1 files changed, 145 insertions, 10 deletions

diff --git a/src/trainMSVMMajdataset.c b/src/trainMSVMMajdataset.c
index 7c3385c..097df85 100644
--- a/src/trainMSVMMajdataset.c
+++ b/src/trainMSVMMajdataset.c
@@ -1,7 +1,28 @@
+/**
+ * @file trainMSVMMajdataset.c
+ * @author Gertjan van den Burg
+ * @date January, 2014
+ * @brief Command line interface for the grid search program
+ *
+ * @details
+ * This is a command line interface to the parameter grid search functionality
+ * of the algorithm. The grid search is specified in a separate file, thereby
+ * reducing the number of command line arguments. See
+ * read_training_from_file() for documentation on the training file. 
+ *
+ * The program runs a grid search as specified in the training file. If
+ * desired the grid search can incorporate consistency checks to find the
+ * configuration among the best configurations which scores consistently high.
+ * All output is written to stdout, unless the quiet mode is specified.
+ *
+ * For further usage information, see the program help function.
+ *
+ */
+
 #include <time.h>
 
 #include "crossval.h"
-#include "MSVMMaj.h"
+#include "msvmmaj.h"
 #include "msvmmaj_pred.h"
 #include "msvmmaj_train.h"
 #include "msvmmaj_train_dataset.h"
@@ -12,11 +33,15 @@
 
 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, char *input_filename);
 void read_training_from_file(char *input_filename, struct Training *training);
 
+/**
+ * @brief Help function
+ */
 void exit_with_help()
 {
 	printf("This is MSVMMaj, version %1.1f\n\n", VERSION);
@@ -28,6 +53,22 @@ void exit_with_help()
 	exit(0);
 }
 
+/**
+ * @brief Main interface function for trainMSVMMajdataset
+ *
+ * @details
+ * Main interface for the command line program. A given training file which
+ * specifies a grid search over a single dataset is read. From this, a Queue
+ * is created containing all Task instances that need to be performed in the
+ * search. Depending on the type of dataset, either cross validation or
+ * train/test split training is performed for all tasks. If specified,
+ * consistency repeats are done at the end of the grid search. Note that
+ * currently no output is produced other than what is written to stdout.
+ *
+ * @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];
@@ -78,6 +119,21 @@ int main(int argc, char **argv)
 	return 0;
 }
 
+/**
+ * @brief Parse command line arguments
+ *
+ * @details
+ * Few arguments can be supplied to the command line. Only quiet mode can be
+ * specified, or help can be requested. The filename of the training file is
+ * read from the arguments. Parsing of the training file is done separately in
+ * read_training_from_file().
+ *
+ * @param[in] 	argc 		number of command line arguments
+ * @param[in] 	argv 		array of command line arguments
+ * @param[in] 	input_filename 	pre-allocated buffer for the training
+ * 				filename.
+ *
+ */
 void parse_command_line(int argc, char **argv, char *input_filename)
 {
 	int i;
@@ -94,7 +150,8 @@ void parse_command_line(int argc, char **argv, char *input_filename)
 				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();
 		}
 	}
@@ -105,6 +162,21 @@ void parse_command_line(int argc, char **argv, char *input_filename)
 	strcpy(input_filename, argv[i]);
 }
 
+/**
+ * @brief Read the Training struct from file
+ *
+ * @details
+ * Read the Training struct from a file. The training file follows a specific
+ * format specified in @ref spec_training_file. 
+ *
+ * Commonly used string functions in this function are all_doubles_str() and
+ * all_longs_str(). 
+ *
+ * @param[in] 	input_filename 	filename of the training file
+ * @param[in] 	training 	Training structure to place the parsed
+ * 				parameter grid.
+ *
+ */
 void read_training_from_file(char *input_filename, struct Training *training)
 {
 	long i, nr = 0;
@@ -117,7 +189,8 @@ void read_training_from_file(char *input_filename, struct Training *training)
 
