frsh: Export information about the last RTP contract and VRES

[frescor/ffmpeg.git] / libavutil / pca.c

diff --git a/libavutil/pca.c b/libavutil/pca.c
index 6e798b6cf9529eeebddced26fb8ca38ec0ea906c..6891026a5bd3bfb6fd7a014dacc6c513ddaabb6b 100644 (file)
--- a/libavutil/pca.c
+++ b/libavutil/pca.c
@@ -1,46 +1,57 @@
 /*
- * 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 "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){
@@ -56,7 +67,8 @@ 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];
 
@@ -108,7 +120,7 @@ int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){
 
                 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;
@@ -152,25 +164,28 @@ int ff_pca(PCA *pca, double *eigenvector, double *eigenvalue){
 #ifdef TEST
 
 #undef printf
-#undef random
 #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;
@@ -179,26 +194,26 @@ int main(){
 /*        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");
     }
@@ -210,7 +225,7 @@ int main(){
         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]);