allow datasets to be stored in libsvm/svmlight format

14 files changed, 783 insertions, 19 deletions

diff --git a/include/gensvm_globals.h b/include/gensvm_globals.h
index 1151069..1aca458 100644
--- a/include/gensvm_globals.h
+++ b/include/gensvm_globals.h
@@ -39,15 +39,17 @@
 #include "gensvm_memory.h"
 
 // all system libraries are included here
+#include <cblas.h>
+#include <ctype.h>
+#include <errno.h>
+#include <limits.h>
+#include <math.h>
 #include <stdarg.h>
+#include <stdbool.h>
 #include <stdio.h>
 #include <stdlib.h>
-#include <stdbool.h>
 #include <string.h>
-#include <math.h>
 #include <time.h>
-#include <cblas.h>
-#include <limits.h>
 
 // ########################### Type definitions ########################### //
 
diff --git a/include/gensvm_io.h b/include/gensvm_io.h
index afcf4a3..c18e9a2 100644
--- a/include/gensvm_io.h
+++ b/include/gensvm_io.h
@@ -37,6 +37,7 @@
 
 // function declarations
 void gensvm_read_data(struct GenData *dataset, char *data_file);
+void gensvm_read_data_libsvm(struct GenData *dataset, char *data_file);
 
 void gensvm_read_model(struct GenModel *model, char *model_filename);
 void gensvm_write_model(struct GenModel *model, char *output_filename);
diff --git a/include/gensvm_sparse.h b/include/gensvm_sparse.h
index 570e19a..f897889 100644
--- a/include/gensvm_sparse.h
+++ b/include/gensvm_sparse.h
@@ -71,6 +71,7 @@ struct GenSparse {
 struct GenSparse *gensvm_init_sparse();
 void gensvm_free_sparse(struct GenSparse *sp);
 long gensvm_count_nnz(double *A, long rows, long cols);
+bool gensvm_nnz_comparison(long nnz, long rows, long cols);
 bool gensvm_could_sparse(double *A, long rows, long cols);
 struct GenSparse *gensvm_dense_to_sparse(double *A, long rows, long cols);
 double *gensvm_sparse_to_dense(struct GenSparse *A);
diff --git a/include/gensvm_strutil.h b/include/gensvm_strutil.h
index 4e86944..1a2dccc 100644
--- a/include/gensvm_strutil.h
+++ b/include/gensvm_strutil.h
@@ -35,6 +35,8 @@
 
 bool str_startswith(const char *str, const char *pre);
 bool str_endswith(const char *str, const char *suf);
+bool str_contains_char(const char *str, const char c);
+char **str_split(char *original, const char *delims, int *len_ret);
 
 void next_line(FILE *fid, char *filename);
 char *get_line(FILE *fid, char *filename, char *buffer);
diff --git a/src/GenSVMgrid.c b/src/GenSVMgrid.c
index 3799de3..41d1070 100644
--- a/src/GenSVMgrid.c
+++ b/src/GenSVMgrid.c
@@ -72,7 +72,8 @@ void exit_with_help(char **argv)
 	printf("Usage: %s [options] grid_file\n", argv[0]);
 	printf("Options:\n");
 	printf("-h | -help : print this help.\n");
-	printf("-q : quiet mode (no output, not even errors!)\n");
+	printf("-q         : quiet mode (no output, not even errors!)\n");
+	printf("-x         : data files are in LibSVM/SVMlight format\n");
 
 	exit(EXIT_FAILURE);
 }
@@ -97,6 +98,7 @@ void exit_with_help(char **argv)
  */
 int main(int argc, char **argv)
 {
+	bool libsvm_format = false;
 	char input_filename[GENSVM_MAX_LINE_LENGTH];
 
 	struct GenGrid *grid = gensvm_init_grid();
@@ -108,12 +110,16 @@ int main(int argc, char **argv)
 			|| gensvm_check_argv_eq(argc, argv, "-h") )
 		exit_with_help(argv);
 	parse_command_line(argc, argv, input_filename);
+	libsvm_format = gensvm_check_argv(argc, argv, "-x");
 
 	note("Reading grid file\n");
 	read_grid_from_file(input_filename, grid);
 
 	note("Reading data from %s\n", grid->train_data_file);
-	gensvm_read_data(train_data, grid->train_data_file);
+	if (libsvm_format)
+		gensvm_read_data_libsvm(train_data, grid->train_data_file);
+	else
+		gensvm_read_data(train_data, grid->train_data_file);
 
