annotate query.cpp @ 206:3c7c8b84e4f3 refactoring

Delete pointQuery() and trackPointQuery()

New convention for tests: an exit code of 14 means "not applicable", as
in n/a = 14/1.  Decorate the newly-failing tests with "exit 14".

#include "audioDB.h"

bool audioDB::powers_acceptable(double p1, double p2) {
  if (use_absolute_threshold) {
    if ((p1 < absolute_threshold) || (p2 < absolute_threshold)) {
      return false;
    }
  }
  if (use_relative_threshold) {
    if (fabs(p1-p2) > fabs(relative_threshold)) {
      return false;
    }
  }
  return true;
}

void audioDB::query(const char* dbName, const char* inFile, adb__queryResponse *adbQueryResponse) {
  switch(queryType) {
  case O2_SEQUENCE_QUERY:
    if(radius==0)
      trackSequenceQueryNN(dbName, inFile, adbQueryResponse);
    else
      trackSequenceQueryRad(dbName, inFile, adbQueryResponse);
    break;
  default:
    error("unrecognized queryType in query()");
  }  
}

// return ordinal position of key in keyTable
unsigned audioDB::getKeyPos(char* key){  
  for(unsigned k=0; k<dbH->numFiles; k++)
    if(strncmp(fileTable + k*O2_FILETABLESIZE, key, strlen(key))==0)
      return k;
  error("Key not found",key);
  return O2_ERR_KEYNOTFOUND;
}

// This is a common pattern in sequence queries: what we are doing is
// taking a window of length seqlen over a buffer of length length,
// and placing the sum of the elements in that window in the first
// element of the window: thus replacing all but the last seqlen
// elements in the buffer the corresponding windowed sum.
void audioDB::sequence_sum(double *buffer, int length, int seqlen) {
  double tmp1, tmp2, *ps;
  int j, w;

  tmp1 = *buffer;
  j = 1;
  w = seqlen - 1;
  while(w--) {
    *buffer += buffer[j++];
  }
  ps = buffer + 1;
  w = length - seqlen; // +1 - 1
  while(w--) {
    tmp2 = *ps;
    *ps = *(ps - 1) - tmp1 + *(ps + seqlen - 1);
    tmp1 = tmp2;
    ps++;
  }
}

void audioDB::sequence_sqrt(double *buffer, int length, int seqlen) {
  int w = length - seqlen + 1;
  while(w--) {
    *buffer = sqrt(*buffer);
    buffer++;
  }
}

void audioDB::sequence_average(double *buffer, int length, int seqlen) {
  int w = length - seqlen + 1;
  while(w--) {
    *buffer /= seqlen;
    buffer++;
  }
}

// k nearest-neighbor (k-NN) search between query and target tracks
// efficient implementation based on matched filter
// assumes normed shingles
// outputs distances of retrieved shingles, max retreived = pointNN shingles per per track
void audioDB::trackSequenceQueryNN(const char* dbName, const char* inFile, adb__queryResponse *adbQueryResponse){
  
  initTables(dbName, inFile);
  
  // For each input vector, find the closest pointNN matching output vectors and report
  // we use stdout in this stub version
  unsigned numVectors = (statbuf.st_size-sizeof(int))/(sizeof(double)*dbH->dim);
  double* query = (double*)(indata+sizeof(int));
  double* queryCopy = 0;

  if(!(dbH->flags & O2_FLAG_L2NORM) )
    error("Database must be L2 normed for sequence query","use -L2NORM");

  if(numVectors<sequenceLength)
    error("Query shorter than requested sequence length", "maybe use -l");
  
  if(verbosity>1) {
    std::cerr << "performing norms ... "; std::cerr.flush();
  }
  unsigned dbVectors = dbH->length/(sizeof(double)*dbH->dim);

  // Make a copy of the query
  queryCopy = new double[numVectors*dbH->dim];
  memcpy(queryCopy, query, numVectors*dbH->dim*sizeof(double));
  qNorm = new double[numVectors];
  sNorm = new double[dbVectors];
  assert(qNorm&&sNorm&&queryCopy&&sequenceLength);    
  unitNorm(queryCopy, dbH->dim, numVectors, qNorm);
  query = queryCopy;

  // Make norm measurements relative to sequenceLength
  unsigned w = sequenceLength-1;
  unsigned i,j;

