/* * principal component analysis (PCA) * Copyright (c) 2004 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 * principal component analysis (PCA) */ #include "common.h" #include "pca.h" typedef struct PCA{ int count; int n; double *covariance; double *mean; double *z; }PCA; PCA *ff_pca_init(int n){ PCA *pca; if(n<=0) return NULL; pca= av_mallocz(sizeof(*pca)); if (!pca) return NULL; pca->n= n; pca->z = av_malloc_array(n, sizeof(*pca->z)); pca->count=0; pca->covariance= av_calloc(n*n, sizeof(double)); pca->mean= av_calloc(n, sizeof(double)); if (!pca->z || !pca->covariance || !pca->mean) { ff_pca_free(pca); return NULL; } return pca; } void ff_pca_free(PCA *pca){ av_freep(&pca->covariance); av_freep(&pca->mean); av_freep(&pca->z); av_free(pca); } void ff_pca_add(PCA *pca, const double *v){ int i, j; const int n= pca->n; for(i=0; i<n; i++){ pca->mean[i] += v[i]; for(j=i; j<n; j++) pca->covariance[j + i*n] += v[i]*v[j]; } pca->count++; } int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){ int i, j, pass; int k=0; const int n= pca->n; double *z = pca->z; memset(eigenvector, 0, sizeof(double)*n*n); for(j=0; j<n; j++){ pca->mean[j] /= pca->count; eigenvector[j + j*n] = 1.0; for(i=0; i<=j; i++){ pca->covariance[j + i*n] /= pca->count; pca->covariance[j + i*n] -= pca->mean[i] * pca->mean[j]; pca->covariance[i + j*n] = pca->covariance[j + i*n]; } eigenvalue[j]= pca->covariance[j + j*n]; z[j]= 0; } for(pass=0; pass < 50; pass++){ double sum=0; for(i=0; i<n; i++) for(j=i+1; j<n; j++) sum += fabs(pca->covariance[j + i*n]); if(sum == 0){ for(i=0; i<n; i++){ double maxvalue= -1; for(j=i; j<n; j++){ if(eigenvalue[j] > maxvalue){ maxvalue= eigenvalue[j]; k= j; } } eigenvalue[k]= eigenvalue[i]; eigenvalue[i]= maxvalue; for(j=0; j<n; j++){ double tmp= eigenvector[k + j*n]; eigenvector[k + j*n]= eigenvector[i + j*n]; eigenvector[i + j*n]= tmp; } } return pass; } for(i=0; i<n; i++){ for(j=i+1; j<n; j++){ double covar= pca->covariance[j + i*n]; double t,c,s,tau,theta, h; if(pass < 3 && fabs(covar) < sum / (5*n*n)) //FIXME why pass < 3 continue; if(fabs(covar) == 0.0) //FIXME should not be needed continue; if(pass >=3 && fabs((eigenvalue[j]+z[j])/covar) > (1LL<<32) && fabs((eigenvalue[i]+z[i])/covar) > (1LL<<32)){ pca->covariance[j + i*n]=0.0; continue; } h= (eigenvalue[j]+z[j]) - (eigenvalue[i]+z[i]); theta=0.5*h/covar; t=1.0/(fabs(theta)+sqrt(1.0+theta*theta)); if(theta < 0.0) t = -t; c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); z[i] -= t*covar; z[j] += t*covar; #define ROTATE(a,i,j,k,l) {\ double g=a[j + i*n];\ double h=a[l + k*n];\ a[j + i*n]=g-s*(h+g*tau);\ a[l + k*n]=h+s*(g-h*tau); } for(k=0; k<n; k++) { if(k!=i && k!=j){ ROTATE(pca->covariance,FFMIN(k,i),FFMAX(k,i),FFMIN(k,j),FFMAX(k,j)) } ROTATE(eigenvector,k,i,k,j) } pca->covariance[j + i*n]=0.0; } } for (i=0; i<n; i++) { eigenvalue[i] += z[i]; z[i]=0.0; } } return -1; }