 	// check labels of training data
 	gensvm_check_outcome_contiguous(train_data);
@@ -196,6 +202,9 @@ void parse_command_line(int argc, char **argv, char *input_filename)
 				GENSVM_ERROR_FILE = NULL;
 				i--;
 				break;
+			case 'x':
+				i--;
+				break;
 			default:
 				fprintf(stderr, "Unknown option: -%c\n",
 						argv[i-1][1]);
@@ -267,7 +276,7 @@ void read_grid_from_file(char *input_filename, struct GenGrid *grid)
 	if (fid == NULL) {
 		fprintf(stderr, "Error opening grid file %s\n",
 				input_filename);
-		exit(1);
+		exit(EXIT_FAILURE);
 	}
 	grid->traintype = CV;
 	while ( fgets(buffer, GENSVM_MAX_LINE_LENGTH, fid) != NULL ) {
diff --git a/src/GenSVMtraintest.c b/src/GenSVMtraintest.c
index 8bebf3a..a275e57 100644
--- a/src/GenSVMtraintest.c
+++ b/src/GenSVMtraintest.c
@@ -77,7 +77,6 @@ void exit_with_help(char **argv)
 			"Huber hinge\n");
 	printf("-l lambda            : set the value of lambda "
 			"(lambda > 0)\n");
-	printf("-s seed_model_file   : use previous model as seed for V\n");
 	printf("-m model_output_file : write model output to file "
 			"(not saved if no file provided)\n");
 	printf("-o prediction_output : write predictions of test data to "
@@ -88,8 +87,11 @@ void exit_with_help(char **argv)
 			"errors!)\n");
 	printf("-r rho               : choose the weigth specification "
 			"(1 = unit, 2 = group)\n");
+	printf("-s seed_model_file   : use previous model as seed for V\n");
 	printf("-t type              : kerneltype (0=LINEAR, 1=POLY, 2=RBF, "
 			"3=SIGMOID)\n");
+	printf("-x                   : data files are in LibSVM/SVMlight "
+			"format\n");
 	printf("\n");
 
 	exit(EXIT_FAILURE);
@@ -108,6 +110,7 @@ void exit_with_help(char **argv)
  */
 int main(int argc, char **argv)
 {
+	bool libsvm_format = false;
 	long i, *predy = NULL;
 	double performance;
 
@@ -130,9 +133,15 @@ int main(int argc, char **argv)
 	parse_command_line(argc, argv, model, &model_inputfile,
 		       	&training_inputfile, &testing_inputfile,
 		       	&model_outputfile, &prediction_outputfile);
+	libsvm_format = gensvm_check_argv(argc, argv, "-x");
+
+	// read data from file
+	if (libsvm_format)
+		gensvm_read_data_libsvm(traindata, training_inputfile);
+	else
+		gensvm_read_data(traindata, training_inputfile);
 
-	// read data from file and check labels
-	gensvm_read_data(traindata, training_inputfile);
+	// check labels for consistency
 	if (!gensvm_check_outcome_contiguous(traindata)) {
 		err("[GenSVM Error]: Class labels should start from 1 and "
 				"have no gaps. Please reformat your data.\n");
@@ -168,7 +177,11 @@ int main(int argc, char **argv)
 	// if we also have a test set, predict labels and write to predictions
 	// to an output file if specified
 	if (testing_inputfile != NULL) {
-		gensvm_read_data(testdata, testing_inputfile);
+		// read the test data
+		if (libsvm_format)
+			gensvm_read_data_libsvm(testdata, testing_inputfile);
+		else
+			gensvm_read_data(testdata, testing_inputfile);
 
 		// check if we are sparse and want nonlinearity
 		if (testdata->Z == NULL && model->kerneltype != K_LINEAR) {
@@ -258,6 +271,7 @@ void parse_command_line(int argc, char **argv, struct GenModel *model,
 	GENSVM_ERROR_FILE = stderr;
 
 	// parse options
+	// note: flags that don't have an argument should decrement i
 	for (i=1; i<argc; i++) {
 		if (argv[i][0] != '-') break;
 		if (++i>=argc) {
@@ -311,6 +325,9 @@ void parse_command_line(int argc, char **argv, struct GenModel *model,
 				GENSVM_ERROR_FILE = NULL;
 				i--;
 				break;
+			case 'x':
+				i--;
+				break;
 			default:
 				// this one should always print explicitly to 
 				// stderr, even if '-q' is supplied, because 
diff --git a/src/gensvm_io.c b/src/gensvm_io.c
index 0d14144..d41b2ef 100644
--- a/src/gensvm_io.c
+++ b/src/gensvm_io.c
@@ -6,7 +6,7 @@
  *
  * @details
  * This file contains functions for reading and writing model files, and data
- * files. It also contains a function for generating a string of the current 
+ * files. It also contains a function for generating a string of the current
  * time, used in writing output files.
  *
  * @copyright
@@ -38,7 +38,7 @@
  * Read the data from the data_file. The data matrix X is augmented
  * with a column of ones, to get the matrix Z. The data is expected
  * to follow a specific format, which is specified in the @ref spec_data_file.
- * The class labels are assumed to be in the interval [1 .. K], which can be 
+ * The class labels are assumed to be in the interval [1 .. K], which can be
  * checked using the function gensvm_check_outcome_contiguous().
  *
  * @param[in,out] 	dataset 	initialized GenData struct
@@ -133,6 +133,265 @@ void gensvm_read_data(struct GenData *dataset, char *data_file)
 	}
 }
 