  // 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;

  double *sPower = 0, *qPower = 0;
  double *spPtr = 0, *qpPtr = 0;

  if (usingPower) {
    if (!(dbH->flags & O2_FLAG_POWER)) {
      error("database not power-enabled", dbName);
    }
    sPower = new double[dbVectors];
    spPtr = sPower;
    memcpy(sPower, powerTable, dbVectors * sizeof(double));
  }

  for(i=0; i<dbH->numFiles; i++){
    if(trackTable[i]>=sequenceLength) {
      sequence_sum(snPtr, trackTable[i], sequenceLength);
      sequence_sqrt(snPtr, trackTable[i], sequenceLength);

      if (usingPower) {
	sequence_sum(spPtr, trackTable[i], sequenceLength);
        sequence_average(spPtr, trackTable[i], sequenceLength);
      }
    }
    snPtr += trackTable[i];
    if (usingPower) {
      spPtr += trackTable[i];
    }
  }
  
  sequence_sum(qnPtr, numVectors, sequenceLength);
  sequence_sqrt(qnPtr, numVectors, sequenceLength);

  if (usingPower) {
    qPower = new double[numVectors];
    qpPtr = qPower;
    if (lseek(powerfd, sizeof(int), SEEK_SET) == (off_t) -1) {
      error("error seeking to data", powerFileName, "lseek");
    }
    int count = read(powerfd, qPower, numVectors * sizeof(double));
    if (count == -1) {
      error("error reading data", powerFileName, "read");
    }
    if ((unsigned) count != numVectors * sizeof(double)) {
      error("short read", powerFileName);
    }

    sequence_sum(qpPtr, numVectors, sequenceLength);
    sequence_average(qpPtr, numVectors, sequenceLength);
  }

  if(verbosity>1) {
    std::cerr << "done." << std::endl;
  }
  
  if(verbosity>1) {
    std::cerr << "matching tracks..." << std::endl;
  }
  
  assert(pointNN>0 && pointNN<=O2_MAXNN);
  assert(trackNN>0 && trackNN<=O2_MAXNN);
  
  // Make temporary dynamic memory for results
  double trackDistances[trackNN];
  unsigned trackIDs[trackNN];
  unsigned trackQIndexes[trackNN];
  unsigned trackSIndexes[trackNN];
  
  double distances[pointNN];
  unsigned qIndexes[pointNN];
  unsigned sIndexes[pointNN];
  

  unsigned k,l,m,n,track,trackOffset=0, HOP_SIZE=sequenceHop, wL=sequenceLength;
  double thisDist;
  
  for(k=0; k<pointNN; k++){
    distances[k]=1.0e6;
    qIndexes[k]=~0;
    sIndexes[k]=~0;    
  }
  
  for(k=0; k<trackNN; k++){
    trackDistances[k]=1.0e6;
    trackQIndexes[k]=~0;
    trackSIndexes[k]=~0;
    trackIDs[k]=~0;
  }

  // Timestamp and durations processing
  double meanQdur = 0;
  double *timesdata = 0;
  double *querydurs = 0;
  double *meanDBdur = 0;
  
  if(usingTimes && !(dbH->flags & O2_FLAG_TIMES)){
    std::cerr << "warning: ignoring query timestamps for non-timestamped database" << std::endl;
    usingTimes=0;
  }
  
  else if(!usingTimes && (dbH->flags & O2_FLAG_TIMES))
    std::cerr << "warning: no timestamps given for query. Ignoring database timestamps." << std::endl;
  
  else if(usingTimes && (dbH->flags & O2_FLAG_TIMES)){
    timesdata = new double[2*numVectors];
    querydurs = new double[numVectors];

    insertTimeStamps(numVectors, timesFile, timesdata);
    // Calculate durations of points
    for(k=0; k<numVectors-1; k++) {
      querydurs[k] = timesdata[2*k+1] - timesdata[2*k];
      meanQdur += querydurs[k];
    }
    meanQdur/=k;
    if(verbosity>1) {
      std::cerr << "mean query file duration: " << meanQdur << std::endl;
    }
    meanDBdur = new double[dbH->numFiles];
    assert(meanDBdur);
    for(k=0; k<dbH->numFiles; k++){
      meanDBdur[k]=0.0;
      for(j=0; j<trackTable[k]-1 ; j++) {
	meanDBdur[k]+=timesTable[2*j+1]-timesTable[2*j];
      }
      meanDBdur[k]/=j;
    }
  }

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

  D = new double*[numVectors];
  assert(D);
  DD = new double*[numVectors];
  assert(DD);

  gettimeofday(&tv1, NULL); 
  unsigned processedTracks = 0;
  unsigned successfulTracks=0;

  double* qp;
  double* sp;
  double* dp;

