Codebase list liblinear / HEAD linear.h
HEAD

Tree @HEAD (Download .tar.gz)

linear.h @HEAD — raw · history · blame 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
#ifndef _LIBLINEAR_H
#define _LIBLINEAR_H

#define LIBLINEAR_VERSION 243

#ifdef __cplusplus
extern "C" {
#endif

extern int liblinear_version;

struct feature_node
{
	int index;
	double value;
};

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

enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR = 11, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL, ONECLASS_SVM = 21 }; /* solver_type */

struct parameter
{
	int solver_type;

	/* these are for training only */
	double eps;             /* stopping tolerance */
	double C;
	int nr_weight;
	int *weight_label;
	double* weight;
	double p;
	double nu;
	double *init_sol;
	int regularize_bias;
};

struct model
{
	struct parameter param;
	int nr_class;           /* number of classes */
	int nr_feature;
	double *w;
	int *label;             /* label of each class */
	double bias;
	double rho;             /* one-class SVM only */
};

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, double *target);
void find_parameters(const struct problem *prob, const struct parameter *param, int nr_fold, double start_C, double start_p, double *best_C, double *best_p, double *best_score);

double predict_values(const struct model *model_, const struct feature_node *x, double* dec_values);
double predict(const struct model *model_, const struct feature_node *x);
double 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);
double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx);
double get_decfun_bias(const struct model *model_, int label_idx);
double get_decfun_rho(const struct model *model_);

void free_model_content(struct model *model_ptr);
void free_and_destroy_model(struct model **model_ptr_ptr);
void destroy_param(struct parameter *param);

const char *check_parameter(const struct problem *prob, const struct parameter *param);
int check_probability_model(const struct model *model);
int check_regression_model(const struct model *model);
int check_oneclass_model(const struct model *model);
void set_print_string_function(void (*print_func) (const char*));

#ifdef __cplusplus
}
#endif

#endif /* _LIBLINEAR_H */