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ffmpeg / libavutil / pca.c @ 80a07f6e

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/*
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 * principal component analysis (PCA)
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 * Copyright (c) 2004 Michael Niedermayer <michaelni@gmx.at>
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 *
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 * This file is part of FFmpeg.
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 *
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 * FFmpeg is free software; you can redistribute it and/or
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 * modify it under the terms of the GNU Lesser General Public
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 * License as published by the Free Software Foundation; either
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 * version 2.1 of the License, or (at your option) any later version.
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 *
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 * FFmpeg is distributed in the hope that it will be useful,
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 * but WITHOUT ANY WARRANTY; without even the implied warranty of
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 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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 * Lesser General Public License for more details.
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 *
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 * You should have received a copy of the GNU Lesser General Public
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 * License along with FFmpeg; if not, write to the Free Software
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 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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 */
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/**
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 * @file libavutil/pca.c
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 * principal component analysis (PCA)
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 */
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#include "common.h"
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#include "pca.h"
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typedef struct PCA{
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    int count;
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    int n;
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    double *covariance;
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    double *mean;
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}PCA;
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PCA *ff_pca_init(int n){
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    PCA *pca;
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    if(n<=0)
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        return NULL;
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    pca= av_mallocz(sizeof(PCA));
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    pca->n= n;
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    pca->count=0;
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    pca->covariance= av_mallocz(sizeof(double)*n*n);
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    pca->mean= av_mallocz(sizeof(double)*n);
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    return pca;
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}
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void ff_pca_free(PCA *pca){
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    av_freep(&pca->covariance);
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    av_freep(&pca->mean);
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    av_free(pca);
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}
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void ff_pca_add(PCA *pca, double *v){
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    int i, j;
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    const int n= pca->n;
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    for(i=0; i<n; i++){
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        pca->mean[i] += v[i];
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        for(j=i; j<n; j++)
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            pca->covariance[j + i*n] += v[i]*v[j];
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    }
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    pca->count++;
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}
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int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){
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    int i, j, pass;
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    int k=0;
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    const int n= pca->n;
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    double z[n];
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    memset(eigenvector, 0, sizeof(double)*n*n);
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    for(j=0; j<n; j++){
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        pca->mean[j] /= pca->count;
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        eigenvector[j + j*n] = 1.0;
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        for(i=0; i<=j; i++){
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            pca->covariance[j + i*n] /= pca->count;
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            pca->covariance[j + i*n] -= pca->mean[i] * pca->mean[j];
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            pca->covariance[i + j*n] = pca->covariance[j + i*n];
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        }
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        eigenvalue[j]= pca->covariance[j + j*n];
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        z[j]= 0;
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    }
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    for(pass=0; pass < 50; pass++){
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        double sum=0;
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        for(i=0; i<n; i++)
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            for(j=i+1; j<n; j++)
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                sum += fabs(pca->covariance[j + i*n]);
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        if(sum == 0){
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            for(i=0; i<n; i++){
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                double maxvalue= -1;
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                for(j=i; j<n; j++){
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                    if(eigenvalue[j] > maxvalue){
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                        maxvalue= eigenvalue[j];
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                        k= j;
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                    }
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                }
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                eigenvalue[k]= eigenvalue[i];
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                eigenvalue[i]= maxvalue;
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                for(j=0; j<n; j++){
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                    double tmp= eigenvector[k + j*n];
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                    eigenvector[k + j*n]= eigenvector[i + j*n];
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                    eigenvector[i + j*n]= tmp;
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                }
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            }
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            return pass;
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        }
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        for(i=0; i<n; i++){
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            for(j=i+1; j<n; j++){
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                double covar= pca->covariance[j + i*n];
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                double t,c,s,tau,theta, h;
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                if(pass < 3 && fabs(covar) < sum / (5*n*n)) //FIXME why pass < 3
