a63ec91d60931d822a7607a5011711c946 /src/gensvm_crossval.c | |
| parent | add doc and test to phony (diff) | |
| download | gensvm-7c8a5e4b2a7cff7573b1a308daf19d2dbd558a9c.tar.gz gensvm-7c8a5e4b2a7cff7573b1a308daf19d2dbd558a9c.zip | |
strip whitespaces
Diffstat (limited to 'src/gensvm_crossval.c')
| -rw-r--r-- | src/gensvm_crossval.c | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/src/gensvm_crossval.c b/src/gensvm_crossval.c index 864e692..6930166 100644 --- a/src/gensvm_crossval.c +++ b/src/gensvm_crossval.c @@ -1,13 +1,13 @@ /** * @file crossval.c - * @author Gertjan van den Burg + * @author Gertjan van den Burg * @date January 7, 2014 * @brief Functions for cross validation * * @details * This file contains functions for performing cross validation. The funtion * gensvm_make_cv_split() creates a cross validation vector for non-stratified - * cross validation. The function gensvm_get_tt_split() creates a train and + * cross validation. The function gensvm_get_tt_split() creates a train and * test dataset from a given dataset and a pre-determined CV partition vector. * See individual function documentation for details. * @@ -22,17 +22,17 @@ * * @details * A pre-allocated vector of length N is created which can be used to define - * cross validation splits. The folds are contain between - * @f$ \lfloor N / folds \rfloor @f$ and @f$ \lceil N / folds \rceil @f$ - * instances. An instance is mapped to a partition randomly until all folds - * contain @f$ N \% folds @f$ instances. The zero fold then contains + * cross validation splits. The folds are contain between + * @f$ \lfloor N / folds \rfloor @f$ and @f$ \lceil N / folds \rceil @f$ + * instances. An instance is mapped to a partition randomly until all folds + * contain @f$ N \% folds @f$ instances. The zero fold then contains * @f$ N / folds + N \% folds @f$ instances. These remaining @f$ N \% folds @f$ - * instances are then distributed over the first @f$ N \% folds @f$ folds. + * instances are then distributed over the first @f$ N \% folds @f$ folds. * * @param[in] N number of instances * @param[in] folds number of folds * @param[in,out] cv_idx array of size N which contains the fold index - * for each observation on exit + * for each observation on exit * */ void gensvm_make_cv_split(long N, long folds, long *cv_idx) @@ -44,7 +44,7 @@ void gensvm_make_cv_split(long N, long folds, long *cv_idx) long big_folds = N%folds; long small_fold_size = N/folds; - + j = 0; for (i=0; i<small_fold_size*folds; i++) while (1) { @@ -73,9 +73,9 @@ void gensvm_make_cv_split(long N, long folds, long *cv_idx) * @details * Given a GenData structure for the full dataset, a previously created * cross validation split vector and a fold index, a training and test dataset - * are created. + * are created. * - * @param[in] full_data a GenData structure for the entire + * @param[in] full_data a GenData structure for the entire * dataset * @param[in,out] train_data an initialized GenData structure which * on exit contains the training dataset @@ -83,7 +83,7 @@ void gensvm_make_cv_split(long N, long folds, long *cv_idx) * on exit contains the test dataset * @param[in] cv_idx a vector of cv partitions created by * gensvm_make_cv_split() - * @param[in] fold_idx index of the fold which becomes the + * @param[in] fold_idx index of the fold which becomes the * test dataset */ void gensvm_get_tt_split(struct GenData *full_data, struct GenData *train_data, |