+/**
+ * @brief Print an error to the screen and exit (copied from LibSVM)
+ *
+ * @param[in] 	line_num 	line number where the error occured
+ *
+ */
+void exit_input_error(int line_num)
+{
+	err("[GenSVM Error]: Wrong input format on line: %i\n", line_num);
+	exit(EXIT_FAILURE);
+}
+
+/**
+ * @brief Read data from a file in LibSVM/SVMlight format
+ *
+ * @details
+ * This function reads data from a file where the data is stored in
+ * LibSVM/SVMlight format. This is a sparse data format, which can be
+ * beneficial for certain applications. The advantage of having this function
+ * here is twofold: 1) existing datasets where data is stored in
+ * LibSVM/SVMlight format can be easily used in GenSVM, and 2) sparse datasets
+ * which are too large for memory when kept in dense format can be loaded
+ * efficiently into GenSVM.
+ *
+ * @note
+ * This code is based on the read_problem() function in the svm-train.c
+ * file of LibSVM. It has however been expanded to be able to handle data 
+ * files without labels.
+ *
+ * @note
+ * This file tries to detect whether 1-based or 0-based indexing is used in 
+ * the data file. By default 1-based indexing is used, but if an index is 
+ * found with value 0, 0-based indexing is assumed.
+ *
+ * @sa
+ * gensvm_read_problem()
+ *
+ * @param[in] 	 data 		GenData structure
+ * @param[in] 	 data_file 	filename of the datafile
+ *
+ */
+void gensvm_read_data_libsvm(struct GenData *data, char *data_file)
+{
+	bool do_sparse, zero_based = false;
+	long i, j, n, m, K, nnz, cnt, tmp, index, row_cnt, num_labels, 
+	     min_index = 1;
+	int n_big, n_small, big_start;
+	double value;
+	FILE *fid = NULL;
+	char *label = NULL,
+	     *endptr = NULL,
+	     **big_parts = NULL,
+	     **small_parts = NULL;
+	char buf[GENSVM_MAX_LINE_LENGTH];
+
+	fid = fopen(data_file, "r");
+	if (fid == NULL) {
+		// LCOV_EXCL_START
+		err("[GenSVM Error]: Datafile %s could not be opened.\n",
+				data_file);
+		exit(EXIT_FAILURE);
+		// LCOV_EXCL_STOP
+	}
+
+	// first count the number of elements
+	n = 0;
+	m = -1;
+
+	num_labels = 0;
+	nnz = 0;
+
+	while (fgets(buf, GENSVM_MAX_LINE_LENGTH, fid) != NULL) {
+		// split the string in labels and/or index:value pairs
+		big_parts = str_split(buf, " \t", &n_big);
+
+		// record if this line has a label (first part has no colon)
+		num_labels += (!str_contains_char(big_parts[0], ':'));
+
+		// check for each part if it is a index:value pair
+		for (i=0; i<n_big; i++) {
+			if (!str_contains_char(big_parts[i], ':'))
+				continue;
+
+			// split the index:value pair
+			small_parts = str_split(big_parts[i], ":", &n_small);
+
+			// convert the index to a number
+			index = strtol(small_parts[0], &endptr, 10);
+
+			// catch conversion errors
+			if (endptr == small_parts[0] || errno != 0 ||
+					*endptr != '\0')
+				exit_input_error(n+1);
+
+			// update the maximum index
+			m = maximum(m, index);
+
+			// update the minimum index
+			min_index = minimum(min_index, index);
+
+			// free the small parts
+			for (j=0; j<n_small; j++) free(small_parts[j]);
+			free(small_parts);
+
+			// increment the nonzero counter
+			nnz++;
+		}
+
+		// free the big parts
+		for (i=0; i<n_big; i++) {
+			free(big_parts[i]);
+		}
+		free(big_parts);
+
+		// increment the number of observations
+		n++;
+	}
+
+	// rewind the file pointer
+	rewind(fid);
+
+	// check if we have enough labels
+	if (num_labels > 0 && num_labels != n) {
+		err("[GenSVM Error]: There are some lines with missing "
+				"labels. Please fix this before "
+				"continuing.\n");
+		exit(EXIT_FAILURE);
+	}
+
+	// don't forget the column of ones
+	nnz += n;
+
+	// deal with 0-based or 1-based indexing in the LibSVM file
+	if (min_index == 0) {
+		m++;
+		zero_based = true;
+	}
+
+	// check if sparsity is worth it
+	do_sparse = gensvm_nnz_comparison(nnz, n, m+1);
+	if (do_sparse) {
+		data->spZ = gensvm_init_sparse();
+		data->spZ->nnz = nnz;
+		data->spZ->n_row = n;
+		data->spZ->n_col = m+1;
+		data->spZ->values = Calloc(double, nnz);
+		data->spZ->ia = Calloc(long, n+1);
+		data->spZ->ja = Calloc(long, nnz);
+		data->spZ->ia[0] = 0;
+	} else {
+		data->RAW = Calloc(double, n*(m+1));
+		data->Z = data->RAW;
+	}
+	if (num_labels > 0)
+		data->y = Calloc(long, n);
+
+	K = 0;
+	cnt = 0;
+	for (i=0; i<n; i++) {
+		fgets(buf, GENSVM_MAX_LINE_LENGTH, fid);
+
+		// split the string in labels and/or index:value pairs
+		big_parts = str_split(buf, " \t", &n_big);
+
+		big_start = 0;
+		// get the label from the first part if it exists
+		if (!str_contains_char(big_parts[0], ':')) {
+			label = strtok(big_parts[0], " \t\n");
+			if (label == NULL) // empty line
+				exit_input_error(i+1);
+
+			// convert the label part to a number exit if there
+			// are errors
+			tmp = strtol(label, &endptr, 10);
+			if (endptr == label || *endptr != '\0')
+				exit_input_error(i+1);
+
+			// assign label to y
+			data->y[i] = tmp;
+
+			// keep track of maximum K
+			K = maximum(K, data->y[i]);
+
+			// increment big part index
+			big_start++;
+		}
+
+		row_cnt = 0;
+		// set the first element in the row to 1
+		if (do_sparse) {
+			data->spZ->values[cnt] = 1.0;
+			data->spZ->ja[cnt] = 0;
+			cnt++;
+			row_cnt++;
+		} else {
+			matrix_set(data->RAW, m+1, i, 0, 1.0);
+		}
+
+		// read the rest of the line
+		for (j=big_start; j<n_big; j++) {
+			if (!str_contains_char(big_parts[j], ':'))
+				continue;
+
+			// split the index:value pair
+			small_parts = str_split(big_parts[j], ":", &n_small);
+			if (n_small != 2)
+				exit_input_error(n+1);
+
+			// convert the index to a long
+			errno = 0;
+			index = strtol(small_parts[0], &endptr, 10);
+
+			// catch conversion errors
+			if (endptr == small_parts[0] || errno != 0 ||
+					*endptr != '\0')
+				exit_input_error(n+1);
+
+			// convert the value to a double
+			errno = 0;
+			value = strtod(small_parts[1], &endptr);
+			if (endptr == small_parts[1] || errno != 0 ||
+					(*endptr != '\0' && !isspace(*endptr)))
+				exit_input_error(n+1);
+
+			if (do_sparse) {
+				data->spZ->values[cnt] = value;
+				data->spZ->ja[cnt] = index + zero_based;
+				cnt++;
+				row_cnt++;
+			} else {
+				matrix_set(data->RAW, m+1, i, 
+						index + zero_based, value);
+			}
+
+			// free the small parts
+			free(small_parts[0]);
+			free(small_parts[1]);
+			free(small_parts);
+		}
+
+		if (do_sparse) {
+			data->spZ->ia[i+1] = data->spZ->ia[i] + row_cnt;
+		}
+
+		// free the big parts
+		for (j=0; j<n_big; j++) {
+			free(big_parts[j]);
+		}
+		free(big_parts);
+	}
+
+	fclose(fid);
+
+	data->n = n;
+	data->m = m;
+	data->r = m;
+	data->K = K;
+
+}
 
