diff options
Diffstat (limited to 'src/msvmmaj_pred.c')
| -rw-r--r-- | src/msvmmaj_pred.c | 27 |
1 files changed, 16 insertions, 11 deletions
diff --git a/src/msvmmaj_pred.c b/src/msvmmaj_pred.c index 5f1b1ae..98b6e0a 100644 --- a/src/msvmmaj_pred.c +++ b/src/msvmmaj_pred.c @@ -1,31 +1,36 @@ /** * @file msvmmaj_pred.c - * @author Gertjan van den Burg (burg@ese.eur.nl) + * @author Gertjan van den Burg * @date August 9, 2013 * @brief Main functions for predicting class labels.. * + * @details + * This file contains functions for predicting the class labels of instances + * and a function for calculating the predictive performance (hitrate) of + * a prediction given true class labels. + * */ #include <cblas.h> #include "libMSVMMaj.h" -#include "MSVMMaj.h" -#include "matrix.h" +#include "msvmmaj.h" +#include "msvmmaj_matrix.h" #include "msvmmaj_pred.h" /** - * @name predict_labels * @brief Predict class labels of data given and output in predy * + * @details * The labels are predicted by mapping each instance in data to the * simplex space using the matrix V in the given model. Next, for each * instance the nearest simplex vertex is determined using an Euclidean * norm. The nearest simplex vertex determines the predicted class label, - * which is recorded in predy + * which is recorded in predy. * - * @param [in] data data to predict labels for - * @param [in] model model with optimized V - * @param [out] predy pre-allocated vector to record predictions in + * @param[in] data MajData to predict labels for + * @param[in] model MajModel with optimized V + * @param[out] predy pre-allocated vector to record predictions in */ void msvmmaj_predict_labels(struct MajData *data, struct MajModel *model, long *predy) { @@ -84,15 +89,15 @@ void msvmmaj_predict_labels(struct MajData *data, struct MajModel *model, long * } /** - * @name msvmmaj_prediction_perf * @brief Calculate the predictive performance (percentage correct) * + * @details * The predictive performance is calculated by simply counting the number * of correctly classified samples and dividing by the total number of * samples, multiplying by 100. * - * @param [in] data the dataset with known labels - * @param [in] predy the predicted class labels + * @param[in] data the MajData dataset with known labels + * @param[in] predy the predicted class labels * * @returns percentage correctly classified. */ |