1 files changed, 121 insertions, 24 deletions

diff --git a/src/trainMSVMMaj.c b/src/trainMSVMMaj.c
index b4b74df..e045a6c 100644
--- a/src/trainMSVMMaj.c
+++ b/src/trainMSVMMaj.c
@@ -1,54 +1,93 @@
+/**
+ * @file trainMSVMMaj.c
+ * @author Gertjan van den Burg
+ * @date August, 2013
+ * @brief Command line interface for training a single model with MSVMMaj
+ *
+ * @details
+ * This is a command line program for training a single model on a given
+ * dataset. To run a grid search over a number of parameter configurations, 
+ * see trainMSVMMajdataset.c. 
+ *
+ */
+
 #include <time.h>
 #include <math.h>
 
+#include "msvmmaj_kernel.h"
 #include "libMSVMMaj.h"
+#include "msvmmaj.h"
+#include "msvmmaj_init.h"
 #include "msvmmaj_train.h"
 #include "util.h"
-#include "MSVMMaj.h"
 
 #define MINARGS 2
 
 extern FILE *MSVMMAJ_OUTPUT_FILE;
 
+// function declarations
 void print_null(const char *s) {}
 void exit_with_help();
 void parse_command_line(int argc, char **argv, struct MajModel *model, 
 		char *input_filename, char *output_filename, char *model_filename);
 
+/**
+ * @brief Help function
+ */
 void exit_with_help()
 {
 	printf("This is MSVMMaj, version %1.1f\n\n", VERSION);
 	printf("Usage: trainMSVMMaj [options] training_data_file\n");
 	printf("Options:\n");
+	printf("-c coef : coefficient for the polynomial and sigmoid kernel\n");
+	printf("-d degree : degree for the polynomial kernel\n");
 	printf("-e epsilon : set the value of the stopping criterion\n");
+	printf("-g gamma : parameter for the rbf, polynomial or sigmoid "
+			"kernel\n");
 	printf("-h | -help : print this help.\n");
 	printf("-k kappa : set the value of kappa used in the Huber hinge\n");
 	printf("-l lambda : set the value of lambda (lambda > 0)\n");
 	printf("-m model_file : use previous model as seed for W and t\n");
 	printf("-o output_file : write output to file\n");
-	printf("-p p-value : set the value of p in the lp norm (1.0 <= p <= 2.0)\n");
+	printf("-p p-value : set the value of p in the lp norm "
+			"(1.0 <= p <= 2.0)\n");
 	printf("-q : quiet mode (no output)\n");
-	printf("-r rho : choose the weigth specification (1 = unit, 2 = group)\n");
+	printf("-r rho : choose the weigth specification (1 = unit, 2 = "
+			"group)\n");
+	printf("-t type: kerneltype (LINEAR=0, POLY=1, RBF=2, SIGMOID=3)\n");
+	printf("-u use_cholesky: use cholesky decomposition when using "
+			"kernels (0 = false, 1 = true). Default 0.\n");
 
 	exit(0);
 }
 
-/*
-	Main
-*/
+/**
+ * @brief Main interface function for trainMSVMMaj
+ *
+ * @details
+ * Main interface for the command line program. A given dataset file is read
+ * and a MSVMMaj model is trained on this data. By default the progress of the
+ * computations are written to stdout. See for full options of the program the
+ * help function.
+ *
+ * @param[in] 	argc 	number of command line arguments
+ * @param[in] 	argv 	array of command line arguments
+ *
+ */
 int main(int argc, char **argv)
 {
 	char input_filename[MAX_LINE_LENGTH];
 	char model_filename[MAX_LINE_LENGTH];
 	char output_filename[MAX_LINE_LENGTH];
 
-	struct MajModel *model = Malloc(struct MajModel, 1);
-	struct MajData *data = Malloc(struct MajData, 1);
+	struct MajModel *model = msvmmaj_init_model();
+	struct MajData *data = msvmmaj_init_data();
 
 	if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help") 
 			|| msvmmaj_check_argv_eq(argc, argv, "-h") ) 
 		exit_with_help();
-	parse_command_line(argc, argv, model, input_filename, output_filename, model_filename);
+	parse_command_line(argc, argv, model, input_filename, 
+			output_filename, model_filename);
 
 	// read data file
 	msvmmaj_read_data(data, input_filename);
@@ -59,22 +98,25 @@ int main(int argc, char **argv)
 	model->K = data->K;
 	model->data_file = input_filename;
 