 /**
  * @brief Read model from file
diff --git a/src/gensvm_sparse.c b/src/gensvm_sparse.c
index ce99b3b..1a9317e 100644
--- a/src/gensvm_sparse.c
+++ b/src/gensvm_sparse.c
@@ -91,6 +91,23 @@ long gensvm_count_nnz(double *A, long rows, long cols)
 }
 
 /**
+ * @brief Compare the number of nonzeros is such that sparsity if worth it
+ *
+ * @details
+ * This is a utility function, see gensvm_could_sparse() for more info.
+ *
+ * @param[in] 	nnz 	number of nonzero elements
+ * @param[in] 	rows 	number of rows
+ * @param[in] 	cols 	number of columns
+ *
+ * @return  		whether or not sparsity is worth it
+ */
+bool gensvm_nnz_comparison(long nnz, long rows, long cols)
+{
+	return (nnz < (rows*(cols-1.0)-1.0)/2.0);
+}
+
+/**
  * @brief Check if it is worthwile to convert to a sparse matrix
  *
  * @details
@@ -100,20 +117,19 @@ long gensvm_count_nnz(double *A, long rows, long cols)
  * the amount of nonzero entries is small enough, the function returns the
  * number of nonzeros. If it is too big, it returns -1.
  *
+ * @sa
+ * gensvm_nnz_comparison()
+ *
  * @param[in] 	A 	matrix in dense format (RowMajor order)
  * @param[in] 	rows 	number of rows of A
  * @param[in] 	cols 	number of columns of A
  *
- * @return
+ * @return  		whether or not sparsity is worth it
  */
 bool gensvm_could_sparse(double *A, long rows, long cols)
 {
 	long nnz = gensvm_count_nnz(A, rows, cols);
-
-	if (nnz < (rows*(cols-1.0)-1.0)/2.0) {
-		return true;
-	}
-	return false;
+	return gensvm_nnz_comparison(nnz, rows, cols);
 }
 
 
diff --git a/src/gensvm_strutil.c b/src/gensvm_strutil.c
index f6bb8f7..7bca2ca 100644
--- a/src/gensvm_strutil.c
+++ b/src/gensvm_strutil.c
@@ -63,6 +63,118 @@ bool str_endswith(const char *str, const char *suf)
 }
 