 	fid = fopen(input_filename, "r");
 	if (fid == NULL) {
-		fprintf(stderr, "Error opening training file %s\n", input_filename);
+		fprintf(stderr, "Error opening training file %s\n", 
+				input_filename);
 		exit(1);
 	}
 	training->traintype = CV;
@@ -126,11 +199,13 @@ void read_training_from_file(char *input_filename, struct Training *training)
 		Memset(lparams, long, MAX_LINE_LENGTH);
 		if (str_startswith(buffer, "train:")) {
 			sscanf(buffer, "train: %s\n", train_filename);
-			training->train_data_file = Calloc(char, MAX_LINE_LENGTH);
+			training->train_data_file = Calloc(char, 
+					MAX_LINE_LENGTH);
 			strcpy(training->train_data_file, train_filename);
 		} else if (str_startswith(buffer, "test:")) {
 			sscanf(buffer, "test: %s\n", test_filename);
-			training->test_data_file = Calloc(char, MAX_LINE_LENGTH);
+			training->test_data_file = Calloc(char,
+				       	MAX_LINE_LENGTH);
 			strcpy(training->test_data_file, test_filename);
 			training->traintype = TT;
 		} else if (str_startswith(buffer, "p:")) {
@@ -167,16 +242,76 @@ void read_training_from_file(char *input_filename, struct Training *training)
 			nr = all_longs_str(buffer, 6, lparams);
 			training->folds = lparams[0];
 			if (nr > 1)
-				fprintf(stderr, "Field \"folds\" only takes one value. "
-						"Additional fields are ignored.\n");
+				fprintf(stderr, "Field \"folds\" only takes "
+						"one value. Additional "
+						"fields are ignored.\n");
 		} else if (str_startswith(buffer, "repeats:")) {
 			nr = all_longs_str(buffer, 8, lparams);
 			training->repeats = lparams[0];
 			if (nr > 1)
-				fprintf(stderr, "Field \"repeats\" only takes one value. "
-						"Additional fields are ignored.\n");
+				fprintf(stderr, "Field \"repeats\" only "
+						"takes one value. Additional "
+						"fields are ignored.\n");
+		} else if (str_startswith(buffer, "kernel:")) {
+			nr = all_longs_str(buffer, 7, lparams);
+			if (nr > 1)
+				fprintf(stderr, "Field \"kernel\" only takes "
+						"one value. Additional "
+						"fields are ignored.\n");
+			switch (lparams[0]) {
+				case 0:
+					training->kerneltype = K_LINEAR;
+					break;
+				case 1:
+					training->kerneltype = K_POLY;
+					break;
+				case 2:
+					training->kerneltype = K_RBF;
+					break;
+				case 3:
+					training->kerneltype = K_SIGMOID;
+					break;
+			}
+		} else if (str_startswith(buffer, "gamma:")) {
+			nr = all_doubles_str(buffer, 6, params);
+			if (training->kerneltype == K_LINEAR) {
+				fprintf(stderr, "Field \"gamma\" ignored, "
+						"linear kernel is used.\n");
+				training->Ng = 0;
+				break;
+			}
+			training->gammas = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->gammas[i] = params[i];
+			training->Ng = nr;
+		} else if (str_startswith(buffer, "coef:")) {
+			nr = all_doubles_str(buffer, 5, params);
+			if (training->kerneltype == K_LINEAR ||
+				training->kerneltype == K_RBF) {
+				fprintf(stderr, "Field \"coef\" ignored with"
+						"specified kernel.\n");
+				training->Nc = 0;
+				break;
+			}
+			training->coefs = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->coefs[i] = params[i];
+			training->Nc = nr;
+		} else if (str_startswith(buffer, "degree:")) {
+			nr = all_doubles_str(buffer, 7, params);
+			if (training->kerneltype != K_POLY) {
+				fprintf(stderr, "Field \"degree\" ignored "
+						"with specified kernel.\n");
+				training->Nd = 0;
+				break;
+			}
+			training->degrees = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->degrees[i] = params[i];
+			training->Nd = nr;
 		} else {
-			fprintf(stderr, "Cannot find any parameters on line: %s\n", buffer);
+			fprintf(stderr, "Cannot find any parameters on line: "
+					"%s\n", buffer);
 		}
 	}