ffmpeg / libavutil / lls.c @ 7c074320
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/*


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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 file is part of FFmpeg.

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*

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* FFmpeg 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 2.1 of the License, or (at your option) any later version.

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*

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* FFmpeg 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 FFmpeg; if not, write to the Free Software

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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA

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*/

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/**

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* @file libavutil/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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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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void av_solve_lls(LLSModel *m, double threshold, int min_order){ 
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int i,j,k;

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double (*factor)[MAX_VARS+1]= (void*)&m>covariance[1][0]; 
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double (*covar )[MAX_VARS+1]= (void*)&m>covariance[1][1]; 
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double *covar_y = m>covariance[0]; 
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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=i1; 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=i1; k>=0; k) 
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sum = factor[i][k]*m>coeff[0][k];

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m>coeff[0][i]= sum / factor[i][i];

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} 
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for(j=count1; j>=min_order; j){ 
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for(i=j; i>=0; i){ 
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double sum= m>coeff[0][i]; 
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for(k=i+1; k<=j; k++) 
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sum = factor[k][i]*m>coeff[j][k]; 
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m>coeff[j][i]= sum / factor[i][i]; 
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} 
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m>variance[j]= covar_y[0];

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for(i=0; i<=j; i++){ 
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double sum= m>coeff[j][i]*covar[i][i]  2*covar_y[i+1]; 
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for(k=0; k<i; k++) 
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sum += 2*m>coeff[j][k]*covar[k][i];

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m>variance[j] += m>coeff[j][i]*sum; 
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} 
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} 
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} 
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double av_evaluate_lls(LLSModel *m, double *param, int order){ 
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int i;

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double out= 0; 
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for(i=0; i<=order; i++) 
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out+= param[i]*m>coeff[order][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(void){ 
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LLSModel m; 
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int i, order;

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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;

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var[0] = (rand() / (double)RAND_MAX  0.5)*2; 
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var[1] = var[0] + rand() / (double)RAND_MAX  0.5; 
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var[2] = var[1] + rand() / (double)RAND_MAX  0.5; 
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var[3] = var[2] + rand() / (double)RAND_MAX  0.5; 
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av_update_lls(&m, var, 0.99); 
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av_solve_lls(&m, 0.001, 0); 
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for(order=0; order<3; order++){ 
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eval= av_evaluate_lls(&m, var+1, order);

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printf("real:%9f order:%d pred:%9f var:%f coeffs:%f %9f %9f\n",

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var[0], order, eval, sqrt(m.variance[order] / (i+1)), 
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m.coeff[order][0], m.coeff[order][1], m.coeff[order][2]); 
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} 
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} 
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return 0; 
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} 
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#endif
