/* * 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 * linear least squares model */ #include <math.h> #include <string.h> #include "lls.h" 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]= (void*)&m->covariance[1][0]; double (*covar )[MAX_VARS+1]= (void*)&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; 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; 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); printf("real:%9f order:%d pred:%9f var:%f coeffs:%f %9f %9f\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