...
Lineaarisessa
...
regressiossa
...
sovitetaan
...
suora
...
pistejoukkoon.
...
Monesti
...
mitattava
...
data
...
on
...
kohinaista
...
ja
...
tapahtumaa
...
kuvaava
...
suora
...
on
...
"haettava"
...
pisteiden
...
seasta.
| Code Block |
|---|
}
double Statistic::LineFit(const double *const x, const double *const y, const size_t n, double *m, double *b)
{
int error = 0;
double sumx = 0, sumy = 0, sumx2 = 0, sumxy = 0;
double dn = (double) n;
size_t i;
if (n <= 1)
{
*m = 0;
*b = 0;
error = 1;
}
else
{
double divisor;
error = 0;
for (i = 0; i < n; i++)
{
sumx += x[i];
sumy += y[i];
sumx2 += (x[i] * x[i]);
sumxy += (x[i] * y[i]);
}
divisor = (sumx2 - ((sumx * sumx) / dn));
if (divisor != 0)
{
*m = (sumxy - ((sumx * sumy) / dn)) / divisor;
*b = (sumy - ((*m) * sumx)) / dn;
}
else
{
*m = 0;
*b = 0;
error = 2;
}
}
return *m;
};
|
| Code Block |
|---|
{code} {code} int main() { // exact line: double x0[] = {-1, 0, 1, 2, 3}; double y0[] = {-1, 0, 1, 2, 3}; // noisy line: double x1[] = {-1.1, 0.01, .9999, 1.99998, 3.01}; double y1[] = {-1.02, -.00001, 1.002, 1.99872, 2.999973}; size_t length = sizeof x0 / sizeof x0[0]; double m; double b; linfit(x0, y0, length, &m, &b); printf("Slope is %f, intercept is %f\n", m, b); linfit(x1, y1, length, &m, &b); printf("Slope is %f, intercept is %f\n", m, b); cin.get(); return 0; } {code} |