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https://gitee.com/openharmony/third_party_ffmpeg
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142 lines
3.6 KiB
C
142 lines
3.6 KiB
C
/*
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* linear least squares model
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*
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* Copyright (c) 2006 Michael Niedermayer <michaelni@gmx.at>
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with this library; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*/
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/**
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* @file lls.c
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* linear least squares model
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*/
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#include <math.h>
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#include <string.h>
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#include "lls.h"
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#ifdef TEST
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#define av_log(a,b,...) printf(__VA_ARGS__)
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#endif
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void av_init_lls(LLSModel *m, int indep_count){
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memset(m, 0, sizeof(LLSModel));
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m->indep_count= indep_count;
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}
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void av_update_lls(LLSModel *m, double *var, double decay){
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int i,j;
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for(i=0; i<=m->indep_count; i++){
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for(j=i; j<=m->indep_count; j++){
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m->covariance[i][j] *= decay;
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m->covariance[i][j] += var[i]*var[j];
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}
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}
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}
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double av_solve_lls(LLSModel *m, double threshold){
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int i,j,k;
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double (*factor)[MAX_VARS+1]= &m->covariance[1][0];
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double (*covar )[MAX_VARS+1]= &m->covariance[1][1];
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double *covar_y = m->covariance[0];
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double variance;
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int count= m->indep_count;
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for(i=0; i<count; i++){
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for(j=i; j<count; j++){
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double sum= covar[i][j];
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for(k=i-1; k>=0; k--)
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sum -= factor[i][k]*factor[j][k];
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if(i==j){
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if(sum < threshold)
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sum= 1.0;
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factor[i][i]= sqrt(sum);
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}else
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factor[j][i]= sum / factor[i][i];
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}
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}
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for(i=0; i<count; i++){
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double sum= covar_y[i+1];
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for(k=i-1; k>=0; k--)
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sum -= factor[i][k]*m->coeff[k];
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m->coeff[i]= sum / factor[i][i];
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}
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for(i=count-1; i>=0; i--){
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double sum= m->coeff[i];
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for(k=i+1; k<count; k++)
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sum -= factor[k][i]*m->coeff[k];
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m->coeff[i]= sum / factor[i][i];
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}
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variance= covar_y[0];
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for(i=0; i<count; i++){
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double sum= m->coeff[i]*covar[i][i] - 2*covar_y[i+1];
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for(j=0; j<i; j++)
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sum += 2*m->coeff[j]*covar[j][i];
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variance += m->coeff[i]*sum;
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}
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return variance;
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}
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double av_evaluate_lls(LLSModel *m, double *param){
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int i;
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double out= 0;
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for(i=0; i<m->indep_count; i++)
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out+= param[i]*m->coeff[i];
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return out;
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}
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#ifdef TEST
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#include <stdlib.h>
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#include <stdio.h>
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int main(){
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LLSModel m;
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int i;
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av_init_lls(&m, 3);
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for(i=0; i<100; i++){
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double var[4];
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double eval, variance;
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var[1] = rand() / (double)RAND_MAX;
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var[2] = rand() / (double)RAND_MAX;
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var[3] = rand() / (double)RAND_MAX;
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var[2]= var[1] + var[3];
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var[0] = var[1] + var[2] + var[3] + var[1]*var[2]/100;
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eval= av_evaluate_lls(&m, var+1);
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av_update_lls(&m, var, 0.99);
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variance= av_solve_lls(&m, 0.001);
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av_log(NULL, AV_LOG_DEBUG, "real:%f pred:%f var:%f coeffs:%f %f %f\n",
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var[0], eval, sqrt(variance / (i+1)),
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m.coeff[0], m.coeff[1], m.coeff[2]);
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}
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return 0;
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}
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#endif
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