mirror of https://github.com/FFmpeg/FFmpeg.git
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
151 lines
4.0 KiB
151 lines
4.0 KiB
/* |
|
* linear least squares model |
|
* |
|
* Copyright (c) 2006 Michael Niedermayer <michaelni@gmx.at> |
|
* |
|
* This file is part of FFmpeg. |
|
* |
|
* FFmpeg is free software; you can redistribute it and/or |
|
* modify it under the terms of the GNU Lesser General Public |
|
* License as published by the Free Software Foundation; either |
|
* version 2.1 of the License, or (at your option) any later version. |
|
* |
|
* FFmpeg is distributed in the hope that it will be useful, |
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of |
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
|
* Lesser General Public License for more details. |
|
* |
|
* You should have received a copy of the GNU Lesser General Public |
|
* License along with FFmpeg; if not, write to the Free Software |
|
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
|
*/ |
|
|
|
/** |
|
* @file lls.c |
|
* linear least squares model |
|
*/ |
|
|
|
#include <math.h> |
|
#include <string.h> |
|
|
|
#include "lls.h" |
|
|
|
#ifdef TEST |
|
#define av_log(a,b,...) printf(__VA_ARGS__) |
|
#endif |
|
|
|
void av_init_lls(LLSModel *m, int indep_count){ |
|
memset(m, 0, sizeof(LLSModel)); |
|
|
|
m->indep_count= indep_count; |
|
} |
|
|
|
void av_update_lls(LLSModel *m, double *var, double decay){ |
|
int i,j; |
|
|
|
for(i=0; i<=m->indep_count; i++){ |
|
for(j=i; j<=m->indep_count; j++){ |
|
m->covariance[i][j] *= decay; |
|
m->covariance[i][j] += var[i]*var[j]; |
|
} |
|
} |
|
} |
|
|
|
void av_solve_lls(LLSModel *m, double threshold, int min_order){ |
|
int i,j,k; |
|
double (*factor)[MAX_VARS+1]= &m->covariance[1][0]; |
|
double (*covar )[MAX_VARS+1]= &m->covariance[1][1]; |
|
double *covar_y = m->covariance[0]; |
|
int count= m->indep_count; |
|
|
|
for(i=0; i<count; i++){ |
|
for(j=i; j<count; j++){ |
|
double sum= covar[i][j]; |
|
|
|
for(k=i-1; k>=0; k--) |
|
sum -= factor[i][k]*factor[j][k]; |
|
|
|
if(i==j){ |
|
if(sum < threshold) |
|
sum= 1.0; |
|
factor[i][i]= sqrt(sum); |
|
}else |
|
factor[j][i]= sum / factor[i][i]; |
|
} |
|
} |
|
for(i=0; i<count; i++){ |
|
double sum= covar_y[i+1]; |
|
for(k=i-1; k>=0; k--) |
|
sum -= factor[i][k]*m->coeff[0][k]; |
|
m->coeff[0][i]= sum / factor[i][i]; |
|
} |
|
|
|
for(j=count-1; j>=min_order; j--){ |
|
for(i=j; i>=0; i--){ |
|
double sum= m->coeff[0][i]; |
|
for(k=i+1; k<=j; k++) |
|
sum -= factor[k][i]*m->coeff[j][k]; |
|
m->coeff[j][i]= sum / factor[i][i]; |
|
} |
|
|
|
m->variance[j]= covar_y[0]; |
|
for(i=0; i<=j; i++){ |
|
double sum= m->coeff[j][i]*covar[i][i] - 2*covar_y[i+1]; |
|
for(k=0; k<i; k++) |
|
sum += 2*m->coeff[j][k]*covar[k][i]; |
|
m->variance[j] += m->coeff[j][i]*sum; |
|
} |
|
} |
|
} |
|
|
|
double av_evaluate_lls(LLSModel *m, double *param, int order){ |
|
int i; |
|
double out= 0; |
|
|
|
for(i=0; i<=order; i++) |
|
out+= param[i]*m->coeff[order][i]; |
|
|
|
return out; |
|
} |
|
|
|
#ifdef TEST |
|
|
|
#include <stdlib.h> |
|
#include <stdio.h> |
|
|
|
int main(void){ |
|
LLSModel m; |
|
int i, order; |
|
|
|
av_init_lls(&m, 3); |
|
|
|
for(i=0; i<100; i++){ |
|
double var[4]; |
|
double eval, variance; |
|
#if 0 |
|
var[1] = rand() / (double)RAND_MAX; |
|
var[2] = rand() / (double)RAND_MAX; |
|
var[3] = rand() / (double)RAND_MAX; |
|
|
|
var[2]= var[1] + var[3]/2; |
|
|
|
var[0] = var[1] + var[2] + var[3] + var[1]*var[2]/100; |
|
#else |
|
var[0] = (rand() / (double)RAND_MAX - 0.5)*2; |
|
var[1] = var[0] + rand() / (double)RAND_MAX - 0.5; |
|
var[2] = var[1] + rand() / (double)RAND_MAX - 0.5; |
|
var[3] = var[2] + rand() / (double)RAND_MAX - 0.5; |
|
#endif |
|
av_update_lls(&m, var, 0.99); |
|
av_solve_lls(&m, 0.001, 0); |
|
for(order=0; order<3; order++){ |
|
eval= av_evaluate_lls(&m, var+1, order); |
|
av_log(NULL, AV_LOG_DEBUG, "real:%f order:%d pred:%f var:%f coeffs:%f %f %f\n", |
|
var[0], order, eval, sqrt(m.variance[order] / (i+1)), |
|
m.coeff[order][0], m.coeff[order][1], m.coeff[order][2]); |
|
} |
|
} |
|
return 0; |
|
} |
|
|
|
#endif
|
|
|