/*
- * Principal component analysis
+ * principal component analysis (PCA)
* Copyright (c) 2004 Michael Niedermayer <michaelni@gmx.at>
*
- * This library is free software; you can redistribute it and/or
+ * 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 of the License, or (at your option) any later version.
+ * version 2.1 of the License, or (at your option) any later version.
*
- * This library is distributed in the hope that it will be useful,
+ * 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 this library; if not, write to the Free Software
- * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
- *
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
- * @file pca.c
- * Principal component analysis
+ * @file libavutil/pca.c
+ * principal component analysis (PCA)
*/
-#include <math.h>
-#include "avcodec.h"
+#include "common.h"
#include "pca.h"
-int ff_pca_init(PCA *pca, int n){
+typedef struct PCA{
+ int count;
+ int n;
+ double *covariance;
+ double *mean;
+}PCA;
+
+PCA *ff_pca_init(int n){
+ PCA *pca;
if(n<=0)
- return -1;
+ return NULL;
+ pca= av_mallocz(sizeof(PCA));
pca->n= n;
pca->count=0;
pca->covariance= av_mallocz(sizeof(double)*n*n);
pca->mean= av_mallocz(sizeof(double)*n);
- return 0;
+ return pca;
}
void ff_pca_free(PCA *pca){
av_freep(&pca->covariance);
av_freep(&pca->mean);
+ av_free(pca);
}
void ff_pca_add(PCA *pca, double *v){
}
int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){
- int i, j, k, pass;
+ int i, j, pass;
+ int k=0;
const int n= pca->n;
double z[n];
if(pass < 3 && fabs(covar) < sum / (5*n*n)) //FIXME why pass < 3
continue;
- if(fabs(covar) == 0.0) //FIXME shouldnt be needed
+ 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;
z[i] -= t*covar;
z[j] += t*covar;
-#define ROTATE(a,i,j,k,l)\
+#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);
+ 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))
return -1;
}
-#if 1
+#ifdef TEST
#undef printf
#include <stdio.h>
#include <stdlib.h>
+#include "lfg.h"
-int main(){
- PCA pca;
+int main(void){
+ PCA *pca;
int i, j, k;
#define LEN 8
double eigenvector[LEN*LEN];
double eigenvalue[LEN];
+ AVLFG prng;
+
+ av_lfg_init(&prng, 1);
- ff_pca_init(&pca, LEN);
+ pca= ff_pca_init(LEN);
for(i=0; i<9000000; i++){
double v[2*LEN+100];
double sum=0;
- int pos= random()%LEN;
- int v2= (random()%101) - 50;
- v[0]= (random()%101) - 50;
+ int pos = av_lfg_get(&prng) % LEN;
+ int v2 = av_lfg_get(&prng) % 101 - 50;
+ v[0] = av_lfg_get(&prng) % 101 - 50;
for(j=1; j<8; j++){
if(j<=pos) v[j]= v[0];
else v[j]= v2;
/* for(j=0; j<LEN; j++){
v[j] -= v[pos];
}*/
-// sum += random()%10;
+// sum += av_lfg_get(&prng) % 10;
/* for(j=0; j<LEN; j++){
v[j] -= sum/LEN;
}*/
// lbt1(v+100,v+100,LEN);
- ff_pca_add(&pca, v);
+ ff_pca_add(pca, v);
}
- ff_pca(&pca, eigenvector, eigenvalue);
+ ff_pca(pca, eigenvector, eigenvalue);
for(i=0; i<LEN; i++){
- pca.count= 1;
- pca.mean[i]= 0;
+ pca->count= 1;
+ pca->mean[i]= 0;
// (0.5^|x|)^2 = 0.5^2|x| = 0.25^|x|
// pca.covariance[i + i*LEN]= pow(0.5, fabs
for(j=i; j<LEN; j++){
- printf("%f ", pca.covariance[i + j*LEN]);
+ printf("%f ", pca->covariance[i + j*LEN]);
}
printf("\n");
}
memset(v, 0, sizeof(v));
for(j=0; j<LEN; j++){
for(k=0; k<LEN; k++){
- v[j] += pca.covariance[FFMIN(k,j) + FFMAX(k,j)*LEN] * eigenvector[i + k*LEN];
+ v[j] += pca->covariance[FFMIN(k,j) + FFMAX(k,j)*LEN] * eigenvector[i + k*LEN];
}
v[j] /= eigenvalue[i];
error += fabs(v[j] - eigenvector[i + j*LEN]);