/*

$Id$
Copyright (c) 2007-2009 The LIBLINEAR Project.
Copyright (c) 2010 Alois Schloegl <alois.schloegl@gmail.com>
This function is part of the NaN-toolbox
http://pub.ist.ac.at/~schloegl/matlab/NaN/

This code was extracted from liblinear-1.51 in Jan 2010 and 
modified for the use with Octave 

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, see <http://www.gnu.org/licenses/>.

*/

#ifndef _LIBLINEAR_H
#define _LIBLINEAR_H

#ifdef __cplusplus
extern "C" {
#endif

struct feature_node
{
	int index;
	double value;
};

struct problem
{
	int l, n;
	int *y;
	struct feature_node **x;
	double bias;            /* < 0 if no bias term */  
	double *W;              /* instance weight */
};

enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR }; /* solver_type */

struct parameter
{
	int solver_type;

	/* these are for training only */
	double eps;	        /* stopping criteria */
	double C;
	int nr_weight;
	int *weight_label;
	double* weight;
};

struct model
{
	struct parameter param;
	unsigned nr_class;		/* number of classes */
	int nr_feature;
	double *w;
	int *label;		/* label of each class */
	double bias;
};

struct model* train(const struct problem *prob, const struct parameter *param);
void cross_validation(const struct problem *prob, const struct parameter *param, int nr_fold, int *target);

int predict_values(const struct model *model_, const struct feature_node *x, double* dec_values);
int predict(const struct model *model_, const struct feature_node *x);
int predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);

int save_model(const char *model_file_name, const struct model *model_);
struct model *load_model(const char *model_file_name);

int get_nr_feature(const struct model *model_);
int get_nr_class(const struct model *model_);
void get_labels(const struct model *model_, int* label);

void destroy_model(struct model *model_);
void destroy_param(struct parameter *param);
const char *check_parameter(const struct parameter *param);
extern void (*liblinear_print_string) (const char *);

#ifdef __cplusplus
}
#endif

#endif /* _LIBLINEAR_H */