 /**
+ * @brief Check if a string contains a char
+ *
+ * @details
+ * Simple utility function to check if a char occurs in a string.
+ *
+ * @param[in] 	str 	input string
+ * @param[in] 	c 	character
+ *
+ * @return 		number of times c occurs in str
+ */
+bool str_contains_char(const char *str, const char c)
+{
+	size_t i, len = strlen(str);
+	for (i=0; i<len; i++)
+		if (str[i] == c)
+			return true;
+	return false;
+}
+
+/**
+ * @brief Count the number of times a string contains any character of another
+ *
+ * @details
+ * This function is used to count the number of expected parts in the function 
+ * str_split(). It counts the number of times a character from a string of 
+ * characters is present in an input string.
+ *
+ * @param[in] 	str 	input string
+ * @param[in] 	chars 	characters to count
+ *
+ * @return  		number of times any character from chars occurs in str
+ *
+ */
+int count_str_occurrences(const char *str, const char *chars)
+{
+	size_t i, j, len_str = strlen(str),
+	    len_chars = strlen(chars);
+	int count = 0;
+	for (i=0; i<len_str; i++) {
+		for (j=0; j<len_chars; j++) {
+			count += (str[i] == chars[j]);
+		}
+	}
+	return count;
+}
+
+/**
+ * @brief Split a string on delimiters and return an array of parts
+ *
+ * @details
+ * This function takes as input a string and a string of delimiters. As 
+ * output, it gives an array of the parts of the first string, splitted on the 
+ * characters in the second string. The input string is not changed, and the 
+ * output contains all copies of the input string parts.
+ *
+ * @note
+ * The code is based on: http://stackoverflow.com/a/9210560
+ *
+ * @param[in] 	original 	string you wish to split
+ * @param[in] 	delims 		string with delimiters to split on
+ * @param[out] 	len_ret 	length of the output array
+ *
+ * @return 			array of string parts
+ */
+char **str_split(char *original, const char *delims, int *len_ret)
+{
+	char *copy = NULL,
+	     *token = NULL,
+	     **result = NULL;
+	int i, count;
+
+	size_t len = strlen(original);
+	size_t n_delim = strlen(delims);
+
+	// add the null terminator to the delimiters
+	char all_delim[1 + n_delim];
+	for (i=0; i<n_delim; i++)
+		all_delim[i] = delims[i];
+	all_delim[n_delim] = '\0';
+
+	// number of occurances of the delimiters
+	count = count_str_occurrences(original, delims);
+
+	// extra count in case there is a delimiter at the end
+	count += (str_contains_char(delims, original[len - 1]));
+
+	// extra count for the null terminator
+	count++;
+
+	// allocate the result array
+	result = Calloc(char *, count);
+
+	// tokenize a copy of the original string and keep the splits
+	i = 0;
+	copy = Calloc(char, len + 1);
+	strcpy(copy, original);
+	token = strtok(copy, all_delim);
+	while (token) {
+		result[i] = Calloc(char, strlen(token) + 1);
+		strcpy(result[i], token);
+		i++;
+
+		token = strtok(NULL, all_delim);
+	}
+	free(copy);
+
+	*len_ret = i;
+
+	return result;
+}
+
+/**
  * @brief Move to next line in file
  *
  * @param[in] 	fid 		File opened for reading
diff --git a/tests/data/test_file_read_data_libsvm.txt b/tests/data/test_file_read_data_libsvm.txt
new file mode 100644
index 0000000..da1c0ff
--- /dev/null
+++ b/tests/data/test_file_read_data_libsvm.txt
@@ -0,0 +1,5 @@
+2 1:0.7065937536993949 2:0.7016517970438980 3:0.1548611397288129
+1 1:0.4604987687863951 2:0.6374142980176117 3:0.0370930278245423
+3 1:0.3798777132278375 2:0.5745070018747664 3:0.2570906697837264
+4 1:0.2789376050039792 2:0.4853242744610165 3:0.1894010436762711
+3 1:0.7630904372339489 2:0.1341546320318005 3:0.6827430912944857
diff --git a/tests/data/test_file_read_data_libsvm_0.txt b/tests/data/test_file_read_data_libsvm_0.txt
new file mode 100644
index 0000000..6169a0c
--- /dev/null
+++ b/tests/data/test_file_read_data_libsvm_0.txt
@@ -0,0 +1,5 @@
+2 0:0.7065937536993949 1:0.7016517970438980 2:0.1548611397288129
+1 0:0.4604987687863951 1:0.6374142980176117 2:0.0370930278245423
+3 0:0.3798777132278375 1:0.5745070018747664 2:0.2570906697837264
+4 0:0.2789376050039792 1:0.4853242744610165 2:0.1894010436762711
+3 0:0.7630904372339489 1:0.1341546320318005 2:0.6827430912944857
diff --git a/tests/data/test_file_read_data_no_label_libsvm.txt b/tests/data/test_file_read_data_no_label_libsvm.txt
new file mode 100644
index 0000000..5452216
--- /dev/null
+++ b/tests/data/test_file_read_data_no_label_libsvm.txt
@@ -0,0 +1,5 @@
+1:0.7065937536993949 2:0.7016517970438980 3:0.1548611397288129
+1:0.4604987687863951 2:0.6374142980176117 3:0.0370930278245423
+1:0.3798777132278375 2:0.5745070018747664 3:0.2570906697837264
+1:0.2789376050039792 2:0.4853242744610165 3:0.1894010436762711
+1:0.7630904372339489 2:0.1341546320318005 3:0.6827430912944857
diff --git a/tests/data/test_file_read_data_sparse_libsvm.txt b/tests/data/test_file_read_data_sparse_libsvm.txt
new file mode 100644
index 0000000..ff41fe7
--- /dev/null
+++ b/tests/data/test_file_read_data_sparse_libsvm.txt
@@ -0,0 +1,10 @@
+2 2:0.7016517970438980
+1 3:0.0370930278245423
+3
+4 2:0.4853242744610165
+3 1:0.7630904372339489
+1
+2
+3
+4
+1
diff --git a/tests/src/test_gensvm_io.c b/tests/src/test_gensvm_io.c
index f1a7d22..29776d6 100644
--- a/tests/src/test_gensvm_io.c
+++ b/tests/src/test_gensvm_io.c
@@ -265,6 +265,321 @@ char *test_gensvm_read_data_no_label()
 	return NULL;
 }
 
