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Diffstat (limited to 'libavutil/pca.c')
| -rw-r--r-- | libavutil/pca.c | 173 |
1 files changed, 173 insertions, 0 deletions
diff --git a/libavutil/pca.c b/libavutil/pca.c new file mode 100644 index 0000000000..4e52c7b362 --- /dev/null +++ b/libavutil/pca.c @@ -0,0 +1,173 @@ +/* + * 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; +} |