  // build track offset table
  off_t *trackOffsetTable = new off_t[dbH->numFiles];
  unsigned cumTrack=0;
  off_t trackIndexOffset;
  for(k=0; k<dbH->numFiles;k++){
    trackOffsetTable[k]=cumTrack;
    cumTrack+=trackTable[k]*dbH->dim;
  }

  char nextKey [MAXSTR];

  // chi^2 statistics
  double sampleCount = 0;
  double sampleSum = 0;
  double logSampleSum = 0;
  double minSample = 1e9;
  double maxSample = 0;

  // Track loop 
  size_t data_buffer_size = 0;
  double *data_buffer = 0;
  lseek(dbfid, dbH->dataOffset, SEEK_SET);

  for(processedTracks=0, track=0 ; processedTracks < dbH->numFiles ; track++, processedTracks++) {

    trackOffset = trackOffsetTable[track];     // numDoubles offset

    // get trackID from file if using a control file
    if(trackFile) {
      trackFile->getline(nextKey,MAXSTR);
      if(!trackFile->eof()) {
	track = getKeyPos(nextKey);
        trackOffset = trackOffsetTable[track];
        lseek(dbfid, dbH->dataOffset + trackOffset * sizeof(double), SEEK_SET);
      } else {
	break;
      }
    }

    trackIndexOffset=trackOffset/dbH->dim; // numVectors offset

    if(sequenceLength<=trackTable[track]){  // test for short sequences
      
      if(verbosity>7) {
	std::cerr << track << "." << trackIndexOffset << "." << trackTable[track] << " | ";std::cerr.flush();
      }
		
      // Sum products matrix
      for(j=0; j<numVectors;j++){
	D[j]=new double[trackTable[track]]; 
	assert(D[j]);

      }

      // Matched filter matrix
      for(j=0; j<numVectors;j++){
	DD[j]=new double[trackTable[track]];
	assert(DD[j]);
      }

      if (trackTable[track] * sizeof(double) * dbH->dim > data_buffer_size) {
	if(data_buffer) {
	  free(data_buffer);
	}
	{ 
	  data_buffer_size = trackTable[track] * sizeof(double) * dbH->dim;
	  void *tmp = malloc(data_buffer_size);
	  if (tmp == NULL) {
	    error("error allocating data buffer");
	  }
	  data_buffer = (double *) tmp;
	}
      }

      read(dbfid, data_buffer, trackTable[track] * sizeof(double) * dbH->dim);

      // Dot product
      for(j=0; j<numVectors; j++)
	for(k=0; k<trackTable[track]; k++){
	  qp=query+j*dbH->dim;
	  sp=data_buffer+k*dbH->dim;
	  DD[j][k]=0.0; // Initialize matched filter array
	  dp=&D[j][k];  // point to correlation cell j,k
	  *dp=0.0;      // initialize correlation cell
	  l=dbH->dim;         // size of vectors
	  while(l--)
	    *dp+=*qp++**sp++;
	}
  
      // Matched Filter
      // HOP SIZE == 1
      double* spd;
      if(HOP_SIZE==1){ // HOP_SIZE = shingleHop
	for(w=0; w<wL; w++)
	  for(j=0; j<numVectors-w; j++){ 
	    sp=DD[j];
	    spd=D[j+w]+w;
	    k=trackTable[track]-w;
	    while(k--)
	      *sp+++=*spd++;
	  }
      }

      else{ // HOP_SIZE != 1
	for(w=0; w<wL; w++)
	  for(j=0; j<numVectors-w; j+=HOP_SIZE){
	    sp=DD[j];
	    spd=D[j+w]+w;
	    for(k=0; k<trackTable[track]-w; k+=HOP_SIZE){
	      *sp+=*spd;
	      sp+=HOP_SIZE;
	      spd+=HOP_SIZE;
	    }
	  }
      }
      
      if(verbosity>3 && usingTimes) {
	std::cerr << "meanQdur=" << meanQdur << " meanDBdur=" << meanDBdur[track] << std::endl;
	std::cerr.flush();
      }

      if(!usingTimes || 
	 (usingTimes 
	  && fabs(meanDBdur[track]-meanQdur)<meanQdur*timesTol)){

	if(verbosity>3 && usingTimes) {
	  std::cerr << "within duration tolerance." << std::endl;
	  std::cerr.flush();
	}