+char *test_gensvm_read_data_libsvm()
+{
+	char *filename = "./data/test_file_read_data_libsvm.txt";
+	struct GenData *data = gensvm_init_data();
+
+	// start test code //
+	gensvm_read_data_libsvm(data, filename);
+
+	// check if dimensions are correctly read
+	mu_assert(data->n == 5, "Incorrect value for n");
+	mu_assert(data->m == 3, "Incorrect value for m");
+	mu_assert(data->r == 3, "Incorrect value for r");
+	mu_assert(data->K == 4, "Incorrect value for K");
+
+	// check if all data is read correctly.
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 1) == 0.7065937536993949,
+			"Incorrect Z value at 0, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 2) == 0.7016517970438980,
+			"Incorrect Z value at 0, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 3) == 0.1548611397288129,
+			"Incorrect Z value at 0, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 1) == 0.4604987687863951,
+			"Incorrect Z value at 1, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 2) == 0.6374142980176117,
+			"Incorrect Z value at 1, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 3) == 0.0370930278245423,
+			"Incorrect Z value at 1, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 1) == 0.3798777132278375,
+			"Incorrect Z value at 2, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 2) == 0.5745070018747664,
+			"Incorrect Z value at 2, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 3) == 0.2570906697837264,
+			"Incorrect Z value at 2, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 1) == 0.2789376050039792,
+			"Incorrect Z value at 3, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 2) == 0.4853242744610165,
+			"Incorrect Z value at 3, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 3) == 0.1894010436762711,
+			"Incorrect Z value at 3, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 1) == 0.7630904372339489,
+			"Incorrect Z value at 4, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 2) == 0.1341546320318005,
+			"Incorrect Z value at 4, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 3) == 0.6827430912944857,
+			"Incorrect Z value at 4, 3");
+	// check if RAW = Z
+	mu_assert(data->Z == data->RAW, "Z pointer doesn't equal RAW pointer");
+
+	// check if labels read correctly
+	mu_assert(data->y[0] == 2, "Incorrect label read at 0");
+	mu_assert(data->y[1] == 1, "Incorrect label read at 1");
+	mu_assert(data->y[2] == 3, "Incorrect label read at 2");
+	mu_assert(data->y[3] == 4, "Incorrect label read at 3");
+	mu_assert(data->y[4] == 3, "Incorrect label read at 4");
+
+	// check if the column of ones is added
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 0) == 1,
+			"Incorrect Z value at 0, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 0) == 1,
+			"Incorrect Z value at 1, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 0) == 1,
+			"Incorrect Z value at 2, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 0) == 1,
+			"Incorrect Z value at 3, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 0) == 1,
+			"Incorrect Z value at 4, 0");
+
+	// end test code //
+
+	gensvm_free_data(data);
+
+	return NULL;
+}
+
+char *test_gensvm_read_data_libsvm_0based()
+{
+	char *filename = "./data/test_file_read_data_libsvm_0.txt";
+	struct GenData *data = gensvm_init_data();
+
+	// start test code //
+	gensvm_read_data_libsvm(data, filename);
+
+	// check if dimensions are correctly read
+	mu_assert(data->n == 5, "Incorrect value for n");
+	mu_assert(data->m == 3, "Incorrect value for m");
+	mu_assert(data->r == 3, "Incorrect value for r");
+	mu_assert(data->K == 4, "Incorrect value for K");
+
+	// check if all data is read correctly.
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 1) == 0.7065937536993949,
+			"Incorrect Z value at 0, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 2) == 0.7016517970438980,
+			"Incorrect Z value at 0, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 3) == 0.1548611397288129,
+			"Incorrect Z value at 0, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 1) == 0.4604987687863951,
+			"Incorrect Z value at 1, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 2) == 0.6374142980176117,
+			"Incorrect Z value at 1, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 3) == 0.0370930278245423,
+			"Incorrect Z value at 1, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 1) == 0.3798777132278375,
+			"Incorrect Z value at 2, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 2) == 0.5745070018747664,
+			"Incorrect Z value at 2, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 3) == 0.2570906697837264,
