comparison audioDB.cpp @ 125:26ec0906adb9

Be a bit more careful about qNorm handling and freeing: don't assign qNorm
anywhere, but use a (new) interior pointer instead.

Also fix test 0023 for the correct results.

   double* ps;
   double tmp1,tmp2;
 
   // Copy the L2 norm values to core to avoid disk random access later on
   memcpy(sNorm, l2normTable, dbVectors*sizeof(double));
+  double* qnPtr = qNorm;
   double* snPtr = sNorm;
   for(i=0; i<dbH->numFiles; i++){
     if(trackTable[i]>=sequenceLength){
       tmp1=*snPtr;
       j=1;

   if(verbosity>4) {
     cerr << "silence thresh: " << SILENCE_THRESH;
   }
   w=sequenceLength-1;
   i=1;
-  tmp1=*qNorm;
+  tmp1=*qnPtr;
   while(w--)
-    *qNorm+=qNorm[i++];
-  ps = qNorm+1;
+    *qnPtr+=qnPtr[i++];
+  ps = qnPtr+1;
   w=numVectors-sequenceLength; // +1 -1
   while(w--){
     tmp2=*ps;
     *ps=*(ps-1)-tmp1+*(ps+sequenceLength-1);
     tmp1=tmp2;
     ps++;
   }
-  ps = qNorm;
+  ps = qnPtr;
   qMeanL2 = 0;
   w=numVectors-sequenceLength+1;
   while(w--){
     *ps=sqrt(*ps);
     qMeanL2+=*ps++;

     else{
       if(verbosity>1) {
 	cerr << "query point: " << queryPoint << endl; cerr.flush();
       }
       query=query+queryPoint*dbH->dim;
-      qNorm=qNorm+queryPoint;
+      qnPtr=qnPtr+queryPoint;
       numVectors=wL;
     }
   
   double ** D = 0;    // Differences query and target 
   double ** DD = 0;   // Matched filter distance

 	}
 
 	// Search for minimum distance by shingles (concatenated vectors)
 	for(j=0;j<=numVectors-wL;j+=HOP_SIZE)
 	  for(k=0;k<=trackTable[track]-wL;k+=HOP_SIZE){
-	    thisDist=2-(2/(qNorm[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
+	    thisDist=2-(2/(qnPtr[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
 	    if(verbosity>10) {
-	      cerr << thisDist << " " << qNorm[j] << " " << sNorm[trackIndexOffset+k] << endl;
+	      cerr << thisDist << " " << qnPtr[j] << " " << sNorm[trackIndexOffset+k] << endl;
             }
 	    // Gather chi^2 statistics
 	    if(thisDist<minSample)
 	      minSample=thisDist;
 	    else if(thisDist>maxSample)

 	      sampleCount++;
 	      sampleSum+=thisDist;
 	      logSampleSum+=log(thisDist);
 	    }
 
-	    // diffL2 = fabs(qNorm[j] - sNorm[trackIndexOffset+k]);
+	    // diffL2 = fabs(qnPtr[j] - sNorm[trackIndexOffset+k]);
 	    // Power test
 	    if(!USE_THRESH || 
 	       // Threshold on mean L2 of Q and S sequences
-	       (USE_THRESH && qNorm[j]>SILENCE_THRESH && sNorm[trackIndexOffset+k]>SILENCE_THRESH && 
+	       (USE_THRESH && qnPtr[j]>SILENCE_THRESH && sNorm[trackIndexOffset+k]>SILENCE_THRESH && 
 		// Are both query and target windows above mean energy?
-		(qNorm[j]>qMeanL2*.25 && sNorm[trackIndexOffset+k]>sMeanL2[track]*.25))) // &&  diffL2 < DIFF_THRESH )))
+		(qnPtr[j]>qMeanL2*.25 && sNorm[trackIndexOffset+k]>sMeanL2[track]*.25))) // &&  diffL2 < DIFF_THRESH )))
 	      thisDist=thisDist; // Computed above
 	    else
 	      thisDist=1000000.0;
 