	// 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/(qnPtr[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
	    if(verbosity>9) {
	      std::cerr << thisDist << " " << qnPtr[j] << " " << sNorm[trackIndexOffset+k] << std::endl;
            }
	    // Gather chi^2 statistics
	    if(thisDist<minSample)
	      minSample=thisDist;
	    else if(thisDist>maxSample)
	      maxSample=thisDist;
	    if(thisDist>1e-9){
	      sampleCount++;
	      sampleSum+=thisDist;
	      logSampleSum+=log(thisDist);
	    }

	    // diffL2 = fabs(qnPtr[j] - sNorm[trackIndexOffset+k]);
	    // Power test
	    if (usingPower) {
	      if (!(powers_acceptable(qpPtr[j], sPower[trackIndexOffset + k]))) {
		thisDist = 1000000.0;
	      }
	    }

	    // k-NN match algorithm
	    m=pointNN;
	    while(m--){
	      if(thisDist<=distances[m])
		if(m==0 || thisDist>=distances[m-1]){
		// Shuffle distances up the list
		for(l=pointNN-1; l>m; l--){
		  distances[l]=distances[l-1];
		  qIndexes[l]=qIndexes[l-1];
		  sIndexes[l]=sIndexes[l-1];
		}
		distances[m]=thisDist;
		if(usingQueryPoint)
		  qIndexes[m]=queryPoint;
		else
		  qIndexes[m]=j;
		sIndexes[m]=k;
		break;
		}
	    }
	  }
	// Calculate the mean of the N-Best matches
	thisDist=0.0;
	for(m=0; m<pointNN; m++) {
          if (distances[m] == 1000000.0) break;
	  thisDist+=distances[m];
        }
	thisDist/=m;
	
	// Let's see the distances then...
	if(verbosity>3) {
	  std::cerr << fileTable+track*O2_FILETABLESIZE << " " << thisDist << std::endl;
        }


	// All the track stuff goes here
	n=trackNN;
	while(n--){
	  if(thisDist<=trackDistances[n]){
	    if((n==0 || thisDist>=trackDistances[n-1])){
	      // Copy all values above up the queue
	      for( l=trackNN-1 ; l > n ; l--){
		trackDistances[l]=trackDistances[l-1];
		trackQIndexes[l]=trackQIndexes[l-1];
		trackSIndexes[l]=trackSIndexes[l-1];
		trackIDs[l]=trackIDs[l-1];
	      }
	      trackDistances[n]=thisDist;
	      trackQIndexes[n]=qIndexes[0];
	      trackSIndexes[n]=sIndexes[0];
	      successfulTracks++;
	      trackIDs[n]=track;
	      break;
	    }
	  }
	  else
	    break;
	}
      } // Duration match
            
      // Clean up current track
      if(D!=NULL){
	for(j=0; j<numVectors; j++)
	  delete[] D[j];
      }

      if(DD!=NULL){
	for(j=0; j<numVectors; j++)
	  delete[] DD[j];
      }
    }
    // per-track reset array values
    for(unsigned k=0; k<pointNN; k++){
      distances[k]=1.0e6;
      qIndexes[k]=~0;
      sIndexes[k]=~0;    
    }
  }

  free(data_buffer);

  gettimeofday(&tv2,NULL);
  if(verbosity>1) {
    std::cerr << std::endl << "processed tracks :" << processedTracks << " matched tracks: " << successfulTracks << " elapsed time:" 
	 << ( tv2.tv_sec*1000 + tv2.tv_usec/1000 ) - ( tv1.tv_sec*1000+tv1.tv_usec/1000 ) << " msec" << std::endl;
    std::cerr << "sampleCount: " << sampleCount << " sampleSum: " << sampleSum << " logSampleSum: " << logSampleSum 
	 << " minSample: " << minSample << " maxSample: " << maxSample << std::endl;
  }  
  if(adbQueryResponse==0){
    if(verbosity>1) {
      std::cerr<<std::endl;
    }
    // Output answer
    // Loop over nearest neighbours
    for(k=0; k < std::min(trackNN,successfulTracks); k++)
      std::cout << fileTable+trackIDs[k]*O2_FILETABLESIZE << " " << trackDistances[k] << " " 
	   << trackQIndexes[k] << " " << trackSIndexes[k] << std::endl;
  }
  else{ // Process Web Services Query
    int listLen = std::min(trackNN, processedTracks);
    adbQueryResponse->result.__sizeRlist=listLen;
    adbQueryResponse->result.__sizeDist=listLen;
    adbQueryResponse->result.__sizeQpos=listLen;
    adbQueryResponse->result.__sizeSpos=listLen;
    adbQueryResponse->result.Rlist= new char*[listLen];
    adbQueryResponse->result.Dist = new double[listLen];
    adbQueryResponse->result.Qpos = new unsigned int[listLen];
    adbQueryResponse->result.Spos = new unsigned int[listLen];
    for(k=0; k<(unsigned)adbQueryResponse->result.__sizeRlist; k++){
      adbQueryResponse->result.Rlist[k]=new char[O2_MAXFILESTR];
      adbQueryResponse->result.Dist[k]=trackDistances[k];
      adbQueryResponse->result.Qpos[k]=trackQIndexes[k];
      adbQueryResponse->result.Spos[k]=trackSIndexes[k];
      sprintf(adbQueryResponse->result.Rlist[k], "%s", fileTable+trackIDs[k]*O2_FILETABLESIZE);
    }
  }

