1 files changed, 76 insertions, 0 deletions

diff --git a/include/msvmmaj_train_dataset.h b/include/msvmmaj_train_dataset.h
index fdcdb4c..5248b4a 100644
--- a/include/msvmmaj_train_dataset.h
+++ b/include/msvmmaj_train_dataset.h
@@ -1,9 +1,39 @@
+/**
+ * @file msvmmaj_train_dataset.h
+ * @author Gertjan van den Burg
+ * @date August, 2013
+ * @brief Structs and functions necessary for the grid search
+ *
+ * @details
+ * The grid search for the optimal parameters is done through a queue.
+ * This file contains struct definitions for this queue and a single
+ * task in a queue, as well as a structure for the complete training
+ * scheme. Function declarations are also included.
+ *
+ */
+
 #ifndef MSVMMAJ_TRAIN_DATASET_H
 #define MSVMMAJ_TRAIN_DATASET_H
 
 #include "globals.h"
 #include "types.h"
 
+/**
+ * @brief A structure for a single task in the queue.
+ *
+ * @param folds 	number of folds in cross validation
+ * @param ID 		numeric id of the task in the queue
+ * @param weight_idx 	parameter for the MajModel
+ * @param p 		parameter for the MajModel
+ * @param kappa 	parameter for the MajModel
+ * @param lambda 	parameter for the MajModel
+ * @param epsilon 	parameter for the MajModel
+ * @param kerneltype 	parameter for the MajModel
+ * @param *kernel_param parameters for the MajModel
+ * @param *train_data 	pointer to the training data
+ * @param *test_data 	pointer to the test data (if any)
+ * @param performance 	performance after cross validation
+ */
 struct Task {
 	KernelType kerneltype;
 	int weight_idx;
@@ -19,14 +49,54 @@ struct Task {
 	double performance;
 };
 
+/**
+ * @brief Simple task queue.
+ *
+ * This struct is basically just an array of pointers to Task instances,
+ * with a length and an index of the current task.
+ *
+ * @param **tasks 	array of pointers to Task structs
+ * @param N 		size of task array
+ * @param i 		index used for keeping track of the queue
+ */
 struct Queue {
 	struct Task **tasks;
 	long N;
 	long i;
 };
 
+/**
+ * @brief Structure for describing the entire grid search
+ *
+ * @param traintype 		type of training to use
+ * @param kerneltype 		type of kernel to use throughout training
+ * @param repeats 		number of repeats to be done after the grid 
+ * 				search to find the parameter set with the 
+ * 				most consistent high performance
+ * @param folds 		number of folds in cross validation
+ * @param Np 			size of the array of p values
+ * @param Nl 			size of the array of lambda values
+ * @param Nk 			size of the array of kappa values
+ * @param Ne 			size of the array of epsilon values
+ * @param Nw 			size of the array of weight_idx values
+ * @param Ng 			size of the array of gamma values
+ * @param Nc 			size of the array of coef values
+ * @param Nd 			size of the array of degree values
+ * @param *weight_idxs 		array of weight_idxs
+ * @param *ps 			array of p values 
+ * @param *lambdas 		array of lambda values
+ * @param *kappas 		array of kappa values
+ * @param *epsilons 		array of epsilon values
+ * @param *gammas 		array of gamma values
+ * @param *coefs 		array of coef values
+ * @param *degrees 		array of degree values
+ * @param *train_data_file 	filename of train data file
+ * @param *test_data_file 	filename of test data file
+ *
+ */
 struct Training {
 	TrainType traintype;
+	KernelType kerneltype;
 	long repeats;
 	long folds;
 	long Np;
@@ -34,11 +104,17 @@ struct Training {
 	long Nk;
 	long Ne;
 	long Nw;
+	long Ng;
+	long Nc;
+	long Nd;
 	int *weight_idxs;
 	double *ps;
 	double *lambdas;
 	double *kappas;
 	double *epsilons;
+	double *gammas;
+	double *coefs;
+	double *degrees;
 	char *train_data_file;
 	char *test_data_file;
 };