Remove unused, never built libavutil/pca.[ch]

Signed-off-by: Mans Rullgard <mans@mansr.com>

1 files changed, 0 insertions, 245 deletions

diff --git a/libavutil/pca.c b/libavutil/pca.c
deleted file mode 100644
index 56bf707421..0000000000
--- a/libavutil/pca.c
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * principal component analysis (PCA)
- * Copyright (c) 2004 Michael Niedermayer <michaelni@gmx.at>
- *
- * This file is part of Libav.
- *
- * Libav 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.
- *
- * Libav 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 Libav; 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;
-}PCA;
-
-PCA *ff_pca_init(int n){
-    PCA *pca;
-    if(n<=0)
-        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 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 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[n];
-
-    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;
-}
-
-#ifdef TEST
-
-#undef printf
-#include <stdio.h>
-#include <stdlib.h>
-#include "lfg.h"
-
-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);
-
-    pca= ff_pca_init(LEN);
-
-    for(i=0; i<9000000; i++){
-        double v[2*LEN+100];
-        double sum=0;
-        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;
-            sum += v[j];
-        }
-/*        for(j=0; j<LEN; j++){
-            v[j] -= v[pos];
-        }*/
-//        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(pca, eigenvector, eigenvalue);
-    for(i=0; i<LEN; i++){
-        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("\n");
-    }
-
-    for(i=0; i<LEN; i++){
-        double v[LEN];
-        double error=0;
-        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] /= eigenvalue[i];
-            error += fabs(v[j] - eigenvector[i + j*LEN]);
-        }
-        printf("%f ", error);
-    }
-    printf("\n");
-
-    for(i=0; i<LEN; i++){
-        for(j=0; j<LEN; j++){
-            printf("%9.6f ", eigenvector[i + j*LEN]);
-        }
-        printf("  %9.1f %f\n", eigenvalue[i], eigenvalue[i]/eigenvalue[0]);
-    }
-
-    return 0;
-}
-#endif