libavutil/pca.c

   1 /*
   2  * Principal component analysis
   3  * Copyright (c) 2004 Michael Niedermayer <michaelni@gmx.at>
   4  *
   5  * This file is part of FFmpeg.
   6  *
   7  * FFmpeg is free software; you can redistribute it and/or
   8  * modify it under the terms of the GNU Lesser General Public
   9  * License as published by the Free Software Foundation; either
  10  * version 2.1 of the License, or (at your option) any later version.
  11  *
  12  * FFmpeg is distributed in the hope that it will be useful,
  13  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  15  * Lesser General Public License for more details.
  16  *
  17  * You should have received a copy of the GNU Lesser General Public
  18  * License along with FFmpeg; if not, write to the Free Software
  19  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
  20  */
  21
  22 /**
  23  * @file pca.c
  24  * Principal component analysis
  25  */
  26
  27 #include "common.h"
  28 #include "pca.h"
  29
  30 typedef struct PCA{
  31     int count;
  32     int n;
  33     double *covariance;
  34     double *mean;
  35 }PCA;
  36
  37 PCA *ff_pca_init(int n){
  38     PCA *pca;
  39     if(n<=0)
  40         return NULL;
  41
  42     pca= av_mallocz(sizeof(PCA));
  43     pca->n= n;
  44     pca->count=0;
  45     pca->covariance= av_mallocz(sizeof(double)*n*n);
  46     pca->mean= av_mallocz(sizeof(double)*n);
  47
  48     return pca;
  49 }
  50
  51 void ff_pca_free(PCA *pca){
  52     av_freep(&pca->covariance);
  53     av_freep(&pca->mean);
  54     av_free(pca);
  55 }
  56
  57 void ff_pca_add(PCA *pca, double *v){
  58     int i, j;
  59     const int n= pca->n;
  60
  61     for(i=0; i<n; i++){
  62         pca->mean[i] += v[i];
  63         for(j=i; j<n; j++)
  64             pca->covariance[j + i*n] += v[i]*v[j];
  65     }
  66     pca->count++;
  67 }
  68
  69 int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){
  70     int i, j, k, pass;
  71     const int n= pca->n;
  72     double z[n];
  73
  74     memset(eigenvector, 0, sizeof(double)*n*n);
  75
  76     for(j=0; j<n; j++){
  77         pca->mean[j] /= pca->count;
  78         eigenvector[j + j*n] = 1.0;
  79         for(i=0; i<=j; i++){
  80             pca->covariance[j + i*n] /= pca->count;
  81             pca->covariance[j + i*n] -= pca->mean[i] * pca->mean[j];
  82             pca->covariance[i + j*n] = pca->covariance[j + i*n];
  83         }
  84         eigenvalue[j]= pca->covariance[j + j*n];
  85         z[j]= 0;
  86     }
  87
  88     for(pass=0; pass < 50; pass++){
  89         double sum=0;
  90
  91         for(i=0; i<n; i++)
  92             for(j=i+1; j<n; j++)
  93                 sum += fabs(pca->covariance[j + i*n]);
  94
  95         if(sum == 0){
  96             for(i=0; i<n; i++){
  97                 double maxvalue= -1;
  98                 for(j=i; j<n; j++){
  99                     if(eigenvalue[j] > maxvalue){
 100                         maxvalue= eigenvalue[j];
 101                         k= j;
 102                     }
 103                 }
 104                 eigenvalue[k]= eigenvalue[i];
 105                 eigenvalue[i]= maxvalue;
 106                 for(j=0; j<n; j++){
 107                     double tmp= eigenvector[k + j*n];
 108                     eigenvector[k + j*n]= eigenvector[i + j*n];
 109                     eigenvector[i + j*n]= tmp;
 110                 }
 111             }
 112             return pass;
 113         }
 114
 115         for(i=0; i<n; i++){
 116             for(j=i+1; j<n; j++){
 117                 double covar= pca->covariance[j + i*n];
 118                 double t,c,s,tau,theta, h;
 119