  // Clean up
  if(trackOffsetTable)
    delete[] trackOffsetTable;
  if(queryCopy)
    delete[] queryCopy;
  if(qNorm)
    delete[] qNorm;
  if(sNorm)
    delete[] sNorm;
  if(qPower)
    delete[] qPower;
  if(sPower) 
    delete[] sPower;
  if(D)
    delete[] D;
  if(DD)
    delete[] DD;
  if(timesdata)
    delete[] timesdata;
  if(querydurs)
    delete[] querydurs;
  if(meanDBdur)
    delete[] meanDBdur;
}

// Radius search between query and target tracks
// efficient implementation based on matched filter
// assumes normed shingles
// outputs count of retrieved shingles, max retreived = one shingle per query shingle per track
void audioDB::trackSequenceQueryRad(const char* dbName, const char* inFile, adb__queryResponse *adbQueryResponse){
  
  initTables(dbName, inFile);
  
  // For each input vector, find the closest pointNN matching output vectors and report
  // we use stdout in this stub version
  unsigned numVectors = (statbuf.st_size-sizeof(int))/(sizeof(double)*dbH->dim);
  double* query = (double*)(indata+sizeof(int));
  double* queryCopy = 0;

  if(!(dbH->flags & O2_FLAG_L2NORM) )
    error("Database must be L2 normed for sequence query","use -l2norm");
  
  if(verbosity>1) {
    std::cerr << "performing norms ... "; std::cerr.flush();
  }
  unsigned dbVectors = dbH->length/(sizeof(double)*dbH->dim);

  // Make a copy of the query
  queryCopy = new double[numVectors*dbH->dim];
  memcpy(queryCopy, query, numVectors*dbH->dim*sizeof(double));
  qNorm = new double[numVectors];
  sNorm = new double[dbVectors];
  assert(qNorm&&sNorm&&queryCopy&&sequenceLength);    
  unitNorm(queryCopy, dbH->dim, numVectors, qNorm);
  query = queryCopy;

  // Make norm measurements relative to sequenceLength
  unsigned w = sequenceLength-1;
  unsigned i,j;

  // 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;

  double *sPower = 0, *qPower = 0;
  double *spPtr = 0, *qpPtr = 0;

  if (usingPower) {
    if(!(dbH->flags & O2_FLAG_POWER)) {
      error("database not power-enabled", dbName);
    }
    sPower = new double[dbVectors];
    spPtr = sPower;
    memcpy(sPower, powerTable, dbVectors * sizeof(double));
  }

  for(i=0; i<dbH->numFiles; i++){
    if(trackTable[i]>=sequenceLength) {
      sequence_sum(snPtr, trackTable[i], sequenceLength);
      sequence_sqrt(snPtr, trackTable[i], sequenceLength);
      if (usingPower) {
        sequence_sum(spPtr, trackTable[i], sequenceLength);
        sequence_average(spPtr, trackTable[i], sequenceLength);
      }
    }
    snPtr += trackTable[i];
    if (usingPower) {
      spPtr += trackTable[i];
    }
  }
  
  sequence_sum(qnPtr, numVectors, sequenceLength);
  sequence_sqrt(qnPtr, numVectors, sequenceLength);

  if (usingPower) {
    qPower = new double[numVectors];
    qpPtr = qPower;
    if (lseek(powerfd, sizeof(int), SEEK_SET) == (off_t) -1) {
      error("error seeking to data", powerFileName, "lseek");
    }
    int count = read(powerfd, qPower, numVectors * sizeof(double));
    if (count == -1) {
      error("error reading data", powerFileName, "read");
    }
    if ((unsigned) count != numVectors * sizeof(double)) {
      error("short read", powerFileName);
    }

    sequence_sum(qpPtr, numVectors, sequenceLength);
    sequence_average(qpPtr, numVectors, sequenceLength);
  }

  if(verbosity>1) {
    std::cerr << "done." << std::endl;    
  }
  
  if(verbosity>1) {
    std::cerr << "matching tracks..." << std::endl;
  }
  
  assert(pointNN>0 && pointNN<=O2_MAXNN);
  assert(trackNN>0 && trackNN<=O2_MAXNN);
  
