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
|
#include "crossval.h"
#include "matrix.h"
#include "MSVMMaj.h"
void msvmmaj_make_cv_split(long N, long folds, long *cv_idx)
{
long i, j, idx;
long big_folds = N%folds;
long small_fold_size = N/folds;
j = 0;
for (i=0; i<small_fold_size*folds; i++)
while (1) {
idx = rand()%N;
if (cv_idx[idx] == 0) {
cv_idx[idx] = j;
j++;
j%=folds;
break;
}
}
j = 0;
i = 0;
while (i < big_folds) {
if (cv_idx[j] == 0) {
cv_idx[j] = i++;
}
j++;
}
}
void msvmmaj_get_tt_split(struct MajData *full_data, struct MajData *train_data,
struct MajData *test_data, long *cv_idx, long fold_idx)
{
long i, j, k, l, test_n, train_n;
long n = full_data->n;
long m = full_data->m;
long K = full_data->K;
test_n = 0;
for (i=0; i<n; i++)
if (cv_idx[i] == fold_idx)
test_n++;
train_n = n - test_n;
test_data->n = test_n;
train_data->n = train_n;
train_data->K = K;
test_data->K = K;
train_data->m = m;
test_data->m = m;
train_data->y = Calloc(long, train_n);
test_data->y = Calloc(long, test_n);
train_data->Z = Calloc(double, train_n*(m+1));
test_data->Z = Calloc(double, test_n*(m+1));
k = 0;
l = 0;
for (i=0; i<n; i++) {
if (cv_idx[i] == fold_idx) {
test_data->y[k] = full_data->y[i];
for (j=0; j<m+1; j++)
matrix_set(test_data->Z, m+1, k, j,
matrix_get(full_data->Z, m+1, i, j));
k++;
} else {
train_data->y[l] = full_data->y[i];
for (j=0; j<m+1; j++)
matrix_set(train_data->Z, m+1, l, j,
matrix_get(full_data->Z, m+1, i, j));
l++;
}
}
}
|
