blob: 5bdc01ff2f8bf8006a3ca12978ace21a2fe5e6e0 (
plain)
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
|
// Linear Regression Slope
//
// linear_regression_slope
//
// Linear regression slope
//
// b = Σ(x-x̄)(y-ȳ) / Σ(x-x̄)²
//
// Inputs: 1
// Outputs: 1
// Constants: 0
#include "ts.h"
typedef struct {
uint_t k;
} ts_linear_regression_slope_params_t;
ts_parameter_t const ts_linear_regression_slope_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_linear_regression_slope_params_t) };
uint_t ts_linear_regression_slope_offset( ts_constant_t const *c )
{
( void )c;
return ( c ? ((ts_linear_regression_slope_params_t const *)c)->k - 1 : 0 );
}
int ts_linear_regression_slope(
fp_t * const *y, uint_t y_n,
fp_t const * const *x, uint_t x_n,
uint_t n, ts_constant_t const *c
)
{
TS_PROLOGUE( ts_linear_regression_slope_p );
if ( !c ) return -1;
uint_t k = ((ts_linear_regression_slope_params_t const *)c)->k;
if (k == 0 || n < k) return -1;
fp_t sum_x = 0, sum_x2 = 0;
for ( uint_t j = 0; j < k; ++j )
{
fp_t xv = (fp_t)j;
sum_x += xv;
sum_x2 += xv * xv;
}
fp_t denom = (fp_t)k * sum_x2 - sum_x * sum_x;
fp_t fk = (fp_t)k;
fp_t sum_y = 0, sum_xy = 0;
for ( uint_t j = 0; j < k; ++j )
{
fp_t yv = x[0][j];
sum_y += yv;
sum_xy += (fp_t)j * yv;
}
if (denom == 0) {
y[0][k - 1] = 0;
} else {
y[0][k - 1] = (fk * sum_xy - sum_x * sum_y) / denom;
}
for ( uint_t i = k; i < n; ++i )
{
fp_t y_new = x[0][i];
fp_t y_old = x[0][i - k];
fp_t sum_y_old = sum_y;
sum_y = sum_y - y_old + y_new;
sum_xy = sum_xy - sum_y_old + y_old + (fk - 1) * y_new;
if (denom == 0) {
y[0][i] = 0;
} else {
y[0][i] = (fk * sum_xy - sum_x * sum_y) / denom;
}
}
return 0;
}
|