  // Make temporary dynamic memory for results
  double trackDistances[trackNN];
  unsigned trackIDs[trackNN];
  unsigned trackQIndexes[trackNN];
  unsigned trackSIndexes[trackNN];
  
  double distances[pointNN];
  unsigned qIndexes[pointNN];
  unsigned sIndexes[pointNN];
  

  unsigned k,l,n,track,trackOffset=0, HOP_SIZE=sequenceHop, wL=sequenceLength;
  double thisDist;
  
  for(k=0; k<pointNN; k++){
    distances[k]=0.0;
    qIndexes[k]=~0;
    sIndexes[k]=~0;    
  }
  
  for(k=0; k<trackNN; k++){
    trackDistances[k]=0.0;
    trackQIndexes[k]=~0;
    trackSIndexes[k]=~0;
    trackIDs[k]=~0;
  }

  // Timestamp and durations processing
  double meanQdur = 0;
  double *timesdata = 0;
  double *querydurs = 0;
  double *meanDBdur = 0;
  
  if(usingTimes && !(dbH->flags & O2_FLAG_TIMES)){
    std::cerr << "warning: ignoring query timestamps for non-timestamped database" << std::endl;
    usingTimes=0;
  }
  
  else if(!usingTimes && (dbH->flags & O2_FLAG_TIMES))
    std::cerr << "warning: no timestamps given for query. Ignoring database timestamps." << std::endl;
  
  else if(usingTimes && (dbH->flags & O2_FLAG_TIMES)){
    timesdata = new double[2*numVectors];
    querydurs = new double[numVectors];

    insertTimeStamps(numVectors, timesFile, timesdata);
    // Calculate durations of points
    for(k=0; k<numVectors-1; k++){
      querydurs[k] = timesdata[2*k+1] - timesdata[2*k];
      meanQdur += querydurs[k];
    }
    meanQdur/=k;
    if(verbosity>1) {
      std::cerr << "mean query file duration: " << meanQdur << std::endl;
    }
    meanDBdur = new double[dbH->numFiles];
    assert(meanDBdur);
    for(k=0; k<dbH->numFiles; k++){
      meanDBdur[k]=0.0;
      for(j=0; j<trackTable[k]-1 ; j++) {
	meanDBdur[k]+=timesTable[2*j+1]-timesTable[2*j];
      }
      meanDBdur[k]/=j;
    }
  }

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

  D = new double*[numVectors];
  assert(D);
  DD = new double*[numVectors];
  assert(DD);

  gettimeofday(&tv1, NULL); 
  unsigned processedTracks = 0;
  unsigned successfulTracks=0;

  double* qp;
  double* sp;
  double* dp;

  // build track offset table
  off_t *trackOffsetTable = new off_t[dbH->numFiles];
  unsigned cumTrack=0;
  off_t trackIndexOffset;
  for(k=0; k<dbH->numFiles;k++){
    trackOffsetTable[k]=cumTrack;
    cumTrack+=trackTable[k]*dbH->dim;
  }

  char nextKey [MAXSTR];

  // chi^2 statistics
  double sampleCount = 0;
  double sampleSum = 0;
  double logSampleSum = 0;
  double minSample = 1e9;
  double maxSample = 0;

  // Track loop 
  size_t data_buffer_size = 0;
  double *data_buffer = 0;
  lseek(dbfid, dbH->dataOffset, SEEK_SET);

  for(processedTracks=0, track=0 ; processedTracks < dbH->numFiles ; track++, processedTracks++){

    trackOffset = trackOffsetTable[track];     // numDoubles offset

    // get trackID from file if using a control file
    if(trackFile) {
      trackFile->getline(nextKey,MAXSTR);
      if(!trackFile->eof()) {
	track = getKeyPos(nextKey);
        trackOffset = trackOffsetTable[track];
        lseek(dbfid, dbH->dataOffset + trackOffset * sizeof(double), SEEK_SET);
      } else {
	break;
      }
    }

    trackIndexOffset=trackOffset/dbH->dim; // numVectors offset

    if(sequenceLength<=trackTable[track]){  // test for short sequences
      
      if(verbosity>7) {
	std::cerr << track << "." << trackIndexOffset << "." << trackTable[track] << " | ";std::cerr.flush();
      }

