35     for (i = 0; i < 100; i++) {
 
   39         var[0] =         (
av_lfg_get(&lfg) / (double) UINT_MAX - 0.5) * 2;
 
   40         var[1] = var[0] + 
av_lfg_get(&lfg) / (double) UINT_MAX - 0.5;
 
   41         var[2] = var[1] + 
av_lfg_get(&lfg) / (double) UINT_MAX - 0.5;
 
   42         var[3] = var[2] + 
av_lfg_get(&lfg) / (double) UINT_MAX - 0.5;
 
   45         for (order = 0; order < 3; order++) {
 
   47             printf(
"real:%9f order:%d pred:%9f var:%f coeffs:%f %9f %9f\n",
 
   48                    var[0], order, eval, sqrt(m.
variance[order] / (i + 1)),
 
Linear least squares model. 
 
double variance[MAX_VARS]
 
av_cold void avpriv_init_lls(LLSModel *m, int indep_count)
 
common internal API header 
 
void avpriv_solve_lls(LLSModel *m, double threshold, unsigned short min_order)
 
double(* evaluate_lls)(struct LLSModel *m, const double *var, int order)
Inner product of var[] and the LPC coefs. 
 
static unsigned int av_lfg_get(AVLFG *c)
Get the next random unsigned 32-bit number using an ALFG. 
 
av_cold void av_lfg_init(AVLFG *c, unsigned int seed)
 
#define LOCAL_ALIGNED(a, t, v,...)
 
void(* update_lls)(struct LLSModel *m, const double *var)
Take the outer-product of var[] with itself, and add to the covariance matrix.