rename execs and fix some unintended renames

1 files changed, 321 insertions, 0 deletions

diff --git a/src/GenSVMgrid.c b/src/GenSVMgrid.c
new file mode 100644
index 0000000..eb1f477
--- /dev/null
+++ b/src/GenSVMgrid.c
@@ -0,0 +1,321 @@
+/**
+ * @file GenSVM_grid.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 "gensvm.h"
+#include "gensvm_io.h"
+#include "gensvm_init.h"
+#include "gensvm_pred.h"
+#include "gensvm_train.h"
+#include "gensvm_train_dataset.h"
+#include "strutil.h"
+#include "util.h"
+
+#define MINARGS 2
+
+extern FILE *GENSVM_OUTPUT_FILE;
+
+// function declarations
+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 GenSVM, version %1.1f\n\n", VERSION);
+	printf("Usage: trainGenSVMdataset [options] training_file\n");
+	printf("Options:\n");
+	printf("-h | -help : print this help.\n");
+	printf("-q : quiet mode (no output)\n");
+
+	exit(0);
+}
+
+/**
+ * @brief Main interface function for trainGenSVMdataset
+ *
+ * @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];
+	
+	struct Training *training = Malloc(struct Training, 1);	
+	struct GenData *train_data = Malloc(struct GenData, 1);
+	struct GenData *test_data = Malloc(struct GenData, 1);
+
+	if (argc < MINARGS || gensvm_check_argv(argc, argv, "-help") 
+			|| gensvm_check_argv_eq(argc, argv, "-h") )
+		exit_with_help();
+	parse_command_line(argc, argv, input_filename);
+
+	training->repeats = 0;
+	note("Reading training file\n");	
+	read_training_from_file(input_filename, training);
+
+	note("Reading data from %s\n", training->train_data_file);
+	gensvm_read_data(train_data, training->train_data_file);
+	if (training->traintype == TT) {
+		note("Reading data from %s\n", training->test_data_file);
+		gensvm_read_data(test_data, training->test_data_file);
+	}
+
+	note("Creating queue\n");
+	struct Queue *q = Malloc(struct Queue, 1);
+	make_queue(training, q, train_data, test_data);
+
+	srand(time(NULL));
+
+	note("Starting training\n");
+	if (training->traintype == TT)
+		start_training_tt(q);
+	else
+		start_training_cv(q);
+	note("Training finished\n");
+
+	if (training->repeats > 0) {
+		consistency_repeats(q, training->repeats, training->traintype);
+	}
+
+	free_queue(q);
+	free(training);
+	gensvm_free_data(train_data);
+	gensvm_free_data(test_data);
+
+	note("Done.\n");
+	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;
+
+	GENSVM_OUTPUT_FILE = stdout;
+
+	for (i=1; i<argc; i++) {
+		if (argv[i][0] != '-') break;
+		if (++i>=argc)
+			exit_with_help();
+		switch (argv[i-1][1]) {
+			case 'q':
+				GENSVM_OUTPUT_FILE = NULL;
+				i--;
+				break;
+			default:
+				fprintf(stderr, "Unknown option: -%c\n", 
+						argv[i-1][1]);
+				exit_with_help();
+		}
+	}
+
+	if (i >= argc)
+		exit_with_help();
+
+	strcpy(input_filename, argv[i]);
+}
+
+KernelType parse_kernel_str(char *kernel_line)
+{
+	if (str_endswith(kernel_line, "LINEAR\n")) {
+		return K_LINEAR;
+	} else if (str_endswith(kernel_line, "POLY\n")) {
+		return K_POLY;
+	} else if (str_endswith(kernel_line, "RBF\n")) {
+		return K_RBF;
+	} else if (str_endswith(kernel_line, "SIGMOID\n")) {
+		return K_SIGMOID;
+	} else {
+		fprintf(stderr, "Unknown kernel specified on line: %s\n",
+				kernel_line);
+		exit(1);
+	}
+}
+
+/**
+ * @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;
+	FILE *fid;
+	char buffer[MAX_LINE_LENGTH];
+	char train_filename[MAX_LINE_LENGTH];
+	char test_filename[MAX_LINE_LENGTH];
+	double *params = Calloc(double, MAX_LINE_LENGTH);
+	long *lparams = Calloc(long, MAX_LINE_LENGTH);
+
+	fid = fopen(input_filename, "r");
+	if (fid == NULL) {
+		fprintf(stderr, "Error opening training file %s\n", 
+				input_filename);
+		exit(1);
+	}
+	training->traintype = CV;
+	while ( fgets(buffer, MAX_LINE_LENGTH, fid) != NULL ) {
+		Memset(params, double,  MAX_LINE_LENGTH);
+		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);
+			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);
+			strcpy(training->test_data_file, test_filename);
+			training->traintype = TT;
+		} else if (str_startswith(buffer, "p:")) {
+			nr = all_doubles_str(buffer, 2, params);
+			training->ps = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->ps[i] = params[i];
+			training->Np = nr;
+		} else if (str_startswith(buffer, "lambda:")) {
+			nr = all_doubles_str(buffer, 7, params);
+			training->lambdas = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->lambdas[i] = params[i];
+			training->Nl = nr;
+		} else if (str_startswith(buffer, "kappa:")) {
+			nr = all_doubles_str(buffer, 6, params);
+			training->kappas = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->kappas[i] = params[i];
+			training->Nk = nr;
+		} else if (str_startswith(buffer, "epsilon:")) {
+			nr = all_doubles_str(buffer, 8, params);
+			training->epsilons = Calloc(double, nr);
+			for (i=0; i<nr; i++)
+				training->epsilons[i] = params[i];
+			training->Ne = nr;
+		} else if (str_startswith(buffer, "weight:")) {
+			nr = all_longs_str(buffer, 7, lparams);
+			training->weight_idxs = Calloc(int, nr);
+			for (i=0; i<nr; i++)
+				training->weight_idxs[i] = lparams[i];
+			training->Nw = nr;
+		} else if (str_startswith(buffer, "folds:")) {
+			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");
+		} 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");
+		} else if (str_startswith(buffer, "kernel:")) {
+			training->kerneltype = parse_kernel_str(buffer);
+		} 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);
+		}
+	}
+
+	free(params);
+	free(lparams);
+	fclose(fid);
+}