      // Sum products matrix
      for(j=0; j<numVectors;j++){
	D[j]=new double[trackTable[track]]; 
	assert(D[j]);

      }

      // Matched filter matrix
      for(j=0; j<numVectors;j++){
	DD[j]=new double[trackTable[track]];
	assert(DD[j]);
      }

      if (trackTable[track] * sizeof(double) * dbH->dim > data_buffer_size) {
	if(data_buffer) {
	  free(data_buffer);
	}
	{ 
	  data_buffer_size = trackTable[track] * sizeof(double) * dbH->dim;
	  void *tmp = malloc(data_buffer_size);
	  if (tmp == NULL) {
	    error("error allocating data buffer");
	  }
	  data_buffer = (double *) tmp;
	}
      }

      read(dbfid, data_buffer, trackTable[track] * sizeof(double) * dbH->dim);

      // Dot product
      for(j=0; j<numVectors; j++)
	for(k=0; k<trackTable[track]; k++){
	  qp=query+j*dbH->dim;
	  sp=data_buffer+k*dbH->dim;
	  DD[j][k]=0.0; // Initialize matched filter array
	  dp=&D[j][k];  // point to correlation cell j,k
	  *dp=0.0;      // initialize correlation cell
	  l=dbH->dim;         // size of vectors
	  while(l--)
	    *dp+=*qp++**sp++;
	}

      // Matched Filter
      // HOP SIZE == 1
      double* spd;
      if(HOP_SIZE==1){ // HOP_SIZE = shingleHop
	for(w=0; w<wL; w++)
	  for(j=0; j<numVectors-w; j++){ 
	    sp=DD[j];
	    spd=D[j+w]+w;
	    k=trackTable[track]-w;
	    while(k--)
	      *sp+++=*spd++;
	  }
      }

      else{ // HOP_SIZE != 1
	for(w=0; w<wL; w++)
	  for(j=0; j<numVectors-w; j+=HOP_SIZE){
	    sp=DD[j];
	    spd=D[j+w]+w;
	    for(k=0; k<trackTable[track]-w; k+=HOP_SIZE){
	      *sp+=*spd;
	      sp+=HOP_SIZE;
	      spd+=HOP_SIZE;
	    }
	  }
      }
      
      if(verbosity>3 && usingTimes) {
	std::cerr << "meanQdur=" << meanQdur << " meanDBdur=" << meanDBdur[track] << std::endl;
	std::cerr.flush();
      }

      if(!usingTimes || 
	 (usingTimes 
	  && fabs(meanDBdur[track]-meanQdur)<meanQdur*timesTol)){

	if(verbosity>3 && usingTimes) {
	  std::cerr << "within duration tolerance." << std::endl;
	  std::cerr.flush();
	}

	// 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/(qnPtr[j]*sNorm[trackIndexOffset+k]))*DD[j][k];
	    if(verbosity>9) {
	      std::cerr << thisDist << " " << qnPtr[j] << " " << sNorm[trackIndexOffset+k] << std::endl;
            }
	    // Gather chi^2 statistics
	    if(thisDist<minSample)
	      minSample=thisDist;
	    else if(thisDist>maxSample)
	      maxSample=thisDist;
	    if(thisDist>1e-9){
	      sampleCount++;
	      sampleSum+=thisDist;
	      logSampleSum+=log(thisDist);
	    }

	    // diffL2 = fabs(qnPtr[j] - sNorm[trackIndexOffset+k]);
	    // Power test
            if (usingPower) {
              if (!(powers_acceptable(qpPtr[j], sPower[trackIndexOffset + k]))) {
                thisDist = 1000000.0;
              }
            }

	    if(thisDist>=0 && thisDist<=radius){
	      distances[0]++; // increment count
	      break; // only need one track point per query point
	    }
	  }
	// How many points were below threshold ?
	thisDist=distances[0];
	
	// Let's see the distances then...
	if(verbosity>3) {
	  std::cerr << fileTable+track*O2_FILETABLESIZE << " " << thisDist << std::endl;
        }