 120                 if(pass < 3 && fabs(covar) < sum / (5*n*n)) //FIXME why pass < 3
 121                     continue;
 122                 if(fabs(covar) == 0.0) //FIXME shouldnt be needed
 123                     continue;
 124                 if(pass >=3 && fabs((eigenvalue[j]+z[j])/covar) > (1LL<<32) && fabs((eigenvalue[i]+z[i])/covar) > (1LL<<32)){
 125                     pca->covariance[j + i*n]=0.0;
 126                     continue;
 127                 }
 128
 129                 h= (eigenvalue[j]+z[j]) - (eigenvalue[i]+z[i]);
 130                 theta=0.5*h/covar;
 131                 t=1.0/(fabs(theta)+sqrt(1.0+theta*theta));
 132                 if(theta < 0.0) t = -t;
 133
 134                 c=1.0/sqrt(1+t*t);
 135                 s=t*c;
 136                 tau=s/(1.0+c);
 137                 z[i] -= t*covar;
 138                 z[j] += t*covar;
 139
 140 #define ROTATE(a,i,j,k,l) {\
 141     double g=a[j + i*n];\
 142     double h=a[l + k*n];\
 143     a[j + i*n]=g-s*(h+g*tau);\
 144     a[l + k*n]=h+s*(g-h*tau); }
 145                 for(k=0; k<n; k++) {
 146                     if(k!=i && k!=j){
 147                         ROTATE(pca->covariance,FFMIN(k,i),FFMAX(k,i),FFMIN(k,j),FFMAX(k,j))
 148                     }
 149                     ROTATE(eigenvector,k,i,k,j)
 150                 }
 151                 pca->covariance[j + i*n]=0.0;
 152             }
 153         }
 154         for (i=0; i<n; i++) {
 155             eigenvalue[i] += z[i];
 156             z[i]=0.0;
 157         }
 158     }
 159
 160     return -1;
 161 }
 162
 163 #ifdef TEST
 164
 165 #undef printf
 166 #undef random
 167 #include <stdio.h>
 168 #include <stdlib.h>
 169
 170 int main(){
 171     PCA *pca;
 172     int i, j, k;
 173 #define LEN 8
 174     double eigenvector[LEN*LEN];
 175     double eigenvalue[LEN];
 176
 177     pca= ff_pca_init(LEN);
 178
 179     for(i=0; i<9000000; i++){
 180         double v[2*LEN+100];
 181         double sum=0;
 182         int pos= random()%LEN;
 183         int v2= (random()%101) - 50;
 184         v[0]= (random()%101) - 50;
 185         for(j=1; j<8; j++){
 186             if(j<=pos) v[j]= v[0];
 187             else       v[j]= v2;
 188             sum += v[j];
 189         }
 190 /*        for(j=0; j<LEN; j++){
 191             v[j] -= v[pos];
 192         }*/
 193 //        sum += random()%10;
 194 /*        for(j=0; j<LEN; j++){
 195             v[j] -= sum/LEN;
 196         }*/
 197 //        lbt1(v+100,v+100,LEN);
 198         ff_pca_add(pca, v);
 199     }
 200
 201
 202     ff_pca(pca, eigenvector, eigenvalue);
 203     for(i=0; i<LEN; i++){
 204         pca->count= 1;
 205         pca->mean[i]= 0;
 206
 207 //        (0.5^|x|)^2 = 0.5^2|x| = 0.25^|x|
 208
 209
 210 //        pca.covariance[i + i*LEN]= pow(0.5, fabs
 211         for(j=i; j<LEN; j++){
 212             printf("%f ", pca->covariance[i + j*LEN]);
 213         }
 214         printf("\n");
 215     }
 216
 217 #if 1
 218     for(i=0; i<LEN; i++){
 219         double v[LEN];
 220         double error=0;
 221         memset(v, 0, sizeof(v));
 222         for(j=0; j<LEN; j++){
 223             for(k=0; k<LEN; k++){
 224                 v[j] += pca->covariance[FFMIN(k,j) + FFMAX(k,j)*LEN] * eigenvector[i + k*LEN];
 225             }
 226             v[j] /= eigenvalue[i];
 227             error += fabs(v[j] - eigenvector[i + j*LEN]);
 228         }
 229         printf("%f ", error);
 230     }
 231     printf("\n");
 232 #endif
 233     for(i=0; i<LEN; i++){
 234         for(j=0; j<LEN; j++){
 235             printf("%9.6f ", eigenvector[i + j*LEN]);
 236         }
 237         printf("  %9.1f %f\n", eigenvalue[i], eigenvalue[i]/eigenvalue[0]);
 238     }
 239
 240     return 0;
 241 }
 242 #endif