-
Notifications
You must be signed in to change notification settings - Fork 0
/
linear.h
107 lines (84 loc) · 3.16 KB
/
linear.h
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
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
#ifndef _LIBLINEAR_H
#define _LIBLINEAR_H
#ifdef __cplusplus
extern "C" {
#endif
// struct graph_edge
// {
// int node1;
// int node2;
// double weight;
// }
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 */
int *numLabels; // multi-label classification
};
enum { L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, L2R_L2LOSS_SVC_GD}; /* solver_type */
struct parameter
{
int solver_type;
/* these are for training only */
double eps; /* stopping criteria */
double eps2;
double C;
int nr_weight;
int *weight_label;
double* weight;
double p;
double *init_sol;
int n_threads; // for parallel
int init_strat;
//int all_neg_init; //
//int mst_schedule;
};
struct model
{
struct parameter param;
int nr_class; /* number of classes */
int nr_feature;
//double *w;
struct feature_node **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, double *target);
// void find_parameter_C(const struct problem *prob, const struct parameter *param, int nr_fold, double start_C, double max_C, double *best_C, double *best_rate);
//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);
//char** predict_all(const struct model *model_, const struct feature_node *x, long long k);
//double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);
//struct model *load_model(const char *model_file_name, struct feature_node **W);
int save_model(const char *model_file_name, const struct model *model_);
//struct model *load_model(const char *model_file_name, struct feature_node **W);
struct model *load_model_stat(const char *model_file_name);
struct feature_node **load_w(const char *model_file_name);
int ** predict(struct feature_node **x, const model *model_, struct feature_node **W, int nr_test, int k, int n_threads);
void evaluate(int ** pred, struct problem * test_prob, int k);
//int save_model(const char *model_file_name, const struct model *model_);
//struct model *load_model(const char *model_file_name, struct feature_node **W);
int get_nr_feature(const struct model *model_);
int get_nr_class(const struct model *model_);
void get_labels(const struct model *model_, long long* 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);
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);
void set_print_string_function(void (*print_func) (const char*));
#ifdef __cplusplus
}
#endif
#endif /* _LIBLINEAR_H */