+			"Incorrect Z value at 2, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 1) == 0.2789376050039792,
+			"Incorrect Z value at 3, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 2) == 0.4853242744610165,
+			"Incorrect Z value at 3, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 3) == 0.1894010436762711,
+			"Incorrect Z value at 3, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 1) == 0.7630904372339489,
+			"Incorrect Z value at 4, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 2) == 0.1341546320318005,
+			"Incorrect Z value at 4, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 3) == 0.6827430912944857,
+			"Incorrect Z value at 4, 3");
+	// check if RAW = Z
+	mu_assert(data->Z == data->RAW, "Z pointer doesn't equal RAW pointer");
+
+	// check if labels read correctly
+	mu_assert(data->y[0] == 2, "Incorrect label read at 0");
+	mu_assert(data->y[1] == 1, "Incorrect label read at 1");
+	mu_assert(data->y[2] == 3, "Incorrect label read at 2");
+	mu_assert(data->y[3] == 4, "Incorrect label read at 3");
+	mu_assert(data->y[4] == 3, "Incorrect label read at 4");
+
+	// check if the column of ones is added
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 0) == 1,
+			"Incorrect Z value at 0, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 0) == 1,
+			"Incorrect Z value at 1, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 0) == 1,
+			"Incorrect Z value at 2, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 0) == 1,
+			"Incorrect Z value at 3, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 0) == 1,
+			"Incorrect Z value at 4, 0");
+
+	// end test code //
+
+	gensvm_free_data(data);
+
+	return NULL;
+}
+
+char *test_gensvm_read_data_libsvm_sparse()
+{
+	char *filename = "./data/test_file_read_data_sparse_libsvm.txt";
+	struct GenData *data = gensvm_init_data();
+
+	// start test code //
+	gensvm_read_data_libsvm(data, filename);
+
+	// check if dimensions are correctly read
+	mu_assert(data->n == 10, "Incorrect value for n");
+	mu_assert(data->m == 3, "Incorrect value for m");
+	mu_assert(data->r == 3, "Incorrect value for r");
+	mu_assert(data->K == 4, "Incorrect value for K");
+
+	// check if dense data pointers are NULL
+	mu_assert(data->Z == NULL, "Z pointer isn't NULL");
+	mu_assert(data->RAW == NULL, "RAW pointer isn't NULL");
+
+	// check sparse data structure
+	mu_assert(data->spZ != NULL, "spZ is NULL");
+	mu_assert(data->spZ->nnz == 14, "Incorrect nnz");
+	mu_assert(data->spZ->n_row == 10, "Incorrect n_row");
+	mu_assert(data->spZ->n_col == 4, "Incorrect n_col");
+
+	// check sparse values
+	mu_assert(data->spZ->values[0] == 1.0,
+			"Incorrect nonzero value at 0");
+	mu_assert(data->spZ->values[1] == 0.7016517970438980,
+			"Incorrect nonzero value at 1");
+	mu_assert(data->spZ->values[2] == 1.0,
+			"Incorrect nonzero value at 2");
+	mu_assert(data->spZ->values[3] == 0.0370930278245423,
+			"Incorrect nonzero value at 3");
+	mu_assert(data->spZ->values[4] == 1.0,
+			"Incorrect nonzero value at 4");
+	mu_assert(data->spZ->values[5] == 1.0,
+			"Incorrect nonzero value at 5");
+	mu_assert(data->spZ->values[6] == 0.4853242744610165,
+			"Incorrect nonzero value at 6");
+	mu_assert(data->spZ->values[7] == 1.0,
+			"Incorrect nonzero value at 7");
+	mu_assert(data->spZ->values[8] == 0.7630904372339489,
+			"Incorrect nonzero value at 8");
+	mu_assert(data->spZ->values[9] == 1.0,
+			"Incorrect nonzero value at 9");
+	mu_assert(data->spZ->values[10] == 1.0,
+			"Incorrect nonzero value at 10");
+	mu_assert(data->spZ->values[11] == 1.0,
+			"Incorrect nonzero value at 11");
+	mu_assert(data->spZ->values[12] == 1.0,
+			"Incorrect nonzero value at 12");
+	mu_assert(data->spZ->values[13] == 1.0,
+			"Incorrect nonzero value at 13");
+
+	// check sparse row lengths
+	mu_assert(data->spZ->ia[0] == 0, "Incorrect ia value at 0");
+	mu_assert(data->spZ->ia[1] == 2, "Incorrect ia value at 1");
+	mu_assert(data->spZ->ia[2] == 4, "Incorrect ia value at 2");
+	mu_assert(data->spZ->ia[3] == 5, "Incorrect ia value at 3");
+	mu_assert(data->spZ->ia[4] == 7, "Incorrect ia value at 4");
+	mu_assert(data->spZ->ia[5] == 9, "Incorrect ia value at 5");
+	mu_assert(data->spZ->ia[6] == 10, "Incorrect ia value at 5");
+	mu_assert(data->spZ->ia[7] == 11, "Incorrect ia value at 5");
+	mu_assert(data->spZ->ia[8] == 12, "Incorrect ia value at 5");
+	mu_assert(data->spZ->ia[9] == 13, "Incorrect ia value at 5");
+	mu_assert(data->spZ->ia[10] == 14, "Incorrect ia value at 5");
+
+	// check sparse column indices
+	mu_assert(data->spZ->ja[0] == 0, "Incorrect ja value at 0");
+	mu_assert(data->spZ->ja[1] == 2, "Incorrect ja value at 1");
+	mu_assert(data->spZ->ja[2] == 0, "Incorrect ja value at 2");