 	    // k-NN match algorithm

   double tmp1,tmp2;
 
   // Copy the L2 norm values to core to avoid disk random access later on
   memcpy(sNorm, l2normTable, dbVectors*sizeof(double));
   double* snPtr = sNorm;
+  double* qnPtr = qNorm;
   for(i=0; i<dbH->numFiles; i++){
     if(trackTable[i]>=sequenceLength){
       tmp1=*snPtr;
       j=1;
       w=sequenceLength-1;

   if(verbosity>4) {
     cerr << "silence thresh: " << SILENCE_THRESH;
   }
   w=sequenceLength-1;
   i=1;
-  tmp1=*qNorm;
+  tmp1=*qnPtr;
   while(w--)
-    *qNorm+=qNorm[i++];
-  ps = qNorm+1;
+    *qnPtr+=qnPtr[i++];
+  ps = qnPtr+1;
   w=numVectors-sequenceLength; // +1 -1
   while(w--){
     tmp2=*ps;
     *ps=*(ps-1)-tmp1+*(ps+sequenceLength-1);
     tmp1=tmp2;
     ps++;
   }
-  ps = qNorm;
+  ps = qnPtr;
   qMeanL2 = 0;
   w=numVectors-sequenceLength+1;
   while(w--){
     *ps=sqrt(*ps);
     qMeanL2+=*ps++;

     else{
       if(verbosity>1) {
 	cerr << "query point: " << queryPoint << endl; cerr.flush();
       }
       query=query+queryPoint*dbH->dim;
-      qNorm=qNorm+queryPoint;
+      qnPtr=qnPtr+queryPoint;
       numVectors=wL;
     }
   
   double ** D = 0;    // Differences query and target 
   double ** DD = 0;   // Matched filter distance

 	}
 
 	// Search for minimum distance by shingles (concatenated vectors)
 	for(j=0;j<=numVectors-wL;j+=HOP_SIZE)
 	  for(k=0;k<=trackTable[track]-wL;k+=HOP_SIZE){
-	    thisDist=2-(2/(qNorm[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
+	    thisDist=2-(2/(qnPtr[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
 	    if(verbosity>10) {
-	      cerr << thisDist << " " << qNorm[j] << " " << sNorm[trackIndexOffset+k] << endl;
+	      cerr << thisDist << " " << qnPtr[j] << " " << sNorm[trackIndexOffset+k] << endl;
             }
 	    // Gather chi^2 statistics
 	    if(thisDist<minSample)
 	      minSample=thisDist;
 	    else if(thisDist>maxSample)

 	      sampleCount++;
 	      sampleSum+=thisDist;
 	      logSampleSum+=log(thisDist);
 	    }
 
-	    // diffL2 = fabs(qNorm[j] - sNorm[trackIndexOffset+k]);
+	    // diffL2 = fabs(qnPtr[j] - sNorm[trackIndexOffset+k]);
 	    // Power test
 	    if(!USE_THRESH || 
 	       // Threshold on mean L2 of Q and S sequences
-	       (USE_THRESH && qNorm[j]>SILENCE_THRESH && sNorm[trackIndexOffset+k]>SILENCE_THRESH && 
+	       (USE_THRESH && qnPtr[j]>SILENCE_THRESH && sNorm[trackIndexOffset+k]>SILENCE_THRESH && 
 		// Are both query and target windows above mean energy?
-		(qNorm[j]>qMeanL2*.25 && sNorm[trackIndexOffset+k]>sMeanL2[track]*.25))) // &&  diffL2 < DIFF_THRESH )))
+		(qnPtr[j]>qMeanL2*.25 && sNorm[trackIndexOffset+k]>sMeanL2[track]*.25))) // &&  diffL2 < DIFF_THRESH )))
 	      thisDist=thisDist; // Computed above
 	    else
 	      thisDist=1000000.0;
 	    if(thisDist>=0 && thisDist<=radius){
 	      distances[0]++; // increment count