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                    continue;
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                if(fabs(covar) == 0.0) //FIXME should not be needed
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                    continue;
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                if(pass >=3 && fabs((eigenvalue[j]+z[j])/covar) > (1LL<<32) && fabs((eigenvalue[i]+z[i])/covar) > (1LL<<32)){
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                    pca->covariance[j + i*n]=0.0;
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                    continue;
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                }
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                h= (eigenvalue[j]+z[j]) - (eigenvalue[i]+z[i]);
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                theta=0.5*h/covar;
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                t=1.0/(fabs(theta)+sqrt(1.0+theta*theta));
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                if(theta < 0.0) t = -t;
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                c=1.0/sqrt(1+t*t);
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                s=t*c;
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                tau=s/(1.0+c);
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                z[i] -= t*covar;
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                z[j] += t*covar;
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#define ROTATE(a,i,j,k,l) {\
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    double g=a[j + i*n];\
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    double h=a[l + k*n];\
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    a[j + i*n]=g-s*(h+g*tau);\
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    a[l + k*n]=h+s*(g-h*tau); }
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                for(k=0; k<n; k++) {
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                    if(k!=i && k!=j){
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                        ROTATE(pca->covariance,FFMIN(k,i),FFMAX(k,i),FFMIN(k,j),FFMAX(k,j))
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                    }
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                    ROTATE(eigenvector,k,i,k,j)
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                }
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                pca->covariance[j + i*n]=0.0;
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            }
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        }
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        for (i=0; i<n; i++) {
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            eigenvalue[i] += z[i];
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            z[i]=0.0;
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        }
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    }
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    return -1;
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}
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#ifdef TEST
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#undef printf
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#include <stdio.h>
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#include <stdlib.h>
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#include "lfg.h"
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int main(void){
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    PCA *pca;
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    int i, j, k;
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#define LEN 8
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    double eigenvector[LEN*LEN];
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    double eigenvalue[LEN];
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    AVLFG prng;
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    av_lfg_init(&prng, 1);
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    pca= ff_pca_init(LEN);
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    for(i=0; i<9000000; i++){
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        double v[2*LEN+100];
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        double sum=0;
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        int pos = av_lfg_get(&prng) % LEN;
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        int v2  = av_lfg_get(&prng) % 101 - 50;
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        v[0]    = av_lfg_get(&prng) % 101 - 50;
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        for(j=1; j<8; j++){
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            if(j<=pos) v[j]= v[0];
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            else       v[j]= v2;
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            sum += v[j];
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        }
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/*        for(j=0; j<LEN; j++){
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            v[j] -= v[pos];
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        }*/
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//        sum += av_lfg_get(&prng) % 10;
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/*        for(j=0; j<LEN; j++){
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            v[j] -= sum/LEN;
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        }*/
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//        lbt1(v+100,v+100,LEN);
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        ff_pca_add(pca, v);
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    }
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    ff_pca(pca, eigenvector, eigenvalue);
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    for(i=0; i<LEN; i++){
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        pca->count= 1;
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        pca->mean[i]= 0;
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//        (0.5^|x|)^2 = 0.5^2|x| = 0.25^|x|
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//        pca.covariance[i + i*LEN]= pow(0.5, fabs
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        for(j=i; j<LEN; j++){
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            printf("%f ", pca->covariance[i + j*LEN]);
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        }
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        printf("\n");
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    }
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#if 1
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    for(i=0; i<LEN; i++){
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        double v[LEN];
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        double error=0;
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        memset(v, 0, sizeof(v));
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        for(j=0; j<LEN; j++){
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            for(k=0; k<LEN; k++){
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                v[j] += pca->covariance[FFMIN(k,j) + FFMAX(k,j)*LEN] * eigenvector[i + k*LEN];
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            }
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            v[j] /= eigenvalue[i];
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            error += fabs(v[j] - eigenvector[i + j*LEN]);
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        }
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        printf("%f ", error);
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    }
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    printf("\n");
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#endif
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    for(i=0; i<LEN; i++){
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        for(j=0; j<LEN; j++){
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            printf("%9.6f ", eigenvector[i + j*LEN]);
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        }
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        printf("  %9.1f %f\n", eigenvalue[i], eigenvalue[i]/eigenvalue[0]);
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    }
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    return 0;
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}
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#endif