+	mu_assert(data->spZ->ja[3] == 3, "Incorrect ja value at 3");
+	mu_assert(data->spZ->ja[4] == 0, "Incorrect ja value at 4");
+	mu_assert(data->spZ->ja[5] == 0, "Incorrect ja value at 5");
+	mu_assert(data->spZ->ja[6] == 2, "Incorrect ja value at 6");
+	mu_assert(data->spZ->ja[7] == 0, "Incorrect ja value at 7");
+	mu_assert(data->spZ->ja[8] == 1, "Incorrect ja value at 8");
+	mu_assert(data->spZ->ja[9] == 0, "Incorrect ja value at 7");
+	mu_assert(data->spZ->ja[10] == 0, "Incorrect ja value at 7");
+	mu_assert(data->spZ->ja[11] == 0, "Incorrect ja value at 7");
+	mu_assert(data->spZ->ja[12] == 0, "Incorrect ja value at 7");
+	mu_assert(data->spZ->ja[13] == 0, "Incorrect ja value at 7");
+
+	// check if labels read correctly
+	mu_assert(data->y[0] == 2, "Incorrect label read at 0");
+	mu_assert(data->y[1] == 1, "Incorrect label read at 1");
+	mu_assert(data->y[2] == 3, "Incorrect label read at 2");
+	mu_assert(data->y[3] == 4, "Incorrect label read at 3");
+	mu_assert(data->y[4] == 3, "Incorrect label read at 4");
+
+	// end test code //
+
+	gensvm_free_data(data);
+
+	return NULL;
+}
+
+char *test_gensvm_read_data_libsvm_no_label()
+{
+	char *filename = "./data/test_file_read_data_no_label_libsvm.txt";
+	struct GenData *data = gensvm_init_data();
+
+	// start test code //
+	gensvm_read_data_libsvm(data, filename);
+
+	// check if dimensions are correctly read
+	mu_assert(data->n == 5, "Incorrect value for n");
+	mu_assert(data->m == 3, "Incorrect value for m");
+	mu_assert(data->r == 3, "Incorrect value for r");
+	mu_assert(data->K == 0, "Incorrect value for K");
+
+	// check if all data is read correctly.
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 1) == 0.7065937536993949,
+			"Incorrect Z value at 0, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 2) == 0.7016517970438980,
+			"Incorrect Z value at 0, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 3) == 0.1548611397288129,
+			"Incorrect Z value at 0, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 1) == 0.4604987687863951,
+			"Incorrect Z value at 1, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 2) == 0.6374142980176117,
+			"Incorrect Z value at 1, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 3) == 0.0370930278245423,
+			"Incorrect Z value at 1, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 1) == 0.3798777132278375,
+			"Incorrect Z value at 2, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 2) == 0.5745070018747664,
+			"Incorrect Z value at 2, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 3) == 0.2570906697837264,
+			"Incorrect Z value at 2, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 1) == 0.2789376050039792,
+			"Incorrect Z value at 3, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 2) == 0.4853242744610165,
+			"Incorrect Z value at 3, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 3) == 0.1894010436762711,
+			"Incorrect Z value at 3, 3");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 1) == 0.7630904372339489,
+			"Incorrect Z value at 4, 1");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 2) == 0.1341546320318005,
+			"Incorrect Z value at 4, 2");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 3) == 0.6827430912944857,
+			"Incorrect Z value at 4, 3");
+	// check if RAW = Z
+	mu_assert(data->Z == data->RAW, "Z pointer doesn't equal RAW pointer");
+
+	// check if labels read correctly
+	mu_assert(data->y == NULL, "Outcome pointer is not NULL");
+
+	// check if the column of ones is added
+	mu_assert(matrix_get(data->Z, data->m+1, 0, 0) == 1,
+			"Incorrect Z value at 0, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 1, 0) == 1,
+			"Incorrect Z value at 1, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 2, 0) == 1,
+			"Incorrect Z value at 2, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 3, 0) == 1,
+			"Incorrect Z value at 3, 0");
+	mu_assert(matrix_get(data->Z, data->m+1, 4, 0) == 1,
+			"Incorrect Z value at 4, 0");
+
+	// end test code //
+
+	gensvm_free_data(data);
+
+	return NULL;
+}
+
 char *test_gensvm_read_model()
 {
 	struct GenModel *model = gensvm_init_model();
@@ -536,6 +851,11 @@ char *all_tests()
 	mu_run_test(test_gensvm_read_data);
 	mu_run_test(test_gensvm_read_data_sparse);
 	mu_run_test(test_gensvm_read_data_no_label);
+	mu_run_test(test_gensvm_read_data_libsvm);
+	mu_run_test(test_gensvm_read_data_libsvm_0based);
+	mu_run_test(test_gensvm_read_data_libsvm_sparse);
+	mu_run_test(test_gensvm_read_data_libsvm_no_label);
+
 	mu_run_test(test_gensvm_read_model);
 	mu_run_test(test_gensvm_write_model);
 	mu_run_test(test_gensvm_write_predictions);