+	// initialize kernel (if necessary)
+	msvmmaj_make_kernel(model, data);
+
 	// allocate model and initialize weights
 	msvmmaj_allocate_model(model);
 	msvmmaj_initialize_weights(data, model);
 
+	// seed the random number generator (only place in programs is in
+	// command line interfaces)
 	srand(time(NULL));
 
 	if (msvmmaj_check_argv_eq(argc, argv, "-m")) {
-		struct MajModel *seed_model = Malloc(struct MajModel, 1);
+		struct MajModel *seed_model = msvmmaj_init_model();
 		msvmmaj_read_model(seed_model, model_filename);
 		msvmmaj_seed_model_V(seed_model, model);
 		msvmmaj_free_model(seed_model);
 	} else {
 		msvmmaj_seed_model_V(NULL, model);
 	}
-	// initialize kernel (if necessary)
-	// msvmmaj_make_kernel(model, data);
 
 	// start training
 	msvmmaj_optimize(model, data);
@@ -92,18 +134,34 @@ int main(int argc, char **argv)
 	return 0;
 }
 
+/**
+ * @brief Parse command line arguments
+ *
+ * @details
+ * Process the command line arguments for the model parameters, and record
+ * them in the specified MajModel. An input filename for the dataset is read
+ * and if specified an output filename and a model filename for the seed
+ * model.
+ *
+ * @param[in] 	argc 			number of command line arguments
+ * @param[in] 	argv 			array of command line arguments
+ * @param[in] 	model 			initialized model
+ * @param[in] 	input_filename 		pre-allocated buffer for the input
+ * 					filename
+ * @param[in] 	output_filename 	pre-allocated buffer for the output
+ * 					filename
+ * @param[in] 	model_filename 		pre-allocated buffer for the model
+ * 					filename
+ *
+ */
 void parse_command_line(int argc, char **argv, struct MajModel *model, 
 		char *input_filename, char *output_filename, char *model_filename)
 {
-	int i;
-
-	// default values
-	model->p = 1.0;
-	model->lambda = pow(2, -8.0);
-	model->epsilon = 1e-6;
-	model->kappa = 0.0;
-	model->weight_idx = 1;
-	
+	int i, tmp;
+	double gamma = 1.0, 
+	       degree = 2.0, 
+	       coef = 0.0;
+
 	MSVMMAJ_OUTPUT_FILE = stdout;
 
 	// parse options
@@ -113,9 +171,18 @@ void parse_command_line(int argc, char **argv, struct MajModel *model,
 			exit_with_help();
 		}
 		switch (argv[i-1][1]) {
+			case 'c':
+				coef = atof(argv[i]);
+				break;
+			case 'd':
+				degree = atof(argv[i]);
+				break;
 			case 'e':
 				model->epsilon = atof(argv[i]);
 				break;
+			case 'g':
+				gamma = atof(argv[i]);
+				break;
 			case 'k':
 				model->kappa = atof(argv[i]);
 				break;
@@ -134,20 +201,50 @@ void parse_command_line(int argc, char **argv, struct MajModel *model,
 			case 'r':
 				model->weight_idx = atoi(argv[i]);
 				break;
+			case 't':
+				model->kerneltype = atoi(argv[i]);
+				break;
+			case 'u':
+				tmp = atoi(argv[i]);
+				if (!(tmp == 1 || tmp == 0)) 
+					fprintf(stderr, "Unknown value %i for"
+							" use_cholesky", tmp);
+				model->use_cholesky = (tmp == 1) ? true : false;
+				break;
 			case 'q':
 				MSVMMAJ_OUTPUT_FILE = NULL;
 				i--;
 				break;
 			default:
-				fprintf(stderr, "Unknown option: -%c\n", argv[i-1][1]);
+				fprintf(stderr, "Unknown option: -%c\n", 
+						argv[i-1][1]);
 				exit_with_help();
 		}
 	}
-
+	
 	// read input filename
 	if (i >= argc)
 		exit_with_help();
 
 	strcpy(input_filename, argv[i]);
-}
 
+	// set kernel parameters
+	switch (model->kerneltype) {
+		case K_LINEAR:
+			break;
+		case K_POLY:
+			model->kernelparam = Calloc(double, 3);
+			model->kernelparam[0] = gamma;
+			model->kernelparam[1] = coef;
+			model->kernelparam[2] = degree;
+			break;
+		case K_RBF:
+			model->kernelparam = Calloc(double, 1);
+			model->kernelparam[0] = gamma;
+			break;
+		case K_SIGMOID:
+			model->kernelparam = Calloc(double, 1);
+			model->kernelparam[0] = gamma;
+			model->kernelparam[1] = coef;
+	}
+}