	// All the track stuff goes here
	n=trackNN;
	while(n--){
	  if(thisDist>trackDistances[n]){
	    if((n==0 || thisDist<=trackDistances[n-1])){
	      // Copy all values above up the queue
	      for( l=trackNN-1 ; l > n ; l--){
		trackDistances[l]=trackDistances[l-1];
		trackQIndexes[l]=trackQIndexes[l-1];
		trackSIndexes[l]=trackSIndexes[l-1];
		trackIDs[l]=trackIDs[l-1];
	      }
	      trackDistances[n]=thisDist;
	      trackQIndexes[n]=qIndexes[0];
	      trackSIndexes[n]=sIndexes[0];
	      successfulTracks++;
	      trackIDs[n]=track;
	      break;
	    }
	  }
	  else
	    break;
	}
      } // Duration match
            
      // Clean up current track
      if(D!=NULL){
	for(j=0; j<numVectors; j++)
	  delete[] D[j];
      }

      if(DD!=NULL){
	for(j=0; j<numVectors; j++)
	  delete[] DD[j];
      }
    }
    // per-track reset array values
    for(unsigned k=0; k<pointNN; k++){
      distances[k]=0.0;
      qIndexes[k]=~0;
      sIndexes[k]=~0;    
    }
  }

  free(data_buffer);

  gettimeofday(&tv2,NULL);
  if(verbosity>1) {
    std::cerr << std::endl << "processed tracks :" << processedTracks << " matched tracks: " << successfulTracks << " elapsed time:" 
	 << ( tv2.tv_sec*1000 + tv2.tv_usec/1000 ) - ( tv1.tv_sec*1000+tv1.tv_usec/1000 ) << " msec" << std::endl;
    std::cerr << "sampleCount: " << sampleCount << " sampleSum: " << sampleSum << " logSampleSum: " << logSampleSum 
	 << " minSample: " << minSample << " maxSample: " << maxSample << std::endl;
  }
  
  if(adbQueryResponse==0){
    if(verbosity>1) {
      std::cerr<<std::endl;
    }
    // Output answer
    // Loop over nearest neighbours
    for(k=0; k < std::min(trackNN,successfulTracks); k++)
      std::cout << fileTable+trackIDs[k]*O2_FILETABLESIZE << " " << trackDistances[k] << std::endl;
  }
  else{ // Process Web Services Query
    int listLen = std::min(trackNN, processedTracks);
    adbQueryResponse->result.__sizeRlist=listLen;
    adbQueryResponse->result.__sizeDist=listLen;
    adbQueryResponse->result.__sizeQpos=listLen;
    adbQueryResponse->result.__sizeSpos=listLen;
    adbQueryResponse->result.Rlist= new char*[listLen];
    adbQueryResponse->result.Dist = new double[listLen];
    adbQueryResponse->result.Qpos = new unsigned int[listLen];
    adbQueryResponse->result.Spos = new unsigned int[listLen];
    for(k=0; k<(unsigned)adbQueryResponse->result.__sizeRlist; k++){
      adbQueryResponse->result.Rlist[k]=new char[O2_MAXFILESTR];
      adbQueryResponse->result.Dist[k]=trackDistances[k];
      adbQueryResponse->result.Qpos[k]=trackQIndexes[k];
      adbQueryResponse->result.Spos[k]=trackSIndexes[k];
      sprintf(adbQueryResponse->result.Rlist[k], "%s", fileTable+trackIDs[k]*O2_FILETABLESIZE);
    }
  }

  // Clean up
  if(trackOffsetTable)
    delete[] trackOffsetTable;
  if(queryCopy)
    delete[] queryCopy;
  if(qNorm)
    delete[] qNorm;
  if(sNorm)
    delete[] sNorm;
  if(qPower)
    delete[] qPower;
  if(sPower) 
    delete[] sPower;
  if(D)
    delete[] D;
  if(DD)
    delete[] DD;
  if(timesdata)
    delete[] timesdata;
  if(querydurs)
    delete[] querydurs;
  if(meanDBdur)
    delete[] meanDBdur;
}

// Unit norm block of features
void audioDB::unitNorm(double* X, unsigned dim, unsigned n, double* qNorm){
  unsigned d;
  double L2, *p;
  if(verbosity>2) {
    std::cerr << "norming " << n << " vectors...";std::cerr.flush();
  }
  while(n--){
    p=X;
    L2=0.0;
    d=dim;
    while(d--){
      L2+=*p**p;
      p++;
    }
    /*    L2=sqrt(L2);*/
    if(qNorm)
      *qNorm++=L2;
    /*
    oneOverL2 = 1.0/L2;
    d=dim;
    while(d--){
      *X*=oneOverL2;
      X++;
    */
    X+=dim;
  }
  if(verbosity>2) {
    std::cerr << "done..." << std::endl;
  }
}